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The effects of acute high-intensity aerobic exercise on motor cortical plasticity and motor learning Mang, Cameron Scott 2015

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THE EFFECTS OF ACUTE HIGH-INTENSITY AEROBIC EXERCISE ON MOTOR CORTICAL PLASTICITY AND MOTOR LEARNING by  Cameron Scott Mang  B.P.E., University of Alberta, 2008 M.Sc., University of Alberta, 2010  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Rehabilitation Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   December 2015  © Cameron Scott Mang, 2015 ii  Abstract Background: Motor learning is mediated by plasticity of neural circuits involved in movement. As such, there is great interest in the development of strategies that maximize brain plasticity to promote motor learning. Aerobic exercise has emerged as an intervention with robust effects on the nervous system, including interactions with mechanisms of neuroplasticity and memory. Yet, the effects of acute aerobic exercise on plasticity and learning in the motor system are not well understood.  Methods: The overall objective of this thesis was to examine the effects of a single bout of high-intensity aerobic exercise on motor cortical (M1) plasticity and motor learning for an upper limb muscle in young healthy individuals. The first three research chapters describe experiments evaluating the effects of acute high-intensity cycling on: M1 plasticity (Chapter 2), continuous motor sequence learning (Chapter 2), activity in cerebello-motor circuits (Chapter 3) and discrete motor sequence learning (Chapter 4). In Chapter 5, a study exploring relationships of genetic and epigenetic variation with acute aerobic exercise effects on M1 plasticity and motor learning is described.  Summary of findings: In Chapter 2, M1 plasticity induced by paired associative stimulation was facilitated when preceded by exercise, compared to a period of rest. Further, continuous motor sequence learning was enhanced when exercise was performed prior to task practice. Transcranial magnetic stimulation assessments utilized in Chapter 3 suggested that modulation of activity in cerebello-motor circuits may contribute to exercise-induced facilitation of M1 plasticity. In Chapter 4, exercise prior to discrete motor sequence task practice enhanced the rate of improvement in task performance at a 24-hour retention test, suggesting an effect of aerobic exercise on motor memory retrieval. Finally, in Chapter 5 genetic variants and DNA methylation iii  patterns impacting brain-derived neurotrophic factor and dopamine signaling pathways were associated with inter-individual variability in exercise effects on M1 plasticity and motor learning.   Conclusions: This dissertation contributes new knowledge towards understanding the effects of acute high-intensity aerobic exercise on plasticity and learning in the motor system. The findings have implications for development of strategies to prime neuroplasticity and motor learning with acute aerobic exercise in sport or rehabilitation settings. iv  Preface This statement is to certify that the work in this dissertation was conceived, designed, conducted, analysed and written by Cameron Mang. All research described in this dissertation was approved by the University of British Columbia’s Clinical Research Ethics Board: “Promoting neuroplasticity and motor learning: the influence of exercise and genetic variation on BDNF”, certificate #H13-02065.  Chapters 1 and 6 were written by Cameron Mang. Dr. Lara Boyd assisted with editing these chapters.   Chapter 2 is based on work conducted by Cameron Mang, Nicholas Snow, Fudan Miao and Drs. Kristin Campbell, Colin Ross and Lara Boyd. Cameron Mang was responsible for conceiving the study, developing the study design, collecting, analysing and interpreting the data, as well as writing and revising the manuscript. Mr. Snow assisted with data collection, interpretation and editing of the manuscript. Ms. Miao performed analyses of serum levels of brain-derived neurotrophic factor. Drs. Campbell, Ross and Boyd contributed to developing the study design and to editing the manuscript.  Chapter 3 is based on work conducted by Cameron Mang, Nicholas Snow, Katlyn Brown, and Drs. Jason Neva, Kristin Campbell and Lara Boyd. Cameron Mang was responsible for conceiving the study, developing the study design, collecting, analysing and interpreting the data, as well as writing and revising the manuscript. Mr. Snow, Ms. Brown and Dr. Neva assisted with v  developing the study design, collecting and interpreting the data and editing the manuscript. Drs. Campbell and Boyd contributed to developing the study design and to editing the manuscript.  Chapter 4 is based on work conducted by Cameron Mang, Nicholas Snow, Katie Wadden and Drs. Kristin Campbell and Lara Boyd. Cameron Mang was responsible for conceiving the study, developing the study design, collecting, analysing and interpreting the data, as well as writing and revising the manuscript. Mr. Snow assisted with data collection, interpretation and editing of the manuscript. Ms. Wadden assisted with analysing and interpreting data, as well as editing the manuscript. Drs. Campbell and Boyd contributed to developing the study design and to editing the manuscript.  Chapter 5 is based on work conducted by Cameron Mang, Lisa McEwen, Michelle Higginson, Nicholas Snow and Drs. Julia MacIsaac, Kristin Campbell, Colin Ross, Michael Kobor and Lara Boyd. Cameron Mang was responsible for conceiving the study, developing the study design, collecting, analysing and interpreting the data, as well as writing and revising the manuscript. Ms. McEwen conducted the bench work and statistical analyses of epigenetic data, provided crucial input to data interpretation and assisted with manuscript writing. Dr. MacIsaac contributed to bench work related to the epigenetic analyses. Ms. Higginson conducted the bench work for the genetic analyses. Mr. Snow assisted with developing the study design, collecting and interpreting the data and to editing the manuscript. Drs. Campbell, Ross, Kobor and Boyd contributed to developing the study design and to editing the manuscript.  vi  In Appendix A an additional manuscript is presented. This work was developed by Cameron Mang and Drs. Kristin Campbell, Colin Ross and Lara Boyd. Cameron Mang was responsible for generating the ideas, reviewing the literature and writing the manuscript. Drs. Campbell, Ross and Boyd provided critical input on ideas and edited the manuscript. The manuscript was developed from literature reviews completed over the course of Cameron’s PhD comprehensive exam.  A version of Chapter 2 has been published: Mang CS, Snow NJ, Campbell KL, Ross CJD, Boyd LA. (2014) A single bout of high-intensity aerobic exercise facilitates response to paired associative stimulation and promotes sequence-specific implicit motor learning. J Appl Physiol. 117 (11): 1325-36.  A version of Chapter 3 is under review for publication: Mang CS, Brown KE, Neva JL, Snow NJ, Campbell KL, Boyd LA. Promoting motor cortical plasticity with acute aerobic exercise: a role for cerebellar circuits.   A version of Chapter 4 is in revision for re-submission for publication: Mang CS, Snow NJ, Wadden KP, Campbell KL, Boyd LA. High-intensity aerobic exercise enhances motor memory retrieval.     vii  A version of Appendix A has been published: Mang CS, Campbell KL, Ross CJD, Boyd LA. (2013) Promoting neuroplasticity for motor rehabilitation after stroke: considering the effects of aerobic exercise and genetic variation on brain-derived neurotrophic factor. Phys Ther. 93 (12): 1707-16.   Chapters of this dissertation that have been published or submitted for publication may include additional details than presented in the published or submitted work to increase clarity and continuity across the chapters of this combined dissertation work. viii  Table of Contents  Abstract .................................................................................................................................... ii Preface ...................................................................................................................................... iv Table of Contents .................................................................................................................. viii List of Tables ......................................................................................................................... xvii List of Figures.......................................................................................................................xviii List of Abbreviations ............................................................................................................... xx Acknowledgements ...............................................................................................................xxiii Dedication ............................................................................................................................. xxiv Chapter 1: General Introduction.............................................................................................. 1 1.1 Preamble......................................................................................................................1 1.2 Motor learning concepts ..............................................................................................2 1.2.1 Stages of motor memory formation ..........................................................................2 1.2.2 Motor performance versus motor learning ................................................................4 1.2.2.1 Measuring motor performance and learning .....................................................4 1.2.3 Task nature ..............................................................................................................5 1.2.3.1 Motor sequence learning and motor adaptation .................................................6 1.2.3.2 Continuous and discrete motor tasks.................................................................6 1.3 Neural substrates of motor learning ..............................................................................8 1.3.1 Long-term potentiation ............................................................................................8 1.3.2 Motor learning brain systems ...................................................................................9 1.3.3 Role of the primary motor cortex in motor learning ................................................ 11 ix  1.3.4 Role of the cerebellum in motor learning ............................................................... 12 1.3.5 Role of the basal ganglia in motor learning ............................................................ 14 1.3.6 Transcranial magnetic stimulation .......................................................................... 15 1.3.6.1 Single-pulse transcranial magnetic stimulation ............................................... 15 1.3.6.2 Paired-pulse transcranial magnetic stimulation ............................................... 16 1.3.6.3 Modulating brain activity with transcranial magnetic stimulation ................... 17 1.4 Modulation of motor learning and its neural substrates by acute aerobic exercise ....... 19 1.4.1 Acute aerobic exercise effects on cognitive function .............................................. 19 1.4.2 Acute aerobic exercise effects on motor learning ................................................... 21 1.4.3 Indirect effects of acute aerobic exercise on the brain ............................................. 22 1.4.4 Acute aerobic exercise modulates neurochemicals ................................................. 22 1.4.4.1 Brain-derived neurotrophic factor ................................................................... 23 1.4.4.2 Dopamine....................................................................................................... 24 1.4.5 Acute aerobic exercise modulates motor cortex electrophysiology ......................... 25 1.5 Considering the potential moderating effect of genetic and epigenetic variation ......... 27 1.5.1 BDNF gene val66met polymorphism ..................................................................... 28 1.5.2 Dopamine-related gene variation ............................................................................ 29 1.5.3 Interactions of genetic variation with aerobic exercise effects on the brain ............. 30 1.5.4 Potential contributions of epigenetic variation ........................................................ 31 1.6 Thesis overview ......................................................................................................... 33 1.6.1 Thesis impact ......................................................................................................... 34 1.6.2 Specific research objectives ................................................................................... 34 x  Chapter 2: A Single Bout of High-Intensity Aerobic Exercise Facilitates Response to Paired Associative Stimulation and Promotes Sequence-Specific Implicit Motor Learning ........... 36 2.1 Introduction ............................................................................................................... 36 2.2 Methods ..................................................................................................................... 38 2.2.1 Participants ............................................................................................................ 38 2.2.2 Experimental design............................................................................................... 39 2.2.3 Exercise procedures ............................................................................................... 40 2.2.3.1 Graded maximal exercise testing .................................................................... 40 2.2.3.2 Standardized acute aerobic exercise bout ........................................................ 41 2.2.4 Paired associative stimulation procedures .............................................................. 43 2.2.4.1 Electromyography .......................................................................................... 43 2.2.4.2 Median nerve stimulation ............................................................................... 43 2.2.4.3 Transcranial magnetic stimulation .................................................................. 44 2.2.4.4 Paired associative stimulation ......................................................................... 45 2.2.5 Continuous tracking task procedures ...................................................................... 45 2.2.6 Serum brain-derived neurotrophic factor ................................................................ 48 2.2.7 Statistical analyses ................................................................................................. 49 2.2.7.1 Paired associative stimulation ......................................................................... 49 2.2.7.2 Continuous tracking task ................................................................................ 49 2.2.7.3 Serum brain-derived neurotrophic factor and correlation analyses .................. 50 2.3 Results ....................................................................................................................... 51 2.3.1 Paired associative stimulation ................................................................................ 51 2.3.2 Continuous tracking task ........................................................................................ 54 xi  2.3.3 Serum brain-derived neurotrophic factor and correlations ...................................... 58 2.4 Discussion ................................................................................................................. 59 2.4.1 A single bout of aerobic exercise facilitates response to paired associative stimulation ........................................................................................................................ 59 2.4.2 A single bout of aerobic exercise promotes implicit sequence-specific motor learning ............................................................................................................................. 63 2.4.3 Systemic brain-derived neurotrophic factor is increased by a single bout of aerobic exercise ............................................................................................................................. 64 2.4.4 Conclusions ........................................................................................................... 66 Chapter 3: Promoting Motor Cortical Plasticity with Acute Aerobic Exercise: A Role for Cerebellar Circuits .................................................................................................................. 67 3.1 Introduction ............................................................................................................... 67 3.2 Methods and results ................................................................................................... 70 3.2.1 Participants ............................................................................................................ 70 3.2.2 Experimental design............................................................................................... 70 3.2.3 Exercise procedures ............................................................................................... 74 3.2.3.1 Graded maximal exercise testing .................................................................... 74 3.2.3.2 Standardized acute aerobic exercise bout ........................................................ 74 3.2.4 Experiment 1 methods ........................................................................................... 75 3.2.4.1 Cerebellar inhibition ....................................................................................... 75 3.2.4.2 Data processing .............................................................................................. 77 3.2.4.3 Statistical analyses ......................................................................................... 78 3.2.5 Experiment 1 results .............................................................................................. 78 xii  3.2.6 Experiment 2 methods ........................................................................................... 82 3.2.6.1 Median nerve stimulation ............................................................................... 82 3.2.6.2 Motor evoked potential recruitment curves ..................................................... 82 3.2.6.3 Paired associative stimulation ......................................................................... 83 3.2.6.4 Data processing .............................................................................................. 83 3.2.6.5 Statistical analyses ......................................................................................... 83 3.2.7 Experiment 2 results .............................................................................................. 85 3.3 Discussion ................................................................................................................. 88 3.3.1 Experiment 1 ......................................................................................................... 89 3.3.2 Experiment 2 ......................................................................................................... 91 3.3.3 Limitations ............................................................................................................ 93 3.3.4 Implications ........................................................................................................... 94 3.3.5 Conclusions ........................................................................................................... 95 Chapter 4: Acute High-Intensity Aerobic Exercise Enhances Motor Memory Retrieval .... 96 4.1 Introduction ............................................................................................................... 96 4.2 Methods ..................................................................................................................... 99 4.2.1 Participants ............................................................................................................ 99 4.2.2 Experimental overview ........................................................................................ 100 4.2.3 Graded maximal exercise testing .......................................................................... 101 4.2.4 Standardized acute aerobic exercise bout ............................................................. 102 4.2.5 Serial targeting task procedures ............................................................................ 104 4.2.6 Serial targeting task analyses ............................................................................... 106 4.2.6.1 Baseline performance ................................................................................... 106 xiii  4.2.6.2 Motor skill acquisition and learning ............................................................. 107 4.2.7 Statistical analyses ............................................................................................... 108 4.2.7.1 Baseline performance ................................................................................... 108 4.2.7.2 Motor skill acquisition and retention ............................................................ 108 4.2.7.3 Normality of data ......................................................................................... 109 4.3 Results ..................................................................................................................... 109 4.3.1 Baseline performance ........................................................................................... 109 4.3.2 Motor skill acquisition ......................................................................................... 110 4.3.3 Motor skill retention ............................................................................................ 110 4.3.4 Recognition ......................................................................................................... 111 4.4 Discussion ............................................................................................................... 115 4.4.1 Implications ......................................................................................................... 119 4.4.2 Conclusions ......................................................................................................... 120 Chapter 5: Acute Aerobic Exercise Effects on Neuroplasticity and Motor Learning: Exploring the Potential Influence of Genetic and Epigenetic Variation ............................. 121 5.1 Introduction ............................................................................................................. 121 5.2 Methods ................................................................................................................... 124 5.2.1 Participants .......................................................................................................... 124 5.2.2 Experimental design............................................................................................. 125 5.2.3 Standardized acute aerobic exercise bout ............................................................. 128 5.2.4 Paired associative stimulation procedures ............................................................ 128 5.2.4.1 Electromyography and median nerve stimulation ......................................... 128 5.2.4.2 Transcranial magnetic stimulation ................................................................ 129 xiv  5.2.4.3 Paired associative stimulation ....................................................................... 130 5.2.5 Motor learning task procedures ............................................................................ 130 5.2.6 Genotyping and DNA methylation analysis procedures ........................................ 133 5.2.6.1 Genotyping .................................................................................................. 133 5.2.6.2 DNA methylation ......................................................................................... 133 5.2.7 Statistical analyses ............................................................................................... 134 5.2.7.1 Genetics ....................................................................................................... 134 5.2.7.2 DNA methylation ......................................................................................... 135 5.3 Results ..................................................................................................................... 137 5.3.1 Genetics ............................................................................................................... 137 5.3.1.1 BDNF val66met polymorphism .................................................................... 137 5.3.1.2 DRD2/ANKK1 glu713lys polymorphism ...................................................... 137 5.3.2 Methylation ......................................................................................................... 138 5.3.2.1 BDNF gene methylation ............................................................................... 138 5.3.2.2 DRD2 gene methylation ............................................................................... 141 5.4 Discussion ............................................................................................................... 146 5.4.1 DRD2/ANKK1 genotype influences facilitation of motor learning by acute aerobic exercise ........................................................................................................................... 146 5.4.2 BDNF genotype and promoter methylation interact to influence motor cortical plasticity .......................................................................................................................... 149 5.4.3 Limitations .......................................................................................................... 151 5.4.4 Conclusions ......................................................................................................... 153 Chapter 6: General Discussion ............................................................................................. 154 xv  6.1 Acute high-intensity aerobic exercise promotes motor cortical plasticity .................. 154 6.2 Acute high-intensity aerobic exercise facilitates implicit sequence-specific learning of temporal precision for a continuous motor sequence task ..................................................... 155 6.3 Acute high-intensity aerobic exercise alters activity of the cerebello-thalamo-cortical pathway ............................................................................................................................... 157 6.4 Acute high-intensity aerobic exercise enhances the rate of implicit sequence-specific motor memory retrieval for a discrete motor sequence task.................................................. 158 6.5 Genetic and epigenetic contributions to inter-individual variability in acute aerobic exercise response ................................................................................................................. 159 6.6 Limitations .............................................................................................................. 160 6.7 Implications and future directions ............................................................................ 163 6.8 Conclusions ............................................................................................................. 165 References.............................................................................................................................. 167 Appendix A: Promoting Neuroplasticity for Motor Rehabilitation After Stroke: Considering the Effects of Aerobic Exercise and Genetic Variation on Brain-Derived Neurotrophic Factor ............................................................................................................. 189 A.1 Introduction ............................................................................................................. 189 A.2 Aerobic exercise to promote neuroplasticity for motor rehabilitation post-stroke ..... 191 A.2.1 BDNF is involved in motor learning and post-stroke motor rehabilitation ........ 193 A.2.2 Aerobic exercise effects on brain function: BDNF and cognitive function ........ 194 A.2.3 Aerobic exercise effects on motor learning ....................................................... 195 A.2.4 Persistence of aerobic exercise effects on the brain........................................... 196 xvi  A.2.5 Prescribing aerobic exercise to prime motor learning and post-stroke motor rehabilitation ................................................................................................................... 198 A.3 Genetics research to inform motor rehabilitation and aerobic exercise prescription post-stroke ………………………………………………………………………………………...201 A.3.1 BDNF gene val66met polymorphism impact on brain health and function ........ 202 A.3.2 BDNF gene val66met polymorphism impact on motor system ......................... 202 A.3.3 BDNF gene val66met polymorphism impact on recovery post-stroke ............... 204 A.3.4 BDNF gene val66met polymorphism to inform the use of aerobic exercise for motor rehabilitation ......................................................................................................... 205 A.4 Conclusions and clinical implications ...................................................................... 208  xvii  List of Tables Table 2.1 Participant characteristics and aerobic exercise data ................................................... 42 Table 3.1 Participant characteristics and aerobic exercise data ................................................... 72 Table 4.1 Participant characteristics and aerobic exercise data ................................................. 103 Table 5.1 Participant characteristics  ....................................................................................... 125 Table 5.2 Group summary of DNA methylation information related to statistically significant findings ................................................................................................................................... 143 Table 5.3 Summary and comparison of mixed-effects models ................................................. 144     xviii  List of Figures Figure 2.1 Overview of experimental procedures....................................................................... 40 Figure 2.2 Schematic of continuous tracking (CT) task. ............................................................ 47 Figure 2.3 Changes in corticospinal excitability evoked by paired associative stimulation (PAS) preceded by rest and aerobic exercise in a single representative participant. .............................. 53 Figure 2.4 Changes in corticospinal excitability evoked by paired associative stimulation (PAS) preceded by rest and aerobic exercise across the group. ............................................................. 54 Figure 2.5 Continuous tracking (CT) task data collected from a single representative participant when practice was preceded by rest (grey) and aerobic exercise (black).  .................................. 57 Figure 2.6 Continuous tracking (CT) task performance in terms of temporal error (Panel A) and spatial error (Panel B) when averaged across the group.  ........................................................... 58 Figure 3.1 Overview of experimental procedures for Experiment 1 (Panel A) and Experiment 2 (Panel B).  ................................................................................................................................. 73 Figure 3.2 Cerebellar inhibition (CBI) in a single representative participant at (A) baseline, (B) pre-exercise and (C) post-exercise. ............................................................................................ 80 Figure 3.3 Cerebellar inhibition (CBI) ratios averaged across the group. ................................... 81 Figure 3.4 Motor evoked potential (MEP) recruitment curve data pre- and post-paired associative stimulation (PAS) uder rest (Panels A and C) and aerobic exercise (Panels B and D) conditions for all participants in both the PAS25 (top panels) and PAS21 (bottom panels) groups.  .............. 87 Figure 3.5 Change in motor evoked potential (MEP) recruitment curve slope evoked by paired associative stimulation (PAS) protocols under rest and aerobic exercise conditions.  ................. 88 Figure 4.1 Experimental overview ........................................................................................... 101 Figure 4.2 Schematic of the serial targeting task ...................................................................... 106 xix  Figure 4.3 Average response time across the group for each movement sequence trial during practice (Panel A) and retention (Panel B) ............................................................................... 112 Figure 4.4 Rate of change parameter (α), obtained from exponential decay curves fit to trial-by-trial practice (Panel A) and retention (Panel B) data, and averaged across the group. ............... 113 Figure 4.5 Acquisition (Panel A) and retention (Panel B) change scores across the group under the rest (red circle) and aerobic exercise conditions (green square) for both repeated (filled) and random (unfilled) sequences. ................................................................................................... 114 Figure 5.1 Overview of experimental procedures to test the effects of an acute bout of aerobic exercise on response to paired associative stimulation (PAS) and motor learning. ................... 127 Figure 5.2 Motor tasks utilized in the two studies analysed in the present work.  ..................... 131 Figure 5.3 Interaction between Condition (rest and aerobic exercise) and DRD2/ANKK1 genotype (glu/glu homozygote and lys carrier) on motor learning. .......................................... 138 Figure 5.4 Relationships between BDNF gene methylation and response to paired associative stimulation (PAS∆) in BDNF gene met allele carriers.............................................................. 140 Figure 5.5 Relationships between DRD2 gene methylation and retention change score (RET∆). ............................................................................................................................................... 142 Figure A.1 Examples of indirect and direct pathways for positive effects of aerobic exercise on the brain. ................................................................................................................................. 192 Figure A.2 Using aerobic exercise to prime motor rehabilitation post-stroke. .......................... 199 Figure A.3 The potential influence of the BDNF val66met polymorphism on the effects of aerobic exercise on motor recovery post-stroke ....................................................................... 207    xx  List of Abbreviations ACQ  acquisition AMPA α-amino-3-hydroxy-5-methy-4-isoxazolepropionic acid AMT  active motor threshold ANKK1 gene encoding for ankyrin repeat and kinase containing 1 ANOVA analysis of variance APB  abductor pollicis brevis BDNF  brain-derived neurotrophic factor BDNF  gene encoding for brain-derived neurotrophic factor  BLa  blood lactate CBI  cerebellar inhibition CMEP cervicomedullary evoked potential CNS  central nervous system CpG  cytosine-guanine dinucleotides CS  conditioning stimulus CT  continuous tracking cTBS  continuous theta burst stimulation DNA  deoxyribonucleic acid DRD2  dopamine D2 receptor DRD2  gene encoding for dopamine D2 receptor D-waves direct waves ELISA enzyme linked immunosorbent assay EMG  electromyography xxi  fMRI  functional magnetic resonance imaging GABA  γ-aminobutyric acid  glu  glutamic acid HR  heart rate ICF  intracortical facilitation ISI  inter-stimulus interval  I-waves indirect waves L-dopa levo-dopa LTD  long-term depression LTP  long-term potentiation lys  lysine M1  motor cortex met  methionine MEP  motor evoked potential Mmax  maximal motor wave NMDA N-methyl D-aspartate  PAS  paired associative stimulation PO  power output PT  perceptual threshold  RER  respiratory exchange ration RET  retention RM  repeated measures RMSE  root mean squared error xxii  RMT  resting motor threshold  RPE  rating of perceived exertion rpm  revolutions per minute rTMS  repetitive transcranial magnetic stimulation SD  standard deviation SI1mV  stimulation intensity to evoke 1 mV response SICI  short-interval intracortical inhibition SNP  single nucleotide polymorphism ST  serial targeting STDP  spike-timing-dependent plasticity TBS  theta-burst stimulation TDCS  transcranial direct current stimulation  TMS  transcranial magnetic stimulation TrkB  tyrosine kinase-B TS  test stimulus TSS  transcription start site val  valine V̇O2  volume of oxygen consumption V̇O2peak peak oxygen consumption    xxiii  Acknowledgements I am extremely grateful for the mentorship of my primary supervisor, Dr. Lara Boyd. Lara, the freedom, generous support and wonderful working environment that you offered over these years enabled me to pursue a fulfilling line of inquiry for my PhD work. I greatly admire and aspire to the balance across family, health, leisure and science that you seem to maintain so effortlessly. I cannot thank you enough. I would also like to thank my supervisory committee members Drs. Kristin Campbell, Colin Ross and Michael Kobor. To Kristin, for facilitating the renewal of my interest and passion for exercise physiology. To Colin and Mike, for your patience and support of me as I naively wandered into the world of genetics and epigenetics research. Also, to Drs. Julien Doyon, Ian Franks, Miriam Spering and Peter Crocker for taking part in my final examination. To the members of the Brain Behaviour Laboratory, for your thoughtfulness, your confidence in me, and most of all, your friendships. I cannot imagine a better group of people to have spent these academic years alongside and I am certain that we will continue to cross paths in the future, both professionally and personally. Also, to my MSc supervisor, Dr. Dave Collins, for your mentorship that continues to this day. Outside of academia, I am blessed with wonderful friends and family who are a constant source of inspiration and encouragement. To Ravi, for a great year in Vancouver together. To Austin, for all the good science and life talks. To my parents (Keith and Gayle; Phillip and Barbara), as well as my siblings and their families (Matt, Chelsea, Lochlan; Jordan, Krista, Matthew, Basil), for your love and support. And of course, to my wife, Annika, and daughters, Etta and Julia, we did it! It has been quite the adventure. I only hope that I can be as supportive of each of you in your endeavours as you have all been of me. Thank you for everything.      xxiv  Dedication  To my Wife and Daughters, For giving me “a place where it’s always safe and warm”, For being my “shelter from the storm”. -Bob Dylan 1  Chapter 1: General Introduction 1.1 Preamble Movement is the primary means through which humans interact with the world. As such, the ability to learn and adapt movement patterns is recognized as an essential characteristic for functioning in everyday life (260). Whether typing on a computer keyboard, playing a sport or engaging in rehabilitation training after injury, motor learning is at play. Although humans demonstrate a remarkable intrinsic capacity for motor learning, there are many contexts in which individuals might benefit from the facilitation of motor learning processes, with neurorehabilitation settings perhaps the most widely posited example (140, 200, 302). Critically, plasticity in the central nervous system (CNS) provides the physiological medium for humans’ ability to undergo motor skill learning (133, 252, 302). Advances in understanding of the neural substrates of motor learning has led to conjecture that the development of strategies to promote motor learning would benefit greatly from integration of recently gained insights into the neurobiology of motor learning (140, 200, 256, 302).   A potential strategy to enhance motor learning that has gained momentum in the literature involves the priming of brain plasticity prior to engagement in motor skill practice (256, 302). The premise for this strategy is that if the CNS is primed into a state with high capacity for physiological change, then subsequent motor practice will be more likely to induce the physiological changes that support the learning of the task. Non-invasive brain stimulation, pharmacological agents and sensorimotor interventions (i.e. electrical stimulation, vibration) that alter brain excitability have gained much attention as potential plasticity priming methods (238, 247, 248).  Notably though, a single bout of aerobic exercise has been shown to induce a range of positive effects on brain physiology, including an up-regulation of multiple neurochemicals 2  involved in motor learning-related neuroplasticity (137, 273, 308). Yet, there is currently little research examining the potential use of acute aerobic exercise to prime neuroplastic change and promote motor learning. Thus, the overall objective of this dissertation work was to provide a comprehensive investigation of the impact of a single bout of aerobic exercise on neuroplasticity in the motor system and motor learning in young healthy humans.    Within this general introduction, a number of important concepts in motor learning research are introduced, followed by a review of the neural substrates supporting the motor learning process. Next, interactions between the physiological effects of acute aerobic exercise and these motor learning processes are discussed, with consideration of the potential influence of genetic and epigenetic variation. Lastly, an overview of the research chapters is presented.     1.2 Motor learning concepts Several behavioural and psychomotor concepts have emerged in the motor learning literature that continually inform further work in the field. In this first introductory section, established concepts and common considerations in motor learning research that will inform interpretation of the forthcoming research chapters are presented.  1.2.1 Stages of motor memory formation Motor learning refers to a relatively permanent change in the internal capability to generate specific movements as a result of practice or experience (260). Although explicit memory functions, such as the processing of task instructions, may be engaged (17), motor skill learning is widely considered to represent a form of implicit, procedural memory. The most common example is learning to ride a bicycle (260); as individuals practice, performance improvements are noted, yet it is not possible for the individuals to consciously express what elements of their performance improved. 3  The formation of such an implicit motor memory can be considered to occur in three overlapping and inter-dependent stages: encoding, consolidation and retrieval (242, 243). Encoding is the most cognitively demanding of the stages, and involves the processing of information related to the task, such as the goal, the necessary motor command and the outcome of that motor command (242). Through encoding, the skilled movement is thought to be converted into a construct that can be stored in the brain in the form of memory trace (174). The consolidation stage then involves transferring and stabilizing the memory trace in long-term memory storage. Consolidation processes may begin while encoding is underway, but continue after practice has ceased and may evolve further with sleep (174, 299). The third stage, retrieval, is dependent on the quality of the prior stages and occurs when the memory trace is called upon to reproduce the movement (123). Related to these stages of memory formation, the motor learning process has also been shown to involve an early, fast phase followed by a later, slow phase. During fast learning, substantial improvements in performance occur quite quickly (e.g. within a single session) as the motor memory is encoded. These rapid improvements can be sustained and stabilized through consolidation processes, but additional performance improvements can also be achieved with further practice. These further improvements occur via slow learning, which is characterized by more gradual improvements in performance, across several training sessions (19, 62, 125, 156). At the beginning of these additional practice sessions the motor memory must be retrieved, and then further encoding and consolidation may occur throughout and following the practice session. The slow phase culminates in consistent, successful completion of the movement task with minimal requisite effort and/or attention (i.e. automatization). Once automatized, the motor memory is highly stable and easily retrieved when necessary. 4  1.2.2 Motor performance versus motor learning Given that motor learning processes evolve over time, a fundamental concept in motor behaviour research is the difference between performance and learning (260). Also of importance is that motor learning is a construct – it refers to a change in the internal capability for movement, and thus cannot be directly observed. Instead, motor learning is inferred from observing changes in motor performance. Motor performance at any given time then will partly reflect an individual’s internal capability for the movement but will also be dependent on which stages of memory formation and phases of learning have been engaged (123). During motor practice, encoding of the motor memory will be underway, and off-line consolidation processes will not yet have begun. As such, motor performance during practice is typically referred to as the skill acquisition period. To more fully evaluate learning per se, memory retrieval must be assessed following completion of practice through the use of a retention test (123). Retention tests may be conducted minutes to hours (i.e. immediate) or days to weeks (i.e. delayed) following practice. A delayed retention test ensures that consolidation processes have been fully engaged when retrieval is tested (123) and determines whether a relatively permanent change in performance (i.e. true motor learning) has occurred. Whether retention tests are employed following early (i.e. fast) or late (i.e. slow) phases of learning may also be of interest when interpreting experimental findings. Within this dissertation work, motor skill acquisition over a single session followed by a 24-hr retention test were examined (Chapters 2 and 4), likely reflecting the evolution of memory processes throughout the fast learning phase. 1.2.2.1 Measuring motor performance and learning As motor learning is commonly inferred from assessing a change in motor performance, it is also important to consider how to evaluate such a change. Generally, some measure of 5  performance relevant to the task is identified (e.g. error distance from a target, time to complete a movement). The change in motor performance measurements obtained at specific points in time within an experiment (i.e. from initial practice to retention) is then traditionally evaluated to determine the extent of learning that occurred (123, 260). Importantly, motor performance at any given time can be temporarily influenced by internal and external factors that may not be of interest in the experimental design, such as arousal, fatigue or the environmental context (197, 260). While the effects of such temporary influences can be mitigated by controlling experimental conditions, some effects are unavoidable and may obscure accurate measurement of learning-related change (197). Recent work has begun to parameterize data using curve fitting methods which take into account trends across multiple data points (21, 197). By considering motor performance trends that emerge across multiple data points (i.e. over the course of acquisition or a retention test) rather than averaging measures at specific time points, curve fitting approaches may be somewhat robust to unsystematic intra-subject variability (21, 197). Of note, motor learning was evaluated with both more traditional evaluations of motor performance change (Chapters 2, 4 and 5) and curve fitting methods (Chapter 4) within this dissertation work. 1.2.3 Task nature A further consideration when interpreting findings in motor learning research relates to the nature of the task that is tested. Similar to the variable movement demands of everyday life, motor learning research has spanned many different types of movement tasks. Two common classifications involve determining whether motor sequence learning or motor adaptation was evaluated, and whether the task involved continuous or discrete movements. 6  1.2.3.1 Motor sequence learning and motor adaptation Many motor skills that are executed on a regular basis involve the combination of isolated movements or the coordination of multi-joint synergies into functional movement sequences (260). Motor sequence learning is then of great functional relevance and is studied in a laboratory setting by assessing individuals’ performance of a specific series of movement actions. Past work has demonstrated that movement sequences can be learned by a purely implicit processes, as demonstrated by reductions in error on a repeated movement sequence concealed from the learner, relative to random movement sequences practiced within the same sessions (18, 43, 56, 298). Motor adaptation is another functionally relevant form of learning, which in contrast to motor sequence learning, involves compensatory changes to movement commands to accommodate for environmental changes or perturbations (e.g. if the keys on a piano had particularly high or low resistance) (264, 291). A common motor adaptation learning paradigm involves the manipulation of a robotic arm to point to a target while a specific force-field is applied to the arm (264, 291). Another motor adaptation paradigm involves the use of a visuomotor rotation task to introduce a directional bias, such as when an upward physical movement results in the downward movement of an on-screen cursor (139). Learning of such motor adaptations can occur without explicit awareness by the learner (i.e. implicitly), for example, when participants are not informed of movement transformations applied incrementally during task practice (120, 128). 1.2.3.2 Continuous and discrete motor tasks Another important classification considers whether a motor task involves continuous or discrete movements. Continuous movements have no clear beginning or end and continue until some arbitrary stopping point (e.g. riding a bicycle) (260). Experimentally, continuous motor 7  tasks typically involve tracking some moving target on a computer monitor with a device, such as a joystick. Discrete movements, then, are characterized by the presence of a specific beginning and end (260). Flicking a light switch is a common discrete movement used in everyday life. In the laboratory setting, a commonly used discrete movement task involves pressing a button on a keyboard in response to the presentation of a series of specific targets on a computer screen (i.e. serial reaction time task). This sort of task can also be referred to as a serial motor task, given that it involve a series or sequence of discrete movements, similar to playing the piano or shifting gears in a car (260).  Importantly, there are thought to be fundamental differences in the memory processes that guide learning of continuous and discrete motor sequence skills (32, 33, 260). For example, the use of a learning strategy termed chunking may differ between these two types of tasks. Chunking involves the storing of separate clusters of movements into chunks that are subsequently linked together to form a larger movement sequence (183). However, the lack of clear boundaries delineating between specific movements may prevent the use of this chunking strategy when learning continuous movement sequences (33). In contrast, chunking is an established motor learning strategy for discrete movement sequences (250). Moreover, past work has also indicated that the effects of breaking a skill down into parts versus teaching it as a whole (260), and providing distributed (i.e. interspersed rest breaks) versus massed (i.e. no rest breaks) practice schedules (147), have disparate effects on the learning of continuous and discrete motor sequence skills. While continuous sequence task learning is generally benefited when the skill is taught as a whole and/or with a distributed practice schedule, the opposite effects occur for discrete tasks (147, 260). This information highlights the importance of considering the nature of a task when interpreting findings in the literature. Although the underlying learning and memory 8  processes likely overlap to an extent between different types of tasks, thorough interpretation and appropriate application of specific findings within the field requires consideration of the nature of the task that was studied. Within this dissertation work, motor tasks were employed that involved varying combinations of sequence learning and visuomotor rotation, as well as continuous and discrete motor sequences (Chapters 2, 4 and 5). 1.3 Neural substrates of motor learning Motor skill learning is mediated by neuroplasticity, the intrinsic capacity of the CNS to undergo functional and structural adaptations in response to experience. Such plasticity occurs in many different forms and may be discussed at a molecular, cellular, synaptic, network and/or regional level. Here, a well-known cellular mechanism of neuroplasticity, termed long-term potentiation (LTP), is reviewed. The overarching brain systems involved in motor learning are then briefly introduced, but a specific focus is placed on the primary motor cortex (M1), cerebellum and basal ganglia. Finally, the utility of transcranial magnetic stimulation (TMS) in investigating motor learning-related plasticity is reviewed. 1.3.1 Long-term potentiation LTP refers to a long-lasting increase in the strength of connection between two neurons that are repeatedly activated together (11). Its discovery was widely heralded as providing neurobiological evidence for the Hebbian rule of learning, which states that “neurons that fire together, wire together” (95). LTP is now well-established as a key cellular mechanism of neuroplasticity underlying various forms of memory. Importantly, the counterpart of LTP is long-term depression (LTD), a decrease in synaptic efficacy related to reductions in synaptic activity (165, 252, 253). While LTP is largely responsible for strengthening and forming neural connections that support a learned behaviour (252), LTD plays an important role in forgetting 9  behaviours, as well as in the pruning and refinement of neural pathways in later stages of skill acquisition (101, 158, 312). In its most commonly studied form, LTP involves the following steps: 1) an excitatory neural connection is activated, such that the neurotransmitter glutamate is released from the pre-synaptic cell; 2) glutamate binds with AMPA (α-amino-3-hydroxy-5-methy-4-isoxazolepropionic acid) and NMDA receptors at the post-synaptic cell; 3) sodium enters the post-synaptic cell via AMPA receptors; 4) depolarization triggers the opening of NMDA receptor ion channels, allowing calcium to rush into the post-synaptic cell; 5) calcium entry prompts a sequence of biological events that increases the density of AMPA receptors on the post-synaptic cell membrane and, via release of a retrograde messenger to the pre-synaptic cell, enhances neurotransmitter release (122). These cellular changes strengthen the activated synaptic connection, such that there is an increased post-synaptic response to future pre-synaptic inputs. Along with the unmasking of latent horizontal cortical connections (105, 106), these LTP-induced changes in synaptic efficacy are thought to contribute to rapid skill acquisition, such as that which occurs in the aforementioned fast learning phase. Structural modifications in the CNS, such as dendritic branching and synaptogenesis (87, 130, 309), follow this early stage of LTP and may play a role in the slow learning phase described previously. Importantly, LTP (and LTD) mechanisms contribute to plasticity across multiple brain regions that support the motor learning process.  1.3.2 Motor learning brain systems Neuroimaging studies, as well as work investigating individuals with CNS damage, have indicated that activity in brain regions comprising the cortico-striatal and cortico-cerebellar systems provides major contributions to motor learning processes (54, 60, 62, 156). Importantly, 10  the involvement of various components of these wider brain systems change over the course of the motor learning process. For example, a network of associative/premotor brain regions, which are linked to higher order cognitive functions and sensory processing, is particularly active during early stages of motor learning (156). This network is generally comprised of the dorsolateral prefrontal cortex, rostral premotor areas, inferior parietal cortex, cerebellar cortex and rostral basal ganglia (156). Activity in the associative/premotor network gradually decreases after a single practice session, congruent with a gradual increase in activity within a sensorimotor network 24 hours after initial practice and over further sessions (156). These findings are consistent with psychomotor theories of motor learning that postulate that motor learning involves transitioning from a period of high cognitive demand, which involve more frontal brain areas, towards automatization, involving purely motor execution brain regions (75).  Although a clearer picture of the brain networks involved in motor learning is beginning to emerge, there is much more to be uncovered. For example, the neural resources underlying motor learning are likely dependent on the specific type of motor skill that is learned, but such intricacies are not yet entirely understood. It has been proposed that activity in the cortico-cerebellar and cortico-striatal systems may differentially support motor sequence and motor adaptation learning (60, 62). There is certainly evidence to support this task-dependent differentiation in motor learning brain systems (60, 62); however, other work emphasizes the more complementary fashion in which the cortico-cerebellar and cortico-striatal systems operate (59). Potential differences in neural substrates supporting continuous and discrete motor task learning are also plausible, given differences in supporting memory processes (32, 33). In terms of motor performance, activation of the superior vermis of the cerebellum was shown to be higher during discrete compared to continuous timed movement (276). Additional experimental 11  and theoretical work will further inform a more comprehensive understanding of the complex brain systems at play during different types of motor learning. 1.3.3 Role of the primary motor cortex in motor learning Of all the brain regions involved in motor learning, the role of the M1 region may be the best characterized. The M1 is located in Broadmann area 4 of the frontal lobe, including the pre-central sulcus and a portion of the pre-central gyrus. Compared to other motor regions in the brain, M1 has the largest and most direct neural pathway to the spinal cord and brainstem (64, 193). As such, the most commonly stated purpose of M1 is to connect the cerebral cortex to the contralateral spinal motor neurons to enable voluntary movement (216, 252). Interestingly, the M1 region has been found to ascribe to a general somatotopic organization with medial-to-lateral topography of leg, arm, head and face representations, and a larger relative size of M1 areas controlling body parts requiring fine (i.e. fingers, hand and face) rather than gross (i.e. trunk and leg) motor control (216). Yet, within this M1 map, which is also termed the motor homonculus (216), areas of M1 corresponding to various musculature have extensive overlap and wide distribution (149, 225, 237, 252). To add further to this complex circuitry, neural connections within M1 are highly modifiable (58, 202, 252, 253).  Initial characterizations of M1 plasticity were obtained through the use of intracortical stimulation mapping techniques in animal models. In both rodent and primate studies, skilled motor training was found to induce an expansion of M1 regions corresponding to muscles involved in the task, at the expense of adjacent M1 representations (131, 202). One of the first studies to document a similar phenomenon in humans showed that the learning of a piano finger sequence over five days resulted in enlarged M1 representations for the trained muscles (209). Further work demonstrated that 30 minutes of motor practice altered M1 organization and 12  increased excitability of the corticospinal pathway for involved muscles, indicating the presence of rapid plasticity within human M1 (41, 315). This robust and rapid neuroplastic change in M1 with motor training has been attributed to LTP mechanisms (315), as described above, as well as the unmasking of latent horizontal connections (105). An important caveat of this work is that plasticity in M1 only occurs in response to skilled movement training, as opposed to simple repetitive movements, strength training or aerobic exercise (129, 211, 221, 292). For example, M1 plasticity was observed in rats trained to perform a skilled reaching movement, but not in rats repeatedly performing a simple bar press (131). Other animal work has reported correlations between the extent of motor map expansion and motor skill learning (186), and human neuroimaging work demonstrated a shift towards greater activity in M1 relative to other brain regions in later stages of learning (156). Overall, these findings have contributed to recognition of M1 as a crucial brain region for motor learning processes that is involved in rapid skill acquisition, but also in the consolidation and long-term storage of motor memories. 1.3.4 Role of the cerebellum in motor learning M1 is not solely an output region, but instead is also the recipient of multiple regulatory inputs from other brain regions which may influence its activity, and consequently plasticity within its circuits (272). Of particular relevance to Chapter 3 of this dissertation work are cerebellar inputs to M1 and their potential influence on M1 plasticity and the motor learning process. The cerebellum is located posteriorly in the brain, underlying the occipital and temporal lobes of the cerebral cortex. Structurally, the cerebellum consists of three layers of cortex overlaying four deep cerebellar nuclei. The cerebellum receives vast sensory input from the proprioceptive and vestibular systems, as well as large projections from motor areas in the brain 13  (199). Cerebellar output to M1, specifically, is mediated by the dentate and interposed nuclei. Fibres from these nuclei transmit a tonic excitatory drive to the contralateral M1 through di-synaptic connections via the thalamus. However, Purkinje cells, the sole output of the cerebellar cortex, suppress this excitatory cerebello-motor drive by inhibiting the deep cerebellar nuclei activity. Purkinje cell activity is evoked by two input pathways. First, mossy fibre excitation of parallel fibres generates action potentials in Purkinje cells, termed simple spikes. In the other pathway, activation of climbing fibres in the inferior olivary nucleus of the medulla leads to powerful excitation and the generation of multiple action potentials in the Purkinje cells, termed a complex spike (199).  Motor learning-related plasticity in the cerebellum is largely thought to occur at the inputs to the Purkinje cells, which impact the output signals projected to M1 from the deep nuclei (199). Climbing fibre induction of complex spikes in Purkinje cells are thought to comprise an error signal, as they cause simultaneously active parallel fibre inputs to be weakened via LTD. On the other hand, parallel fibre-Purkinje cell inputs may also be strengthened through LTP mechanisms with a lack of synchronous climbing fibre activity (i.e. lack of error) (199). Consistent with a role in such error-feedback learning, cerebellar activity is closely associated with early phases of motor learning (61, 310). Past work has also indicated reductions in the inhibitory drive transmitted from the cerebellum to M1 with the learning of a motor adaptation (114) and a visuomotor rotation (259) task, with more prominent changes noted in early learning stages followed by a regression to baseline levels (259). Importantly, activity in the dentate nucleus in the later, slow learning phase has also been reported (63), highlighting the complexities of the neural processes that underlie motor learning within even a single brain structure. 14  1.3.5 Role of the basal ganglia in motor learning Another key neural circuit involved in motor learning processes is comprised of connections between the basal ganglia and motor areas (60, 62, 199, 297). The basal ganglia are located at the base of the forebrain and consist of multiple subcortical nuclei, including the caudate nucleus and putamen (collectively known as the dorsal striatum), as well as the globus pallidus, substantia nigra and subthalamic nucleus (199). The basal ganglia receive multiple projections from sensorimotor and frontal regions of the cortex, and project major outputs back to the cerebral cortex via the thalamus (99, 182). Generally, the basal ganglia exert a tonic inhibition on cortical areas that may then be released by cortico-striatal signals. The caudate and putamen are the primary recipients of frontal and sensorimotor cortical inputs, respectively, and project neurons to the globus pallidus which then interacts with the thalamus (199). Also of note, the substantia nigra projects dopaminergic neurons that tend to elevate activity in the dorsal striatum via the nigrostriatal pathway (79).  The extensive connections of the basal ganglia with frontal and sensorimotor cortices described above underpin its involvement in movement and motor learning processes (14, 60, 62, 85, 86, 184). Increased activation of the caudate in early stages followed by the putamen in later stages of motor learning suggest a basal ganglia-supported evolution of the motor learning processes, from higher-order cognitive functions towards automatization (148). Several other roles of the basal ganglia have been identified that may also contribute to motor learning, including movement planning (116, 281), movement selection and execution (31), switching between motor responses (92) and utilization of feedback via a dopamine-mediated reward system (208). Interestingly, a specific role of basal ganglia circuits in mediating motor chunking processes during the learning of discrete movement sequences has also been identified. For 15  example, individuals with Parkinson’s disease (85), a neurodegenerative condition of the substantia niagra, or stroke impacting the basal ganglia (14) demonstrate impairments in chunking during discrete motor sequence learning. 1.3.6 Transcranial magnetic stimulation Many of the advances in understanding of the neural basis of human motor learning, particularly with regards to M1, can be attributed to experimental TMS techniques. Certainly, a number of the experimental findings in humans that have been discussed to this point were achieved through the use of TMS (41, 114, 209, 259, 315). TMS is a non-invasive brain stimulation technique that utilizes the principles of electromagnetic induction to probe and modulate cortical activity. When a TMS coil is positioned on the scalp to overlie the M1 region of one cerebral hemisphere, neurons in M1 can be preferentially stimulated to generate contralateral muscle activity. The muscle activity can then be captured by electromyographic techniques in the form of a motor evoked potential (MEP). The MEP is comprised of descending volleys generated by direct (D-waves) and indirect, synaptic (I-waves) activation of pyramidal tract neurons (23, 65). Importantly, the muscle activity generated by TMS is dependent on neuronal excitability in both M1 and the spinal cord, and is typically considered to be a marker of corticospinal excitability. A number of TMS coil variants have been developed, including a figure-of-eight coil that improves the focality of the stimulus, and a double-cone coil that allows activation of deeper brain regions, such as the cerebellum (135, 249). 1.3.6.1 Single-pulse transcranial magnetic stimulation A variety of TMS techniques have been developed to assess and modulate M1 activity and organization. In terms of assessments, these techniques generally involve the delivery of single stimulus pulses or paired pulses delivered in quick succession. Single-pulse TMS 16  assessments used in the present dissertation work (Chapters 2, 3 and 5) include MEP threshold and MEP recruitment curves (also known as input-output curves). MEP threshold is commonly defined as the lowest TMS intensity required to elicit an MEP with a peak-to-peak amplitude of 50 μV in 50% of successive trials using the same intensity of stimulation (249). The MEP threshold is measured by TMS over the cortical site that produces the largest MEP at a given TMS intensity. MEP threshold is representative of membrane excitability of cortical neurons and lower motor neurons and can be determined while a muscle is maintained at rest (resting motor threshold, RMT) or during an active contraction (active motor threshold, AMT) (121). MEP recruitment curves can then be constructed by measuring the amplitude of MEPs elicited by TMS at varying stimulation intensities relative to threshold (28, 57). With increasing stimulation intensity, neurons that are spatially further from the centre point of the M1 representation and neurons with higher excitatory thresholds are activated (268). As such, information obtained from MEP recruitment curve data overlaps considerably with TMS procedures involving delivery of single-pulse TMS at multiple sites to map the size of M1 representations, although mapping procedures can reveal unique information, such as asymmetric changes in M1 representation size (268). Nevertheless, the slope of an MEP amplitude by TMS intensity plot (i.e. recruitment curve) is considered to provide a comprehensive assessment of corticospinal excitability for a given muscle (268). 1.3.6.2 Paired-pulse transcranial magnetic stimulation The delivery of two TMS pulses in quick succession (i.e. paired-pulse TMS) allows the assessment of excitatory and inhibitory neural connections within M1 and from various brain regions to M1. For example, short intracortical inhibition and intracortical facilitation can be explored depending on the stimulation intensities and the inter-stimulus intervals (ISIs) of paired 17  TMS pulses delivered over M1 (143). Likewise, inter-hemispheric or inter-regional connections can be explored utilizing dual-coil paired-pulse protocols. In the research presented in chapter 3, a dual-coil paired-pulse TMS technique was utilized to evaluate cerebellar influences on M1 activity by eliciting cerebellar inhibition (CBI). CBI involves delivery of a cerebellar stimulus 5-7 ms prior to M1 stimulation (51, 89, 220, 293). The cerebellar stimulus is termed the conditioning stimulus and is delivered with a double cone coil placed 3 cm lateral (opposite of the figure-of-eight coil described below) of the midpoint along a line between the inion and the mastoid process. The cerebellar stimulation, which is thought to activate Purkinje cells that inhibit the tonic excitatory drive from the dentate nucleus to M1, results in a suppression of the amplitude of the MEP elicited by M1 stimulation alone (30, 220, 293). Previous studies have confirmed this CBI circuit, ruling out a potential effect of the cerebellar stimulus on activation of the brachial plexus or corticospinal tract (220, 304). 1.3.6.3 Modulating brain activity with transcranial magnetic stimulation Besides assessments of M1 activity, TMS techniques have also been designed to alter cortical excitability. Two common techniques are repetitive TMS (rTMS) and theta-burst TMS TBS. rTMS refers to the delivery of TMS at a continuous frequency (~1-5 Hz) for several minutes (e.g. 15 minutes) (36, 210), while TBS involves delivery of successive bursts of high-frequency TMS (e.g. 50 Hz) over a shorter time period (e.g. 40 s to three minutes) (104). Alteration of stimulus frequencies and patterns of rTMS and TBS influence whether M1 excitability is up- or down-regulated. This frequency-dependent homeotopic plasticity relies on the repetitive activation of the same neural input. TMS can also be utilized in concert with peripheral electrical stimulation to elicit spike-timing dependent plasticity, relying on synchronous activation of two inputs to the same cell. This technique is commonly termed paired 18  associative stimulation (PAS) (42), and was used extensively in this dissertation work (Chapters 2, 3 and 5).  PAS involves the repetitive delivery of an electrical stimulus to a peripheral nerve, to transmit an afferent volley to M1, followed by a TMS pulse over M1 (42). When the ISI between the sensory stimulus and the M1 stimulus is greater than or equal to the time for the sensory volley to reach M1, PAS has excitatory effects on M1. Typically, excitatory PAS is considered to occur at ISIs ranging from 21-25 ms (189, 191, 279, 303, 311). As per spike-timing dependent plasticity, if the ISI is less than the time for the sensory volley to reach M1, PAS may have suppressive effects on M1 excitability (42, 246). Parameters of PAS have varied between studies, but paired stimuli are commonly delivered at frequencies of 0.25 Hz or 0.1 Hz for 10-30 minutes. During excitatory PAS, the afferent input is generally thought to reach M1 via the dorsal column-medial lemniscus pathway to thalamus followed by a relay through somatosensory cortex or direct thalamic inputs to M1 (279, 311). Nevertheless, recent work suggests that PAS utilizing longer ISIs (i.e. 25 ms) may also utilize more indirect afferent pathways to influence M1, such as through the cerebello-thalamo-motor circuit (90, 228).   PAS was developed based on principles derived from studies of LTP conducted in animal models and cortical slices, whereby stimulation of cortical afferents was paired with depolarization or stimulation-induced firing of the post-synaptic cell (7, 110, 279). The employment of these LTP principles in the design of PAS, combined with findings of rapid and persistent effects of PAS on M1 excitability, have been taken to suggest that PAS effects are dependent on trans-synaptic LTP-like mechanisms (279). Further evidence is derived from a study showing that the excitatory effects of PAS on M1 are abolished by administration of an NMDA receptor antagonist (278). Although reciprocal inhibition in spinal circuits has been 19  shown to be modified by PAS (181), other work has shown no effect of PAS on F-waves or potentials evoked by electrical brainstem stimulation (279). As a result, PAS is widely considered to alter corticospinal excitability primarily through an impact on M1 excitability.  1.4 Modulation of motor learning and its neural substrates by acute aerobic exercise Increased understanding of the neural substrates of motor learning has led to interest in the development of strategies that optimize motor learning by modulating the underlying neurobiological processes. The main objective of this section is to consider the potential of using a single bout of aerobic exercise to prime neuroplasticity and facilitate motor learning. Behavioural research indicating an impact of acute aerobic exercise on cognitive function and, specifically, motor learning is described. The neurophysiological effects of acute aerobic exercise that may facilitate memory and motor learning processes are then explored. Within this section, a neurochemical hypothesis for acute aerobic exercise effects on the brain is considered, with an emphasis placed on the potential roles of brain-derived neurotrophic factor (BDNF) and dopamine. The information presented in this section is pertinent to all of the included research projects (Chapters 2-5), given their general aim of further elucidating acute aerobic exercise effects on M1 plasticity and motor learning. 1.4.1 Acute aerobic exercise effects on cognitive function The study of acute aerobic exercise effects on cognition was initially guided by the concept of exercise-induced arousal, which led to a focus on information processing and decision-making tasks (e.g.. simple and choice-reaction time tasks) (20, 288). Lambourne and Tomporowski (145) conducted a meta-analysis of 29 studies, including 109 effect sizes, investigating the influence of acute aerobic exercise on cognitive function. The influence of the exercise prescription and the type of cognitive assessment employed were considered as 20  moderating factors (145). Overall, cognitive performance was significantly enhanced immediately following aerobic exercise compared to pre-exercise assessments (145). The effect was consistent across studies that employed protocols involving steady-state exercise, exercise-to-exhaustion and brief periods of high-intensity exercise (i.e. intervals), but cycling benefited cognitive function more than running (145). Notably, the largest effects were observed in a small number of studies that employed cognitive assessments of declarative memory (nine effect sizes in total), rather than the more common investigations of information processing and decision-making (145).  Evidence that acute aerobic exercise may impact memory processes is significant, as it suggests that acute aerobic exercise effects could extend beyond temporary cognitive benefits associated with arousal, and may have the potential to prime lasting changes in behaviour. Interestingly, the most robust benefits of acute aerobic exercise have been reported for long-term (i.e. recall of information more than two minutes following exposure), rather than short-term, memory processes (244). For example, moderate-intensity cycling immediately prior to information exposure enhanced verbal memory recall 12 minutes (46) and 35 minutes (144) later in young healthy adults. Likewise, high-intensity running accelerated acquisition rate and enhanced recall seven days later for a vocabulary learning task in young adults (308). In another study, moderate-intensity cycling performed immediately after exposure enhanced image recall one hour following exposure in older adults (262). Together, these findings suggest that acute aerobic exercise may impact long-term declarative memory through an interaction with both encoding and consolidation processes (145, 244). 21  1.4.2 Acute aerobic exercise effects on motor learning Reports of acute aerobic exercise benefits for declarative memory have inspired two recent studies evaluating whether similar effects extend to motor memory processes. Roig and colleagues examined the effects of acute high-intensity interval cycling on acquisition and retention of a continuous motor tracking task involving a visuomotor rotation (245). Compared to a resting control group, individuals who exercised immediately before or after task practice demonstrated enhanced long-term retention of the skill (i.e. 24 hours and seven days post-practice), despite no effects on performance during practice (i.e. acquisition) or at a one-hour retention test (245). Interestingly, the positive effects of exercise were greatest in the group that exercised after motor practice and most pronounced at the seven-day retention test (245). A more recent study examined the potential of an acute bout of moderate-to-vigorous intensity aerobic exercise to protect against motor memory interference effects (240). In this study, off-line consolidation gains 24 hours after practice of a discrete motor sequence task were disrupted by subsequent practice of a competing motor sequence; however, the offline gains were maintained in individuals that performed aerobic exercise after the initial practice and immediately prior to the competing sequence practice. Together, given the findings in these studies that exercise after motor practice influenced delayed retention test performance (240, 245), it has been suggested that acute aerobic exercise primarily interacts with motor memory consolidation processes. Nevertheless, the acute aerobic exercise effect on motor learning is far from an established finding and, given the complexity of the motor learning process, many questions remain. Thus, additional work is necessary to replicate findings, examine different types of tasks, and generally provide further insights into the nature of acute aerobic exercise benefits for motor learning. 22  1.4.3 Indirect effects of acute aerobic exercise on the brain Acute aerobic exercise has far-reaching effects on human physiology (172), many of which could indirectly interact with the neurobiological mechanisms of motor learning. Two prime examples are aerobic-exercise induced changes in energy metabolism and vascular function. For example, alterations in cerebral metabolism have been demonstrated during and immediately following aerobic exercise (107). Further, as energy sources are delivered to the brain via the vascular system, changes in cerebral blood flow induced by aerobic exercise also likely have a role to play (162, 230, 274). Although not a focal point of this dissertation work, these broad physiological effects of acute aerobic exercise must be acknowledged when considering acute aerobic exercise effects on brain function. The remainder of this subsection will focus on more direct effects of acute aerobic exercise on the brain, specifically related to acute aerobic exercise-induced alterations in the concentration of neurochemicals, as well as modulation of brain activity and electrophysiology. Overlap between these aerobic exercise effects and the neural substrates of motor learning will be emphasized. 1.4.4 Acute aerobic exercise modulates neurochemicals One of the predominant hypotheses for the mechanism underpinning acute aerobic exercise effects on brain function relates to alterations in neurochemical concentrations (137, 244, 273, 308). Animal work has demonstrated vast changes in gene expression across numerous CNS regions following aerobic exercise training (47, 289). Critically, many of the genes with altered expression encode for molecules involved in synaptic plasticity processes (289). Complementary work in humans has demonstrated transient, acute aerobic exercise-induced increases in systemic levels of various neurotrophic factors, dopamine, epinephrine, norepinephrine and lactate (137, 244, 273, 308) – all neurochemicals thought to play an active 23  role in neuroplasticity and memory. Two molecular signaling pathways that are implicated in aerobic exercise effects on brain function and were specifically probed within the research presented in this dissertation involve the neurochemicals BDNF (Chapters 2 and 5) and dopamine (Chapter 5). 1.4.4.1 Brain-derived neurotrophic factor BDNF is a member of the neurotrophin family of proteins that is found in high concentrations in multiple brain regions, including the hippocampus, hypothalamus, cortex and cerebellum (192). BDNF has gained notoriety in the literature for its role as a key modulator of synaptic plasticity (10, 158, 227). Upon activity-dependent release from a neuron, BDNF binds with its TrkB (tyrosine kinase-B) receptor at the pre- and post-synaptic cell membranes to promote trafficking of neurotransmitter vesicles and enhance the probability of NMDA receptor channel opening, thereby facilitating LTP mechanisms (158, 227). Notably, the precursor form of BDNF (pro-BDNF) has also been implicated in LTD processes, suggesting BDNF may play a regulatory role in bidirectional synaptic plasticity (157). Consistent with its cellular mechanisms, BDNF has been implicated in multiple cognitive processes, including motor learning. BDNF levels are increased in rat M1 concurrent with motor map expansion following motor practice (111, 134). Moreover, disruption of BDNF signaling with pharmacological intervention in rats impairs skilled motor performance and disrupts M1 map plasticity, but subsequent application of exogenous BDNF into M1 partially restores these functions (294).  A pivotal study by Vaynman and colleagues provided strong evidence that BDNF is an important contributor to the effects of aerobic exercise on memory processes (295). Compared to sedentary controls, rats that engaged in one week of aerobic exercise training demonstrated increased levels of BDNF in the hippocampus and improved performance on a spatial memory 24  test (295). Critically, when the same aerobic exercise training program was employed, but synthesis of BDNF in the hippocampus was blocked, the learning and recall abilities of exercising animals were reduced to the levels of sedentary controls (295). Although BDNF cannot be directly measured or blocked in vivo in the human brain, multiple human studies have now demonstrated that acute aerobic exercise enhances systemic BDNF levels (137, 273, 308). The increases are positively related to the intensity of the exercise bout, but are transient, with BDNF concentration typically returning to baseline levels by 60 minutes post-exercise (137). Moreover, the magnitude of BDNF increase following acute aerobic exercise correlated with facilitation of declarative memory and motor learning in previous work (308). Although the relevance of systemic BDNF measurements to central BDNF levels is questionable due to limited transport across the blood-brain barrier (194), these correlations in human work combined with the previous animal studies suggest that BDNF contributes to acute aerobic-exercise induced increases in declarative and motor memory. 1.4.4.2 Dopamine Dopamine is another important CNS molecule that is thought to be up-regulated by acute aerobic exercise (217, 308). Dopamine is highly involved in the prefrontal cortex, and as such is implicated in high level cognitive functions, including attention, problem-solving and memory (201). On a cellular level, dopamine facilitates synaptic plasticity by interacting with protein kinases and phosphatases to amplify the NMDA receptor signal during LTP (113, 161). Of relevance to motor learning, the ventral tegmental area of the basal ganglia is the primary source of dopamine production in the brain (199). Critically, elimination of dopaminergic projections from basal ganglia to M1 impairs motor skill learning and M1 plasticity in a rat model (102, 187, 241). In line with a role of dopamine in motor learning discerned from animal work, 25  administration of a dopamine precursor (levo-dopa, L-dopa) in humans enhances motor learning in young (212) and older adults (76).   A role for dopamine in mediating aerobic exercise-induced increases in brain function has been speculated (217, 244, 308), although empirical findings remain unclear. Multiple animal studies have demonstrated that acute aerobic exercise increases the concentration of extracellular dopamine in the basal ganglia (93, 180, 307). Nevertheless, positron emission topography imaging did not detect changes in dopamine signaling in the human basal ganglia immediately following 30 minutes of moderate-intensity treadmill running; although the null finding was largely attributed to low sensitivity of the imaging technique compared to techniques used in animal work (301). In contrast, another study in humans reported that systemic dopamine levels were increased immediately following high-intensity treadmill running and were positively correlated with improvements in immediate and seven-day retention tests of declarative memory (308). A more recent study also found that acute high-intensity aerobic exercise increased systemic dopamine levels, but the change in dopamine concentration was not related to aerobic exercise-induced improvements in motor learning (273). As with BDNF though, inferences of central levels of dopamine through measurement from the periphery are limited as dopamine does not cross the blood-brain barrier (284). In sum, its known involvement in memory processes and evidence that it is modulated by aerobic exercise have distinguished dopamine as a candidate molecule for mediating acute aerobic exercise effects on the brain, yet further work is needed to evaluate this hypothesis. 1.4.5 Acute aerobic exercise modulates motor cortex electrophysiology   TMS-based assessments of M1 excitability and plasticity have provided further insight into potential mechanisms by which acute aerobic exercise might interact with motor learning. 26  Interestingly, past work has demonstrated that the excitability of the corticospinal output, as evaluated with single-pulse TMS, is not changed during or following moderate-intensity cycling (267, 270, 275). In contrast, the excitability of intracortical circuits appear to be altered with exercise. For example, short-interval intracortical inhibition elicited from M1 representations of exercised muscles undergoes phasic modulation during aerobic exercise (266), and is decreased immediately following aerobic exercise (313). Further work has demonstrated that moderate-intensity cycling also decreases intracortical inhibition (270, 275) and increases intracortical facilitation (270) for a non-exercised upper limb muscle for up to 30 minutes post-exercise. Intracortical inhibition and facilitation in M1 are thought to be mediated by GABAergic (γ-aminobutyric acid) and glutamatergic interneurons, respectively, both of which are highly implicated in neuroplastic processes (i.e. LTP and LTD) (35, 151). For example, animal (52) and human studies (315) have demonstrated that decreased GABAergic inhibition is a necessary precursor to motor learning and the underlying M1 plasticity. These alterations in M1 intracortical networks have been taken to suggest that, while acute aerobic exercise does not directly modulate the excitability of M1 output neurons, it alters the balance of excitatory and inhibitory input to these cells (270). Further, the net effect appears to be the creation of an M1 environment that may be particularly supportive of neuroplastic change, such as that which underpins motor learning (275). Findings from two recent studies provide preliminary support for this interpretation (173, 271).  McDonnell and colleagues reported the first evidence for an acute aerobic exercise effect on M1 plasticity (173). In this work, continuous TBS designed to induce LTD-like effects suppressed M1 excitability for a muscle of the hand when it was preceded by low-intensity aerobic cycling, but not moderate-intensity aerobic exercise or rest (173). The authors attributed 27  the absence of an effect of moderate-intensity aerobic exercise to increased levels of cortisol, a stress hormone that negatively affects M1 plasticity, compared to the low-intensity bout (173). In another study, a PAS protocol designed to induce LTP-like effects resulted in immediate increases in M1 excitability when it was preceded by moderate-intensity aerobic exercise, compared to a period of rest; however, 30 minutes post-PAS, M1 excitability was increased to the same extent by both conditions (271). PAS preceded by aerobic exercise also induced a greater release of intracortical inhibition at both time points as compared to the rest condition (271). Although the literature base is small, these findings suggest that acute aerobic exercise may have the capacity to promote rapid plasticity underpinned by LTD- and LTP-like processes in M1, and could plausibly represent a neural substrate through which motor learning processes may be modulated. Further, the effects described here could be underpinned by cellular changes related to neurochemical levels described in the previous section. A primary objective of this dissertation work was to further elucidate these electrophysiological findings and their relevance for motor learning. 1.5 Considering the potential moderating effect of genetic and epigenetic variation Prior sections have included information related to the basic molecular signaling pathways underlying motor learning-related plasticity and their potential modulation by acute aerobic exercise. Yet, these signaling pathways are dependent on the expression of a number of genes and thus, genetic and epigenetic variation could conceivably moderate aforementioned effects (214). Consideration of such genetic and epigenetic variations could contribute to the clarification of basic molecular mechanisms and the identification of individuals that are best suited to benefit from neural effects of acute aerobic exercise (214). In the current section, the influence of genetic variation on motor learning and related plasticity is reviewed, considering 28  potential interactions with the effects of acute aerobic exercise. Single nucleotide polymorphisms (SNPs) impacting BDNF and dopamine signaling pathways are emphasized, given evidence that both molecules are involved in motor learning and upregulated by acute aerobic exercise. DNA methylation, an epigenetic modulator of gene expression, is also discussed. 1.5.1 BDNF gene val66met polymorphism Approximately 30-50% of North Americans carry a SNP on the gene that encodes for BDNF. This particular missense SNP causes an amino acid switch from valine (val) to methionine (met) at codon 66 (val66met). Egan and colleagues demonstrated that the met form of BDNF is associated with an approximately 25% reduction in activity-dependent secretion, a crucial property for its involvement in plasticity processes (i.e. LTP and LTD) (66). Since its initial discovery, this common polymorphism has been associated with a variety of changes in brain structure and function in humans, likely through an impact on the role of BDNF in plasticity, neurogenesis and neuroprotection. For example, the met allele is linked to reduced volume of the prefrontal cortex (219) and hippocampus (22, 219), abnormal hippocampal activation when performing a working memory task during functional magnetic resonance imaging (66) and impaired performance on hippocampal-dependent memory tasks (66, 91, 97).   The effects of the BDNF val66met polymorphism on hippocampal function have been studied most extensively, but BDNF is widely distributed throughout the brain and involved in multiple forms of plasticity, including those which support motor learning (111, 134, 294). A number of studies have demonstrated that a single session of fast index finger movement training or complex visuomotor task practice induces less M1 map expansion and less increase in M1 excitability in met allele carriers, compared to val/val homozygotes (132); however, plasticity deficits in met allele carriers may be overcome with practice over several days (176). Related 29  work has demonstrated decreased plasticity in hand and pharyngeal muscle representations induced by non-invasive brain stimulation and electrical stimulation, respectively, in met allele carriers compared to val/val individuals (34).There is also evidence for behavioural differences in motor performance and learning between met allele carriers and val/val homozygotes (177). Notably though, null BDNF genotype effects on M1 plasticity and motor behaviour have also been reported, particularly in terms of behavioural assessments (39, 132, 150). It has been suggested that the complexity of the processes determining motor behaviour and variation in motor task demands may contribute to the lack of robust findings in motor behaviour and learning assessments (39, 132). 1.5.2 Dopamine-related gene variation The known role of dopamine in modulating synaptic plasticity has generated interest in the study of its modulation by genetic variation. Yet, a key challenge to this work is that SNPs impacting dopaminergic transmission have been identified on a number of genes (214). Key dopaminergic genes with functional SNPs include catechol-o-methyltransferase and dopamine transporter protein, which regulate synaptic dopamine levels, as well as the D1, D2 and D3 dopamine receptors (212-214). These genes code for their respective proteins in various brain regions, but are primarily expressed in the basal ganglia and cortex (213, 214). Similar to findings for the BDNF gene variant described above, these dopaminergic genes have been associated with a variety of brain and behavioural changes, including structural changes in the prefrontal cortex, anterior cingulate, as well as alterations in attention and declarative memory (213, 214). 30   Although dopaminergic neurotransmission contributes to motor learning processes, only one study has examined the potential influence of variation in dopamine genes specifically on motor learning (212). In this study, a dopamine gene score was determined based on SNP variants of the five genes mentioned above. Gene scores corresponding to greater expected dopaminergic neurotransmission were associated with greater motor learning under a placebo condition but less facilitation of motor learning by oral administration of the dopamine precursor, L-dopa (212). On the other hand, gene scores associated with lower dopamine transmission were related to greater M1 plasticity under the placebo condition, and less plasticity under the L-dopa condition (212). The contrasting patterns of behavioural and neural change between genotypes are counter-intuitive to past evidence indicating positive correlations between the extent of motor learning and M1 activity, but nonetheless indicate a potential role of variation in dopamine genes on motor learning processes (212). Interestingly, within this study a SNP associated with an approximately 40% reduction in dopamine D2 receptor availability in the basal ganglia (DRD2/ANKK1 glu716lys) was found to have the strongest relationship with motor learning outcomes (212). Lys allele carriers demonstrated impaired learning at placebo but were benefited more by L-dopa administration, compared to glu/glu homozygotes (212).  1.5.3 Interactions of genetic variation with aerobic exercise effects on the brain Whether acute exercise effects on motor learning might be impacted by genetic variation, such as that described above for BDNF and several dopamine genes, is relatively unknown. One study found that object recognition memory was enhanced following four weeks of aerobic exercise training in BDNF gene val/val individuals, but not met carriers (100). On the other hand, physical activity has been found to protect against depression to a greater degree in met carriers than in val/val homozygotes (170). Of potentially more relevance to motor learning processes, a 31  small study (6 individuals of each genotype) found a non-significant trend for a greater modulation of intracortical inhibition by acute aerobic exercise in BDNF met allele carriers, compared to val/val individuals (270). In terms of dopamine genes, a SNP on the catechol-o-methyltransferase gene influenced the effects of a long-term aerobic exercise program on cognitive function in young adults (282). Other work found that the impact of acute aerobic exercise on cognitive function in adolescent children was modulated by a SNP targeting the dopamine transporter protein gene (9).  Although findings to date are limited and somewhat ambiguous, various trends and significant findings related to BDNF and dopamine gene variants and the motor system, combined with known involvement of these molecules in motor learning and acute aerobic exercise effects (76, 217, 273, 295, 308), suggest that further investigation may be fruitful. 1.5.4 Potential contributions of epigenetic variation Epigenetic modifications refer to functionally relevant changes to the genome that do not involve alteration of the nucleotide sequence (118). While such epigenetic changes may occur in a number of different forms, DNA methylation is the most characterized within the literature. DNA methylation involves the addition of a methyl group to a cytosine residue, typically at a cytosine-phosphate-guanine dinucleotide (CpG site) and is partly dependent on external or environmental factors (e.g. aging, parenting, lifestyle, etc.). Once attached, these methyl groups can alter the activity and expression of the gene, depending on their position within the transcriptional unit (118). Increasing research utilizes DNA methylation patterns as markers of an epigenetic state that impacts gene expression and may consequently contribute to the development of specific phenotypic traits (138). As a result of this research, DNA methylation 32  patterns are now widely recognized as an important factor influencing health and disease in humans (67, 72, 290).  Importantly though, epigenetic markers are highly dependent on cell type (103, 316). Certainly, epigenetic regulatory processes modify gene expression to facilitate cell differentiation throughout growth and development (316). Nevertheless, concordant DNA methylation patterns have been observed across cell types, such as between samples obtained from peripheral blood and post-mortem brain tissue (71, 300). Consistent with these findings, DNA methylation patterns in peripheral blood cells have been implicated in various conditions and diseases of the CNS (50, 108, 300). Thus, while DNA methylation patterns are highly cell type-dependent, there is ample work suggesting that blood can serve as a surrogate tissue to detect methylation patterns that may relate to brain functions. Further, reports of associations between DNA methylation patterns and multiple neurological diseases indicate that epigenetic modifications play an important role in neuronal processes, and consequently, cognitive functions (152). For example, methylation of both the BDNF and DRD2 genes described above have been found to relate to various psychiatric disorders in humans, including depression and schizophrenia (77, 127, 314).  Considering this information, it seems plausible that, through an influence on the expression of genes such as BDNF and DRD2, inter-individual variability in DNA methylation could contribute to differences between people in both motor learning and physiological responses to acute aerobic exercise. Such effects could be independent from or interactive with potential effects of genetic variation described above. In terms of the latter, methylation patterns may even contribute to previously noted discrepancies between studies examining the impact of the BDNF gene val66met polymorphism on motor learning and related plasticity. Critically 33  though, it must also be recognized that epigenetic modifications are dynamic in nature (316). For example, DNA methylation and other epigenetic modifications within the BDNF gene, among others, are thought to contribute to changes in gene expression associated with late phase LTP in long-term memory processes (152, 160). Similarly, epigenetic mechanisms are also implicated in changes in gene expression induced by aerobic exercise (83). Nevertheless, the baseline epigenetic state of a gene is thought to partly underlie individuals’ predispositions for various traits and potentially individuals’ responsiveness to treatments or interventions based on environmental triggers (126). Thus, preliminary work investigating relationships between DNA methylation patterns, motor learning and neural effects of acute aerobic exercise appear warranted (see Chapter 5). 1.6 Thesis overview The overarching objective of this thesis is to examine the effects of a single bout of aerobic exercise on brain plasticity and motor learning in young healthy adults. Chapters 2 through 5 comprise the original research contributions of the dissertation, beginning with an investigation of the effects of a single bout of aerobic exercise on neuroplastic response evoked by PAS and the learning of a continuous movement sequence task. In Chapter 3, experiments utilizing TMS techniques to examine acute aerobic exercise effects on cerebello-motor circuits are described. Chapter 4 then involves evaluation of the effects of acute aerobic exercise on the learning of a discrete movement sequence task. The research contributions are concluded in Chapter 5 with a retrospective analysis of data collected for the first three research projects to explore potential relationships between genetic and epigenetic variation and the effects of acute aerobic exercise on the motor system. Finally, a general discussion of the main findings of the thesis, the limitations, future directions and conclusion is provided in Chapter 6. 34  1.6.1 Thesis impact Rehabilitation strategies for many individuals with neurological conditions aim to promote the re-learning of movement skills lost due to the condition or injury. This re-learning process is mediated, at least in part, through similar neural substrates to those underpinning motor learning in healthy individuals (140, 302). As such, the concept of applying neurobiological principles and motor learning concepts in rehabilitation settings has generated great interest (140, 200, 302). This dissertation work was designed to contribute basic scientific knowledge towards the newly posited idea that scheduling aerobic exercise bouts in close temporal proximity to task-specific movement training sessions may enhance response to motor rehabilitation partly through an influence on neuroplasticity and memory processes (244, 245, 270, 271, 273). The findings provide insights into the specific aspects of motor learning that are affected and the neurophysiological underpinnings, as well as genetic and epigenetic contributions to inter-individual variability in acute aerobic exercise response in young healthy individuals. The results also directly impact learning strategies for young, healthy people, showing a positive effect of acute aerobic exercise on learning and memory systems in the brain. Further, these studies provide an important starting point for future research and consideration of whether the posited strategy might have meaningful applications in clinical settings. The potential clinical importance of work related to this dissertation is further elaborated in Appendix A, which includes a perspective essay offering a theoretical basis for the potential use of acute aerobic exercise to prime plasticity and promote motor rehabilitation after stroke. 1.6.2 Specific research objectives Chapter 2: To investigate the impact of a single bout of high-intensity aerobic exercise on M1 plasticity evoked by excitatory PAS and motor learning of a continuous motor sequence task. 35  Hypothesis: PAS will evoke a greater increase in M1 excitability when preceded by a bout of aerobic exercise, compared to a period of rest. Likewise, participants will demonstrate enhanced learning of a continuous motor sequence task when motor practice is immediately preceded by aerobic exercise, relative to seated rest. Chapter 3: To evaluate the involvement of cerebello-motor circuits in mediating acute aerobic exercise effects on M1 plasticity. Hypothesis: Acute aerobic exercise will reduce CBI on M1. Additionally, excitatory response in M1 evoked by a PAS protocol bypassing cerebellar circuitry will be facilitated to a lesser extent than by a protocol involving cerebellar circuits.  Chapter 4: To determine the effects of acute high-intensity aerobic exercise on the extent and rate of learning of a discrete motor sequence task.  Hypothesis: Compared to a period of seated rest, acute aerobic exercise performed prior to task practice will enhance the extent and rate of learning of a discrete motor sequence task. Chapter 5: To explore the potential influence of variation in genetic and epigenetic markers on acute aerobic exercise effects on M1 plasticity and motor learning. Hypothesis: Genetic polymorphisms and DNA methylation patterns impacting the BDNF and DRD2 genes will be associated with inter-individual variability in the neurophysiological and behavioural response to acute aerobic exercise.  36  Chapter 2: A Single Bout of High-Intensity Aerobic Exercise Facilitates Response to Paired Associative Stimulation and Promotes Sequence-Specific Implicit Motor Learning 2.1  Introduction Multiple studies demonstrate that engaging in regular exercise has positive effects on cognitive function (45, 244). Complementary research indicates that engagement in a single bout of aerobic exercise can also positively impact cognitive function, with the most robust effects occurring on learning and memory processes (145, 244). For example, aerobic exercise performed immediately prior to task practice facilitated vocabulary learning in young healthy individuals (308) and enhanced image recall in healthy elderly individuals, as well as those with mild cognitive impairment (262). More recently, a high-intensity aerobic exercise bout performed immediately before or after skilled motor practice increased long-term retention of the motor skill (24 hours and seven days after practice), suggesting that pairing aerobic exercise with motor practice has potential to facilitate motor learning (245). This finding has led to further speculation that pairing aerobic exercise with motor training may facilitate response to motor rehabilitation training after neurological injury (166, 244, 245). However, the mechanisms driving these effects of aerobic exercise on learning are not well understood. The present study was designed to investigate the priming effects of an acute bout of high-intensity aerobic exercise on neuroplasticity and implicit motor skill learning in young healthy individuals. The immediate effects of aerobic exercise on memory may be driven in part by exercise-induced increases in neurochemicals, which facilitate long-term potentiation (LTP) (244, 271). While ample work demonstrates systemic increases in catecholamines and neurotrophic growth 37  factors immediately after aerobic exercise (26, 38, 308), limited work directly evaluates altered capacity for neuroplastic change evoked acutely by aerobic exercise. A continuous theta burst stimulation (cTBS) protocol designed to suppress motor cortical (M1) excitability via long-term depression (LTD)-like mechanisms evoked a greater suppressive effect when preceded by a low-intensity bout of aerobic exercise, compared to a moderate-intensity bout or a period of rest (173). Recently, an acute bout of moderate-intensity aerobic exercise was found to facilitate response to paired associative stimulation (PAS) administered to increase M1 excitability via LTP-like mechanisms (271). While the only study to demonstrate an effect of aerobic exercise on motor learning utilized a high-intensity exercise bout (245), the immediate effect of a high-intensity exercise bout on neuroplasticity has yet to be investigated. Importantly, aerobic exercise-induced alterations in neurochemicals, which can both up- and down-regulate neuroplasticity, are dependent on exercise intensity (137, 173, 251, 308). Thus, additional research is necessary to determine whether exercise-induced changes in LTP may underlie the effects of high-intensity aerobic exercise on motor learning. Currently, only one study has evaluated the effect of acute aerobic exercise on motor learning (245). Motor learning is a complex process, involving multiple brain systems that support different aspects of skill acquisition (277). For example, motor learning can involve non-specific improvements in motor control, as well as implicit (i.e. acquired without conscious awareness) sequence-specific improvements in performance (16, 179, 298). Additionally, motor performance can be decomposed into elements of temporal precision and spatial accuracy (16, 298). An improved understanding of which aspects of motor learning are acutely affected by aerobic exercise could provide insights into the brain regions primarily impacted by aerobic 38  exercise, as well as whether aerobic exercise has different effects on the learning of different types of motor skills. We hypothesized that an acute bout of high-intensity aerobic exercise would facilitate LTP-like neuroplasticity in a group of young healthy individuals. We used single-pulse transcranial magnetic stimulation (TMS) to test changes in corticospinal excitability evoked by a PAS paradigm designed to induce LTP-like effects (246, 279). PAS was preceded by either a period of rest or an acute bout of high-intensity aerobic exercise. Sequence-specific implicit motor learning was assessed in terms of temporal precision and spatial accuracy via practice of a joy-stick based continuous tracking (CT) task that was preceded by either a period of rest or an acute bout of high-intensity aerobic exercise. As aerobic exercise triggers a cascade of neurobiological events that up-regulate neurochemicals in multiple brain regions (47, 48), we hypothesized that an acute aerobic exercise bout prior to motor practice would facilitate both temporal and spatial implicit motor sequence learning, as reflected by improved performance from early practice to a 24-hour retention test. Finally, we measured systemic levels of brain-derived neurotrophic factor (BDNF) immediately before and after aerobic exercise. BDNF is a neurotrophic growth factor that is involved in LTP and is susceptible to up-regulation by aerobic exercise (10, 48, 137, 227). We hypothesized that aerobic exercise-induced increases in neuroplasticity and motor learning would be positively correlated with the up-regulation of systemic BDNF following the aerobic exercise bout. 2.2 Methods 2.2.1 Participants Eight men and eight women between ages 19 and 33 (mean ± SD; 23.9 ± 3.7 years) participated in this study. Participants had no known neurological disorders and were of adequate 39  health to complete our exercise protocols. All participants gave written informed consent prior to testing. The Clinical Research Ethics Board at the University of British Columbia approved all experimental procedures.  2.2.2 Experimental design Each participant first completed a graded maximal exercise test. Next, every participant completed six experimental sessions designed to assess the effects of a 20-minute period of rest and a 20-minute standardized bout of high-intensity cycling (aerobic exercise; 90% of maximal power output [PO] in watts) on both change in corticospinal excitability evoked by PAS and motor learning. The six experimental sessions included: (1) rest followed by PAS; (2) aerobic exercise followed by PAS; (3) rest followed by skilled motor practice using a joystick-based CT task and (4) a no-exercise 24-hour retention test; and (5) aerobic exercise followed by CT task practice and (6) a no-exercise 24-hour retention test. Capillary blood samples were collected via finger stick during the aerobic exercise sessions to determine blood lactate (BLa) response to the aerobic exercise bout, as well as serum levels of BDNF before and after the aerobic exercise bout. Session order was pseudo-randomized and performed at the same time of day (± 2 hours) for each participant to account for diurnal fluctuations in M1 excitability (286) and serum BDNF levels (24). On all testing days, participants were instructed to refrain from any exercise besides that involved in the experimental sessions. All sessions were separated by at least 48 hours with two exceptions: retention tests were conducted 24 ± 2 hours following CT practice sessions, and there was a minimum washout-period of two weeks between CT practice under the different experimental conditions (rest or aerobic exercise). The procedures are depicted in their experimental order in Figure 2.1.  40   Figure 2.1 Overview of experimental procedures. PAS, paired associative stimulation; CT Task, continuous tracking task. 2.2.3  Exercise procedures 2.2.3.1 Graded maximal exercise testing A maximal exercise test was conducted on a cycle ergometer (Ergoselect 200, Ergoline GmbH, Bitz, Germany) and began with a workload of 100 W for men and 50 W for women, and was increased by 30 W increments every 2 minutes until exhaustion. Participants were instructed to maintain a pedaling cadence of 70-90 revolutions per minute (rpm) and to remain seated throughout testing. During exercise testing, the following measurements were monitored: expired O2 and CO2 concentrations and air flow via a metabolic cart (ParvoMedicsTrueOne 2400, Sandy, 41  UT, USA); heart rate (HR) via a HR monitor (Polar Electro, Oy, Kempele, Finland); and Borg’s 6-20 scale rating of perceived exertion (RPE) (12). Finger-stick BLa was determined immediately following the exercise test using an automated portable blood lactate analyzer and test strips (Lactate Pro, Arkray Inc., Kyoto, Japan). Peak O2 consumption (V̇O2peak) criteria included at least one of the following: a plateau in O2 uptake (V̇O2) and HR with further increase in workload, a respiratory exchange ratio (RER) greater than 1.1, a RPE greater than 17, BLa greater than 10 Mmol/L, an inability to maintain a cadence of 70 rpm and volitional exhaustion. Exercise testing results for each individual are presented in Table 2.1. 2.2.3.2 Standardized acute aerobic exercise bout Maximal PO determined by the exercise test was used to inform prescription of a standardized acute aerobic exercise bout. The bout lasted 20 minutes and included a five-minute warm-up at 50 W and self-selected cadence, followed by three sets of three-minute high-intensity cycling intervals interspersed with two minutes of low-intensity cycling. The high-intensity intervals consisted of cycling at 90% of maximal PO from the final fully completed stage of the maximal exercise test and the low-intensity intervals involved cycling at 50 W, always maintaining a cadence greater than 70 rpm. The aerobic exercise bout was based on previous work demonstrating systemic increases in neurochemicals with minimal long-term fatigue or dehydration (20, 308) and is similar to that previously employed by Roig et al. (245). Participants performed the aerobic exercise bout on two occasions: once immediately before PAS procedures and once immediately before CT task practice. 42  Table 2.1 Participant characteristics and aerobic exercise data     Exercise Test - Final stage Exercise bout  Participant Age Gender Dom. Hand V̇O2peak PO HR RER BLa RPE PO  HR  BLa RPE  01 27 M R 63.4 280 179 1.11 14.1 - 250 156 9.8 - 02 22 M R 45.7 280 187 1.16 10.0 - 250 183 11.7 - 03 25 M R 40.4 250 195 1.22 14.7 20 225 187 13.7 19 04 25 F R 37.0 140 191 1.18 8.2 19 125 163 8.8 15 05 33 M R 59.7 310 182 1.12 13.6 19 180 176 6.7 14.5 06 24 F R 46.2 230 188 1.10 12.8 20 205 170 6.9 14 07 24 F R 46.7 260 178 1.14 10.4 19 235 166 10.7 13 08 27 F R 47.2 230 178 1.17 12.7 18 205 180 11.3 19 09 21 M R 60.1 310 183 1.08 11.4 19 280 171 11.6 15 10 19 F R 40.2 170 188 1.23 13.1 17 145 185 12.4 16.5 11 24 F R 45.1 200 174 1.15 11.8 18 180 172 12.7 16.25 12 19 F R 30.4 110 194 1.35 10.0 19 80 199 12.0 19.5 13 28 M R 44.2 220 193 1.25 17.1 19 200 189 12.1 17.5 14 20 F R 33.9 140 183 1.27 12.8 15 125 184 15.7 18 15 22 M R 49.8 280 180 1.21 13.0 19 250 179 19.7 19 16 22 M L 35.8 130 188 1.35 12.1 15 115 195 14.9 14 Mean 23.9   45.4 221 185 1.19 12.4 18.3 191 178 11.9 16.4 SD 3.7   9.5 66 6.4 0.08 2.1 1.6 58 11.7 3.2 2.2 Dom., dominant; 𝐕̇  O2peak, peak oxygen consumption (ml/kg/min); PO, power output (W); HR, heart rate (beats/min); RER, respiratory exchange ratio; BLa, blood lactate (Mmol/L); RPE, Borg’s Rating of Perceived Exertion; SD, standard deviation. PO in the ‘Exercise bout’ section was consistent across all three high-intensity intervals for each bout, the remaining values were collected at the end of the third (final) interval within an exercise bout. HR and RPE listed in the ‘Exercise bout’ section are averages of the values collected on the two bouts (once for PAS and once for the CT task). RPE was not collected during exercise for the first two participants.  43  2.2.4 Paired associative stimulation procedures The following procedures were conducted with each participant under each experimental condition (rest and aerobic exercise). 2.2.4.1 Electromyography Surface electromyography (EMG) was collected from 1 cm by 1 cm square surface recording electrodes (Covidien, Mansfield, MA) placed over the belly of the abductor pollicis brevis muscle (APB) of the non-dominant hand. EMG signals were collected using LabChart software (LabChart 7.0, AD instruments, Colorado Springs, CO) and were pre-amplified (1000 times) and band-pass filtered at 10-1000 Hz with PowerLab amplification and EMG systems (AD instruments, Colorado Springs, CO). Data for all evoked potentials were sampled at 2000 Hz and recorded from 100 ms before to 400 ms after stimulus delivery. 2.2.4.2 Median nerve stimulation Rectangular pulses of 0.2 ms duration were delivered over the median nerve at the wrist of the non-dominant hand using a constant current stimulator (DS7A, Digitimer, Hertfordshire, UK). Immediately before motor evoked potential (MEP) recruitment curve collection (see below), electrical stimulation intensity was increased over 5-10 stimuli from below motor threshold to 150% of the minimum current to evoke the maximal motor-wave (Mmax) in APB. Mmax was determined as the largest peak-to-peak amplitude M-wave evoked in APB in these stimuli. Mmax is considered a stable measure of muscle activity during maximal muscle fibre recruitment (27), and was used as a reference from which to normalize MEPs evoked by TMS (173). 44  2.2.4.3 Transcranial magnetic stimulation TMS was delivered using a figure-of-eight coil (Magstim 70 mm P/N 9790, Magstim Co., Carmarthenshire,UK) and Magstim 2002 stimulator (Magstim Co., Carmarthenshire,UK) over the non-dominant APB M1 representation. Prior to the rest period or aerobic exercise bout, the coil was moved over the M1 to find the site that elicited the largest amplitude MEP at the lowest stimulation intensity for APB. Using Brainsight™ image-guided neuronavigation software (Rogue Resolutions Inc., Montréal, QC, Canada), this stimulation site was recorded and used to maintain orientation for all TMS delivery. All MEPs were evoked at rest. Resting motor threshold (RMT) was determined by finding the lowest stimulation intensity that evoked MEPs of at least 50 µV in 5 out of 10 consecutive trials (249). An MEP recruitment curve (baseline) was then conducted to determine corticospinal excitability via measurement of the amplitude of MEPs elicited at varying TMS intensities. Ten stimuli were delivered at 0.25 Hz in a random order at intensities ranging from 90-150% of RMT, in 10% increments for a total of 70 stimuli collected over approximately five minutes. Recruitment curves were collected using the same stimulation site and intensities immediately pre- (beginning within three minutes post-rest/exercise) and post-PAS (beginning within three minutes post-PAS). Including the delivery of PAS, all assessments were completed within approximately 45 minutes following the rest period or exercise bout. MEP recruitment curve data were processed using a custom MATLAB script (Mathworks, Natick, MA, USA). To ensure all MEPs were obtained at rest, MEPs were inspected post-hoc and discarded if EMG activity during the 100 ms prior to the TMS pulse exceeded two standard deviations of the average pre-stimulus signal. Less than 0.4% of all responses were removed from further analyses based on this criterion. Plots of stimulation 45  intensity (%RMT) by MEP amplitude (peak-to-peak amplitude expressed as %Mmax) were then constructed for each individual at each time point and under each condition. Previous studies have fit MEP recruitment curve data with both linear (215, 246) and sigmoidal (57, 171) functions. The range of stimulation intensities (90-150% RMT) was chosen to primarily capture the ascending portion of the MEP recruitment curve and, upon visual inspection, appeared to be best suited to a linear fit. This was verified by conducting leave-one-out cross validation procedures on all recruitment curves, which yielded, on average, a lower mean squared error for the linear (8.16 ± 10.31) versus sigmoidal (11.48 ± 14.28) functions. The linear function fit the data with an average R2 of 0.71 ± 0.21. A larger recruitment curve slope value following PAS indicated an increase in corticospinal excitability. 2.2.4.4 Paired associative stimulation Electrical stimulation was delivered over the median nerve of the non-dominant limb with 0.2 ms duration pulses at 300% perceptual threshold (PT) 25 ms prior to the delivery of TMS. TMS was applied over the APB M1 representation for the non-dominant limb at a stimulation intensity that evoked a MEP of approximately 1 mV (SI1mV). In total, 450 paired stimuli were delivered at 0.25 Hz (30 minutes of stimulation). Similar PAS protocols have previously been shown to enhance corticospinal excitability at rest (246, 279). 2.2.5 Continuous tracking task procedures Continuous tracking (CT) task practice took place following both the rest and aerobic exercise conditions for each participant, with at least 2 weeks between conditions. CT task practice involved manipulation of a finger joystick (Figure 2.2; Current Designs, Philadelphia, PA, USA) with the thumb of the non-dominant hand. The joystick was used to move a cursor up and down to track the vertical path of a target moving at a constant horizontal velocity from the 46  right to the left of a computer screen. Joystick position sampling and all stimuli were presented at 50 Hz using custom software developed on LabView (v. 9.0, National Instruments, Austin, TX, USA). The CT task was presented in 30-second trials, preceded by a 2-second normalization period where the cursor became zeroed to the target. For every 30-second trial, the first and last 10 seconds contained a random sequence while the middle 10 seconds contained a repeated sequence that was identical across practice and retention blocks. Random and repeated sequences were controlled for difficulty level in terms of range of motion and velocity of the target movements (298). Participants were not informed of the existence of a repeated sequence but instructed to track the target with the cursor as accurately as possible on each occasion. The inclusion of repeated and random sequences allows separation between improvements in motor control (random sequences) and those associated with sequence-specific implicit learning (repeated sequences) (16, 179, 298). In the experimental sessions involving CT task practice, participants completed one trial of the CT task (30 seconds of movement) prior to the rest period or the aerobic exercise bout for task familiarization. For CT practice following rest or the aerobic exercise bout, participants completed two blocks of 10 trials, for a total of 10 minutes of CT practice. The following day (24 ± 2 hours after motor practice) participants completed another single block of CT trials (delayed retention test).  For each participant, the control of the joystick was reversed between rest and aerobic exercise conditions, such that left and right joystick movements resulted in up and down cursor movements for one condition and down and up cursor movements for the other condition. Participants were informed of the direction of joystick control at the beginning of each CT practice and retention session. Additionally, repeated sequences presented for each condition were reversed, such that the sequences differed but shared equivalent difficulty. The order of 47  presentation of conditions (rest and aerobic exercise), joystick control (left-up, right-down versus left-down, right-up) and sequences (regular or reversed) were balanced across the sample.  Following the final retention test, participants were tested for explicit recognition of the repeated sequences within the CT task from both conditions (rest and aerobic exercise). For the recognition testing, participants viewed a series of 13 CT trials, seven of which included only random sequences and six of which included repeated sequences from the rest and aerobic exercise sessions. A group average of eight or more correctly identified sequences (four of the seven random sequences and four of the six repeated sequences) would indicate that explicit knowledge of the repeated sequence was acquired across the group (16, 179, 298).  Figure 2.2 Schematic of the continuous tracking (CT) task. A) Example of target movements during the random and repeated sequences within a single trial of tracking. Each black line depicts a different possible tracking pattern presented over one trial. The middle portion of the trial depicts the repeated sequence. Five possible trials are depicted in the figure. B) Participant view of target (black circle) and cursor (black dot) presented on computer monitor. C) Participant manipulating joystick device with thumb of non-dominant hand. 48  CT task data were processed using a custom MATLAB script (Mathworks, Natick, MA, USA). Root mean squared error (RMSE) was calculated for each sequence. RMSE was separated into temporal and spatial error components using a cross correlation analysis (16). Time lag of tracking represents the temporal distance from the target in milliseconds, with more negative numbers indicating that the cursor lags further behind the target. Removal of the calculated lag from the tracking signal prior to calculating the tracking error allowed for determination of spatial tracking error or shifted RMSE (16, 298). The time lag and shifted RMSE were then considered separately for the first and second random sequences combined (i.e. first and last 10 seconds of a trial) and the repeated sequence. 2.2.6 Serum brain-derived neurotrophic factor In the experimental session involving aerobic exercise followed by CT task practice, a 100 µL capillary blood sample was drawn by finger stick and collected using a microvette capillary blood collection tube (Sarstedt, Numbrecht, Germany) immediately before and after the aerobic exercise bout. Participants were instructed not to eat within 30 minutes, exercise or drink coffee within 24 hours, or drink alcohol or smoke within 48 hours of this session (24). These samples were placed on wet ice immediately following collection and allowed 30 minutes to clot. The samples were then centrifuged at 1000g for 15 minutes and serum was aliquoted into 0.6 mL aliquots and stored at −80º Celsius. Serum BDNF concentrations were measured in duplicate by technicians blinded to sample time points using a Quantikine (R&D Systems, Minneapolis, MN, USA) sandwich enzyme-linked immunosorbent assay (ELISA) kit according to the manufacturer’s instructions. All samples were assayed at the same time in a single, batched, analysis. 49  2.2.7 Statistical analyses 2.2.7.1 Paired associative stimulation To determine whether Mmax amplitude changed across time in each experimental session, a one-way repeated measures analysis of variance (RM-ANOVA) was conducted for each condition with the factor Time (baseline, pre-PAS and post-PAS). PAS parameters were also tested for any potential differences between conditions (rest and aerobic exercise). Separate paired t-tests were performed to compare the 300% PT stimulation intensity (mA), RMT (% mean TMS output), and SI1mV intensity (% mean TMS output) between the rest and aerobic exercise conditions. An additional paired t-test was conducted to determine whether MEP recruitment curve slope was well-matched between the rest and aerobic exercise conditions at the pre-PAS time point. The percent change in linear slope of the recruitment curves from the baseline to pre-PAS measurements and the pre-PAS to post-PAS measurements was calculated and used as the dependent variable for analysis of the effects of PAS. A two-way RM-ANOVA with the factors Condition (rest, aerobic exercise) and Time (baseline to pre-PAS, pre-PAS to post-PAS) was then conducted. 2.2.7.2 Continuous tracking task For each trial, the lag and shifted RMSE were determined separately for the random sequences and the repeated sequence. For the first random sequence (i.e. first 10 seconds of a trial), the first trial of the CT blocks was excluded from the analyses, as it included the first movement of a block and as such, commonly showed higher error relative to the rest of the block. As such, the second trial was considered the first trial of a practice block when determining error for the first random sequence (i.e. first 10 seconds of a trial). Next, the first 50  three trials of the first practice block (early practice), the last three trials of the second practice block (late practice) and the first three trials of the retention block (retention) were averaged for the two random sequences together and the repeated sequence separately. Two separate three-way RM-ANOVAs with dependent variables lag and shifted RMSE were then conducted with factors Condition (rest, aerobic exercise), Sequence (random, repeated) and Time (early practice, late practice, retention). 2.2.7.3 Serum brain-derived neurotrophic factor and correlation analyses A paired t-test was conducted to determine whether there was a change in serum BDNF concentration from pre- to post-aerobic exercise. Simple bivariate correlation analyses (Pearson’s r or Spearman’s rs) between the following variables were conducted: percent change in serum BDNF concentration from before to after aerobic exercise, percent change in linear recruitment curve slope from pre- to post-PAS for the aerobic exercise condition and, based on the results of the CT task analysis, percent change in lag on the repeated sequence from practice blocks to retention for the aerobic exercise condition. Bivariate correlation analyses were also conducted between the difference in percent change in linear recruitment curve slope between rest and aerobic exercise conditions and the difference in percent change in lag on the repeated sequence from practice blocks to retention between the rest and aerobic exercise conditions. These analyses were conducted to determine whether those individuals with greater facilitation of PAS effects following exercise were the same as those that showed greater facilitation of sequence-specific motor learning following exercise.  Following visual inspection of the data and objective testing using the Shapiro-Wilkes test (significance level p < 0.001) (78), all data were found to be normally distributed (W ≥ 0.894, p ≥ 0.06) except the percent change in serum BDNF (W=0.793, p < 0.001). For all other 51  statistical tests, significance level was p < 0.05. For all ANOVAs, post-hoc analyses (Fisher’s least significant difference tests) were conducted where appropriate. All descriptive statistics are reported as mean ± SD. All statistical analyses were conducted using SPSS software (SPSS 21.0, IBM Corporation, Armonk, NY, USA). 2.3 Results 2.3.1 Paired associative stimulation Mmax did not change across time (baseline, pre-PAS and post-PAS) in either the rest (F(2, 28) = 1.45, p = 0.25) or aerobic exercise (F(2, 28) = 0.25, p = 0.78) conditions. Additionally, 300% PT (t(15) = −0.28, p = 0.78), RMT (t(15) = −1.57, p = 0.14), SI1mV intensities (t(15) = −0.09, p = 0.93) and pre-PAS MEP recruitment curve slope (t(15) = −0.11, p = 0.91) were not different between the rest and aerobic exercise conditions. These analyses indicate that when considering the entire study sample, there were no differences in PAS procedures or pre-PAS recruitment curve slope across conditions (rest and aerobic exercise).  Figure 2.3 shows MEP recruitment curve plots and mean MEP waveforms collected at all intensities of the recruitment curves pre- and post-PAS for each condition for a single representative participant. Figure 2.4 depicts changes in MEP recruitment curve slope evoked by PAS across the group for both rest and aerobic exercise conditions. The two-way RM-ANOVA detected a significant main effect of Time (F(1, 15) = 6.86, p = 0.02) and a significant Condition by Time interaction (F(1, 15) = 7.86, p = 0.01). There was no main effect of Condition (F(1, 15) = 1.18, p = 0.30). To determine whether increases in recruitment curve slope following PAS were statistically significant under each condition, post-hoc comparisons utilized the baseline to pre-PAS change in recruitment curve slope under the rest condition as a control assessment (i.e. change in recruitment curve slope when no intervention is administered). The results 52  demonstrate that under the rest condition the change in slope from pre-to post-PAS (14.18 ± 32.70%) was not different from the change in slope from baseline to pre-PAS under the rest condition (7.59 ± 34.11%, p = 0.69), suggesting that PAS preceded by rest did not have a significant effect on recruitment curve slope. In contrast, under the exercise condition the change in slope from pre-to post-PAS (59.81 ± 73.49%) was significantly greater than the change from baseline to pre-PAS under the rest condition (7.59 ± 34.11%, p = 0.001), suggesting that PAS preceded by exercise significantly increased recruitment curve slope. Moreover, the change in recruitment curve slope from pre- to post-PAS was significantly greater when PAS was preceded by exercise than by rest (p = 0.02). Finally, the change in recruitment curve slope from the baseline to pre-PAS time points was not significantly different between the rest and aerobic exercise conditions (p = 0.38), indicating that aerobic exercise itself did not have a significantly different effect on MEP recruitment curve slope than the period of seated rest.   53   Figure 2.3 Changes in corticospinal excitability evoked by paired associative stimulation (PAS) preceded by rest and aerobic exercise in a single representative participant. Panels A and B represent motor evoked potential (MEP) recruitment curves collected pre- (grey) and post-PAS (black) for each condition (rest and aerobic exercise). Panel C depicts average raw MEP waveforms (n = 10 MEPs) elicited pre- (grey) and post-PAS (black) for each condition (rest and aerobic exercise) at each transcranial magnetic stimulation (TMS) intensity. 54   Figure 2.4 Changes in corticospinal excitability evoked by paired associative stimulation (PAS) preceded by rest and aerobic exercise across the group. Panel A depicts the percent change in linear slope of motor evoked potential (MEP) recruitment curves from baseline to pre-PAS and pre-PAS to post-PAS for the rest and aerobic exercise conditions when averaged across the group. Error bars represent one standard deviation. Horizontal bars and asterisks indicate statistically significant differences (p<0.05). Panel B shows the percent change in MEP recruitment curve slope from pre- to post-PAS under the rest and aerobic exercise conditions for each participant. Unlinked data points represent the mean percent change across the group under each condition (as shown also by bar graph in Panel A). Error bars around the unlinked data points demonstrate the 95% confidence interval for percent change in recruitment curve slope. Panels C and D show the MEP recruitment curves with MEP amplitudes averaged across the group for all transcranial magnetic stimulation (TMS) intensities pre- and post-PAS for the rest and aerobic exercise conditions, respectively. 2.3.2 Continuous tracking task Figure 2.5A and 2.5B shows trial by trial time lag of tracking for repeated and random sequences under both the rest and aerobic exercise conditions for a single representative 55  participant. Figure 2.6A shows the group averages for time lag of tracking from early practice, late practice and retention for the rest and aerobic exercise conditions.  With time lag as the dependent variable, a three-way RM-ANOVA revealed a significant main effect of Sequence (F(1, 15) = 16.27, p = 0.001), a significant main effect of Time (F(2, 30) = 5.70, p = 0.01) and a significant three-way Condition by Sequence by Time interaction (F(2, 30) = 3.39, p = 0.04). The main effect of Condition was not significant (F(1, 15) = 2.04, p = 0.17). Post-hoc analyses indicated that under the rest condition, participants’ time lag of tracking did not significantly improve from early to late practice or early practice to retention for either the random (p > 0.05) or repeated sequences (p > 0.16).  In contrast, CT time lag performance after the aerobic exercise bout did not change for random sequences (p > 0.22), but improved significantly for the repeated sequence from early (−100.83 ± 52.10) to late practice (−75.21 ± 45.57; p < 0.001), and this improvement was maintained at retention (−79.17 ± 42.28; p = 0.004). Furthermore, time lag was significantly faster for repeated compared to random sequences at both late practice (p = 0.001) and retention (p = 0.001) for the exercise condition. Moreover, time lag performance of the repeated sequences was not different between conditions during early practice (p = 0.95) but significantly faster under the aerobic exercise condition at late practice (p = 0.03) and retention (p = 0.02). These results demonstrate that sequence-specific learning of the temporal aspect of the CT task occurred when practice was preceded by aerobic exercise but not rest.   Figure 2.5C and 2.5D shows trial by trial spatial error via shifted RMSE for the repeated and random sequences under both rest and aerobic exercise conditions for a single representative participant. Figure 2.6B shows the group averages for shifted RMSE across practice and 56  retention under the rest and aerobic exercise conditions. The inlaid plot shows the effects of Time and Sequence when collapsed across both conditions (rest and aerobic exercise).  The three-way RM-ANOVA with shifted RMSE as the dependent measure yielded a significant main effect of Sequence (F(1, 15) = 6.01, p = 0.003) and a significant main effect of Time (F(1, 15) = 14.31, p = 0.001). There were no main effects of Condition (F(1, 15) = 0.21, p = 0.66) or interactions (all p > 0.05). Post-hoc analyses of the Sequence effect (collapsed across conditions and time) showed that shifted RMSE of tracking on the random sequence was on average worse than performance on the repeated sequence (p = 0.003). Post-hoc analyses of the effect of Time (collapsed across conditions) demonstrated that performance improved on average from early to late practice (p = 0.01) and from both early and late practice to retention (p = 0.001 and p = 0.002, respectively). Further analyses indicated that random and repeated sequence spatial error was not significantly different during early (p = 0.61) or late practice (p = 0.07), but that performance on repeated sequences was significantly better than on random sequences at retention (p = 0.04). These results indicate that implicit sequence-specific learning of the spatial aspect of the CT task occurred with practice but was not affected by condition (rest or aerobic exercise).  Across the group, participants did not demonstrate explicit knowledge of a repeated sequence during the recognition testing. The sequences were correctly identified at a level consistent with chance (7.2 ± 2.1/13 or 55.4 ± 16.5% of the sequences). 57   Figure 2.5 Continuous tracking (CT) task data collected from a single representative participant when practice was preceded by rest (grey) and aerobic exercise (black). Panels A and B show CT performance in terms of temporal error (lag behind the target) and Panels C and D in terms of spatial error corrected for the contribution of time lag (shifted root mean squared error, RMSE). For Panels A and B, more negative lag values reflect greater temporal error. Panels A and C demonstrate repeated sequence performance, while Panels B and D show the random sequence performance. Solid lines demonstrate performance on each trial, and data points represent performance averaged over 3-4 trials. Vertical dashed lines indicate changes in the CT block (early practice, late practice, and 24-hour retention block). 58   Figure 2.6 Continuous tracking (CT) task performance in terms of temporal error (Panel A) and spatial error corrected for time lag (Panel B) when averaged across the group. In Panel A, more negative lag values reflect greater temporal error. The small inlaid plot in Panel B depicts the effects of Time and Sequence on shifted root mean squared error (RMSE) detected by the statistical analyses. Early practice refers to the beginning of practice block 1, late practice to the end of practice block 2, and retention to the beginning of the 24-hour retention block. Error bars represent one standard deviation. ┼ indicates a statistically significant difference from early practice (p<0.05). Horizontal bars and asterisks indicate statistically significant differences between corresponding values (p<0.05). 2.3.3 Serum brain-derived neurotrophic factor and correlations In a paired t-test analysis comparing pre- and post-exercise blood samples, individual serum BDNF concentrations were increased on average by 3.4-fold following the aerobic 59  exercise bout (5258 ± 3258 pg/ml to 11918 ± 6404 pg/ml; t(14) = −3.61, p = 0.003). However, there were no significant correlations between the change in serum BDNF concentration with change in linear recruitment curve slope for the aerobic exercise condition (rs = −0.17, p = 0.54), or with change in lag on the repeated sequence from practice blocks to retention for the aerobic exercise condition (rs = −0.15, p = 0.59). The change in recruitment curve slope and lag on the repeated sequences under the aerobic exercise condition were not correlated (r = −0.20, p = 0.48). Finally, the difference in percent change in linear recruitment curve slope between rest and aerobic exercise conditions and the difference in percent change in lag on the repeated sequence from practice blocks to retention between the rest and aerobic exercise conditions were not correlated (r = 0.42, p = 0.11). 2.4 Discussion Our findings support the hypothesis that an acute bout of high-intensity aerobic exercise facilitates LTP-like neuroplasticity in the human motor system. In the same individuals, aerobic exercise prior to motor practice promoted sequence-specific implicit motor learning associated with improvements in temporal precision of continuous tracking.  The aerobic exercise bout also increased systemic levels of BDNF, but these increases did not relate to the neurophysiological or behavioural data. Overall, these findings imply that modulation of LTP-like neuroplasticity may contribute to high-intensity aerobic exercise-induced improvements in implicit motor learning. 2.4.1 A single bout of aerobic exercise facilitates response to paired associative stimulation Recent studies demonstrate that aerobic exercise can modulate neuroplasticity in the motor system (173, 271). For example, a moderate-intensity bout of cycling enhanced the LTP-60  like effects of a PAS protocol on corticospinal excitability for a muscle in the hand (271) and a low-intensity bout of cycling enhanced the LTD-like effects of a cTBS protocol on corticospinal excitability for a muscle of the hand (173). Both LTP and LTD are key neural processes involved in learning and memory, which respectively drive the strengthening of neural pathways as a memory is initially formed and then the focusing or pruning of neural pathways as preferential pathways develop (312). The rapid effects of PAS and cTBS suggest that these protocols trigger the early stages of such neuroplasticity that are dependent on chemical, rather than structural, changes at the synapse. Likewise, the systemic levels of a number of neurochemicals, such as dopamine, norepinephrine and BDNF, which are known to influence M1 plasticity (132, 187, 223), are also up-regulated with acute aerobic exercise (26, 38, 308). Systemic increases in dopamine were observed both immediately and 30 minutes following exercise (308). Similarly, reports of systemic increases in BDNF occur as late as 60 minutes post-exercise (137). While increases in such neurochemicals are greater with higher intensity exercise (137, 308), increasing intensities of exercise also induce higher levels of cortisol, a steroid hormone which has negative effects on plasticity in M1 (251). This finding has led to a focus in the recent literature on the effects of moderate- or low-intensity exercise on neuroplasticity (173, 271); however, previous work demonstrated that higher intensity aerobic exercise facilitated vocabulary learning to a greater extent than moderate-intensity exercise (308). Likewise, the only study to demonstrate acute exercise effects on motor learning utilized a high-intensity exercise bout (245). The present study adds to the literature by showing that a single bout of high-intensity cycling can also facilitate LTP-like neuroplasticity evoked by PAS for a muscle of the hand. The time frame for the delivery of the PAS protocol (30 minutes duration, commenced within five minutes post-exercise) and the intensity of the exercise align with a neurochemical mechanism for the exercise 61  effect. As cortisol was not measured, we cannot speculate on how it was affected by exercise or whether it may have modulated our effects.   Additional research has investigated how an acute bout of aerobic exercise impacts the activity of intracortical circuits in M1 representations of non-exercised upper limb muscles. Smith and colleagues (275) reported a reduction in short interval intracortical inhibition (SICI) in a small hand muscle immediately and 15 minutes after low-to-moderate and moderate-to-high-intensity cycling. Other work demonstrated a reduction in SICI and an increase in intracortical facilitation (ICF) in a non-exercised upper limb muscle immediately and 30 minutes following a bout of moderate-intensity exercise (270). The extent of SICI is thought to reflect GABAA (γ-aminobutyric acid) activity (35), for which reduced activity is strongly implicated in the induction of LTP (96) and M1 plasticity (315). Similarly, ICF is thought to depend on activity of glutamatergic interneurons and N-methyl D-aspartate (NMDA) receptors (151), which are also key players in LTP-like plasticity. Given the modulation of these intracortical networks by exercise, and their involvement in LTP-like mechanisms, it is likely that such changes contribute to the observed effect of high-intensity exercise on responsiveness to PAS. Moreover, modulation of intracortical networks has been shown to persist for at least 30 minutes post-exercise (270), which indicates that such changes would have been sustained during the administration of the PAS protocol in the present experiments. The lack of change in MEP recruitment curve slope immediately following PAS under the rest condition in the present experiments was not expected, but is consistent with recent work by Singh et al. (271) in which PAS preceded by rest facilitated MEP recruitment curves 30 minutes, but not immediately, following the delivery of the PAS. Other studies have also shown a greater effect of PAS 30 minutes following the stimulation as compared to immediately after 62  (34, 55, 222). Thus, by only collecting MEP recruitment curves immediately following PAS, we may have missed the effect of PAS under the rest condition. Regardless, our findings demonstrate that a bout of high-intensity aerobic exercise potentiated the immediate response to PAS compared to a period of rest, and is consistent with work investigating the effects of moderate-intensity exercise (271). An additional factor that may have impacted the present results relates to exercise-induced arousal and attention. The magnitude of the effect of PAS on corticospinal excitability has previously been shown to be highly dependent on attention to the stimuli (280). Presently, participants were not provided with any explicit instructions regarding attention to the stimuli in either condition. Anecdotally, participants did not report any differences in their level of attention between sessions, but it is plausible that an increase in arousal after exercise (145) enhanced participants’ attentiveness to the stimuli and that this increase in attention contributed to the modulation of the response to PAS. However, Singh et al. (271) recently demonstrated a similar effect of moderate-intensity exercise on response to PAS when attention was monitored by instructing participants to count the stimuli under both rest and aerobic exercise conditions. Finally, while cycling was used for the exercise bout and predominantly involves the lower limbs, it is possible that upper limb muscle activity, potentially via the gripping of the cycle ergometer handle bars, occurred during the cycling session and influenced the response to the subsequent PAS session. Nevertheless, the accumulating evidence for aerobic exercise influences on neurochemicals (26, 38, 308) and intracortical networks (270, 275) seem more likely candidates for the observed effects. 63  2.4.2 A single bout of aerobic exercise promotes implicit sequence-specific motor learning Participants demonstrated implicit sequence-specific learning of the temporal element of the motor task when practice was preceded by high-intensity aerobic exercise, but not rest. Implicit learning of the spatial component of the task was similarly impacted by rest and aerobic exercise. Thus, our findings show that, compared to a period of rest, a single bout of aerobic exercise preceding motor task practice specifically promoted the learning of the temporal element of an implicit motor sequence. The lack of implicit learning of the temporal component of the task under the rest condition was not wholly unexpected. We attempted to minimize participants’ exposure to the task to reduce any potential carryover effects between the conditions. As implicit learning typically requires relatively high volumes of practice (124, 277), it seems that the volume of practice was not sufficient for implicit learning of the temporal element of the task under the rest condition, but that the priming effect of exercise reduced the amount of practice necessary for formation of the implicit memory. While implicit motor learning is supported by a distributed network of brain regions (62, 84, 94, 98), previous work has demonstrated a distinct role for the cerebellum in improvements in temporal precision during implicit motor learning (16). Thus, our finding that aerobic exercise specifically influenced CT task improvements in temporal precision suggests that cerebellar function may be impacted by a single bout of aerobic exercise. Additionally, previous work demonstrated that PAS delivered with a 25 ms inter-stimulus interval, as was employed in the present study, is influenced by modulation of cerebellar activity via transcranial direct current stimulation (90). As such, it is possible that an aerobic exercise effect on cerebellar function may also have contributed to our concurrent finding that aerobic exercise facilitated the response of the M1 to PAS. Although our 64  findings point toward a cerebellar role, it is unlikely that aerobic exercise exclusively affects the cerebellum. For instance, aerobic exercise can impact gene expression of multiple proteins involved in neuroplasticity (289) across multiple central nervous system (CNS) regions (82, 196). Moreover, as cerebellar involvement was not directly evaluated in the present study we cannot conclusively ascertain the extent of its involvement in the observed effects.  Previously, aerobic exercise prior to task practice impacted motor performance at retention but not over practice blocks (i.e. acquisition) (245). This finding (245) contrasts previous data demonstrating improved performance on various cognitive tasks immediately following a bout of aerobic exercise (145). For example, the acquisition rate of novel words during an associative vocabulary learning task was immediately increased by 20% following high-intensity interval running compared to rest (308). Roig and colleagues (245) suggested that the lack of effect of aerobic exercise on short-term motor skill acquisition may have been due to fatigue from the aerobic exercise bout that masked improvements in tracking accuracy during the practice session. Our results demonstrate an effect of the aerobic exercise bout on acquisition of temporal precision over acquisition (i.e. early to late practice) and retention. As we utilized an aerobic exercise bout of similar structure and intensity to that employed by Roig et al. (245), our separation of the spatial and temporal components of implicit sequence-specific learning may have allowed us to more sensitively detect the impact of the aerobic exercise bout on skill acquisition. 2.4.3 Systemic brain-derived neurotrophic factor is increased by a single bout of aerobic exercise BDNF is a member of the neurotrophin family of proteins, which are heavily involved in neuroprotection, neurogenesis and neuroplasticity (10, 227). Animal research has demonstrated a 65  crucial role of BDNF in LTP and motor learning (111, 134, 294), as well as increased BDNF gene expression throughout the CNS induced by aerobic exercise (82, 196, 289). Consistent with the majority of previous research in humans (137), we found a marked increase in systemic BDNF after the high-intensity aerobic exercise bout; however, the increase in serum BDNF was not associated with the effects of aerobic exercise on neuroplasticity and motor learning. Although animal work suggests that systemic BDNF levels correlate with centrally-derived measures (204, 254), BDNF is released in an activity-dependent manner in the spinal cord and periphery (82) and whether it readily crosses the blood-brain barrier in humans remains a point of contention (194). Thus, the lack of relationships in our data may relate to differences in BDNF levels between the periphery and the brain. Additionally, up-regulation of other neurotrophic factors and catecholamines (26, 38, 308) and the presence of genetic variants that impact such neurochemicals (100, 213) could also mask the presence of any potential relationships. While elevated levels of BDNF throughout the brain following exercise could plausibly facilitate the LTP-like processes involved in PAS and motor learning, our inability to measure centrally-derived BDNF in humans limits our capacity to speculate further on such effects.  Our results also showed no relationship between the effects of aerobic exercise on LTP-like neuroplasticity and implicit motor learning. This finding may point to more complex, and potentially global, actions of aerobic exercise on the brain. For example, our measure of neuroplastic response was obtained from M1, but the specificity of our behavioural results to changes in temporal lag indicated a potential influence of aerobic exercise on the cerebellum (16). Given that the PAS effects on M1 may have involved cerebellar circuitry (90), a more direct measure of aerobic exercise impact on plasticity in the cerebellum may have correlated more strongly with the behavioural outcome. Additionally, while the repeated measures design 66  for the motor learning task was designed to balance the order of sessions across the group, it may have impacted the magnitude of the effects observed within individuals and affected our ability to detect relationships. Despite the absence of significant correlations within our results, the concurrent finding of increased LTP-like neuroplasticity, motor learning and systemic BDNF following a single bout of aerobic exercise suggests a potential interplay between these effects. 2.4.4 Conclusions In the current study we discovered that a single bout of high-intensity aerobic exercise facilitated LTP-like responses evoked by excitatory PAS and promoted sequence-specific implicit motor learning specifically associated with improvements in temporal precision. These effects were neither related to aerobic exercise-induced increases in systemic BDNF, nor to each other. Our findings have implications for motor rehabilitation strategies for individuals with neurological injury, such as stroke. As long-term aerobic exercise programs are increasingly prescribed for secondary prevention following stroke (163), the priming effects of aerobic exercise on neuroplasticity may be exploited through the strategic pairing of aerobic exercise bouts with motor rehabilitation training. Additional research into underlying mechanisms, as well as how characteristics of the aerobic exercise bout (i.e. intensity, mode, duration), influence these effects will be important for the optimization of exercise strategies to enhance motor skill learning.   67  Chapter 3: Promoting Motor Cortical Plasticity with Acute Aerobic Exercise: A Role for Cerebellar Circuits 3.1 Introduction While aerobic exercise is commonly prescribed to promote cardiorespiratory and musculoskeletal health benefits, it is now also well-established that it exerts powerful effects on the brain (45, 48). These aerobic exercise effects on the brain include an impact on neuroplasticity and have been largely studied in terms of chronic effects associated with long-term aerobic exercise training (40, 47, 48). Notably, recent studies of the human sensorimotor system have utilized non-invasive brain stimulation techniques to demonstrate that a single bout of aerobic exercise can modulate plasticity in the primary motor cortex (M1) (167, 173, 271). For example, two studies demonstrated that lower-limb cycling facilitated long-term potentiation (LTP)-like plasticity evoked by paired associative stimulation (PAS) targeting the hand region of M1 (167, 271). Additionally, the long-term depression-like effects of continuous theta-burst stimulation on M1 excitability for a muscle of the hand were enhanced when stimulation was preceded by low-intensity cycling (173). Taken together, these studies indicate that acute aerobic exercise has robust effects on neuroplasticity in humans, impacting both the up- and down-regulation of M1 excitability in non-exercised muscles (167, 173, 271).  Importantly, the aforementioned work indicates that acute aerobic exercise modulates induction of plasticity, rather than overall excitability, in M1 corticospinal projections to non-exercised muscles (167, 173, 271). Multiple studies have found that overall corticospinal excitability for hand muscles is not changed from before to after a bout of aerobic exercise alone (167, 173, 275). In contrast, mounting evidence suggests that intracortical excitability within 68  hand muscle M1 representations is altered by a single bout of cycling (270, 275). Specifically, acute aerobic exercise decreased short-interval intracortical inhibition (SICI) (270, 275) and increased intracortical facilitation (ICF) (270) for a non-exercised hand muscle. These intracortical changes do not appear to agree with the lack of change in overall corticospinal excitability, but importantly, SICI and ICF measures are thought to reflect specific interneuronal pools that represent a portion of a multitude of inputs determining overall corticospinal excitability (35, 109). Thus, instead of altering the overall excitability of the M1 output, these more subtle changes in SICI and ICF following aerobic exercise may reflect a mechanism that serves to prepare the upper motor neurons to subsequently undergo changes in excitability (i.e. plasticity) (270, 275). In concert with alterations in intracortical circuits (270, 275), it is plausible that changes in the excitability of neural inputs projecting from other cortical and subcortical sources to M1 could also contribute to aerobic exercise-induced increases in M1 plasticity. In a recent study examining the effects of acute aerobic exercise on motor learning, our behavioural findings suggested a possible aerobic exercise-induced potentiation of cerebellar function (167). Specifically, high-intensity interval cycling immediately prior to practicing a continuous tracking task with a visuomotor rotation enhanced the acquisition and delayed retention of the temporal, but not spatial, element of the motor skill (167). Cerebellar circuits are known to play an important role in motor control and learning (30), especially for tasks involving visuomotor rotations (234, 291). Previous work in individuals with a cerebellar infarct also suggested that motor sequence learning of the temporal element of a continuous tracking task was highly dependent on cerebellar function (16). Thus, our previous work may be taken to suggest a potential impact of acute aerobic exercise on the cerebellum (167). Further, cerebellar circuits 69  have been shown to modulate plasticity in M1 (228). Nevertheless, without any direct measures of cerebellar function in our previous study (167), a postulated impact of acute aerobic exercise on cerebellar circuits is highly speculative. Therefore, the present study is comprised of two experiments designed to evaluate our hypothesis that acute aerobic exercise-induced effects on M1 may be partially mediated by cerebellar circuits projecting to M1. The purpose of Experiment 1 was to examine whether activity in the cerebello-thalamo-cortical pathway is altered by acute high-intensity aerobic exercise. We tested the activity of the cerebello-thalamo-cortical pathway using a dual-coil paired-pulse transcranial magnetic stimulation (TMS) technique, termed cerebellar inhibition (CBI) (51, 220, 293), before and after both a period of rest and a bout of high-intensity aerobic exercise. We hypothesized that, similar to the decrease in SICI reported in previous work (270, 275), there would be a decrease in CBI following acute aerobic exercise. In Experiment 2, we examined the role of cerebellar circuits in mediating acute aerobic exercise effects on LTP-like plasticity in M1. Both studies that have previously reported a positive effect of acute aerobic exercise on responses to excitatory PAS utilized a 25 ms inter-stimulus interval (PAS25, ISI) between the peripheral and cortical stimuli (167, 271). Interestingly, previous work demonstrated that the effects of PAS25 on M1 excitability are mediated, in part, by indirect trans-cerebellar sensory pathways (90, 228). In contrast, it is thought that excitatory PAS with a shorter ISI (~21 ms, PAS21) does not provide enough time for sensory inputs via this indirect route to influence M1, and thus exerts its effects via only more direct sensory pathways (i.e. dorsal column-medial lemniscus pathway) (90). Thus, it is possible that previously documented effects of acute aerobic exercise on PAS25 could be partly mediated by an aerobic exercise effect on cerebellar circuits. Here, we evaluated the impact of acute high-intensity aerobic exercise, versus a period of rest, on LTP-like plasticity in 70  M1 evoked by both PAS25 and PAS21. Due to the involvement of cerebellar pathways, we hypothesized that the response to PAS25 would be facilitated by aerobic exercise to a greater extent than PAS21. 3.2 Methods and results 3.2.1 Participants Experiments for the current study were conducted on a total of 34 young healthy participants between ages 19 and 34 (mean ± SD; 24.8 ± 4.1 years, 14 M). Participants had no known neurological disorders, were of adequate health to complete exercise protocols and were screened for potential contraindications to TMS. All participants gave written informed consent prior to testing. The Clinical Research Ethics Board at the University of British Columbia approved all experimental procedures. 3.2.2 Experimental design This study consisted of two separate experiments designed to evaluate the impact of acute high-intensity cycling on cerebello-motor circuits for a non-exercised muscle of the hand (abductor pollicis brevis, APB). Prior to participation in experimental sessions, each participant completed a graded maximal exercise test, for the purpose of subsequent exercise intensity prescription. For Experiment 1, participants completed one single session to evaluate the impact of a standardized bout of high-intensity interval cycling on CBI. The session involved an assessment of CBI at three time points: baseline, immediately following 20 minutes of seated rest (pre-exercise), and immediately following a 20-minute high-intensity aerobic exercise interval session (post-exercise). There was a total of 13 participants in this experiment; however, the session was not completed in three participants due to a lack of CBI at the baseline time point (n = 2) and discomfort with cerebellar stimulation (n = 1). Thus, the final dataset included a total 71  of 10 participants. For Experiment 2, 32 participants completed two sessions designed to assess the potential effects of the same high-intensity interval cycling bout on change in corticospinal excitability evoked by PAS. The experimental sessions included: (1) rest followed by PAS and (2) aerobic exercise followed by PAS. Half of the participants (n = 16) underwent the experiments with PAS25 and the other half underwent PAS21. PAS groups were similar in terms of age, sex and cardiorespiratory fitness (Table 3.1). Session order was pseudo-randomized and performed at the same time of day (± 2 hours) for each participant to account for diurnal fluctuations in M1 excitability (286). Data from participants that underwent the PAS25 protocol were also included in previous work (Chapter 2) (167). Of the 32 participants involved in Experiment 2, 11 also participated in Experiment 1. On all testing days, participants were instructed to refrain from any exercise besides that involved in the experimental sessions. All sessions conducted on the same individuals were separated by at least 48 hours. The procedures are depicted in their experimental order in Figure 3.1. 72  Table 3.1 Participant characteristics and aerobic exercise data     Exercise Test - Final stage Exercise bout  Experimental group n Age Sex V̇O2peak PO HR RER BLa RPE PO  HR  BLa RPE  CBI 10 24.5 7 F 45.3 227 181 1.20 11.6 18.4 205 170 11.9 15.3   ±3.3  ±11.4 ±62.9 ±10.4 ±0.16 ±2.0 ±1.5 ±61.4 ±7.9 ±4.1 ±2.2 PAS25 16 23.9 8 F 45.4 221 185 1.19 12.4 18.3 191 178 11.9 16.4   ±3.7  ±9.5 ±66.1 ±6.4 ±0.08 ±2.1 ±1.6 ±58.4 ±11.7 ±3.2 ±2.2 PAS21 16 25.7 10 F 43.3 221 181 1.17 12.8 18.2 196 170 11.0 16.0   ±4.6  ±8.4 ±61.7 ±10.6 ±0.06 ±2.2 ±1.3 ±59.8 ±10.3 ±4.3 ±2.0 𝐕̇  O2peak, peak oxygen consumption (ml/kg/min); PO, power output (W); HR, heart rate (beats/min); RER, respiratory exchange ratio; BLa, blood lactate (Mmol/L); RPE, Borg’s Rating of Perceived Exertion (6-20 point scale). PO in the ‘Exercise bout’ section was consistent across all three high-intensity intervals for each bout; the remaining values were collected at the end of the third (final) interval within an exercise bout. 73   Figure 3.1 Overview of experimental procedures for Experiment 1 (Panel A) and Experiment 2 (Panel B). Each session was approximately two hours in duration. CBI, cerebellar inhibition; MEP, motor evoked potential; PAS25, paired associative stimulation with 25 ms inter-stimulus interval (ISI); PAS21, paired associative stimulation with 21 ms ISI.74  3.2.3 Exercise procedures 3.2.3.1 Graded maximal exercise testing A graded maximal exercise test was conducted on a stationary cycle ergometer (Ergoselect 200, Ergoline GmbH, DE), beginning with a power output (PO) of 100 W for men and 50 W for women, and increased by 30 W increments every two minutes until volitional exhaustion. Participants were instructed to maintain a pedaling cadence of 70-90 revolutions per minute (rpm) and to remain seated throughout testing. During exercise testing, the following measurements were monitored: expired O2 and CO2 concentrations and air flow via a metabolic cart (ParvoMedicsTrueOne 2400, USA); heart rate (HR) via a wireless HR monitor (Polar Electro, FIN); and Borg’s 6-20 scale rating of perceived exertion (RPE) (12). Finger-stick blood lactate (BLa) was measured immediately after completion of the exercise test using an automated portable BLa analyzer and test strips (Lactate Pro, Arkray Inc., Japan). Peak O2 consumption (V̇O2peak) criteria included at least one of the following: a plateau in O2 uptake (V̇O2) and HR with further increase in workload, a respiratory exchange ratio greater than 1.1, a RPE greater than 17, BLa greater than 10 Mmol/L, an inability to maintain a cadence of 70 rpm, and/or volitional exhaustion. Exercise testing results averaged across the study groups are presented in Table 3.1. 3.2.3.2 Standardized acute aerobic exercise bout Maximal PO determined by the exercise test was used to inform prescription of a standardized acute aerobic exercise bout. The bout lasted 20 minutes and included a five-minute warm-up at 50 W and self-selected cadence, followed by three sets of three-minute high-intensity cycling intervals interspersed with two minutes of low-intensity cycling. The high-intensity intervals involved cycling at 90% of maximal PO from the final fully completed stage of the 75  maximal exercise test. The low-intensity active rest intervals involved cycling at 50 W. During the intervals, participants were instructed to maintain a cadence greater than 70 rpm. The aerobic exercise bout was prescribed based on previous work demonstrating systemic increases in neurochemicals with minimal long-term fatigue or dehydration (20, 308) and is similar to that previously employed by Roig and colleagues (245) and our lab (Chapter 2) (167) when examining aerobic exercise effects on motor learning and neuroplasticity. 3.2.4 Experiment 1 methods As shown in Figure 3.1A, CBI was evaluated in participants at three time points over a single session: baseline, pre-exercise (following 20 minutes of seated rest) and post-exercise. This experimental design allowed evaluation of changes in CBI following a period of seated rest (i.e. control) versus a high-intensity aerobic exercise bout. 3.2.4.1 Cerebellar inhibition Surface electromyography (EMG) was collected from 1 cm by 1 cm square surface recording electrodes (Covidien, USA) placed over the belly of the APB of the non-dominant hand. EMG signals were collected using LabChart software (LabChart 7.0, AD instruments, USA) and were pre-amplified (1000 times) and band-pass filtered at 10-1000 Hz with PowerLab amplification and EMG systems (AD instruments, USA). Data for all evoked potentials were sampled at 2000 Hz and recorded from 100 ms before to 400 ms after stimulus delivery.  TMS was first delivered using a figure-of-eight coil (Magstim 70 mm P/N 9790, Magstim Co., UK) and Magstim 2002 stimulator (Magstim Co., UK) over the non-dominant APB M1 representation. At the baseline time point, the coil was moved over M1 to find the site that elicited the largest amplitude motor evoked potential (MEP) at the lowest stimulation intensity for APB. Using Brainsight™ image-guided neuronavigation software (Rogue Resolutions Inc., 76  Canada) and a standardized neuroanatomical template, this stimulation site was recorded and used to maintain coil orientation for all TMS delivery. All MEPs were evoked at rest. Resting motor threshold (RMT) was determined by finding the lowest stimulation intensity that evoked MEPs of at least 50 µV in 5 out of 10 consecutive trials (249). CBI was then studied with a protocol similar to previous work (159, 293). Cerebellar stimulation was delivered with a double cone coil (Magstim P/N 9902-00, Magstim Co., UK). The centre of the double cone coil was placed 3 cm lateral (ipsilateral to the non-dominant hand) of the midpoint along a line between the inion and the mastoid process. The coil junction was oriented vertically to induce an upward electric current in the underlying tissue. The conditioning stimulus (CS) was delivered through this coil immediately prior to a test stimulus (TS) delivered by the figure-eight coil placed over the non-dominant APB M1 representation.  When examining CBI in previous work, the CS intensity has been commonly set to 5-10% mean stimulator output below the threshold for eliciting a cervicomedullary evoked potential (CMEP) by double cone coil stimulation over the inion (159, 293). This past method ensures that CBI waveforms are not contaminated with CMEPs (293). Yet, CMEPs are commonly of low amplitude, can be painful for participants and cannot be elicited in all participants (168). Here, we opted to circumvent the CMEP threshold process and utilized RMT, determined by TMS delivery with the figure-eight coil over the APB M1 representation, as a reference point for setting the CS intensity of the double cone coil to a sufficient intensity to elicit CBI in each participant. Of the 10 individuals that completed the experiment, a CS intensity equivalent to the previously established RMT (i.e. 1.0 × RMT) was adequate to evoke CBI in seven individuals, while a CS intensity of 1.2 × RMT was needed to evoke CBI for the other three participants. The TS was set to the intensity that evoked a MEP in the non-dominant 77  APB of 1 mV (SI 1 mV). CBI was tested at ISIs of 5, 6 and 7 ms between the CS and TS. For each ISI, a separate block of 20 stimuli was delivered involving 10 CS-TS trials flanked by five TS alone trials (i.e. 10 TS alone trials total). The order of blocks was randomized at each time point that CBI was evaluated. TS intensity was determined prior to collection of each block of stimuli. All data was collected within approximately 20 minutes of the completion of the rest period and aerobic exercise bout. 3.2.4.2 Data processing CBI data were processed using a custom MATLAB script (Mathworks, USA). All CBI trials were inspected post-hoc and discarded if EMG activity during the 100 ms prior to the TMS pulse for each individual trial exceeded two standard deviations of the average pre-stimulus signal. Data were also visually inspected post-hoc and trials removed in instances that the CS elicited corticospinal, cervical root or antidromic activity (4). Less than 0.1% of all responses were removed from further analyses based on these criteria. CBI was determined for each ISI at each time point as the ratio of the mean conditioned MEP amplitude to the mean unconditioned MEP amplitude collected in the same block of stimuli, where a lower value indicates more CBI. Baseline CBI data were analyzed online. Initially, five participants did not demonstrate CBI at baseline with a CS intensity of 1.0 × RMT, and were re-tested for CBI with a CS intensity of 1.2 × RMT. Three of these individuals demonstrated CBI at this higher CS intensity and were tested at the remaining experimental time points with the higher CS intensity. Two participants did not show CBI even at the higher CS intensity and thus, their experimental sessions were discontinued following the baseline measurement. CBI was considered present at baseline, and the experiment continued, if a one-sample one-direction t-test indicated that CS-TS trials were on average lower than TS-alone trials in the same block at any ISI at baseline (p < 0.05). The ISI 78  that yielded the lowest CBI ratio at baseline was identified for each individual. CBI at this specific ISI for each individual was then compared between time points in the statistical analyses. Previous work has recommended that in order to measure a change in CBI induced by an intervention (i.e. aerobic exercise), it is necessary to initially conduct CBI procedures with stimulus parameters that yield an approximate 50% suppression of the TS (4). The above described procedures ensured that CBI was evoked at approximately this amplitude in each participant at the outset of the experiment. 3.2.4.3 Statistical analyses A one-way repeated measures analysis of variance (RM-ANOVA) was conducted with the factor Time (baseline, pre-exercise and post-exercise) to ensure that TS amplitude was similar across time points. Next, to evaluate whether aerobic exercise impacted CBI, a second one-way RM-ANOVA with factor Time (baseline, pre-exercise and post-exercise) was conducted with CBI ratio as the dependent variable. Post-hoc Tukey’s honest significant difference tests were conducted on the main effect of Time. Following visual inspection for skewness and kurtosis and objective testing for normality with the Shapiro-Wilk test with a significance level set at p < 0.001 (78), all variables were found to be normally distributed (W(10) ≥ 0.916, p ≥ 0.33). For all statistical tests, significance level was p < 0.05. All descriptive statistics are reported as mean ± SD. Statistical analyses were conducted using SPSS (v. 23.0, IBM Corporation, USA) and Statistica (v 12.0, Statsoft Inc., Dell Software, USA) software. 3.2.5 Experiment 1 results TS amplitude during CBI collection did not change across experimental time points (F(2, 18) = 1.07, p = 0.37, baseline: 0.99 ± 0.51 mV, pre: 1.22 ± 0.50 mV, post: 1.12 ± 0.55 mV). 79  Figure 3.2 shows mean MEP waveforms collected for CBI in a single representative participant at each time point. Figure 3.3 depicts the CBI ratios averaged across the group at each time point. The one-way RM-ANOVA conducted on CBI ratio detected a significant main effect of Time (F(2, 18) = 6.11, p = 0.01). Post-hoc analyses indicated that CBI ratio was significantly higher following aerobic exercise compared to the baseline (p = 0.01) and pre-exercise time points (p = 0.04). In contrast, CBI ratio did not change from before to after the period of seated rest (baseline to pre-exercise, p = 0.84). 80   Figure 3.2 Cerebellar inhibition (CBI) in a single representative participant at (A) baseline, (B) pre-exercise and (C) post-exercise. Motor evoked potential (MEP) waveforms are averaged from 10 MEPs. Dashed waveforms show MEPs when the test stimulus (TS) is delivered alone. Solid line waveforms show MEP waveforms when the TS was preceded by a conditioning stimulus (CS) delivered over the cerebellum. For this individual, the CS was delivered at 1.0 × resting motor threshold and preceded the TS by 7 ms. 81               Figure 3.3 Cerebellar inhibition (CBI) ratios averaged across the group. A value of 1.0 on the y-axis (depicted by dashed line) indicates the amplitude of the test stimulus (TS) response alone. Asterisks indicate statistical significance (p < 0.05). Error bars represent one standard deviation (SD). 82  3.2.6 Experiment 2 methods The following procedures were conducted with each participant under each experimental condition (rest and aerobic exercise). The order of conditions was randomized for each participant (Figure 3.1B). During Experiment 2, EMG was collected as described above for Experiment 1. 3.2.6.1 Median nerve stimulation Rectangular pulses of 0.2 ms duration were delivered over the median nerve at the wrist of the non-dominant hand using a constant current stimulator (DS7A, Digitimer, UK). Immediately before MEP recruitment curve collection (see below), electrical stimulation intensity was increased over 5-10 stimuli from below motor threshold to 150% of the minimum current to evoke the maximal M-wave (Mmax) in APB. Mmax was determined as the largest peak-to-peak amplitude M-wave evoked in APB in these stimuli. Mmax is a stable measure of muscle activity during maximal muscle fibre recruitment (27), and was used as a reference from which to normalize MEPs evoked by TMS (167, 173). 3.2.6.2 Motor evoked potential recruitment curves The figure-of-eight coil (Magstim 70 mm P/N 9790, Magstim Co., UK) was used to locate the non-dominant APB hotspot and RMT, as above. Next, a MEP recruitment curve (pre-PAS) determined corticospinal excitability via measurement of the amplitude of MEPs elicited at varying TMS intensities. Ten stimuli were delivered at 0.25 Hz in a random order at intensities ranging from 90-150% of RMT, in 10% increments for a total of 70 stimuli collected over approximately five minutes (188). Recruitment curves were collected using the same stimulation site and intensities immediately pre-PAS (beginning within three minutes following rest or aerobic exercise) and post-PAS (beginning within three minutes post-PAS). Including the 83  delivery of PAS, all assessments were completed within approximately 45 minutes following the rest period or aerobic exercise bout. 3.2.6.3 Paired associative stimulation Electrical stimulation was delivered over the median nerve of the non-dominant limb with 0.2 ms duration pulses at 300% perceptual threshold (PT) 25 ms (PAS25) or 21 ms (PAS21) prior to delivery of supra-threshold single-pulse TMS. TMS was applied over the APB M1 representation for the non-dominant limb at an intensity that evoked a MEP of approximately 1 mV (SI1mV). In total, 450 paired stimuli were delivered at 0.25 Hz (30 minutes of stimulation). Similar PAS protocols have previously been shown to enhance corticospinal excitability (167, 246, 279). 3.2.6.4 Data processing MEP recruitment curve data were processed using a custom MATLAB script (Mathworks, USA). As in Experiment 1, MEPs were inspected post-hoc and discarded in the case of EMG activity prior to the TMS pulse (< 0.5% of responses removed). Plots of stimulation intensity (% RMT) by MEP amplitude (peak-to-peak amplitude expressed as % Mmax) were constructed for each individual at each time point and under each condition. As with previous work (167, 215, 246), a linear regression line was fit to the MEP recruitment curve plots (90-150% RMT), with a larger recruitment curve slope value following PAS indicating an increase in corticospinal excitability. 3.2.6.5 Statistical analyses To determine whether Mmax amplitude changed across time in each experimental session, paired samples t-tests were conducted for each condition and each PAS group on pre-PAS and post-PAS time points. PAS parameters were also tested for any potential differences between 84  conditions (rest and aerobic exercise) and PAS groups (PAS25 and PAS21). Two-way mixed ANOVAs (Condition by PAS group) were performed to compare the 300% PT stimulation intensity (mA), RMT (% mean stimulator output, MSO), SI1mV intensity (% MSO), and pre-PAS recruitment curve slope between the conditions and PAS groups.  A two-way mixed ANOVA was conducted to evaluate the impact of aerobic exercise on PAS response. The dependent variable was percent change in recruitment curve slope from pre-PAS to post-PAS. The within-subject factor was Condition (rest and aerobic exercise) and the between-groups factor was PAS Group (PAS25 and PAS 21). Given evidence from Experiment 1 that activity in the cerebello-thalamo-cortical pathway may be modulated by exercise, and our hypothesis that acute aerobic exercise would facilitate response to PAS25 to a greater extent than PAS21, we conducted planned comparisons to evaluate the difference in change in recruitment curve slope evoked under the rest and aerobic exercise conditions for each PAS group separately. To further explore any potential differences in the magnitude of the effect of aerobic exercise on response to PAS25 versus PAS21, effect sizes (η2partial) were calculated on the difference in PAS-induced change in MEP recruitment curve slope between the rest and aerobic exercise conditions separately for the PAS25 and PAS21 groups. Effect size calculations were interpreted based on previously developed guidelines (44). Using criteria as described above in Experiment 1, all variables were found to be normally distributed (W(16) ≥ 0.894, p ≥ 0.06). All statistical tests were conducted with a significance level of p < 0.05, all descriptive statistics are reported as mean ± SD, and statistical analyses were conducted using SPSS (v. 23.0, IBM Corporation, USA) and Statistica (v 12.0, Statsoft Inc., Dell Software, USA) software. 85  3.2.7 Experiment 2 results Mmax did not change across time (pre-PAS and post-PAS) in the rest or aerobic exercise conditions in either the PAS25 or PAS21 groups (t(15)  ≤ |1.62|, p ≥ 0.13). Additionally, there were no effects of condition, PAS group or interactions for 300% PT, RMT, SI1mV intensities and pre-PAS MEP recruitment curve slope (F(1, 30)  ≤ 1.42, p ≥ 0.24). These analyses indicate that when considering the entire study sample, there were no differences in PAS procedures or initial MEP recruitment curve slope across conditions (rest and aerobic exercise) or PAS group (PAS25 and PAS21).  Figure 3.4 shows the data points comprising pre-PAS and post-PAS MEP recruitment curve plots for each participant from both the PAS25 and PAS21 groups under each condition. The group average linear regression lines for the MEP recruitment curve plots, depicting the average slope across the study sample at pre-PAS and post-PAS time points under each condition and in each PAS group, are also depicted in Figure 3.4. The mixed ANOVA detected a significant main effect of Condition on change in recruitment curve slope evoked by PAS (F(1, 30) = 6.49 p = 0.02), a trend for an effect of PAS group (F(1, 30) = 3.75, p = 0.06) and no interaction effect (F(1, 30) = 1.10, p = 0.30). The hypothesis that the effect of the acute aerobic exercise on PAS response would differ between PAS25 and PAS21 protocols was tested by planned comparisons. MEP recruitment curve slope was increased to a greater extent by PAS25 under the aerobic exercise condition (59.8 ± 73.5% increase) compared to the rest condition (14.2 ± 32.7% increase (F(1, 30) = 6.47, p = 0.02), but there was no significant difference between conditions in the magnitude of change evoked by PAS21 (rest: 3.7 ± 36.3%, aerobic exercise: 22.7 ± 46.7%; F(1, 30) = 1.12, p = 0.30) (Figure 3.5). Further, effect size calculations indicated that aerobic exercise had a large 86  facilitatory effect on response to PAS25 (η2partial = 0.27), and a small-medium facilitatory effect on response to PAS21 (η2partial = 0.09).87   Figure 3.4 Motor evoked potential (MEP) recruitment curve data pre-and post-paired associative stimulation (PAS) under rest (Panels A and C) and aerobic exercise (Panels B and D) conditions for all participants in both the PAS25 (top panels) and PAS21 (bottom panels) groups. Unfilled circles and filled squares depict MEP amplitude at each stimulator intensity for each participant at pre- and post-PAS time points, respectively. Likewise, dashed and solid lines depict linear regression lines showing average MEP recruitment curve slope for the group at pre- and post-PAS time points, respectively. Mmax, maximal motor-wave; PAS25, paired associative stimulation with 25 ms inter-stimulus interval (ISI); PAS21, paired associative stimulation with 21 ms ISI; TMS, transcranial magnetic stimulation; RMT, resting motor threshold.88   Figure 3.5 Change in in motor evoked potential (MEP) recruitment curve slope evoked by paired associative stimulation (PAS) protocols under rest and aerobic exercise conditions. Asterisks indicate statistical significance (p < 0.05). Error bars represent one standard deviation (SD). PAS25, paired associative stimulation with 25 ms inter-stimulus interval (ISI); PAS21, paired associative stimulation with 21 ms ISI. 3.3 Discussion We conducted two experiments to examine: 1) the impact of acute aerobic exercise on activity in the cerebello-thalamo-cortical pathway, and 2) the potential role of these cerebellar circuits in mediating acute aerobic exercise-induced modulation of M1 plasticity. In Experiment 1, we found that CBI was decreased immediately following acute high-intensity aerobic exercise. In Experiment 2, the M1 excitatory response to PAS25, but not PAS21, was significantly facilitated by acute aerobic exercise. Previous work demonstrated that the LTP-like effects of PAS25, but not PAS with shorter ISIs (i.e. PAS21), on M1 excitability are partly mediated by cerebellar circuits (90). Thus, the present work suggests that acute aerobic exercise modulates 89  activity in the cerebello-thalamo-cortical circuit, and further, that these effects may contribute to aerobic exercise-induced facilitation of LTP-like plasticity in M1. These findings have implications for understanding the motor circuits underpinning acute aerobic exercise influences on neuroplasticity in M1. 3.3.1 Experiment 1 The majority of research investigating the mechanisms by which acute aerobic exercise affects the brain has focused on the role of neurochemicals, such as catecholamines and neurotrophic growth factors, which are transiently elevated after a bout of aerobic exercise (26, 38, 137, 273, 308). These increases in neurochemicals are thought to promote the development of a cortical environment that is supportive of plasticity, and hence, receptive to meaningful experience (e.g. cognitive training, skilled motor practice, etc.) (166, 244). The contention that acute aerobic exercise creates a particularly neuroplastic milieu in the cortex is further supported by work that has demonstrated reduced SICI (270, 275) and enhanced ICF (270) in M1 representations for non-exercised muscles. SICI and ICF reflect activity of GABA (γ-amino butyric acid) (35) and NMDA (N-methyl D-aspartate) (151) receptors, both of which are highly implicated in the induction of LTP-like plasticity in M1 (96, 315). Thus, while acute aerobic exercise alone does not necessarily induce neuroplasticity in M1, it appears to prepare or prime the brain for plasticity to occur. Nevertheless, changes in SICI and ICF represent just two M1 interneuron pools that could contribute to enhanced M1 plasticity following aerobic exercise. Activity in the descending corticospinal neurons is influenced by multiple inputs from a range of brain regions, any of which could plausibly be affected by aerobic exercise and subsequently influence M1 plasticity. 90   The results from Experiment 1 suggest that acute aerobic exercise modulates activity in cerebellar circuits that project to M1. Specifically, we found that CBI in a non-exercised muscle of the hand was reduced immediately following a single bout of high-intensity cycling (Figure 3.3). CBI, as measured with dual-coil paired-pulse TMS, involves cerebellar stimulation 5-7 ms prior to M1 stimulation, which results in a suppression of the MEP elicited by M1 stimulation alone (51, 89, 220, 293). The cerebellar stimulation is thought to activate Purkinje cells, which inhibit the tonic excitatory drive from the dentate nucleus to M1 via the ventral lateral thalamus (30, 220, 293). Previous studies have verified this CBI circuit, ruling out a potential effect of the cerebellar stimulus on activation of the brachial plexus, corticospinal tract or other potential subcortical influences (220, 304). Past work has also indicated no direct effect of acute aerobic exercise on corticospinal excitability (167, 173, 270, 275) or spinal excitability (190) of non-exercised upper limb muscles, suggesting that the observed release of CBI was likely not mediated by changes at these sites. However, interactions between interneuronal populations involved in SICI and those which receive cerebellar projections must also be considered (51). Specifically, a contribution of reduced SICI after acute aerobic exercise (270, 275) in mediating the presently observed reduction in CBI cannot be entirely excluded. Yet, SICI represents one of many interneuronal populations in M1 and is likely not the sole recipient of cerebellar inputs to M1 (35, 51). Regardless, our results indicate that inhibition of M1 induced by activation of the cerebello-thalamo-cortical pathways is reduced immediately following acute aerobic exercise. The driving force behind modulation of neural inputs to M1 by acute aerobic exercise may relate back to aforementioned increases in neurochemicals following aerobic exercise (26, 38, 137, 273, 308). For example, animal work has demonstrated aerobic exercise-induced elevations in brain-derived neurotrophic factor (BDNF) in multiple brain regions, including the 91  cerebellum (196). Although highly speculative, it is plausible then that the bout of aerobic exercise altered BDNF levels within the cerebellum, which subsequently influenced CBI. While past work has focused on the impact of acute aerobic exercise on excitatory neurotransmitters and growth factors (26, 38, 137, 273, 308), the present findings, in combination with reports of reduced SICI after aerobic exercise (270, 275), may also be taken to suggest an impact of aerobic exercise on GABA. GABA is the chief inhibitory neurotransmitter in the central nervous system (CNS) and both inhibitory interneurons in M1 (35) and Purkinje cells in the cerebellum are GABAergic (203). However, without a direct measure of GABA levels in the CNS, we cannot conclusively determine its involvement in the presently reported effects. Nevertheless, such changes in neurochemicals and inhibitory systems could contribute towards the creation of a favourable M1 environment for induction of plasticity, as suggested in other work (167, 173, 271). 3.3.2 Experiment 2 In Experiment 2, we examined the effects of acute aerobic exercise on changes in corticospinal excitability evoked by PAS25 and PAS21. Although there is some evidence for involvement of spinal circuits (181), changes in corticospinal excitability evoked by PAS are thought to largely reflect alterations in M1 excitability, given a lack of change in F-waves and potentials evoked by electrical brainstem stimulation following PAS (279). PAS exerts its effects on M1 excitability via spike-timing-dependent plasticity (STDP), with excitatory effects evoked when the ISI between the sensory and cortical stimuli is equal to or slightly greater than the latency of the N20 sensory evoked potential (i.e. the time for a sensory volley to reach M1) (191). As such, excitatory PAS is typically delivered using ISIs ranging from approximately 21 ms to 25 ms, with the sensory input generally thought to reach M1 by rapid conduction via the 92  dorsal column-medial lemniscal system to the sensory thalamus, followed by either direct thalamic connections or via somatosensory cortex (90, 189, 191, 279, 303, 311). Importantly though, past work demonstrated that both anodal and cathodal transcranial direct current stimulation of the cerebellum blocks PAS25 effects on M1 excitability but has no effect on response to excitatory PAS delivered with a slightly shorter ISI of 21.5 ms (90). Further work has demonstrated that continuous theta burst stimulation of the cerebellum modulates changes in M1 excitability evoked by PAS25 (228). Thus, PAS25 appears to utilize trans-cerebellar sensory pathways to enhance M1 excitability, in addition to the more direct sensory pathways involved in PAS with slightly shorter ISIs (90). This interpretation is consistent with animal work demonstrating indirect sensory pathways to M1 that travel through the cerebellum (25, 53, 305). Our results demonstrate that acute aerobic exercise significantly facilitated response to PAS25, but not PAS21 (Figure 3.5). Effect size calculations showed that the aerobic exercise bout had a large effect on response to PAS25 (η2partial = 0.27) and a small-moderate effect on the PAS21 protocol (η2partial = 0.09). As the primary mechanistic difference between PAS25 and PAS21 is the involvement of a trans-cerebellar sensory pathway (90), this finding combined with an acute aerobic exercise effect on CBI in Experiment 1, suggests that activity in the cerebello-thalamo-cortical pathway is likely modulated by acute aerobic exercise and may subsequently play a role in facilitating M1 plasticity. Nevertheless, it is unlikely that the effects of acute aerobic exercise on response to excitatory PAS of M1 are mediated solely by excitability changes in cerebellar circuits. Although non-significant, effect size calculations on the PAS21 protocol demonstrated that aerobic exercise had a facilitatory effect of nearly moderate magnitude (44). Thus, it is more probable that excitability changes in this trans-cerebellar sensory pathway summated with 93  changes in other motor circuits, such as SICI and ICF (270, 275), to amplify the facilitatory effects of aerobic exercise on M1 plasticity evoked by PAS25, compared to PAS21.  We also observed a trend (p = 0.06) for a main effect of PAS protocol, indicating that the change in slope evoked by PAS was greater with PAS25 compared to PAS21 when collapsed across conditions (rest and aerobic exercise). Participant characteristics and experimental procedures were comparable between PAS groups and are unlikely to have contributed to this trend. Importantly, the trend for an effect of PAS protocol appears to be driven by the difference between PAS groups under the aerobic exercise condition (PAS25: 59.8 ± 73.5%, PAS21: 22.7 ± 46.7% increases), rather than the rest condition (PAS25: 14.2 ± 32.7%, PAS21: 3.7 ± 36.3% increases). However, a slight difference in the magnitude of the PAS effect at rest may relate to differences in STDP between PAS25 and PAS21. For example, PAS21 may be on the cusp of the appropriate ISI range to produce LTP-like effects and consequently elicit smaller effects. Further, the involvement of additional sensory pathways in PAS25 compared to PAS21 (90) could also plausibly affect the size of the resting PAS response. Regardless, we were not interested in comparing the magnitude of the PAS response between protocols, but rather in comparing the magnitude of the aerobic exercise effect on PAS response between the protocols.  3.3.3 Limitations Although participants were instructed to minimize upper limb muscle activity during the aerobic exercise bout, EMG activity was not monitored. Thus, it is possible that gripping of the cycle ergometer handle bars could have contributed to the observed effects in both experiments. Also, it is difficult to definitively determine what aspects of the aerobic exercise bout caused the observed effects; for example, whether the results might be influenced by fatiguing, rather than non-fatiguing, or active versus passive leg movements cannot be elucidated from the current 94  experimental design. Nevertheless, given the accumulating evidence for acute aerobic exercise effects on excitability of intracortical circuits (270, 275) and neurochemicals (273), we suggest that the observed effects are more likely related to a direct impact of aerobic exercise on the brain. In Experiment 2, an exercise-induced change in arousal and/or attention (145) could have influenced our results, as response to PAS has been previously shown to depend on attention to the stimuli (280). However, participants were not instructed to attend to the PAS stimuli under either condition or in either PAS group. Also, Singh and colleagues (271) demonstrated a similar facilitatory effect of acute aerobic exercise, compared to rest, on PAS25 response when participants’ attention levels were monitored, suggesting that attentional changes likely do not drive these acute aerobic exercise effects. Nevertheless, our decision to not instruct participants to attend to the PAS stimuli may have attenuated the PAS responses observed in our study. Finally, we tested different participants for the PAS25 and PAS21 protocols. Utilizing a full repeated-measures experimental design would have removed any potential influence of participant characteristics on the magnitude of the aerobic exercise effect; however, the characteristics of the groups were well-matched (i.e. age, sex and cardiorespiratory fitness). Therefore, despite this limitation, we are confident that the current results were not significantly influenced by our experimental design. 3.3.4 Implications The impact of acute aerobic exercise on neuroplasticity in human M1 is a relatively recent discovery (167, 173, 271) that has led to speculation that acute aerobic exercise may be used to prime the learning of motor skills in sport and neurorehabilitation settings (166, 244). This idea is supported by work showing benefits of acute aerobic exercise on motor learning tasks (Chapter 2) (167, 245). Our current findings, suggesting an impact of acute aerobic 95  exercise on cerebellar circuits, are in line with the results of our previous behavioural experiments (Chapter 2) (167) that demonstrated an effect of high-intensity aerobic exercise specifically on complex motor task elements associated with cerebellar function (16). Interestingly, reductions in CBI, similar to those observed in Experiment 1 following high-intensity aerobic exercise, were also demonstrated following the learning of a locomotor adaptation (114) and a visual hand perturbation task (259). Thus, acute aerobic exercise may have the capacity to initiate physiological processes in the cerebellum that underpin motor learning. Given our findings here, further studies might consider whether the learning of motor tasks known to involve cerebellar function is particularly amenable to augmentation by acute aerobic exercise. 3.3.5 Conclusions This study provides evidence that acute aerobic exercise may impact the excitability of cerebellar circuits (Experiment 1), which in turn influence LTP-like plasticity in M1 (Experiment 2). Taken with previous work investigating intracortical M1 excitability after acute aerobic exercise (270, 275), our results suggest that aerobic exercise may promote a somewhat global decrease in inhibition within and projecting to M1, which could contribute to the creation of a favourable neural environment for the induction of LTP-like plasticity.  96  Chapter 4: Acute High-Intensity Aerobic Exercise Enhances Motor Memory Retrieval 4.1 Introduction There is a growing body of evidence demonstrating that aerobic exercise can benefit memory processes (145, 244). Interestingly, a recent meta-analysis concluded that the acute effects of a single bout of aerobic exercise on memory were larger than the chronic effects associated with aerobic exercise training over several weeks or months (244). Further, acute aerobic exercise effects were greatest in studies examining long-term memory processes, indexed by tests of recall of information greater than two minutes and up to eight months following exposure (244). Although interesting, it should be noted that the majority of evidence for these effects was obtained from evaluation of declarative learning and memory processes, such as verbal/vocabulary learning and image recall tests (145, 244). Less is known about the effects of a single bout of aerobic exercise on motor learning. Procedural motor learning is supported by memory systems that are unique from those involved in declarative tasks (156, 306), but that are also specific to the nature of the motor task being learned (32, 62). Only two studies investigated the effects of a single bout of aerobic exercise specifically on motor learning and, notably, both employed highly similar motor tasks (167, 245). These studies indicated that, when paired closely in time to practice of a continuous motor sequence, acute aerobic exercise reduced performance error at the end of skill acquisition (167) and at no-exercise retention tests conducted 24 hours (167, 245) and seven days following practice (245). In the study conducted in our laboratory (Chapter 2) we found that the aerobic exercise effect was specific to improvements in temporal precision (167), a component of motor 97  learning that is linked to cerebellar function (16). Additionally, the tasks utilized in both of the aforementioned studies involved a visuomotor rotation (167, 245), a form of motor learning for which the cerebellum is also implicated (139, 259). Thus, current evidence for acute aerobic exercise benefits on motor learning were observed in tasks and outcome measures that were highly cerebellum-dependent. A further consideration when interpreting the aforementioned work relates to the key distinction between discrete versus continuous movement tasks. Critically, the principles which guide sequence learning in these two classes of movement are thought to be fundamentally different (32, 33, 147, 260). Discrete movements are characterized by a definite beginning and end, and may be combined into a sequence or series, known as a serial movement (260). A well-studied feature of discrete motor sequence learning is the chunking of individual movements into sub-sequences or clusters (183, 250). Chunks of approximately two or three discrete movements can be stored as separate memory units and then linked to allow faster and more accurate performance of a larger movement sequence (250). Interestingly, past work demonstrated a key role of the basal ganglia for supporting chunking in discrete motor sequence learning (14, 85). In contrast to discrete movements, continuous movements, as used in the prior studies of acute aerobic exercise and motor learning, have no recognizable beginning or end (260). A role for chunking has not been substantiated in continuous motor sequence learning, possibly because the lack of movement boundaries within a continuous sequence precludes the detection and use of movement chunks to facilitate learning (32, 33). In line with the apparent difference in how sequences are learned, practice conditions, such as part versus whole (260) and distributed versus blocked practice (147), have disparate effects on continuous and discrete motor sequence 98  learning. Thus, a practice condition (e.g. acute aerobic exercise) that benefits one class of movement cannot be assumed to translate to another. Importantly, recent work (240) examined the effects of acute aerobic exercise on motor memory interference, but not motor learning per se, using a discrete motor sequence task akin to the serial reaction time task. Briefly, an aerobic exercise bout performed two hours after practice of a motor sequence, but immediately before practice of an alternative sequence, protected the learning of the original sequence from interference, assessed by a 24-hour retention test (240). The authors suggested that, to some extent, the results contrasted findings of enhanced continuous motor sequence learning by aerobic exercise prior to motor practice. For instance, had aerobic exercise prior to practice of the alternative sequence benefited its learning, then a greater interference effect on the original sequence learning might have been expected (240). Thus, these finding suggest that a more direct evaluation of the effects of acute aerobic exercise prior to motor practice on the learning of a sequence containing discrete movements would advance the current understanding of interactions between acute aerobic exercise and motor learning. The main objective of the present work was to evaluate the effects of acute aerobic exercise performed immediately prior to motor practice on the learning of a discrete motor sequence task without visuomotor rotation. In contrast to previous work, which demonstrated acute aerobic exercise-induced learning benefits on comparatively cerebellar-mediated tasks (167, 245), the task utilized in the present work likely involved a relatively greater contribution from basal ganglia circuits (14, 85). Although the specific neurobiological mechanisms for acute aerobic exercise effects on human memory are not conclusively known, evidence from animal studies suggested that exercise-induced increases in neurochemicals (48, 295), including 99  dopaminergic effects in the basal ganglia (217), may play a role in facilitating memory processes. Thus, we hypothesized that acute aerobic exercise would benefit the acquisition and learning of the motor task currently employed. Similar to previous work (167, 245), we examined change in performance over the practice period, which may reflect memory encoding processes (123). We also assessed change in performance from practice to a no-exercise 24-hour delayed retention test to evaluate motor learning following the evolution of off-line consolidation processes (123). In line with prior evidence suggesting potential acute exercise effects on encoding (167) and consolidation (167, 245) processes, we cautiously expected positive effects of acute aerobic exercise on these measures. We also evaluated the rate of performance improvement during practice and during the retention test, which may reflect the rates of memory encoding and memory retrieval or re-learning, respectively (123). We anticipated that rates of skill improvement at both time points would be enhanced in the aerobic exercise condition, possibly due to exercise effects on the speed of information processing (145), which might facilitate discrete motor sequence learning rate by speeding up the detection and subsequent use of motor chunks. 4.2 Methods 4.2.1 Participants Six men and 10 women between ages 19 and 34 (mean ± SD; 25.7 ± 4.6) participated. Participants had no known neurological diagnoses and were of adequate health to complete the exercise protocols. All participants provided written informed consent prior to testing. The Clinical Research Ethics Board at the University of British Columbia approved all testing procedures.  100  4.2.2 Experimental overview On a separate day, at least two days prior to all other experimental sessions, participants completed a graded maximal exercise test on a cycle ergometer. Each individual then participated in four sessions in a within-subjects experimental design to compare the effects of a 20 min period of rest and a 20-minute bout of high-intensity interval cycling on the acquisition and learning of a discrete motor sequence task. The four experimental sessions were completed on separate days, and included: (1) rest immediately prior to skilled motor practice, followed by (2) a no-exercise 24-hour retention test; and (3) aerobic exercise immediately prior to motor practice, followed by (4) a no-exercise 24-hour retention test. On all testing days, participants were instructed to refrain from any exercise other than that involved in the experimental sessions. There was a minimum washout period of two weeks between motor practice under the different experimental conditions (i.e. rest or aerobic exercise). Participants were randomly allocated to an experimental order such that half of the sample completed the rest condition first and the other half the aerobic exercise condition. The experimental procedures are depicted in Figure 4.1. 101   Figure 4.1 Experimental overview. 4.2.3 Graded maximal exercise testing A maximal exercise test was conducted on a cycle ergometer (Ergoselect 200, Ergoline GmbH, Bitz, Germany), beginning with a power output (PO) of 100 W for men and 50 W for women, and was increased by 30 W increments every two minutes until exhaustion. Participants were instructed to maintain a pedaling cadence of 70-90 rotations per minute (rpm) and to remain seated throughout testing. The following measurements were monitored throughout exercise testing: expired O2 and CO2 concentrations and air flow via a metabolic cart (ParvoMedicsTrueOne 2400, Sandy, UT, USA); heart rate (HR) via a HR monitor (Polar Electro, Oy, Kempele, Finland); and Borg’s 6-20 scale rating of perceived exertion (RPE). Finger-stick 102  blood lactate (BLa) was determined immediately following the exercise test using an automated portable blood lactate analyzer and test strips (Lactate Pro, Arkray Inc., Kyoto, Japan). Peak O2 consumption (V̇O2peak) criteria included at least one of the following: a plateau in O2 uptake (V̇O2) and HR with further increase in workload, a respiratory exchange ratio (RER) greater than 1.1, a RPE greater than 17, BLa greater than 10 Mmol/L, an inability to maintain a cadence of 70 rpm, and volitional exhaustion. Exercise testing results for each individual are presented in Table 4.1.  4.2.4 Standardized acute aerobic exercise bout Maximal PO determined by the exercise test was used to inform prescription of a standardized acute aerobic exercise bout in the same manner as our previous work (Chapters 2 and 3) (167). The bout lasted 20 minutes and included a five-minute warm-up at 50 W and self-selected cadence, followed by three sets of three-minute high-intensity cycling intervals interspersed with two minutes of low-intensity cycling. The high-intensity intervals consisted of cycling at 90% of maximal PO from the final fully completed stage of the maximal exercise test, and the low-intensity intervals involved cycling at 50 W, always maintaining a cadence greater than 70 rpm. The high-intensity exercise bout was utilized based on previous work indicating a dose-response relationship between exercise intensity and increases in neurochemicals (137, 308). Aerobic exercise prescriptions and physiological responses of each participant are presented in Table 4.1. 103  Table 4.1 Participant characteristics and aerobic exercise data     Aerobic Exercise Test - Final stage Aerobic Exercise Bout  Participant Age Sex Dom. Hand V̇O2peak PO HR RER BLa RPE PO  HR  Bla RPE  01 23 M R 37.8 220 184 1.22 16.7 19 200 170 14.2 18 02 30 M R 42.0 280 195 1.13 12.8 19 250 169 20.3 15 03 30 F R 45.0 320 182 1.10 11.1 19 288 165 8.3 18 04 34 F L 34.2 170 196 1.19 12.2 - 150 188 12.2 19 05 29 F R 53.6 230 179 1.11 16.4 19 207 166 6.3 14 06 24 F R 47.7 200 177 1.20 15.1 16 180 167 - 18 07 29 F R 58.9 290 163 1.11 9.7 19 261 154 11 17 08 22 F R 39.2 170 167 1.17 12.9 18 140 165 15.9 18 09 19 F R 24.0 110 168 1.27 8.8 15 72 170 3.3 13 10 27 F R 49.1 200 178 1.17 13.3 18 180 172 12.3 16 11 25 M R 42.8 220 180 1.16 15.2 18 198 156 10.2 15 12 25 F R 37.6 140 187 1.28 12.2 19 126 176 - 13 13 20 M R 52.5 280 182 1.17 13.7 18 252 169 10 16 14 22 F R 40.5 170 200 1.14 10.9 18 153 195 9.1 14 15 20 M R 40.9 220 188 1.21 12.3 18 198 170 7.4 15 16 32 M R 47.0 310 171 1.09 12.1 20 279 161 13.6 18 Mean 25.7   43.3 220.6 181.1 1.17 12.8 18.2 195.9 170 11.0 16.0 SD 4.6   8.4 61.7 10.6 0.06 2.2 1.3 59.8 10.3 4.3 2.0 Dom., dominant; V̇O2peak, peak oxygen consumption (ml/kg/min); PO, power output (W); HR, heart rate (beats/min); RER, respiratory exchange ratio; BLa, blood lactate (Mmol/L); RPE, Borg’s Rating of Perceived Exertion; SD, standard deviation. Power output (PO) in the ‘Aerobic Exercise Bout’ section was consistent across all three high-intensity intervals for each bout, the remaining values were collected at the end of the third (final) interval within an exercise bout. 104  4.2.5 Serial targeting task procedures Practice of a discrete motor sequence task, herein referred to as the serial targeting task, was completed following the rest and aerobic exercise conditions for each participant, with at least two weeks between conditions. Serial targeting task practice involved manipulation of a computer mouse (Wheel Mouse Optical, Microsoft Corporation, Redmond, WA, USA) housed in a custom frame that was held in a pronated grasp with the non-dominant hand (Figure 4.2). The mouse was used to move a cross-hair cursor between a series of discretely-presented targets. To initiate the appearance of the next target, participants placed their cursor in the current target for 500 ms, which was followed by an additional 500 ms inter-target interval. Each target was the same size (28 mm diameter) and could appear at one of nine locations. One location was central; the other eight locations formed an equidistant circular array. Cursor position sampling was presented at 200 Hz with custom software developed on Labview (v.8.1; National Instruments Corporation, Austin, TX, USA). The target movement response time was defined as the time in seconds from target appearance to the presentation of the next target (corrected for the 500 ms stationary period and the 500 ms inter-target interval). All measures were extracted using custom Labview software (v.8.1; National Instruments Corporation, Austin, TX, USA). Embedded within the target movements that were presented, there was a repeated 6-target sequence that was flanked by 7-target random sequences (Figure 4.2). The same repeated sequence was practiced for each phase of each experimental condition (rest and aerobic exercise). For each participant, the target movements within the sequences presented during practice and retention blocks were reversed between conditions, such that the sequences differed but shared equivalent difficulty. The order of presentation of conditions (rest and aerobic exercise) and sequences (regular and reversed) assigned to each participant were pseudo-105  randomized such that it was balanced across the sample. The random sequences were different at each presentation throughout a block. Repeated and random sequence difficulty was controlled and equated using Fitt’s Law (75). Participants were not informed of the existence of the repeated sequence but instructed to move the cursor to all targets as quickly and accurately as possible while taking the most direct route. The inclusion of repeated and random sequences allows separation of change associated with implicit sequence-specific learning (repeated sequences) and those associated with generalized improvements in motor control (random sequences) (13, 15, 17, 218). In the experimental sessions involving serial targeting task practice, participants completed three sequences (20 target movements; one repeated sequence and two random sequences) prior to the rest period or the aerobic exercise bout for task familiarization. For serial targeting task practice following rest or the aerobic exercise bout, participants completed three blocks of 110 target movements (8 × 6-target repeated sequences and 9 × 7-target random sequences). The following day (24 ± 2 hours after motor practice) participants completed another single block of the serial targeting task (no-exercise delayed retention test).  Following the final retention test, participants were tested for explicit recognition of the repeated sequence within the serial targeting task from both conditions (rest and aerobic exercise). For the recognition testing, participants viewed a series of 20 target sequences and were asked to select ‘yes’ or ‘no’ with a computer mouse after the presentation of each sequence to indicate whether or not it was recognized. Of the sequences presented, 14 were random sequences and 6 were the repeated sequences from the rest and aerobic exercise sessions. If explicit knowledge of the repeated sequence was acquired, we expected that, on average, sequences would be identified correctly at a level higher than that associated with chance (i.e. 4/6 repeated sequences and 8/14 random sequences) (13, 15, 17). 106   Figure 4.2 Schematic of the serial targeting task. Performance of the task involved manipulation of a computer mouse housed in a custom frame that was held in a pronated grasp with the non-dominant hand. Participants were instructed to use the mouse to move a cross-hair cursor between a series of discretely presented targets as quickly and accurately as possible while taking the most direct route. 4.2.6 Serial targeting task analyses Sequence response time, the sum of the reaction and movement times to complete an entire target sequence, was used to measure task performance. To obtain this measure, the sum response time of all target movements within a sequence was calculated for each movement sequence within a block. Response times were calculated separately for random and repeated sequences. 4.2.6.1 Baseline performance To allow for comparison of serial targeting task performance at the outset of experimental sessions under each condition (rest, exercise), the response time for the three sequences presented in the familiarization period and the first three sequences in the practice period (initial practice) were averaged. The random and repeated sequence response times at these time points were averaged together for analysis of baseline performance, as during this 107  early stage of practice the participants were exposed to the repeated sequence only twice (once during familiarization and once during initial practice), and are thus unlikely to have yet acquired any sequence-specific skill. 4.2.6.2 Motor skill acquisition and learning Response time from the first four sequences of the first practice block (early acquisition), the last four sequences of the third practice block (late acquisition) and the first four sequences of the retention block (retention) were averaged separately for random and repeated sequences. This method is consistent with past work investigating acute aerobic exercise effects on continuous motor sequence task learning (Chapter 2) (167) and other work using this serial targeting task (178). Change in response time was then calculated between early and late acquisition (acquisition change score, Δ-ACQ) to assess practice-related changes in motor performance, and between early acquisition and retention (retention change score, Δ-RET) to assess motor learning (123) for each individual.  Next, we utilized an exponential decay (decreasing form) curve fitting approach to obtain information about participants’ rate of improvement in response time for random and repeated sequences separately. Given the evolution of memory processes that contribute to motor performance during practice and at a delayed retention test (123), we fit curves to data obtained over practice (skill acquisition) and at the delayed retention test (skill retrieval) separately. A moving average of the response time for every three consecutive sequences was calculated. Response times that differed from the moving average by more than two standard deviations were filtered out of the data set (˂ 5 % of sequences). The remaining sequence response times from the practice blocks (blocks 1-3) and at retention were fit with a least squares regression analysis using the following equation (21): 108  E(RTN) = A +Be-αN E(RTN) is the expected value of the response time on sequence trial N; A is the expected value of response time after practice has been completed; B is the change in the expected value of response time from the beginning to the end of practice; e is a mathematical constant (Euler’s number) characteristic of an exponential function; Alpha (α) is the expected exponential rate of change in response time from the beginning to the end of practice. We were specifically interested in α, which represented the rate of motor skill acquisition (α-ACQ) during practice and the rate of retrieval of the motor memory (α-RET) during the retention test. All curve fitting was conducted using a custom MATLAB script (Mathworks, Natick, MA, USA). 4.2.7 Statistical analyses 4.2.7.1 Baseline performance Data were evaluated for differences between conditions in serial targeting task performance (i.e. response time) at the outset of the experimental sessions. To achieve this, a two-way repeated measures analysis of variance (RM-ANOVA) was conducted with Condition (rest, aerobic exercise) and Time (familiarization, initial practice) as the within-subjects factors and response time as the dependent variable. 4.2.7.2 Motor skill acquisition and retention To examine the effects of acute aerobic exercise on sequence-specific learning of the serial targeting task, separate two-way Condition (rest or aerobic exercise) by Sequence (repeated or random) RM-ANOVAs were conducted for the following dependent variables: Δ-ACQ, Δ-RET, α-ACQ and α-RET. For all RM-ANOVAs, post-hoc analyses (Fisher’s least-significant-difference tests) were conducted where appropriate. 109  4.2.7.3 Normality of data All data were visually inspected for skewness and kurtosis and objectively tested for normality with the Shapiro-Wilk test with a significance level set at p < 0.001 (78). The following data were found to be non-normal (W(16) ≤ 0.760, p < 0.001): α-ACQ for the repeated sequence under both conditions and the random sequence under the rest condition, as well as α-RET for both repeated and random sequences under the rest condition. For statistical analyses, square root transformations were applied to all α-ACQ and α-RET data. Following square root transformation, all α-ACQ and α-RET data were found to be normal (W(16) ≥ 0.774, p > 0.001). All baseline performance and change score data were found to be normally distributed (W(16) ≥ 0.784, p > 0.001) and were subsequently analyzed in their raw form. All descriptive statistics are reported as mean ± SD. For all statistical tests, significance level was set at p < 0.05. Statistical analyses were conducted using SPSS software (SPSS 21.0, IBM Corporation, Armonk, NY, USA). 4.3 Results 4.3.1 Baseline performance The RM-ANOVA examining initial performance yielded a significant main effect of Time (F(1, 15) = 24.14, p < 0.001), with an average response time of 9.03 ± 1.66 s at familiarization and 8.06 ± 1.40 s at initial practice across conditions. There was no effect of Condition (F(1, 15) = 3.01, p = 0.10) and no interaction effect (F(1, 15) = 0.63, p = 0.44), indicating that, across the group, baseline serial targeting task performance improved following familiarization, but was not significantly different between conditions at either time point. 110  4.3.2 Motor skill acquisition Group average response times for each movement sequence trial over the course of motor practice are presented in Figure 4.3A. A RM-ANOVA indicated that there was a significant main effect of Sequence on α-ACQ (F(1, 15) = 6.26, p = 0.02), with a faster rate of acquisition for the repeated sequence relative to the random sequence, regardless of condition (Figure 4.4A). There was no effect of Condition (F(1, 15) = 0.28, p = 0.61) and no interaction effect (F(1, 15) = 0.28, p = 0.61) on α-ACQ. When considering ΔACQ, there was no effect of Condition (F(1, 15) = 0.02, p = 0.89), Sequence (F(1, 15) ˂ 0.01, p = 0.97) or their interaction (F(1, 15) = 0.34, p = 0.57) (Figure 4.5A). These results suggest that there was a sequence-specific increase in the rate (α-ACQ), but not the extent (ΔACQ), of skill acquisition over practice; however, this effect was not different between rest and aerobic exercise conditions. 4.3.3 Motor skill retention Group average response times for each movement sequence trial over the course of the retention test are presented in Figure 4.3B. A RM-ANOVA demonstrated a significant Condition by Sequence interaction effect on α-RET (F(1, 15) = 7.13, p = 0.02). There was no main effect of Condition (F(1, 15) = 3.14, p = 0.10) or Sequence (F(1, 15) = 1.35, p = 0.26) on α-RET. Post-hoc analyses of the significant interaction indicated a significantly greater α-RET for the repeated sequence relative to the random sequence for the aerobic exercise condition (p = 0.01). In contrast, there was no difference in α-RET values between sequences for the rest condition (p = 0.33). Further, α-RET for the repeated sequence under the aerobic exercise condition was also greater than α-RET for both the repeated (p ˂ 0.01) and random sequences (p = 0.01) under the rest condition (Figure 4.4B). On the other hand, the RM-ANOVA evaluating ∆-RET demonstrated no effect of Condition (F(1, 15) = 0.09, p = 0.77), Sequence (F(1, 15) = 0.20, p = 0.66) 111  or their interaction (F(1, 15) = 0.07, p = 0.79). Altogether, these results suggest that there was a sequence-specific increase in the rate of retrieval or re-learning of the motor skill in the aerobic exercise, but not the rest, condition, and that the retention change score was not different between sequences or conditions (Figure 4.5B). 4.3.4 Recognition Across the group, participants did not demonstrate explicit knowledge of a repeated sequence during the recognition testing. The repeated sequence was correctly identified at a level consistent with chance (3.0 ± 2.3/6 or 50.0 ± 38.0 % correct). 112   Figure 4.3 Average response time across the group for each movement sequence trial during practice (Panel A) and retention (Panel B). Trial-by-trial data points are depicted for the rest (red circles) and aerobic exercise (green squares) conditions for the repeated (filled) and random (unfilled) sequences. The moving average (n = 2 trials) of trial-by-trial data is depicted for the rest (red) and aerobic exercise (green) conditions for the repeated (solid) and random (dashed) sequences. 113   Figure 4.4 Rate of change parameter (α), obtained from exponential decay curves fit to trial-by-trial practice (Panel A) and retention (Panel B) data, and averaged across the group. Red and green bars denote α obtained from data collected under rest and aerobic exercise conditions, respectively. Filled and patterned bars show data for the repeated and random sequences. Error bars depict one standard deviation. α-ACQ, rate of acquisition; α-RET, rate of retrieval.  114   Figure 4.5 Acquisition (Panel A) and retention (Panel B) change scores across the group under the rest (red circle) and aerobic exercise conditions (green square) for both repeated (filled) and random (unfilled) sequences. Error bars depict one standard deviation. Δ-ACQ, acquisition change score; Δ-RET, retention change score. 115  4.4 Discussion This study was designed to evaluate the impact of a single bout of high-intensity aerobic exercise, performed immediately prior to practice of a discrete motor sequence task (i.e. serial targeting task), on the extent and rate of implicit sequence-specific skill acquisition and learning. There was a positive impact of acute aerobic exercise, compared to a period of seated rest, on the rate of improvement in performance at a 24-hour no-exercise delayed retention test. In contrast, there were no significant effects of acute aerobic exercise on the rate of improvement during practice (i.e. acquisition), or the overall change in performance across practice and from practice to retention. Thus, our main finding was that acute aerobic exercise enhanced implicit sequence-specific motor learning of a discrete motor sequence task, expressly by increasing the rate of motor memory retrieval at retention.  Motor learning can involve memories that are accessed implicitly (i.e. without conscious awareness) (260, 277) and rely on multiple brain regions, including: primary sensory and motor cortices, pre-motor cortex, supplementary motor area, pre-frontal cortex, and subcortical structures such as the cerebellum and basal ganglia (62, 156). Within this over-arching implicit motor learning brain network, the relative contributions of specific brain regions vary based on the characteristics of the learned task (32, 62). Yet, the only studies to examine acute aerobic exercise effects specifically on motor learning have utilized tasks of a very similar nature, which required learning of a continuous motor sequence during a visuomotor rotation (167, 245). Given the use of visuomotor rotations, and the observation in one study that learning benefits were specific to improvements in temporal precision (167), the previously studied acute exercise effects were likely dependent on cerebellar function (16, 139, 259). Here, we found that acute aerobic exercise performed prior to motor task practice also benefited the learning of a discrete 116  motor sequence task with no visuomotor rotation. The task used here, termed the serial targeting task, was similar in nature to the commonly used serial reaction time (1) and discrete sequence production motor tasks (2), but instead of fine movements involving pressing a key with a finger, gross movements of the wrist and arm were used to move a computer mouse. Learning of the motor sequence in this serial targeting task may be expected to rely less on cerebellar function than the tasks used in prior work examining acute aerobic exercise effects on motor learning (167, 245). Although not directly measured here, learning of the current task may involve motor chunking, a memory process involving key contributions from basal ganglia circuits (14, 85).   A recent study yielded results that could be taken as indirect evidence that acute aerobic exercise prior to motor practice did not benefit discrete motor sequence task learning (240). Specifically, moderate-to-vigorous cycling performed following practice of a discrete movement sequence ‘A’, but immediately before sequence ‘B’, enhanced performance of sequence ‘A’ on a 24 hour retention test (240). This finding is consistent with a benefit of aerobic exercise on consolidation processes for sequence ‘A’; but, on the other hand, if the aerobic exercise prior to sequence ‘B’ had benefited sequence ‘B’ learning, then a greater interference effect with aerobic exercise may have been anticipated (240). The present findings add some clarity to the interpretation of this recent study, and indicate that, in addition to its effects on continuous motor sequence learning, acute aerobic exercise prior to motor practice can also enhance implicit learning of a sequence of discrete movements. Thus, the findings reported from the motor memory interference experiment (240) likely relate to the specific experimental paradigm that was employed (i.e. motor practice followed by aerobic exercise followed by motor practice) rather than to the nature of the task. 117  In the current experiments, there was an enhanced rate of acquisition of the repeated, relative to the random, sequence during practice that was independent of condition. This result suggests that implicit sequence-specific encoding processes occurred to the same extent under the rest and aerobic exercise conditions. However, when evaluating learning at retention, the rate of retrieval for the repeated sequence under the aerobic exercise condition was greater than for the random sequence and for both sequences under the rest condition. Also, rate of retrieval at retention for the rest condition did not differ between sequences. These findings indicate that implicit sequence-specific learning occurred when practice was preceded by aerobic exercise, but not rest. The enhanced rate of retrieval, but not acquisition, under the aerobic exercise condition suggests a role for off-line consolidation processes, as described in previous work (245), as well as retrieval and re-learning processes (123). Thus, our observation of an increased rate of memory retrieval at a delayed retention test when practice was preceded by exercise demonstrates a significant benefit of acute high-intensity aerobic exercise for implicit learning of sequences of discrete movements. The results partly align with our previous work that evaluated implicit sequence-specific motor learning with a continuous tracking task (167). In the previous study (167) and the current experiment, we attempted to minimize participants’ exposure to the task to reduce the potential of carryover effects between conditions, but this also resulted in the use of a relatively small dose of practice. As in the prior study (167), it appears that the practice dose was not sufficient for implicit sequence-specific learning to occur under the rest condition, but that acute aerobic exercise effects reduced the amount of practice necessary to allow formation of the implicit motor memory. Thus, the results suggest that acute aerobic exercise can benefit implicit motor learning, regardless of whether tasks are continuous or discrete in nature. Nevertheless, our 118  findings are also somewhat contradictory to our previous study using a continuous motor tracking task (167). Previously, we observed an effect of acute aerobic exercise on the overall change in motor performance of a repeated sequence across practice and from practice to retention (167), whereas presently we observed no effect of exercise on the change score measures with the serial targeting task. The preferential effect of acute aerobic exercise on rate of retrieval rather than change in performance for the serial targeting task used here may relate to the specific processes that underpin discrete motor sequence learning (5, 257). For example, past work suggests that individuals commonly show a greater degree of forgetting of discrete, rather than continuous, motor tasks over delayed retention intervals (5, 257), possibly due to the need to re-identify motor sequence chunks at retention. Perhaps, initial retention test performance of the serial movement sequence task in the present study was compromised by such forgetting, and instead, the speed at which the movement sequences were re-processed, remembered or re-learned over multiple trials (i.e. rate of motor memory retrieval) provided a better indicator of the learning process. Thus, we speculate that the benefits of aerobic exercise may have been most amenable to an influence on rate of memory retrieval, rather than initial retention test performance, due to the discrete nature of the movements. Although an increased rate of improvement at retention could occur as a statistical artifact (individuals who perform poorly at the outset of the retention test have greater room for improvement), the lack of difference in retention change score between conditions suggests that this was not the case for the observed effects. It is also important to note that all of the significant findings in the current study were found when considering the rate of improvement in serial targeting task performance. Overall change in performance did not differ between sequences or conditions during either acquisition 119  or at retention. The preferential effects on rate of improvement in motor performance may then also speak to the merits of utilizing an exponential curve fitting approach when analysing motor learning data. Commonly, motor skill acquisition and learning is assessed through the calculation of change scores determined from performance measurements taken at single points in time (123, 260). While this approach has value for evaluating motor learning, it is also susceptible to dilution of effects due to within-subject variability in motor performance (197). A curve fitting approach that utilizes measures obtained across multiple time points is less vulnerable to misrepresentation of effects due to unsystematic within-subject variability (21, 197). Thus, under the present experimental conditions, our curve fitting analysis approach to examine rate of improvement may have been more sensitive than the change score calculations for detection of implicit sequence-specific motor learning and its interactions with acute aerobic exercise.  Regardless of how the learning benefit manifests (i.e. change score or rate of improvement), a positive impact of acute aerobic exercise on motor learning across varying motor tasks is consistent with the general hypothesis that the effects of acute aerobic exercise on memory are driven by transient increases in neurochemicals (244, 273). The neurochemicals upregulated by aerobic exercise are expressed across multiple brain regions (192, 198) and thus, may be most utilized by the brain regions which are activated by subsequent experiences, such as specific types of motor practice or exposure to declarative information. 4.4.1 Implications Humans have a remarkable ability to learn and perform movement that is crucial for participation in everyday life. However, the memory processes supporting motor skill learning are highly dependent on the nature of the learned task, and as such, practice conditions which benefit one type of skill learning do not necessarily translate across tasks (32, 62, 147, 260). The 120  present findings suggest that acute high-intensity aerobic exercise can benefit the learning of a discrete movement sequence, in addition to its positive effects on continuous motor sequence learning which have been shown in past work (167, 245). The extension of this acute aerobic exercise effect across different movement tasks suggests that it may provide a means to facilitate varying movement tasks in sport or rehabilitation settings, including those involving meaningful sequences of discrete movements (e.g. playing the piano, throwing a ball or shifting gears in a car). Nevertheless, the past and present work demonstrating these effects have utilized high-intensity aerobic exercise bouts, based on indications of a dose-dependent effect of exercise intensity on neurochemical production (137, 308). Thus, determining how the exercise prescription impacts motor learning will be an important consideration in further work on this topic. 4.4.2 Conclusions  Our results indicate that a single bout of high-intensity aerobic exercise enhances discrete motor sequence task learning, preferentially through an increase in the rate of motor memory retrieval, rather than an overall change in performance as shown previously with continuous motor sequence tasks (167, 245). Thus, the positive effects of acute aerobic exercise on motor learning appear to occur for both continuous and discrete tasks, but the specific learning benefits of acute aerobic exercise may be somewhat task-specific. In conclusion, these data add to a growing body of evidence suggesting that acute bouts of high-intensity aerobic exercise may be utilized to facilitate motor skill learning in sport and rehabilitation contexts.   121  Chapter 5: Acute Aerobic Exercise Effects on Neuroplasticity and Motor Learning: Exploring the Potential Influence of Genetic and Epigenetic Variation 5.1 Introduction There is increasing evidence that a single bout of aerobic exercise can enhance both neuroplasticity and memory (145, 244). Recent work studied these effects in the context of the human motor system. For example, the neuroplastic response to non-invasive brain stimulation techniques targeting primary motor cortex (M1) is facilitated when stimulation is immediately preceded by aerobic exercise (167, 173, 271). Another line of work demonstrated that, if performed either immediately before or after skilled motor practice, acute aerobic exercise can enhance motor learning (167, 245). Together, these findings suggest that acute aerobic exercise may prime the motor system for neuroplastic change that supports learning (244). Nevertheless, in our work on this topic we noted substantial inter-individual variability in the acute effects of aerobic exercise on M1 plasticity and motor learning (Chapters 2-4) (167). Consideration of the origins of this variability may provide insights into both the basic mechanisms underlying exercise effects on the brain, and determination of which individuals might best respond to aerobic exercise-induced priming of the brain (166). Thus, the present work was designed to explore the potential contribution of genetic, and secondarily epigenetic, variation to differences in the effects of acute high-intensity interval cycling on M1 plasticity and motor learning.  Past studies suggested that genetic variation may contribute to inter-individual variability in M1 plasticity and motor learning in humans under a resting or baseline state (213, 214). Considerable work focused on a common single nucleotide polymorphism (SNP) on the gene 122  encoding for brain-derived neurotrophic factor (BDNF, rs6265) (34, 39, 115, 132, 176, 177). This SNP causes a valine-to-methionine substitution at codon 66 (val66met) and is associated with impaired activity-dependent secretion of BDNF (37, 66). The identification of this functional genetic variant gained much attention due to the known involvement of BDNF in long-term potentiation (LTP), a key neuroplastic mechanism underpinning memory formation (10, 158, 227). A number of studies demonstrated significant associations between the BDNF met allele and reduced M1 plasticity (3, 34, 39, 115, 132, 150), as well as reduced motor skill performance and learning (176, 177) in young healthy individuals; however, these BDNF genotype effects have not been observed for all measures of plasticity and all motor tasks (3, 39, 132, 150). Similar to BDNF, dopamine is a key orchestrator of neuroplasticity and memory processes (102, 187, 241) with signaling pathways that are susceptible to modulation by genetic variation (213, 214, 226, 287). One study investigated five SNPs known to affect dopaminergic signaling and demonstrated particularly robust effects of a SNP near the DRD2 gene and within the ANKK1 gene (rs1800497) on motor learning (212). This SNP causes a glutamic acid to lysine substitution at position 713 (glu713lys) of the ANKK1 gene and is associated with reduced dopamine D2 receptor availability in the human striatum in vivo and reduced D2 binding in human post-mortem brain tissue (226, 287). In the aforementioned work, individuals with the lys allele demonstrated reduced motor learning under a placebo condition, but experienced greater improvements in motor learning following the pharmacologic administration of levo-dopa (L-Dopa) compared to glu/glu homozygotes (212).  Interestingly, the priming effects of acute aerobic exercise on the brain are largely attributed to exercise-induced increases in neurochemicals, with potential roles posited for both BDNF and dopamine (244, 273). Evidence for this neurochemical mechanism is obtained from 123  animal work, in which direct roles for BDNF (81, 295) and dopaminergic pathways (217) in mediating aerobic exercise effects on brain function were demonstrated. Although neither molecule readily crosses the blood-brain barrier (194, 284), work in humans demonstrated positive relationships between both increased systemic BDNF (273) and dopamine (308) with improvements in memory following acute aerobic exercise. Thus, given evidence for roles of BDNF and dopamine in aerobic exercise effects on the brain (81, 217, 295), it is plausible that genetic variation impacting these molecules may influence acute aerobic exercise effects on M1 plasticity and motor learning in humans. Yet, little work on this topic has considered such genetic influences. Singh et al. (270) found no influence of the BDNF val66met SNP on acute aerobic exercise-induced alteration of M1 intracortical excitability. In contrast, another study found that a 4-week aerobic exercise program enhanced declarative memory in BDNF val/val homozygotes, but not met carriers (100). Nevertheless, both studies employed small sample sizes (n = 12) and examined BDNF genotype effects secondarily (100, 270). Genetic variation impacting dopaminergic pathways has not been considered in relation to acute aerobic exercise effects on plasticity and memory in humans.  The BDNF val66met and DRD2/ANKK1 glu713lys SNPs cause changes in the DNA sequence, which alter the amino acid structure of the encoded proteins. While these SNPs have significant consequences for BDNF and dopamine signaling pathways, epigenetic modifications within DNA may also exert significant effects. Epigenetics encompasses the study of modifications to DNA and DNA packaging, which can potentially affect gene expression, without changing the nucleotide sequence. The most characterized epigenetic modification is DNA methylation, the addition of a methyl group to the 5’carbon of a cytosine nucleotide, most commonly followed by a guanine base and referred to as a cytosine-phosphate-guanine 124  dinucleotide (CpG) (118). The binding of a methyl group to DNA is determined by a complex relationship between genetic and environmental factors, and is a dynamic but stable process that has been identified as a major contributor in regulating gene transcription (118). Differential DNA methylation patterns of both the BDNF (169) and DRD2 (229) genes impact gene expression and are associated with psychiatric disorders (77, 127, 314), suggesting phenotypic consequences of methylation within these genes. It is possible then, that by influencing the synthesis of BDNF and DRD2 molecules in the CNS, variation in DNA methylation patterns may contribute to inter-individual variability that impacts response to acute aerobic exercise. However, no previous work on this topic has examined epigenetic effects. In the present study, we evaluated relationships between the BDNF gene val66met (rs6265) and DRD2/ANKK1 glu713lys (rs1800497) genetic variants and response to acute high-intensity interval cycling indexed by M1 plasticity and motor learning. In an exploratory analysis, we considered whether DNA methylation epigenetic markers also contribute to inter-individual variability in acute aerobic exercise effects. We hypothesized that BDNF gene met allele carriers, compared to val/val homozygotes, and DRD2/ANKK1 lys allele carriers, compared to glu/glu homozygotes, would be less receptive to the positive effects of acute aerobic exercise on M1 plasticity and motor learning. Secondarily, we expected that DNA methylation patterns near or within the BDNF and DRD2 genes would further moderate these effects. 5.2 Methods 5.2.1 Participants The study sample consisted of 32 participants (14 men) between ages 19 and 34 (mean ± SD; 24.6 ± 4.2 years). Participants had no known neurological disorders and were of adequate health to complete the exercise protocols employed. Participant characteristics are presented in 125  Table 5.1. All participants gave written informed consent prior to testing. The Clinical Research Ethics Board at the University of British Columbia approved all experimental procedures.  Table 5.1 Participant characteristics  All BDNF val/val BDNF met carrier DRD2/ANKK1 glu/glu DRD2/ANKK1 lys carrier N 32 14 18 17 15     Study 1 16 8 8 6 10     Study 2 16 6 10 11 5 Age 24.8 ± 4.2 25.9 ± 5.0 23.9 ± 3.3 24.9 ± 4.3 24.7 ± 4.3 Sex 18F 8F 10F 10F 8F Handedness 30R 14R 16R 17R 13R V̇O2peak 44.3 ± 8.9 46.7 ± 11.9 42.5 ± 5.2 46.0 ± 8.1 42.4 ± 9.6 Ethnicity          White 22 11 11 11 11     East Asian 4 2 2 1 3     South Asian 4 1 3 4 0     Hispanic 2 0 2 1 1  5.2.2 Experimental design The genetic and epigenetic analyses in the present study were conducted retrospectively on data collected for previous work in our laboratory (Chapters 2-4) (167). For these studies, all participants completed a graded maximal exercise test prior to experimental sessions. On a separate day from all other testing procedures, venous blood samples were obtained from participants’ antecubital vein for genetic and epigenetic analyses. All participants then completed a series of experimental sessions designed to assess acute aerobic exercise effects on M1 plasticity and motor learning when preceded by a 20-minute period of seated rest or a 20-minute standardized bout of high-intensity cycling (90% of maximal power output [PO] in watts). M1 plasticity was indexed through the use of excitatory paired associative stimulation (PAS), a non-invasive brain stimulation protocol designed to induce LTP-like plasticity in M1 (278, 279). 126  Motor learning was assessed using novel motor sequence tracking tasks. Specifically, six experimental sessions were completed in all participants in a pseudo-randomized order and included: 1) rest followed by PAS; 2) aerobic exercise followed by PAS; 3) rest followed by skilled motor practice and 4) a no-exercise 24-hour retention test; and 5) aerobic exercise followed by skilled motor task practice and 6) a no-exercise 24-hour retention test. All sessions were separated by at least 48 hours with two exceptions: retention tests were conducted 24 ± 2 hours following motor task practice sessions, and there was a minimum of two weeks between motor task practice sessions under the different experimental conditions (rest or aerobic exercise) (Chapters 2-4) (167).  Due to differences in the experimental designs of the prior studies (Chapters 2-4) (167), 16 participants underwent PAS with a 25 ms inter-stimulus interval (PAS25, ISI) and were evaluated for motor learning with a continuous tracking task, while the other 16 individuals received PAS with a 21 ms ISI (PAS21) and were tested with a serial targeting task. For the purpose of the present study, data from participants across these sets of experiments were combined to provide a larger sample size from which to explore possible genetic and epigenetic influences on acute aerobic exercise effects. Importantly, we accounted for differences in the PAS and motor learning experimental protocols in our statistical analyses (see below). All procedures are depicted in Figure 5.1. 127   Figure 5.1 Overview of experimental procedures to test the effects of an acute bout of aerobic exercise on response to paired associative stimulation (PAS) and motor learning. All participants completed a graded maximal exercise test prior to the experimental sessions. A venous blood draw was collected on a separate day from all experimental sessions. TMS, transcranial magnetic stimulation. 128  5.2.3 Standardized acute aerobic exercise bout Exercise procedures are described in further detail in our previous work (Chapters 2-4) (167). Participants performed the standardized aerobic exercise bout on two occasions: once immediately before PAS procedures and once immediately before motor task practice. In short, the bout involved 20 minutes of cycling, including a five-minute warm-up at 50 W and self-selected cadence and three sets of three-minute high-intensity cycling intervals (90% maximal PO, ≥ 70 rpm) interspersed with two minutes of low-intensity cycling (50 W, 70-90 rpm). The high-intensity interval PO was prescribed based on performance in a previously completed graded maximal exercise test. 5.2.4 Paired associative stimulation procedures PAS procedures were conducted with each participant under each experimental condition (rest and aerobic exercise). 5.2.4.1 Electromyography and median nerve stimulation Surface electromyography (EMG) was collected from the abductor pollicis brevis muscle (APB) of the non-dominant hand using LabChart software (LabChart 7.0, AD instruments, Colorado Springs, CO). During data collection procedures, rectangular pulses of 0.2 ms duration were delivered over the median nerve at the wrist of the non-dominant hand using a constant current stimulator (DS7A, Digitimer, Hertfordshire, UK). The amplitude of the maximal M-wave (Mmax) in APB evoked by median nerve stimulation was monitored throughout data collection, and utilized to normalize the amplitude of motor evoked potentials (MEP) elicited by transcranial magnetic stimulation (TMS) for MEP recruitment curves (see below) (27, 173). 129  5.2.4.2 Transcranial magnetic stimulation TMS was delivered with a figure-of-eight coil (Magstim 70 mm P/N 9790, Magstim Co., Carmarthenshire, UK) and Magstim 2002 stimulator (Magstim Co., Carmarthenshire,UK). TMS was guided by Brainsight™ image-guided neuronavigation software (Rogue Resolutions Inc., Montréal, QC, Canada), to stimulate the non-dominant APB M1 representation. Prior to the rest period or aerobic exercise bout, resting motor threshold (RMT) for APB was determined (249). MEP recruitment curves were then conducted immediately following the rest period or aerobic exercise bout (pre-PAS) and following the PAS procedure (post-PAS, see below) to determine corticospinal excitability. For MEP recruitment curves, ten stimuli were delivered in a random order at intensities ranging from 90-150% of RMT, in 10% increments. The same stimulation intensities and sites were used to collect MEP recruitment curves at pre- and post-PAS time points.  MEP recruitment curve data were processed using a custom MATLAB script (Mathworks, Natick, MA, USA). To ensure all MEPs were obtained at rest, MEPs were inspected post-hoc and discarded if EMG activity during the 100 ms prior to the TMS pulse exceeded two standard deviations of the average baseline signal recorded at rest before stimuli in the same respective recruitment curve. Less than 0.5% of all responses were removed. Plots of stimulation intensity (%RMT) by MEP amplitude (peak-to-peak amplitude expressed as %Mmax) were then constructed. Percent change in linear slope of the recruitment curves from pre- to post-PAS was calculated for each condition (rest and aerobic exercise, PAS∆), with a greater increase in linear slope of the recruitment curves from pre-to post-PAS indicating a greater increase in corticospinal excitability (167, 215, 246, 268). 130  5.2.4.3 Paired associative stimulation Electrical stimulation was delivered over the median nerve of the non-dominant limb with 0.2 ms duration pulses at 300% perceptual threshold 25 ms (n = 16) or 21 ms (n = 16) prior to the delivery of TMS. TMS was applied over the APB M1 representation for the non-dominant limb at an intensity that evoked a MEP of approximately 1 mV (SI1mV). In total, 450 paired stimuli were delivered at 0.25 Hz (30 minutes of stimulation) (167). 5.2.5 Motor learning task procedures Motor practice took place immediately following the rest and aerobic exercise conditions for each participant, with at least two weeks between respective practice sessions. We utilized two motor learning tasks previously studied within our laboratory, a continuous tracking task (Chapter 2) (167) and a serial targeting task (Chapter 4). For both tasks, participants performed the task for ~30 s for familiarization prior to the rest period or the aerobic exercise bout. Continuous tracking task performance involved manipulation of a finger joystick (Current Designs, Philadelphia, PA, USA) with the thumb of the non-dominant hand (Chapter 2) (167). The joystick was used to move a cursor up and down (via side-to-side movements) to track the vertical path of a target moving at a constant horizontal velocity across a computer screen (Figure 5.2A). For task practice following rest or the aerobic exercise bout, participants completed two blocks of 10 × 30 s trials, for a total of 10 minutes of motor practice. The following day (24 ± 2 hours after motor practice) participants completed another block of continuous tracking task trials (no-exercise delayed retention test). Serial targeting task practice involved manipulation of a computer mouse (Wheel Mouse Optical, Microsoft Corporation, Redmond, WA, USA) housed in a custom frame that was held in a pronated grasp with the non-dominant hand (Figure 5.2B) (Chapter 4). The mouse was used to move a cross-hair cursor 131  between a series of discretely-presented targets. To initiate the appearance of the next target, participants placed their cursor in the current target for 500 ms. For serial targeting task practice following rest or the aerobic exercise bout, participants completed three blocks of 110 target movements (~10 minutes). The following day (24 ± 2 hours after motor practice) participants completed another single block of the serial targeting task (no-exercise delayed retention test).  Figure 5.2 Motor tasks utilized in the two studies analysed in the present work. The continuous tracking task involved tracking the vertical path of a target moving from the right to left of the monitor at a constant horizontal velocity (A, top panel). The target is depicted by the black filled circle, and the cursor by the black unfilled circle. The dashed line shows an example of a target path. The cursor was controlled by manipulating a joystick with the non-dominant thumb (A, bottom panel). The other task (serial targeting task) involved performing a series of discrete movements (B, top panel). After a cursor was placed within a target circle for 0.5 s, the next target circle was revealed. Targets are depicted by the black unfilled circles and the cursor by the crosshair. The arrows between targets show an example of a sequence of target movements. The cursor was controlled by manipulating an adapted computer mouse with the non-dominant hand (B, bottom panel). 132  In both tasks, participants were unknowingly presented with a repeated sequence of movements throughout task practice and retention tests (Chapters 2 and 4) (167). The inclusion of the repeated sequences in these paradigms allows evaluation of sequence-specific implicit learning (16, 179, 298). The movement sequences were reversed between conditions, such that the sequences differed but were of equivalent difficulty for evaluation of motor learning under each condition (rest and aerobic exercise). The order of presentation of conditions (rest and aerobic exercise) and movement sequences (regular or reversed) were balanced across the sample. Following the final retention test session, participants were tested for explicit recognition of the repeated sequences within the tracking tasks. Across the group, repeated sequences were identified at a level consistent with chance for both tasks (52.2 ± 27.0%).  Motor task data were processed using custom MATLAB scripts (Mathworks, Natick, MA, USA). As our previous work indicated preferential effects of exercise on implicit sequence-specific motor learning (Chapters 2 and 4) (167), only repeated sequence performance was considered in the present analyses. For the continuous tracking task, root mean squared error (RMSE) was calculated for each sequence. Response time, the sum of the reaction and movement times, to complete a target sequence was used to measure serial targeting task performance. From these data, we then combined the two data sets by calculating percent change in performance from early practice to retention (RET∆), such that higher percent change scores indicate greater learning-related improvements in task performance. Further information on these tasks can be found in our prior work (Chapters 2 and 4) (167). 133  5.2.6 Genotyping and DNA methylation analysis procedures 5.2.6.1 Genotyping A 4 ml blood sample was collected into an EDTA tube and stored at -20º Celsius until all samples were collected. The QiaSymphony automated DNA purification system (Qiagen) was used to purify DNA from the blood with optimal DNA yield and quality. DNA concentrations were assessed using a Picogreen DNA quantification assay, and samples were diluted to 50 ng/µl in preparation for genotyping. The BDNF val66met and DRD2/ANKK1 glu713lys polymorphisms were genotyped using a custom Illumina panel (VC0013722-OPA) on an Illumina BeadXpress platform. During the three-day protocol, DNA was amplified overnight, fragmented, hybridized overnight to Illumina BeadArrays, washed, stained, coated and scanned on the last day. Both SNPs were in Hardy-Weinberg equilibrium. 5.2.6.2 DNA methylation 8 ml of whole blood was drawn into a cell preparation tube from each individual at baseline and stored at room temperature for up to four hours, until peripheral blood mononuclear cell (PBMC) isolation was conducted. 1 μg of genomic DNA isolated from each PBMC sample was bisulfite-treated, which selectively deaminates unmethylated cytosine nucleotides, leaving methylated cytosines intact, using the Zymo EZ DNA Methylation Kit. As per manufacturer’s instructions, bisulfite converted DNA was hybridized on the Infinium HumanMethylation 450 BeadChip, an epigenome wide array covering 450, 000 CpG sites; however, analyses a priori focused on methylation at CpG sites located on or near the BDNF and DRD2 genes. Raw intensity files were imported into Illumina’s GenomeStudio software to obtain colour-corrected and background-subtracted β-values, a value representing percent methylation, which were then imported into R Statistical Programming for the remainder of data processing. We used log-134  transformed β-values, termed m-values, which were obtained by a function in the ‘lumi’ bioconductor package and were used for all statistical tests. M-values have been identified as being less heteroscedastic and more normally distributed than β-values; however, all biological interpretations were made with consideration of corresponding β-values (207). Initially, probe filtering included removal of probes that targeted SNPs, polymorphic CpGs, or either sex chromosome to reduce inter-individual variability that may introduce biased results. We also removed cross-hybridizing probes and probes with a low detection p-value < 0.01. After preprocessing, 442,117 probes were carried forward (231).  To account for the differing β value distributions of the type I and type II probe designs on the array, subset-quantile-within array normalization (SWAN) (164) was employed. ComBat, a function in the Surrogate Variable Analysis (sva) (117) package in bioconductor, was used to remove known batch effects, namely sentrix ID and sentrix position.   Due to the vast heterogeneity of cell types in blood tissue, and DNA methylation variation primarily driven by cell type, a cellular deconvolution method (103) was used to estimate cell type proportions in blood, specifically: CD4+ and CD8+ T cells, natural killer cells, B cells, monocytes and granulocytes. As additive dependent variables, predicted cell counts were regressed against DNA methylation and residuals were extracted, values representing variance unassociated with approximated cell types. Residuals were then added to the mean methylation of each CpG site resulting in cell-type corrected methylation values for downstream analysis. 5.2.7 Statistical analyses 5.2.7.1 Genetics We examined the potential effects of BDNF val66met and DRD2/ANKK1 glu713lys genotype on acute aerobic exercise-induced increases in M1 plasticity and motor learning in 135  separate models with the same structure (see model equation below). In separate analyses, the expected PAS∆ or RET∆ was modeled as a function of Condition (rest and aerobic exercise), Genotype (SNP carrier and non-carrier) and the Condition by Genotype interaction for each gene variant. The models controlled for the following variables: age, sex, ethnicity and study protocol (i.e. PAS25 versus PAS21, and continuous tracking versus serial targeting task). We also controlled for any potential interaction between the study protocol and condition in the mixed model. Additionally, a subject-specific intercept (i.e. a random intercept) was included to account for between-subject differences. All other effects were considered to be fixed. Specifically, the mixed-effects model employed was: E[Y] = μi + β0 + β1(Condition) + β2(Genotype) + β3(Condition × Genotype) + β4-8(covariates) where μi is the subject-specific intercept, and β0 to β8 are the fixed effects. This model was run four times in total for each combination of dependent variable (PAS∆ or RET∆) and genotype of interest (BDNF val66met or DRD2/ANKK1 glu713lys). 5.2.7.2 DNA methylation Secondarily, we explored the potential effects of DNA methylation on acute aerobic exercise-induced increases in M1 plasticity and motor learning. We employed expanded versions of the models described in the ‘Genetics’ section, adding the main effect of Methylation, the Condition by Methylation interaction and the Condition by Genotype by Methylation interaction. For models including BDNF genotype as a predictor term, methylation of CpG sites near or within the BDNF gene were considered. For models investigating DRD2/ANKK1 genotype, methylation of CpG sites surrounding or within the DRD2 gene were considered. Given the presence of multiple CpG sites (81 associated with the BDNF gene and 27 near or within the 136  DRD2 gene) located along different regulatory gene regions and potentially conferring independent functions (118), we fit the linear model multiple times with methylation at each individual CpG site used as the methylation predictor term for each separate run of the model (i.e. 81 runs for the BDNF gene model and 27 runs for the DRD2/ANKK1 gene model). We then examined the statistical significance of regression β coefficients describing methylation effects in the context of a Benjamini-Hochberg false discovery rate (FDR) correction (q ˂ 0.20). Specifically, the mixed-effects models employed were: E[Y] = μi + β0 + β1(Condition) + β2(Genotype) + β3(Methylation) + β4(Condition × Genotype) + β5(Condition × Methylation) + β6(Genotype × Methylation) + β7(Condition × Genotype × Methylation) + β8-12(covariates) where annotation is consistent with the prior section. Also as above, this model was evaluated separately for each combination of dependent variable (PAS∆ or RET∆) and gene of interest (BDNF or DRD2).  All statistical analyses were conducted using R, specifically the ‘lme4’ package used to test mixed-effects models. Post-hoc analyses of simple slopes, while controlling for covariates, were conducted to decompose significant interaction effects. We were specifically interested in testing the significance of regression β coefficients corresponding to main effects and interactions of the genotype and methylation predictor variables. Significance level for effects was set at p ˂ 0.05 (‘Genetics’ section), besides those described above that employed the FDR correction (‘DNA methylation’ section). The Akaike information criterion (AIC) was calculated to determine the goodness of fit of models with significant genotype or methylation effects (206). 137  5.3 Results 5.3.1 Genetics 5.3.1.1 BDNF val66met polymorphism The effects of BDNF genotype and the Condition by BDNF genotype interaction were not significant for the models predicting PAS∆ (p = 0.87 and p = 0.90, respectively) and RET∆ (p = 0.70 and p = 0.67, respectively). 5.3.1.2 DRD2/ANKK1 glu713lys polymorphism There was a significant Condition by DRD2/ANKK1 genotype interaction for the model predicting RET∆ (t(29) = 2.52, p = 0.02). Post-hoc analyses indicated that aerobic exercise significantly enhanced RET∆ in glu/glu homozygotes (t(15) = 3.00, p = 0.01), but that there was no effect of Condition for lys allele carriers (p = 0.55) (Figure 5.3). The effects of DRD2/ANKK1 genotype (p = 0.28) and the Condition by DRD2/ANKK1 Genotype interaction (p = 0.08) were not significant for the models predicting PAS∆.   138   Figure 5.3 Interaction between Condition (rest and aerobic exercise) and DRD2/ANKK1 genotype (glu/glu homzygote and lys carrier) on motor learning. There was a significant effect of Condition on retention change score (RETΔ) under the exercise condition for glu/glu homozygotes, as indicated by the asterisk. In contrast, the Condition effect was not significant in lys allele carriers. Marginal means and standard error bars, calculated using the average values for all covariates, are presented separately for each genotype to illustrate the specific Condition by Genotype interaction. 5.3.2 Methylation 5.3.2.1 BDNF gene methylation There was a significant two-way Condition by Methylation interaction involving one CpG site (cg10635145), which is 109bp upstream from the transcription start site (TSS) of BDNF for the model predicting PASΔ (t(27) = 4.35, p = 1.75 × 10-4, q = 0.01). However, this two-way interaction was further dependent on BDNF genotype, as determined by a significant three-way Condition by Genotype by Methylation interaction (t(27) = -4.56, p = 9.91 × 10-5, q = 0.01) involving the same CpG site in the same model. Post-hoc analyses of the model utilizing methylation at cg10635145 indicated that for BDNF met allele carriers there was a significant 139  negative relationship between PASΔ and methylation under the rest condition (t(12) = −2.59, p = 0.02), but a significant positive relationship between the same variables under the aerobic exercise condition (t(12) = 2.52, p = 0.03) (Figure 5.4). In contrast, PASΔ and methylation were not significantly related under either condition for val/val homozygotes (p ≥ 0.21). Regression β coefficients representing the effects of methylation at other BDNF gene-associated CpG sites were not significantly related to PASΔ (q ≥ 0.26). Additionally, there were no significant findings when evaluating regression β coefficients involving BDNF gene methylation for the prediction of RETΔ (q ≥ 0.74). 140   Figure 5.4 Relationships between BDNF gene methylation and response to paired associative stimulation (PASΔ) in BDNF gene met allele carriers. Methylation in the BDNF gene promoter region (cg10635145) was negatively associated with PASΔ under the rest condition (A and B), but positively associated with PASΔ under the exercise condition (C and D) in met carriers. Although not presented here, methylation and PASΔ were not significantly associated in BDNF gene val/val homozygotes. Plots presented in Panels A and C are partial residual plots generated from post-hoc analyses of the mixed-effects model with m-values on the x-axis, and covariates held at their average values. Asterisks indicate statistically significant regression β coefficients. Panels B and D show simple bivariate plots with methylation β-values on the x-axis.  141  5.3.2.2 DRD2 gene methylation The main effect of Methylation was significant at one CpG site (cg21330703) 25bp downstream of the DRD2 TSS for the model predicting RETΔ (t(46) = 3.21, p = 2.40 × 10-3, q = 0.06). Further, methylation at this CpG site was also found to have a significant two-way interaction with DRD2/ANKK1 genotype (t(46) = −3.15, p = 2.82 × 10-3, q = 0.08). Post-hoc analyses of the Genotype by Methylation interaction were conducted utilizing this CpG site (cg21330703) and the RETΔ collapsed across the experimental conditions (rest and aerobic exercise). As depicted in Figure 5.5, methylation at this CpG had a significant positive relationship with RETΔ in individuals carrying the lys allele (t(9) = −2.34, p = 0.04). In contrast, methylation in glu/glu homozygotes was not significantly related to RETΔ (t(12) = −0.34, p = 0.74). Nevertheless, the range of methylation β-values was small at this CpG site (β range: 0.14 to 0.17, Table 5.2), indicating that, although statistically significant, this relationship is likely not physiologically relevant. For the model predicting RETΔ, all other regression β coefficients involving methylation, including those interacting with the condition effect, were not significant for any CpGs associated with the DRD2 gene (q ≥ 0.21). There were also no significant findings when evaluating regression β coefficients involving DRD2 gene methylation for the prediction of PASΔ (q ≥ 0.27). A group summary of information related to the CpG sites with significant findings and results from the mixed-effects models are presented in Tables 5.2 and 5.3.142   Figure 5.5 Relationships between DRD2 gene methylation and retention change score (RETΔ). There was a significant positive relationship between methylation in the DRD2 gene (cg21330703) and RETΔ in lys allele carriers, but not glu/glu homozygotes (glu/glu homozygote data not shown here). Given that the relationships between methylation and RETΔ were not dependent on the condition, RETΔ values utilized in these plots were collapsed across conditions. The plot presented in Panel A is a partial residual plot generated from the statistical analyses with m-values on the x-axis, and covariates held at their average values. Asterisks indicate statistically significant regression β coefficients. Although statistically significant, the relationship was identified over a very small range of methylation β-values, as shown in the simple bivariate plot presented in Panel B.    143  Table 5.2 Group summary of DNA methylation information related to statistically significant findings                 Gene CpG site Location  (bp rel. to TSS) Genotype β-values m-values BDNF cg10635145 −109 All 0.48 ± 0.03 −0.10 ± 0.20    Val/Val 0.49 ± 0.03 −0.03 ± 0.18    Met carrier 0.47 ± 0.03 −0.16 ± 0.20 DRD2 cg21330703 25 All 0.16 ± 0.01 −2.44 ± 0.11    Glu/Glu 0.16 ± 0.01 −2.44 ± 0.09    Lys carrier 0.16 ± 0.01 −2.44 ± 0.10 144  Table 5.3 Summary and comparison of mixed-effects models  Model: AIC PASΔ ~ Condition × BDNF genotype + covariates 636.6 Predictor β estimate Std. Error p-value     Condition 44.0 21.6 0.04     Genotype  2.9 18.3 0.87     Condition × Genotype 3.2 25.3 0.90 Model: AIC PASΔ ~ Condition × BDNF genotype × BDNF methylation + covariates 583.7 Predictor β estimate Std. Error p-value/q-value     Condition 98.1 19.8 <0.01     Genotype  23.6 18.6 0.21     Condition × Genotype −51.8 22.4 0.03     Condition × Methylation 283.9 65.3 <0.01/0.01     Genotype × Methylation 215.8 93.9 0.03/1.00     Condition × Genotype × Methylation −511.3 112.1 <0.01/<0.01 Model: AIC RETΔ ~ Condition × DRD2/ANKK1 genotype + covariates 453.8 Predictor β estimate Std. Error p-value     Condition 1.2 3.4 0.73     Genotype  −1.3 3.7 0.74     Condition × Genotype 11.0 4.4 0.02       145  Table 5.3 Summary and comparison of mixed-effects models (continued) Model: AIC RETΔ ~ Condition × DRD2/ANKK1 genotype × DRD2 methylation + covariates 418.2 Predictor β estimate Std. Error p-value/q-value     Condition −156.6 82.5 0.07     Genotype  −268.3 84.8 <0.01     Condition × Genotype 179.7 104.9 0.10     Condition × Methylation −65.0 33.9 0.07/0.52     Genotype × Methylation −110.0 34.8 <0.01/0.08     Condition × Genotype × Methylation 69.2 43.0 0.12/1.00 The model descriptions indicate only the highest order interaction term. All lower order effects, as well as covariates were also included in the models. The Akaike Information Criterion (AIC) is a measure of the quality of a statistical model for a given set of data. The AIC is calculated based on a balance between the goodness of fit and the complexity of the model. Lower AIC values indicate a model of higher relative quality. p-values represent the significance of each regression β coefficient. q-values represent the Benjamini-Hochberg corrected significance values and are only presented for terms where methylation at multiple CpG sites were evaluated. 146  5.4 Discussion We investigated relationships between genetic and epigenetic variation and inter-individual differences in the effects of acute aerobic-exercise on M1 plasticity and motor skill learning. Our main finding was that the DRD2/ANKK1 glu713lys SNP was associated with the extent to which acute aerobic exercise facilitated motor learning across individuals. A functional SNP on the BDNF gene (rs6265) alone did not significantly affect exercise-induced changes in either M1 plasticity or motor learning. However, in an exploratory analysis we found an interaction effect involving the BDNF genotype and BDNF gene-related methylation. Specifically, we identified a relationship between methylation in the BDNF promoter region and M1 plasticity that was dependent on the experimental condition (rest or aerobic exercise) and specific to BDNF gene met allele carriers. Overall, these results add to a growing body of evidence suggesting that dopamine and BDNF signaling pathways likely contribute to acute aerobic exercise effects on plasticity and learning in the motor system. 5.4.1 DRD2/ANKK1 genotype influences facilitation of motor learning by acute aerobic exercise Dopamine is a key neurotransmitter and modulator of synaptic plasticity with diverse functions across the central nervous system (CNS) (113, 201), including a role in M1 plasticity and motor learning (76, 102, 187, 212, 241). Given animal work demonstrating that acute aerobic exercise increases dopamine levels in the brain (93, 180, 307), past findings that acute aerobic exercise positively impacts motor learning have been speculated to involve a dopaminergic mechanism (244). However, a positron emission tomography study indicated no change in dopamine signaling in the human basal ganglia following acute aerobic exercise (301). Also, while human studies demonstrated increases in systemic dopamine after acute aerobic 147  exercise, both a positive correlation (308) and a lack of correlation (273) with concurrent improvements in long-term declarative memory and motor learning, respectively, were reported. It is important to note that dopamine signaling pathways in the brain are complex, and interact with multiple receptor subtypes (D1-D5) that each yields specific neuronal outcomes (198). Further, a series of experiments in an animal model of Parkinson’s disease suggested that aerobic exercise effects on cognitive function may be more dependent on increases in the availability and binding of dopamine receptors (e.g. D2) than a change in extracellular concentration of dopamine (217). Recent work also indicated that a common SNP impacting DRD2 expression (DRD2/ANKK1 glu713lys) was associated with motor learning in humans (212). Here, using an a priori candidate gene approach, we found that this same SNP influenced the facilitation of motor learning by acute aerobic exercise. Specifically, acute aerobic exercise prior to motor skill practice enhanced motor sequence learning in DRD2/ANKK1 glu/glu homozygotes, but not in lys allele carriers. Our findings suggest that dopamine signaling pathways contribute to acute aerobic exercise effects on motor learning in humans. However, the results are somewhat contradictory to other work, which found that lys allele carriers demonstrated impaired motor learning under a placebo condition but a greater benefit of L-dopa on motor learning, compared to glu/glu homozygotes (212). First, we did not find any difference in learning between DRD2/ANKK1 genotypes under the resting condition (i.e. equivalent to placebo). Although a number of factors may contribute to this difference in findings, the previous study observed greater genotype differences when learning a complex, rather than simple, motor task (212) and past work has indicated higher involvement of the dopaminergic system with more challenging tasks (142). Thus, performance and learning of a more complex task than was currently used may have been 148  more sensitive to baseline DRD2/ANKK1 genotype differences. Second, we found that motor learning was benefited by acute aerobic exercise for glu/glu homozygotes but not for lys allele carriers, whereas the opposite effect was observed for L-dopa administration (212). Although a speculative explanation, these disparate findings may point towards important differences in the dopaminergic pathways activated by endogenous physiological events elicited by aerobic exercise versus the exogenous stimulus provided by administration of L-dopa. Together, the findings suggest a potentially important gene-environment interaction, which might be exploited to promote motor learning in individuals based on genetic differences (i.e. L-dopa treatment for lys allele carriers and acute aerobic exercise for glu/glu homozygotes).  Somewhat surprisingly, DRD2/ANKK1 glu713lys SNP effect was specific to motor learning outcomes and did not impact M1 plasticity evoked by PAS. Past work suggested that PAS induces plasticity in M1 through similar cellular mechanisms to those underpinning motor learning (252, 278), and thus similar genotype effects across PAS-induced M1 plasticity and motor learning might be expected. Yet, in our previous work, acute aerobic exercise-induced increases in motor learning and PAS-induced M1 plasticity were not correlated within participants (167). Previously, Pearson-Fuhrhop et al. also found that the DRD2/ANKK1 genotype effect was specific to motor learning, rather than M1 plasticity measured by TMS (212). The dopaminergic system plays an important role in synaptic plasticity (113), but is also associated more generally with various cognitive functions (e.g. attention) and psychological constructs such as arousal, motivation and reward (201). It is possible then that the DRD2/ANKK1 glu713lys SNP influenced exercise-induced facilitation of motor learning through physiological mechanisms other than those which specifically underpin M1 plasticity evoked by PAS. 149  5.4.2 BDNF genotype and promoter methylation interact to influence motor cortical plasticity BDNF is another CNS molecule that is up-regulated by acute aerobic exercise (137, 273, 308) and also plays a role in synaptic plasticity underpinning memory processes, including motor learning (111, 134, 294). Notably, the magnitude of aerobic exercise-induced increases in systemic BDNF correlated with facilitation of declarative memory (308) and motor learning (273). Thus, given evidence that BDNF is a likely contributor to the effects of aerobic exercise on brain function (47, 48), it is plausible that the BDNF val66met SNP could influence the neural effects of acute aerobic exercise. For example, BDNF val/val homozygotes, but not met allele carriers, demonstrated improvements in declarative memory following four weeks of aerobic exercise training in previous work (100). However, we currently found no influence of the BDNF genotype alone on M1 plasticity or motor learning in either the rest or acute aerobic exercise condition. This discrepancy with previous work (100) may relate to differences in the physiological effects of acute versus long-term aerobic exercise (244). Interestingly, while our evaluation of the impact of BDNF genotype alone did not yield significant findings, we identified a significant interaction effect of the BDNF val66met genotype and methylation at a CpG site (cg10635145, 109bp upstream from TSS) in the BDNF gene promoter IV region (232) on M1 plasticity. The relationship between methylation was specific to met allele carriers, indicating that higher methylation was related to less M1 plasticity evoked by PAS under the rest condition but greater plasticity evoked under the aerobic exercise condition.  The influence of DNA methylation on gene expression is dependent on the gene locus (118) and the BDNF gene has a highly complex structure (232). However, higher methylation percentages in promoter regions are typically related to a suppression of gene expression (118). 150  Further, past work in rodents found that reduced methylation of the BDNF promoter IV region, including the CpG site identified in the current work, was related to greater BDNF transcription in neurons (169). Thus, the present findings suggest that met allele carriers with lower expected BDNF gene expression (i.e. higher promoter methylation) experienced less M1 plasticity induced by PAS under the resting control condition. This finding is consistent with previous work indicating that LTP-like mechanisms, which mediate PAS effects on M1 (278), are facilitated by BDNF (227). The reversal of this relationship under the aerobic exercise condition is more challenging to interpret, but suggests that an exercise-induced increase in BDNF gene expression (47, 289) reduced M1 plasticity in met allele carriers with higher baseline levels of BDNF expression (i.e. lower promoter methylation). This relationship could potentially be taken to suggest an optimal level of BDNF expression for induction of plasticity in met allele carriers. Interestingly, methylation was not significantly related to M1 plasticity under either condition in BDNF gene val/val homozygotes, possibly suggesting that greater activity-dependent secretion in val/val homozygotes (66) overcomes the physiological effects of BDNF gene methylation on M1 plasticity. Another important consideration in interpreting these data is that DNA methylation is a dynamic process that is partly dependent on environmental factors (316). For example, reductions in methylation of the BDNF promoter IV region contributed to increases in neuronal gene expression induced by seven days of aerobic exercise training in rats (83). Further, epigenetic modifications within multiple genes, including BDNF, may contribute to changes in gene expression associated with late phase LTP in long-term memory processes (152, 160). Although this study investigated the effects of only a single bout of aerobic exercise, and PAS-induced plasticity is thought to engage only early stages of LTP that do not involving altered 151  gene expression (42, 278), changes in DNA methylation may have interacted with the present results. Critically, the interaction of BDNF genotype and methylation was only observed in the neurophysiologic measures, and not the behavioural measures of motor learning. Past work examining the BDNF val66met SNP alone on the motor system yielded a greater number of significant findings related to motor system physiology, rather than motor behaviour (3, 34, 39, 115, 132, 150, 176, 177). Although BDNF facilitates cellular mechanisms underlying motor learning processes (i.e. LTP), motor behaviour is ultimately determined by a host of additional factors that may obscure detection of relationships between BDNF gene variation and measures of motor learning. Given that the DRD2 gene glu713lys SNP effects were specific to the motor learning outcomes, our results could suggest that BDNF and dopamine signaling pathways may provide unique contributions to acute aerobic exercise effects on the brain. 5.4.3 Limitations Several limitations must be taken into account when interpreting the results of this study. First, the retrospective analysis involved combining data sets utilizing different iterations of similar experimental techniques. Nevertheless, the objectives and techniques employed were comparable and any differences related to experimental conditions were accounted for in our statistical approach. Further, the combination of data across studies provided a larger sample size in which to evaluate potential genetic and epigenetic influences. Although still relatively small, the current sample of 32 participants is similar to those used in related work investigating genetic influences on the motor system (3, 34, 39, 115, 132, 150, 176, 177). 152   It is also important to note that the genes studied in the current work are expressed across multiple regions of the body and brain and are involved in numerous biological processes. As such, inferences related to the mechanisms through which genetic and epigenetic variation influence the current results are speculative. Moreover, the functional effects of genetic and epigenetic variations are likely dependent on complex interactions across the genome and epigenome. Due to our modest sample size, we restricted our study to evaluation of SNPs and DNA methylation related to the BDNF and DRD2/ANKK1 genes (100, 132, 212).  Of particular importance to interpreting epigenetic findings in the current study is that DNA methylation is highly dependent on cell type (103, 316). Our outcome measures of interest were largely indicative of neural processes, yet methylation data was obtained from PBMCs in whole blood. Nonetheless, concordant DNA methylation patterns between samples obtained from peripheral blood and post-mortem brain tissue in humans were previously reported (71, 300). Further, DNA methylation patterns in peripheral blood cells relate to various conditions and diseases of the brain (50, 108, 300). Thus, blood cells can serve as a surrogate tissue to examine methylation patterns that may relate to neural processes. A further consideration is that we detected an effect of methylation at a DRD2-related CpG on motor learning; however, methylation at this CpG spanned a range of less than 3% across the study sample (Figure 5.5). Although statistically significant, the physiological relevance of such small differences in methylation percentage are questionable and thus was not interpreted further in the current work. In contrast, the statistically significant finding related to the BDNF gene involved a CpG with a range of methylation percentages greater than 10% and is more likely to have physiological consequences. 153  5.4.4 Conclusions In the present study, lys allele carriers of the DRD2/ANKK1 glu713lys SNP were less receptive to the benefits of acute aerobic exercise on motor learning than glu/glu homozygotes. In an exploratory epigenetics analyses, an interaction effect of BDNF genotype and BDNF promoter methylation was significantly related to M1 plasticity under rest and aerobic exercise conditions in met allele carriers. Thus, our results provide evidence that dopamine and BDNF signaling pathways may contribute to acute aerobic exercise effects on M1 plasticity and motor learning. Further, such genetic and epigenetic information could have potential application for individualized prescription of acute aerobic exercise to promote plasticity and motor learning. Nevertheless, these findings provide only a preliminary step in elucidating highly complex relationships between neuroplasticity, motor learning, aerobic exercise and variation in the human (epi)genome.  154  Chapter 6: General Discussion The experiments described in this thesis were designed to evaluate the effects of a single bout of high-intensity interval cycling on plasticity in the motor cortex (M1) and motor learning in young healthy individuals. Summarized below are the major findings of each research chapter. Aspects of the experimental techniques and approach that limit the interpretation of the present findings are considered, followed by a discussion of the implications of the work, as well as possible directions for future research on this topic. 6.1 Acute high-intensity aerobic exercise promotes motor cortical plasticity The primary aim of the first experiment described in Chapter 2 was to evaluate the impact of a single bout of high-intensity cycling on neuroplasticity for an upper limb muscle. This research aim was derived from prior work demonstrating that a bout of high-intensity cycling facilitated the learning of an upper extremity motor task (245), which led to speculation that aerobic-exercise induced modulation of neuroplasticity might contribute to the observed behavioural effects (244, 245, 273). In our experiment, we focused our assessment of neuroplasticity on M1 due to its known involvement in motor learning (131, 156, 186, 209). M1 plasticity was indexed by change in corticospinal excitability evoked by excitatory paired associative stimulation (PAS) targeting a muscle of the hand, when preceded by a period of rest or acute high-intensity cycling. Although changes in spinal excitability could not be entirely ruled out with this experimental approach (181), response to PAS is generally thought to reflect long-term potentiation like mechanisms in cortical circuits (278, 279). As hypothesized, participants demonstrated a greater excitatory response to PAS when preceded by acute aerobic exercise compared to rest, suggesting that the aerobic exercise bout facilitated the induction of M1 plasticity. This facilitation of plasticity for a non-exercised upper 155  limb muscle following high-intensity aerobic exercise is consistent with other work utilizing low- and moderate-intensity cycling and different transcranial magnetic stimulation (TMS) techniques (173, 271). Nonetheless, the underlying mechanisms for the acute aerobic exercise-induced modulation of plasticity are not definitively known. One thought is that decreased intracortical inhibition (270, 275) and increased intracortical facilitation (270) for M1 representations of non-exercised muscles immediately following aerobic exercise may prime M1 for subsequent neuroplastic change. An additional, and likely related, explanation focuses on aerobic-exercise induced changes in neurochemicals involved in plasticity (273). For example, peripheral levels of brain-derived neurotrophic factor (BDNF) were elevated immediately after aerobic exercise in the current research (Chapter 2) and in other work (137, 273); however, the importance of such peripheral changes in BDNF for neuroplasticity are unknown given that BDNF may not readily cross the blood-brain barrier (194). Regardless, our results, combined with past findings (173, 270, 271, 275), suggest that acute aerobic exercise of varying intensities may promote the development of a favourable neural environment for induction of plasticity, and aligns with previously documented acute high-intensity aerobic exercise effects on motor learning (245). 6.2 Acute high-intensity aerobic exercise facilitates implicit sequence-specific learning of temporal precision for a continuous motor sequence task Complementary to the electrophysiological study described above, a behavioural experiment was also described in Chapter 2. In this experiment, we evaluated the effects of acute high-intensity interval cycling prior to motor practice on the acquisition and learning of a continuous motor sequence task with a visuomotor rotation. We utilized a similar exercise bout and motor task as that employed in previous work demonstrating a positive effect of acute 156  aerobic exercise on motor learning (245); however, important differences in our motor learning task paradigm and analysis approach enabled us to take a novel perspective in our examination of the effects. Importantly, we employed motor task practice that involved random and repeated movement sequences to allow evaluation of acute aerobic exercise effects on implicit sequence-specific motor learning versus more generalized improvements in motor control (43, 218, 298). Further, we decomposed the overall error in the task to evaluate how acute aerobic exercise impacted motor performance and learning associated with temporal precision and spatial accuracy of movements (16).  Our main finding was that acute aerobic exercise expressly benefited implicit sequence-specific acquisition and learning of the temporal element of continuous motor task performance. There was no difference between conditions in terms of generalized improvements in motor control (i.e. random sequence performance) or spatial accuracy of movements. The results suggest that acute aerobic exercise effects on motor learning are not due to generalized changes in motor control, but rather relate to an interaction between exercise effects and implicit sequence-specific motor memory. Such motor learning processes are crucial for the ability of humans to learn and perform a variety of motor skills in everyday life with minimal cognitive effort (260). Moreover, our results indicated that acute aerobic exercise effects were particularly beneficial for elements of task performance involving temporal precision. Notably, improvements in temporal precision during implicit sequence-specific continuous motor task learning were previously attributed to cerebellar function (16). Although aerobic exercise likely impacts multiple brain regions, this finding suggests a potential interaction between aerobic exercise and cerebellar function to promote motor learning. 157  6.3 Acute high-intensity aerobic exercise alters activity of the cerebello-thalamo-cortical pathway Building on the Chapter 2 findings, the experiments described in Chapter 3 were designed to further investigate the possibility that high-intensity aerobic exercise may impact activity in cerebellar circuits. We utilized a TMS technique, termed cerebellar inhibition (CBI), to examine activity in the cerebello-thalamo-cortical pathway before and after aerobic exercise. We found that CBI for an upper limb muscle was relatively stable from before to after a period of rest, but was significantly reduced immediately following high-intensity cycling. The result suggests that acute aerobic exercise modulated the impact of cerebello-thalamo-cortical activity, which is largely inhibitory, on M1. However, the locus of the change along the cerebello-thalamo-cortical pathway remains unclear. Nevertheless, if cerebello-thalamo-cortical activity is modulated by acute aerobic exercise, then such changes could possibly work alongside aforementioned intracortical changes to promote subsequent neuroplastic change in M1 (228). Interestingly, similar changes in CBI were observed over the course of learning a motor adaptation (114) and visuomotor rotation (259) tasks, suggesting that acute aerobic exercise may initiate neural effects that support cerebellum-dependent motor learning tasks.   In a second experiment described in Chapter 3, we evaluated whether acute aerobic exercise-induced changes in the cerebello-thalamo-cortical pathway contribute to facilitation of M1 plasticity induced by PAS. To address this objective we examined the effects of acute aerobic exercise on response to excitatory PAS delivered with a 25 ms (PAS25) or 21 ms inter-stimulus interval (PAS21). Previous work indicated that PAS25 effects on corticospinal excitability are partly dependent on indirect sensory signals that reach M1 via the cerebellum, while PAS21 involves only more direct dorsal column-medial lemniscal sensory pathways to M1 158  (90). We found that, compared to a period of rest, acute high-intensity aerobic exercise facilitated the excitatory response to PAS25, but not PAS21. Thus, these results, in conjunction with findings of reduced CBI in the prior experiment, suggest that acute aerobic exercise effects on M1 plasticity are partly dependent on activity in the cerebello-thalamo-cortical pathway. 6.4 Acute high-intensity aerobic exercise enhances the rate of implicit sequence-specific motor memory retrieval for a discrete motor sequence task Although the work described in Chapter 3 focused in on modulation of cerebellar circuits by acute aerobic exercise, it seems likely that acute aerobic exercise could also impact numerous other brain regions and their involvement in motor learning. For instance, aerobic exercise alters neurochemical concentrations across multiple regions of the central nervous system (47). Nevertheless, whether or not such physiological changes are similarly beneficial for different aspects of motor skill acquisition and learning is not known. Thus, given fundamental differences in the learning processes involved in different motor tasks (32, 147, 260), it remains plausible that acute aerobic exercise benefits for motor learning might not fully translate across different types of motor tasks. This postulation then informed our research objective in Chapter 4, where we examined the effects of acute high-intensity aerobic exercise on implicit sequence-specific learning of a motor targeting task that requires multiple discrete movements. The specific characteristics of this task may demand the use of somewhat different neural substrates compared to the continuous tracking task (32) used in Chapter 2, which was biased towards cerebellar function due to the use of a visuomotor rotation (139, 259). For example, the learning of discrete motor sequence tasks involves a memory strategy termed chunking (183, 250), which is largely mediated by basal ganglia circuits (14, 85), and has not been demonstrated for continuous motor sequence task learning (32, 33).  159  Our main finding was that acute aerobic exercise facilitated motor learning through a sequence-specific increase in the rate of improvement in task performance at a 24-hour retention test, suggesting a positive impact of acute aerobic exercise on implicit motor memory retrieval. Thus, acute aerobic exercise benefits on motor learning appear to occur across varying motor tasks. A plausible, but speculative, physiological interpretation is that acute aerobic exercise elicits global changes across numerous brain regions that are then exploited based on subsequent experiences, such as motor task practice. Nevertheless, we surprisingly found no effects of acute aerobic exercise on rate of skill acquisition or overall change in motor performance over acquisition or at retention. The lack of an acquisition effect suggests that aerobic exercise may interact with consolidation, but not encoding, processes (123). We further conjectured that the preferential effect on motor memory retrieval rate might relate to an exercise-induced increase in the speed of processing cues that invoke the use of learned motor chunks at retention. Thus, although the benefits may manifest somewhat differently, the results suggest that positive effects of acute aerobic exercise can occur for motor tasks with varying characteristics and potentially different neural substrates. 6.5 Genetic and epigenetic contributions to inter-individual variability in acute aerobic exercise response The final research chapter shifted focus away from potential differences in acute aerobic exercise effects across motor tasks, to explore differences in acute aerobic exercise effects on M1 plasticity and motor learning between people. As mentioned previously, a general hypothesis for acute aerobic exercise effects on memory centres on exercise-induced increases in neurochemicals, such as BDNF and dopamine (244, 273). Yet, genetic variation and epigenetic modifications can impact such molecular signaling pathways, and thus could plausibly moderate 160  a given individual’s response to acute aerobic exercise. As such, we focused our analyses on two common genetic polymorphisms (BDNF val66met, DRD2/ANKK1 glu713lys) known to influence M1 plasticity and motor learning (132, 177, 212), as well as DNA methylation related to the BDNF and dopamine D2 receptor (DRD2) genes.  Our main findings was that DRD2 gene glu/glu homozygotes, but not lys carriers, experienced an increase in motor learning associated with acute aerobic exercise compared to rest. When considering genotype alone, there were no other significant influences on the aerobic exercise effects on M1 plasticity or motor learning. However, in a secondary analysis we considered the interaction between genotype and DNA methylation on the observed effects. Interestingly, BDNF met allele carriers demonstrated a negative relationship between BDNF promoter methylation and M1 plasticity (i.e. response to PAS) at rest, but a positive relationship between these variables under the aerobic exercise condition. Given the relatively small sample size, these genetic and epigenetic findings must be considered somewhat preliminary in nature. Nevertheless, the results suggest that genetic and epigenetic variation may contribute to inter-individual variability in exercise response, and provide further evidence that BDNF and dopamine signaling pathways are likely involved in mediating the effects of acute aerobic exercise on M1 plasticity and motor learning. 6.6 Limitations The interpretation of the research findings of this thesis must be made with consideration of a number of limitations associated with the design of the experiments. Perhaps the most important and general consideration is that engagement in acute aerobic exercise has robust physiological effects across multiple body systems. As a result, it is difficult to specifically identify the basic physiological mechanisms involved in acute aerobic exercise effects on 161  neuroplasticity and motor learning, as well as what aspects of the aerobic exercise (e.g. leg movement, arousal, attention, metabolism, etc.) elicited the effects. Our understanding of the physiological mechanisms involved are also somewhat limited by the use of TMS techniques as the primary neurophysiological assessment tool in the present experiments (Chapters 2 and 3). For example, we were particularly interested in studying aerobic exercise-induced changes in M1 circuits; however, spinal changes could influence the measures obtained with TMS and thus, cannot be entirely excluded from contributing to the observed effects. Despite these drawbacks, our findings, combined with other work (173, 245, 270, 271, 275), suggest an effect of acute aerobic exercise on M1 plasticity and motor learning.  A further consideration for our findings relates to the relevance of the neurophysiological measures to the behavioural results. In Chapter 2, acute aerobic exercise increased systemic levels of BDNF, M1 plasticity evoked by PAS and motor learning; however, these changes were not correlated. The lack of relationships between these effects may call into question the importance of the BDNF and plasticity changes in supporting the motor learning benefits. Past work also demonstrated a lack of relationship between neuroplastic response to non-invasive brain stimulation protocols and motor learning, and noted that neurophysiologic effects detected with TMS techniques represent changes within a subset of an extensive motor learning brain network (150). As such, it is possible that detection of relationships between TMS measures and learning are obscured by the many other physiological processes involved. Also, the lack of correlation between systemic BDNF levels and measures of plasticity and learning were not entirely surprising. Although other work showed a positive relationship between systemic BDNF levels and motor learning benefits associated with acute aerobic exercise (273), BDNF does not 162  readily cross the blood-brain barrier and inferences of brain levels from peripheral measures must be made with caution (137, 194).  The behavioural results from the motor learning experiments described in this thesis must also be interpreted with consideration of the specific experimental conditions. Both of our learning experiments utilized only a very small dose of practice and a single 24-hour retention test. The practice doses were determined to minimize carryover effects of task practice between conditions for the within-subjects design that was used. As a result, the acute aerobic exercise effects on motor learning were likely only observed for the early, fast learning phase (125), and whether acute aerobic exercise might impact later, slow phases of motor learning were not tested. In retrospect, a matched between-groups experimental design, as well as additional delayed retention tests, may have provided further insights into the aspects of motor learning that are impacted by acute aerobic exercise. Also, we utilized motor sequence learning tasks with different characteristics between studies (i.e. continuous tracking task and serial targeting task) to allow consideration of how acute aerobic exercise might interact with the learning of tasks relying on different neural substrates. However, the neural underpinnings of each task were inferred from previous work and brain activity during task performance was not measured. In the final study, we performed an exploratory analysis to evaluate relationships of genetic and epigenetic variation with the previously observed acute aerobic exercise effects on M1 plasticity and motor learning. An important consideration when interpreting these results is that the data were compiled from multiple studies (i.e. Chapters 2-4) using different iterations of similar experimental techniques. One consequence of this approach was that for motor learning data, the measures that were most sensitive to acute aerobic exercise effects (i.e. improvements in temporal precision, Chapter 2; increased rate of memory retrieval, Chapter 4) were not 163  explored because they could not be readily combined. Thus, although influences of study conditions were controlled for in the statistical analyses, this factor cannot be entirely ignored when interpreting the study results. 6.7 Implications and future directions Despite limitations in the research conducted for this thesis, our findings contribute knowledge to an important topic of study. There is great interest within both the scientific and the greater community in the study of acute aerobic exercise strategies to promote neuroplasticity and facilitate motor learning. For example, the first study to demonstrate an impact of acute aerobic exercise on motor learning (245), published three years ago, was soon after the topic of an article in the New York Times (239). The high level of interest in these findings is generated, at least in part, from the diverse range of plausible applications for such an effect, spanning from sport to neurorehabilitation settings. Potential implications for neurorehabilitation after stroke are explored further in a narrative review article included in Appendix A of this thesis (166). Nevertheless, given the recentness of these findings, the study of acute aerobic exercise effects on motor learning is a topic that remains ripe for further scientific inquiry. Despite the contributions of this thesis work to our knowledge base, an abundance of research questions related to acute aerobic exercise effects on neuroplasticity and motor learning remain unanswered. The neurophysiological experiments conducted in this thesis research utilized TMS techniques to explore acute aerobic exercise effects on M1 and cerebello-motor circuits. Future work utilizing functional magnetic resonance imaging techniques could evaluate the impact of acute aerobic exercise on more diverse brain regions/networks, as well as whether such changes contribute to motor learning benefits. Such experiments could also clarify whether acute aerobic exercise has regionally specific effects on brain activity that may be particularly 164  amenable to promoting certain aspects of memory, or instead if it elicits more global effects that are then exploited by the brain regions/networks activated by subsequent experiences (i.e. motor practice). Further investigation of the motor learning processes that are impacted by acute aerobic exercise will provide another exciting avenue of future research. The experiments described in this thesis, as well as other work on the topic (240, 245, 273), utilized very low volumes of motor practice and as a result considered only acute aerobic exercise effects on fast learning phases (62, 125). Investigation of the effects of pairing acute aerobic exercise with motor practice over several days could provide insights into potential interactions with later, slow phases of motor learning (62, 125). A recent study demonstrated that moderate-to-vigourous intensity cycling two hours following motor sequence practice, but immediately prior to practice of an alternative motor task, protected against motor memory interference (240). Thus, additional examination of the effects of performing acute aerobic exercise before versus after, and at various durations of time in relation to motor practice may be another means of investigating interactions between acute aerobic exercise and motor learning. Another potential line of future research on this topic relates to the exercise prescription and characteristics of the study samples used in this thesis research. High-intensity interval cycling bouts were utilized presently based on previous work suggesting an intensity-dependent increase in neurochemicals following aerobic exercise and a potential role of those neurochemicals in facilitating memory (137, 308). Whether differing aerobic exercise prescriptions have similar effects on plasticity and motor learning will be important when considering how to implement such acute aerobic exercise strategies in sport or clinical settings. Other work suggests that low- and moderate-intensity bouts of aerobic exercise may also 165  modulate M1 plasticity (173, 271), but behavioural studies of motor learning with low- and moderate-intensity have not yet been conducted. Additionally, all experiments for this thesis were conducted in young, healthy, and relatively fit participants. Plasticity, memory and physiological effects elicited by aerobic exercise may all vary based on individual traits related to cardiorespiratory fitness, age and health conditions (40, 69, 141). Thus, investigation of acute aerobic exercise effects on motor learning in various populations will be a key step towards elucidating potential applications of this work. The last future research direction that will be proposed relates to the findings from the final research study. Specifically, we discovered that genetic and epigenetic variations may contribute to inter-individual variability in acute aerobic exercise effects on motor learning and M1 plasticity. Firstly, it should be noted that the BDNF gene is highly complex (232), and that the epigenetic findings in our final research chapter would benefit greatly from replication, and possibly from corroborative animal work examining DNA methylation in central nervous system tissue. Nevertheless, a potential direction for future work relates to the dynamic nature of DNA methylation and its susceptibility to modification by environmental factors, including engagement in aerobic exercise (83). An interesting study then might evaluate whether modification of DNA methylation profiles, perhaps through long-term aerobic exercise training, might alter individuals’ neuroplastic and motor learning responses to acute aerobic exercise. 6.8 Conclusions The research presented in this thesis contributes to a relatively new line of inquiry investigating the effects of acute aerobic exercise on motor learning and its neural substrates. Specifically, the results provide information about interactions between acute aerobic exercise and implicit sequence-specific learning of different motor tasks. Further, neurophysiological 166  experiments determined an acute aerobic exercise effect on M1 plasticity, and possible contributions of activity in the cerebello-thalamo-cortical pathway to these effects. Lastly, contributions of genetic and epigenetic variations to inter-individual variability in the acute aerobic exercise response were explored. 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J.Physiol.Pharmacol. 59: Suppl 7: 119-132, 2008.   189  Appendix A: Promoting Neuroplasticity for Motor Rehabilitation After Stroke: Considering the Effects of Aerobic Exercise and Genetic Variation on Brain-Derived Neurotrophic Factor The manuscript presented here was developed over the course of my PhD comprehensive examination and prior to conducting my thesis research. As a result, the information included here does not refer to the research findings presented in this thesis, but rather discusses related work and its potential implications for motor rehabilitation after stroke. A.1  Introduction Stroke is the leading cause of long-term disability in North America (88). Deficits in motor function are common following stroke with up to 75% of individuals experiencing upper extremity impairments that persist into the chronic stage (88). Over the first six months after stroke onset, some spontaneous motor recovery occurs (119), but further advances in motor function rely on motor rehabilitation training. The process of motor rehabilitation is a form of motor learning (302), which refers to a relatively permanent change in motor behaviour evoked by practice or experience (260). As such, individuals with stroke engage in motor rehabilitation training in an effort to re-learn motor skills that were lost due to injury. Consistent with motor learning in healthy adults, this re-learning process is mediated by neuroplasticity (302): the ability of the central nervous system (CNS) to undergo structural and functional change in response to new experiences (133). This neuroplasticity is detected in humans with a number of experimental techniques including non-invasive brain stimulation to measure shifts in size, location and excitability of motor cortical maps, as well as functional magnetic resonance imaging (fMRI) to measure altered activation and recruitment of brain regions involved in 190  movement (302). Initially, learning-related plasticity involves the strengthening of existing, and the formation of new, neural connections that support learned behaviours (101, 302). It is followed by pruning or focusing of neural connections, as skill and preferential pathways develop (74, 101). Current research focuses on maximizing the functional benefits of post-stroke motor rehabilitation by developing interventions to promote motor learning-related neuroplasticity (302). Despite major progress in the understanding of neuroplasticity, very few new treatment interventions have resulted from this research (302). Thus, there is a critical need for the development of novel and more effective approaches for post-stroke motor rehabilitation.  Recent advancements in the understanding of the role of brain-derived neurotrophic factor (BDNF) in neuroplasticity may provide important information for the development of new post-stroke rehabilitation strategies. BDNF is a member of the neurotrophin family; a group of proteins involved in neuroprotection, neurogenesis and neuroplasticity and has been identified as a key mediator of motor learning and rehabilitation after stroke. New areas of research are beginning to inform the development of rehabilitation strategies that take into account the importance of BDNF for motor recovery after stroke. These include consideration of aerobic exercise effects on brain function and the incorporation of genetic information to individualize therapy. Converging evidence suggests that aerobic exercise is a valuable intervention for improving brain function (45, 136, 141, 145, 235), and that these effects are mediated, in part, by up-regulation of BDNF (47, 48). Thus, capitalizing on aerobic exercise-induced increases in BDNF could plausibly facilitate motor learning-related neuroplasticity for rehabilitation after stroke. Nevertheless, the basic processes that drive neuroplasticity, such as BDNF signaling, are dependent on the expression of genes. As a result, genetic variation could impact an individual’s response to motor rehabilitation training, aerobic exercise training and overall motor recovery 191  after stroke (213). Thus, the primary aims of the present perspectives article are to: 1) discuss evidence that aerobic exercise enhances brain function by increasing BDNF production and consider how these effects might be harnessed to facilitate motor rehabilitation post-stroke, and 2) discuss the potential impact of a common variant of the BDNF gene on motor learning, response to motor rehabilitation training and aerobic exercise effects on the brain post-stroke. A.2  Aerobic exercise to promote neuroplasticity for motor rehabilitation post-stroke Aerobic exercise affects the brain indirectly through improvements in general health and fitness, as well as through alterations in molecular signaling pathways that act directly on the CNS (47, 48) (Figure A.1). The primary focus of this section will be on the direct pathway of exercise-induced up-regulation of BDNF in the CNS (47, 48). To begin to consider how exercise-induced increases in BDNF may be a key contributor towards the positive effects of aerobic exercise on brain health and function we will address three main topics in this article. First, we will discuss the involvement of BDNF in facilitating neuroplasticity, motor learning and post-stroke motor rehabilitation. Next, the effects of aerobic exercise on BDNF and its role in mediating exercise-induced increases in brain function will be reviewed. Lastly, we will consider how the effects of exercise on the brain could be best harnessed to promote neuroplasticity and facilitate motor rehabilitation post-stroke. The present paper will focus on aerobic exercise, although a growing body of evidence suggests that resistance exercise may have similar or complementary effects (153-155).  192   Figure A.1 Examples of indirect and direct pathways for positive effects of aerobic exercise on the brain. Indirect effects refer to improvements in general health and reduction of peripheral risk factors that consequently impact brain health. Direct effects refer to aerobic exercise influences on the molecular signaling pathways of the brain itself. The present paper focuses on the direct effect of exercise on BDNF production in the brain. BDNF, brain-derived neurotrophic factor; NT-3, neurotrophin-3; CNS, central nervous system.193  A.2.1 BDNF is involved in motor learning and post-stroke motor rehabilitation BDNF is involved in many facets of brain function, including neuroplastic changes that underlie motor learning. BDNF exerts its effects on neuroplasticity by facilitating long-term potentiation (LTP), a long-lasting increase in the strength of connection between two neurons that are repeatedly activated together, and promoting dendritic growth and remodeling (10, 227). Unlike other growth factors, BDNF is secreted in the CNS through both a constitutive and an activity-dependent pathway. The activity-dependent secretion is crucial to the role of BDNF in promoting neuroplasticity in circuits activated in response to experience (227). Evidence for the role of BDNF specifically in motor learning can be found in animal work demonstrating that disrupting BDNF synthesis with a pharmacological intervention impaired skilled motor performance and diminished training-induced cortical map plasticity (294). Subsequent application of BDNF by intracortical injection into primary motor cortex (M1) partially restored these functions (294). Similarly, in rat models of focal ischemia, recovery of skilled reaching movements with rehabilitation training were abolished when BDNF was blocked in the CNS (224). In a similar study, response to post-stroke rehabilitation training was enhanced when exogenous BDNF was administered through an intravenous bolus in rats (255). Equivalent data in humans showing direct BDNF involvement in motor learning and post-stroke rehabilitation is not available due to the invasive nature of intracortical injections and limited capacity to target BDNF application to specific brain regions in humans. Nevertheless, given the strong evidence for BDNF involvement in neuroplasticity within the motor system in animal research, it is plausible that motor rehabilitation strategies that capitalize on the beneficial effects of BDNF in the CNS will be effective for facilitating recovery after stroke.  194  A.2.2 Aerobic exercise effects on brain function: BDNF and cognitive function Aerobic exercise may be a particularly effective means to enhance BDNF levels, as it induces a cascade of events that leads to increased BDNF gene expression in multiple regions of the CNS, including the hippocampus, cerebellum, cerebral cortex and spinal cord (47). Moreover, considerable evidence shows that exercise-induced increases in BDNF benefit cognitive function (69, 136, 146, 235). In rats, completion of a one-week aerobic exercise program enhanced spatial memory test performance, but these effects were abolished in an experimental group that also received pharmacological blockage of hippocampal BDNF (295). Such a causal effect is more difficult to demonstrate in humans as BDNF cannot be blocked and typically cannot be measured from the CNS in vivo. However, systemic levels of BDNF are increased for approximately10-60 minutes following a bout of aerobic exercise in humans (137). These systemic BDNF measurements are often considered to reflect CNS levels in humans, as BDNF undergoes bidirectional transport across the blood-brain barrier (204, 205) and is released from the brain into the periphery at rest and during aerobic exercise (236). There have also been reports of increased basal levels of systemic BDNF following several weeks of aerobic exercise training (6, 263, 317), but other studies report no effect of aerobic exercise training programs on basal BDNF values (29, 258, 261). The return to baseline levels of systemic BDNF levels after one hour following aerobic exercise and the lack of training effects on basal systemic levels in some studies are thought to be a result of a subsequent increase in BDNF absorption in the CNS following aerobic exercise-induced increases in production (137).  Complementary work demonstrates that aerobic exercise training enhances multiple aspects of cognitive function in healthy individuals and across a range of chronic health conditions, including stroke (69, 136, 146, 235). A meta-analysis of 18 aerobic exercise training 195  intervention studies in older adults concluded that the largest effects on cognition occur in the executive control domain, including functions such as planning, scheduling, working memory and multi-tasking (45). The majority of studies included in this meta-analysis involved aerobic exercise three times per week at a moderate-intensity (i.e. approximately 70% maximum heart rate). Programs that involved aerobic exercise sessions greater than 30 minutes, training periods of more than six months and a combination of aerobic and resistance training had the largest effects (45). Similar positive effects of aerobic exercise training on cognition have been shown in individuals with stroke. Exercise programs combining aerobic and resistance training performed at moderate ratings of perceived exertion on two to three days per week for 12 weeks (136) and six months (235) improved executive function and memory in individuals with chronic stroke. Combined with animal work (295), the findings of aerobic exercise-induced increases in systemic BDNF (137) and cognitive function (136, 235) in humans are commonly taken as evidence that BDNF contributes, at least in part, to the positive effects of aerobic exercise on cognitive function in humans (137). Nevertheless, a key limitation of current evidence is that relatively few human studies concurrently assess aerobic exercise-induced changes in both BDNF and cognitive function (73, 80, 308). A.2.3 Aerobic exercise effects on motor learning As well as improving cognitive function, aerobic exercise training also enhances mobility, balance and motor function post-stroke (112, 136, 235, 283). Increased physical fitness is undoubtedly a large contributor to these improvements in motor function; however, exercise-induced increases in neuroplasticity and motor learning abilities via up-regulation of BDNF within the CNS may also contribute to these beneficial effects. Only one study has examined the effects of engaging in aerobic exercise over several weeks on motor learning and it was 196  conducted in individuals with stroke (233). In this study by Quaney et al., participation in an eight-week aerobic cycling program (70% maximum heart rate, 45 minutes, three times per week) improved within-session performance of an upper limb motor sequence task compared to those who participated in an eight-week stretching program (233). This study demonstrated that, at least in the short-term, aerobic exercise training improves motor skill acquisition. However, motor performance at a delayed retention test (≥ 24 hours post-practice) is required to indicate motor learning (123), and unfortunately this was not examined. Nevertheless, Quaney and colleagues’ findings suggest a potential interaction between motor learning abilities and aerobic exercise. A.2.4 Persistence of aerobic exercise effects on the brain  Another important finding by Quaney et al. was that the greater within-session performance among aerobic exercisers was not maintained at a follow-up test eight weeks after exercise training had stopped (233). This finding raises an important issue concerning the persistence of aerobic exercise-induced increases in brain function that has not been well addressed in the literature. Many randomized clinical trials of aerobic exercise training programs report improvements in performance on cognitive tests performed immediately before and after participation in an aerobic exercise program (45). However, to our knowledge, there is no evidence that the benefits of aerobic exercise on brain function persist at follow-up after aerobic exercise is stopped. Similar to the finding by Quaney et al. (233), a recent study of young healthy adults found that improvements in object memory retrieval following a four-week treadmill training program occurred only when individuals performed an exercise bout on the final testing day (100). A possible explanation for these findings may be found within research investigating the effects of an acute bout of aerobic exercise on cognitive performance in humans. A meta-197  analysis of 29 studies of young healthy adults concluded that information processing and memory are significantly enhanced immediately following a single bout of aerobic exercise (145). Thus, enhanced cognitive function induced by aerobic exercise training programs may simply be due to continuous exposure to acute bouts of aerobic exercise (145); when regular training is stopped, these effects are no longer regularly evoked. Traditionally, enhanced cognitive function following an acute bout of aerobic exercise has been attributed to a temporary increase in arousal, and thus, expected to dissipate as arousal levels return to baseline (145). However, the aforementioned meta-analysis determined that the aspects of cognitive function most positively affected by an acute bout of aerobic exercise were short- and long-term memory (145). These effects were greatest when cycling exercise was used, rather than treadmill exercise (145). There is also evidence that short intervals of high-intensity aerobic exercise (i.e. three by three-minute intervals above ventilatory threshold) may enhance memory more than long duration, low-to-moderate-intensity aerobic exercise (i.e. 40 minutes below ventilatory threshold) in young adults (308). Interestingly, if an acute bout of aerobic exercise alters memory processes, then it could also impact learning and thus, promote relatively permanent changes in motor behaviour that persist even after aerobic exercise training has stopped. An important caveat to this idea is that learning and neuroplasticity are dependent on experience. By increasing BDNF production, aerobic exercise may facilitate the neuroplastic processes that underlie learning, such as LTP and dendritic branching, but aerobic exercise alone is not capable of inducing these neuroplastic processes. Thus, for aerobic exercise to have the most meaningful and lasting effects on behaviour, it likely needs to be paired closely in time with sufficient and meaningful practice or experience that is consistent with the desired 198  behavioural change. For example, two months of a combination of aerobic exercise training and mental training increases cognitive function more than either intervention alone (70).  A.2.5 Prescribing aerobic exercise to prime motor learning and post-stroke motor rehabilitation  The basis of engaging in aerobic exercise training in close temporal proximity with behavioural training is that the aerobic exercise will serve to prime the CNS for the neuroplastic change that underlies the desired behaviour change (i.e. learning). With this approach, the positive effects of aerobic exercise on brain function may be more effectively harnessed to facilitate functional improvements in populations with chronic disease, such as stroke. In a recent study, Roig et al. found that high-intensity interval cycling (three sets of three-minute intervals, above ventilatory threshold) immediately before or after practice of a motor task enhanced motor performance on retention tests conducted at one and seven days post-practice, demonstrating that a single bout of aerobic exercise enhanced motor learning in young healthy individuals (245). Aerobic exercise immediately before motor task practice was thought to facilitate the detection and encoding of information relevant to the task during the subsequent motor practice, while aerobic exercise immediately after motor task practice was thought to facilitate processes involved in motor memory consolidation (245). As motor leaning underlies improvements in motor function evoked by rehabilitation following stroke, these findings suggest that acute bouts of aerobic exercise might have the potential to be used to facilitate response to post-stroke motor rehabilitation training (Figure A.2). 199   Figure A.2 Using aerobic exercise to prime motor rehabilitation post-stroke. Performing aerobic exercise immediately before motor rehabilitation training may facilitate improvements in motor function by capitalizing on aerobic exercise-induced increases in the capacity for neuroplasticity. Alternatively, aerobic exercise could be performed immediately after motor training to facilitate motor memory consolidation processes. BDNF, brain-derived neurotrophic factor; LTP, long-term potentiation.200  Current guidelines recommend that individuals with stroke engage in a minimum of 20 minutes of moderate-intensity aerobic exercise three days per week (163). The intensity of the exercise should be greater than 30% of heart rate reserve, the minimal effective training intensity for very unfit individuals (285), but based on individual exercise stress test results and health status (163). Although these recommendations are sufficient to obtain general health benefits for individuals with stroke, there is limited research examining the specific exercise dose necessary to elicit direct effects on brain function and facilitate motor rehabilitation in individuals with stroke. Thus, findings from studies investigating aerobic exercise effects on BDNF production and other cognitive functions may currently be the best source of information when speculating on how to best prescribe aerobic exercise for this purpose. Based on this literature, to induce large positive effects on cognitive function and increase BDNF levels, exercise training studies should employ: 1) aerobic exercise sessions of more than 30 minutes (45), 2) training intensities of approximately 70% heart rate maximum (137), 3) a frequency of four days per week (137) and 4) a combination of aerobic and resistance exercises (45). Cycling (145) and high-intensity intervals (308) may be especially effective for immediate benefits of acute aerobic exercise on cognitive function; although there is also evidence that just 30 minutes of aerobic exercise at 60% maximum heart rate is effective for increasing BDNF in individuals with chronic disease (137). Lastly, the effects of aerobic exercise training on the brain may be most effectively harnessed if performed at a point close in time to performance of motor rehabilitation training (245). Although further research is needed to determine the precise time course of BDNF effects, it appears that one hour post-exercise is the most likely window of time in which motor learning will be most facilitated (137, 145, 245). These findings provide a reference point for prescription of aerobic exercise in future research evaluating the effects of exercise on motor learning and 201  response to rehabilitation post-stroke. The idea of priming motor rehabilitation with aerobic exercise is speculative, but with additional study further insights may be achieved into whether and, if so how, aerobic exercise can be prescribed to facilitate the acquisition and retention of motor skills for rehabilitation. A.3 Genetics research to inform motor rehabilitation and aerobic exercise prescription post-stroke The premise for rehabilitation interventions that promote neuroplasticity is that if the CNS can be primed for greater capacity for physiological change, then functional improvements mediated by those physiological changes will be more likely to occur (302). Since many of the neuronal processes that drive such changes are dependent on the expression of specific genes, genetic variation may influence the efficacy of rehabilitation strategies that engage these processes (213). For example, while up-regulation of BDNF following aerobic exercise may be beneficial for neuroplasticity, a common single nucleotide polymorphism (SNP) on the human BDNF gene could impact the effects of aerobic exercise on the brain (37, 66, 100). Thus, improved understanding of how genetic variation influences neuroplasticity and motor learning after CNS injury might allow for better individualization of rehabilitation strategies to maximize motor outcome post-stroke. In the current section of this paper, the effects of the BDNF val66met polymorphism on neuroplasticity, motor learning and post-stroke motor rehabilitation will be discussed. Next, ideas about how knowledge of the effects of this polymorphism could be utilized when prescribing aerobic exercise to prime motor rehabilitation will be considered. Given the role of BDNF in mediating aerobic exercise effects on the brain (137, 295), focus is placed on the well-characterized BDNF val66met gene variation throughout this section; 202  however, this is just one of many genetic variants that could potentially impact aerobic exercise effects on the brain and post-stroke motor rehabilitation (213). A.3.1 BDNF gene val66met polymorphism impact on brain health and function  In approximately 30-50% of the human population, a SNP exists on the BDNF gene that results in an amino acid change from valine (val) to methionine (met) at position 66 (val66met) of the precursor peptide proBDNF (265). The presence of the met allele results in a 25% reduction in activity-dependent secretion of BDNF in the CNS (37, 66). Due to the importance of activity-dependent secretion of BDNF to brain health and function, much research has been dedicated to studying the effects of the BDNF val66met polymorphism on the CNS. In humans, presence of the BDNF met allele is associated with abnormalities in brain structure and physiology (8). For example, compared to those without the polymorphism, met allele carriers demonstrate reduced volume of the prefrontal cortex (219) and hippocampus (22, 219), reduced hippocampal levels of N-acetyl-aspartate, a marker for neuronal health (66) and abnormal activation of the hippocampus when performing a working memory task during fMRI (66). These changes in the brain coincide with altered cognitive function. For instance, multiple studies have demonstrated that met allele carriers demonstrate impaired performance on hippocampal-dependent memory tasks compared to those without the polymorphism (66, 91, 97). A.3.2 BDNF gene val66met polymorphism impact on motor system  The first study to demonstrate an effect of the BDNF gene val66met polymorphism on activity-dependent plasticity associated with movement was conducted by Kleim and colleagues (132). Following 30 minutes of fast index finger movement training, individuals without the 203  polymorphism demonstrated a greater expansion of motor maps and greater increase in M1 excitability, as measured by transcranial magnetic stimulation, compared to individuals with the met allele (132). Another study demonstrated similar results utilizing the same simple motor training task and fMRI techniques in young healthy individuals (177). Interestingly, it has also been demonstrated that after one day of training on a similar index finger motor task, individuals without the polymorphism had greater motor map plasticity compared to those individuals with it, but that after five and 12 days of training there was no difference in measures of plasticity between genotypes (176). These results suggest that extensive motor training could overcome deficits in neuroplasticity in met allele carriers. In contrast, two other studies found no effect of BDNF genotype on change in cortical excitability evoked by a single session of fast finger movement tasks in young adults (39, 150); however, one of these studies found a significant effect of BDNF genotype when a more complex visuomotor task was practiced (39). Additionally, McHughen et al. recently found that there was no BDNF val66met polymorphism effect on motor map plasticity evoked by the same fast index finger movement paradigm as described above when performed in healthy older adults, suggesting that the BDNF genotype effects may be attenuated with advanced age (175). However, null effects of BDNF genotype on motor map plasticity within this body of research may also relate to the nature of the motor tasks employed. For example, given the importance of BDNF for motor learning (10, 134, 227, 294), plasticity may be more dependent on the BDNF val66met polymorphism when induced by tasks that involve learning of a novel motor skill than by tasks that involve simple repetition of a familiar movement.  Despite differences in neuroplasticity, most studies thus far have found no effect of BDNF genotype on motor performance in young healthy individuals (39, 132, 150). It has been 204  suggested that detecting behavioural effects of the polymorphism may require more sensitive measures of motor performance (132). It is also possible that reduced neuroplasticity may have larger and more detectable behavioural effects if other CNS functions were compromised, such as after stroke. Further, the majority of studies have only tested motor performance during and immediately following motor task training and as a result may have missed any longer lasting effects that would be detected with a delayed retention test (i.e. true motor learning effects) (39, 132, 150). The only study to employ a delayed retention test found that individuals without the val66met polymorphism demonstrated greater relative retention on a motor learning task compared to those with the polymorphism (177). Thus, altered neuroplasticity as a result of the BDNF val66met polymorphism may manifest behaviourally as deficits in motor learning. Nevertheless, additional research specifically examining motor learning is needed to further elucidate the effects of the BDNF genotype on the motor system. A.3.3 BDNF gene val66met polymorphism impact on recovery post-stroke   Evidence for a BDNF genotype effect on neuroplasticity and motor learning in young healthy individuals has led to speculation that the BDNF val66met polymorphism may also influence recovery after stroke (213). Three studies have demonstrated an association between the met allele and poorer recovery relative to those without the polymorphism in the acute and sub-acute stages following hemorrhagic stroke. However, there are conflicting findings regarding the long-term impact of the polymorphism (i.e. > one month post-stroke) and limited evidence to support an impact amongst individuals with ischemic stroke (49, 185, 269). Moreover, these studies have all used global outcome scales that do not differentiate between recovery of cognitive and motor function (49, 185, 269). Thus far, only one study of the BDNF val66met polymorphism has been conducted in individuals with chronic stroke (296). In this study, 205  reductions in visual memory after subarachnoid hemorrhage were greater in met allele carriers compared to individuals without the polymorphism; however, this genotype effect was not present in individuals with concurrent cerebral infarctions (296). Thus, more research is needed to understand BDNF genotype effects on different aspects of recovery and long-term outcome, as well as how the type of stroke impacts these effects. Additionally, effects of the BDNF gene val66met polymorphism on motor learning could potentially modulate response to motor rehabilitation in the chronic stage of stroke, but this has not yet been investigated. A.3.4 BDNF gene val66met polymorphism to inform the use of aerobic exercise for motor rehabilitation Previously, we considered the idea of priming the CNS by prescribing an acute bout of aerobic exercise in concert with motor rehabilitation training. As up-regulation of BDNF is thought to partly mediate the benefits of aerobic exercise on brain function (137, 295), aerobic exercise effects on motor learning and rehabilitation might be attenuated in individuals with the BDNF gene val66met polymorphism. Supporting this contention is the finding that improvements in object recognition memory following four weeks of aerobic exercise are attenuated in individuals with the BDNF val66met polymorphism, compared to those without the polymorphism (100). It is possible then, that any beneficial effects of aerobic exercise on cognitive domains involved in motor learning and rehabilitation would also be reduced in individuals with the BDNF val66met polymorphism. Nevertheless, aerobic exercise may still be beneficial for motor rehabilitation in BDNF val66met carriers, but may need to be prescribed in greater amounts or at higher intensity compared to those without the polymorphism. As more intensive motor practice can overcome the negative effects of the met allele on motor map plasticity (176), more intensive aerobic exercise might also overcome such negative effects. 206  Alternatively, it could be that rehabilitation strategies that target BDNF are not effective for met carriers, and as a result, other approaches might need to be developed to promote motor recovery in these individuals. Figure A.3 illustrates how the BDNF gene val66met polymorphism may influence the effects of aerobic exercise on motor rehabilitation post-stroke. Nevertheless, many other factors and molecular pathways, besides BDNF signaling, could impact the effects of aerobic exercise on the brain (68, 195, 289). As a result, aerobic exercise may be a uniquely powerful intervention that has positive effects on brain function across many genetic profiles. Regardless, an improved understanding of the role of genetics in motor rehabilitation could potentially enhance the understanding of what effects aerobic exercise might have on specific individuals, and as such, inform how it could be most effectively prescribed.    207   Figure A.3 The potential influence of the BDNF val66met polymorphism on the effects of aerobic exercise on motor recovery post-stroke. Aerobic exercise increases the production of BDNF which may then increase the amount of BDNF available for secretion via its activity-dependent pathway. Increased amounts of BDNF secreted via the activity-dependent pathway could then enhance neuroplasticity, resulting in an increase in response to motor rehabilitation, and ultimately, an increase in motor recovery. However, the BDNF val66met polymorphism impairs the intracellular trafficking of BDNF to the activity-dependent pathway by 25%. As a result, the effect of aerobic exercise on neuroplasticity, response to rehabilitation and motor recovery may be attenuated in individuals with the BDNF val66met polymorphism compared to those without it. + and – signs indicate positive and negative effects, respectively.    208  A.4  Conclusions and clinical implications Rehabilitation strategies that promote motor learning-related neuroplasticity hold promise for improving functional outcomes post-stroke (302). Aerobic exercise may be a particularly effective means of enhancing the capacity of the motor system for plasticity by up-regulation of neurotrophins, such as BDNF (47, 48, 137). Importantly, aerobic exercise alone does not induce neuroplasticity, but rather promotes the development of a neural environment that is supportive of plasticity (129). To capitalize on this effect for motor rehabilitation, aerobic exercise bouts may need to be performed in close temporal proximity to purposeful motor skill practice or experience. This idea is supported by evidence that an acute bout of aerobic exercise immediately before or after skilled motor practice enhanced motor learning in healthy young adults (245); further research is needed to test this idea in individuals with stroke. Additionally, the basic neuronal processes that mediate aerobic exercise effects on the brain and facilitate motor learning-related neuroplasticity, such as the production and activity-dependent secretion of BDNF, depend on the expression of specific genes (213). For example, the effects of aerobic exercise on motor learning-related neuroplasticity may be attenuated in individuals with a variant of the BDNF gene (val66met) that reduces activity-dependent secretion of BDNF (37, 66). Knowledge of the effects of this genetic variant could be used to better individualize motor rehabilitation strategies. Although genetics research is a promising avenue for the development of individualized rehabilitation strategies for people with stroke, it is important to note that a number of other factors, including demographic or environmental variables, can modulate the functional effects of genetic variation (69, 175, 195). Nevertheless, as personalized health care and specifically rehabilitation strategies become more refined, the effects of interventions may be optimized by the incorporation of genetic information. In 209  conclusion, future research into aerobic exercise and genetics may provide exciting new directions for the development of rehabilitation strategies designed to promote optimal neuroplasticity to improve motor recovery after stroke. 

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