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Characterizing cortical-cerebellar neurophysiology during visuomotor adaptation in chronic middle cerebral… Feldman, Samantha Jean 2018

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CHARACTERIZING CORTICAL-CEREBELLAR NEUROPHYSIOLOGY DURING VISUOMOTOR ADAPTATION IN CHRONIC MIDDLE CEREBRAL ARTERY STROKE   by  Samantha Jean Feldman  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Neuroscience)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2018 © Samantha Jean Feldman, 2018 ii  Abstract The acquisition of complex motor skills shapes our behavior and ability to adapt in everyday life. The underlying biological processes are complex, involving multiple neuroanatomic pathways and efficient communication within functional brain networks. Understanding the neural underpinnings of motor learning and adaptation within the context of health and disease is fundamental, and particularly relevant to brain injury, recovery, and rehabilitation. Following middle cerebral artery (MCA) stroke, individuals often experience upper limb motor impairment that continues into the chronic phase. Motor recovery is influenced by the reorganization of brain networks compensating for tissue damage and by well-designed motor rehabilitation programs that promote use of the remaining motor output. However, predicting the outcome of rehabilitation has been difficult and recovery is often incomplete. Impairments in cortical-cerebellar pathways may underlie motor deficits and influence motor function, yet, to date, little work has examined the influence of cortical-cerebellar relationships in relation to motor adaptation in chronic MCA stroke. The overall objective of the present thesis was to investigate the impact of MCA stroke on cortical-cerebellar neurophysiology and motor adaptation using transcranial magnetic stimulation (TMS). TMS was employed to investigate cortical-cerebellar excitability at baseline, and then during a sensorimotor adaptation task in a group of chronic MCA stroke and a group healthy older controls. Both groups had similar cortical-cerebellar excitability before and during the adaptation task. However, individuals with MCA stroke are impaired compared to healthy age matched controls in sensorimotor adaptation. This suggests that adaptation deficits after MCA stroke may be influenced by motor network substrates beyond the cerebellum. The present thesis contributes new knowledge towards understanding the impact of chronic MCA stroke on cortical-cerebellar pathways and iii  sensorimotor adaptation. The finding of sufficient cortical-cerebellar connectivity suggests that it may be a candidate pathway for TMS stimulation to modulate motor related networks for stroke rehabilitation.       iv  Lay Summary After a neurological injury, such as stroke, people often experience motor impairment on one side of their body. To recover motor function, individuals with stroke partake in rehabilitation programs which focus on relearning movements they previously were able to complete. In addition to engaging in rehabilitation programs, different regions of the brain that remain intact after stroke are responsible for generating movement and help to contribute to motor recovery. It is not fully understood how activity in different motor regions of the brain relate to movement after stroke. In order to create effective rehabilitation programs, it is important to determine how motor regions of the brain contribute to motor function. This research examined the relationship between two areas of the brain which help to create fluid coordinated movement and how they influence one another during a motor task in people with stroke.  v  Preface The work in this thesis was conceived, designed, conducted, analysed, and written by Samantha Feldman under the supervision of Dr. Lara A. Boyd. The University of British Columbia’s Clinical Research Ethics Board approved all research included in this dissertation. Certificate #: H16-01928.  This thesis will be submitted to a peer review journal for publication as a manuscript. Samantha Feldman was principally responsible for conceiving the study, developing the study design, collecting, analysing, and interpreting the data. Drs. Katlyn Brown, Julia Schmidt, Jason Neva, and Lara Boyd contributed to the research process. Drs. Katlyn Brown and Lara Boyd contributed to the development of the study design and data interpretation. Drs. Katlyn Brown and Julia Schmidt assisted with data collection and analysis. Dr. Jason Neva aided in data analysis and interpretation. Dr. Lara Boyd, Dr. Katlyn Brown, Dr. Jason Neva, and Dr. Matthieu Boisgontier assisted with editing of the current manuscript.    vi  Table of Contents  Abstract ..................................................................................................................................... ii Lay Summary ........................................................................................................................... iv Preface ....................................................................................................................................... v Table of Contents ..................................................................................................................... vi List of Tables ............................................................................................................................ ix List of Figures ........................................................................................................................... x List of Abbreviations ............................................................................................................... xi Acknowledgements ................................................................................................................. xii Dedication............................................................................................................................... xiii Chapter 1: Introduction............................................................................................................ 1 1.1 General Introduction .................................................................................................... 1 1.2 Overview of Motor Learning and Sensorimotor Adaptation ......................................... 4 1.3 Neural Substrates for Sensorimotor Adaptation............................................................ 5 1.4 The Cortical-Cerebellar Circuit in Stroke ..................................................................... 8 1.5 Assessing Sensorimotor Adaptation ............................................................................. 9 1.6 Assessing Neurophysiology using Non-Invasive Stimulation in Humans ................... 10 Chapter 2: Specific Aims and Hypotheses ............................................................................. 14 Chapter 3: Methods ................................................................................................................ 15 3.1 Participants ................................................................................................................ 15 3.2 Experimental Protocol ............................................................................................... 18 3.2.1.1 Functional Assessments ................................................................................. 18 vii  3.2.1.2 Neurophysiological Assessment ..................................................................... 19 3.2.1.3 EMG Recordings and TMS Protocol .............................................................. 19 3.2.1.4 Cerebellar Inhibition (CBI) ............................................................................ 20 3.2.1.5 Data Processing.............................................................................................. 22 3.2.1.6 Behavioural Assessment – Visuomotor Adaptation Task ................................ 22 3.2.1.6.1 Experimental Set-up ................................................................................. 22 3.2.1.6.2 Procedure ................................................................................................. 23 3.2.2 Statistical Analysis ............................................................................................. 27 3.2.2.1 Neurophysiological Assessment ..................................................................... 27 3.2.2.2 Behavioural Assessment: Visuomotor Adaptation .......................................... 28 3.2.2.3 Neurophysiological and Behavioural Relationships ........................................ 28 Chapter 4: Results................................................................................................................... 30 4.1 Cerebellar Inhibition: Baseline Comparisons in Stroke and Healthy Controls ............ 30 4.2 Cerebellar Inhibition: Over the Course of Visuomotor Adaptation ............................. 30 4.3 Behavioural Assessment: Visuomotor Adaptation...................................................... 33 4.3.1 RMSE ................................................................................................................ 33 4.3.2 APV................................................................................................................... 36 4.3.3 Visuomotor Adaptation: Retention ..................................................................... 39 4.3.3.1 RMSE Retention and Re-adaptation ............................................................... 39 4.3.3.2 APV Retention and Re-adaptation .................................................................. 40 4.3.4 Neurophysiological and Behavioural Relationships ............................................ 41 Chapter 5: Discussion ............................................................................................................. 42 5.1 Cortical-cerebellar neurophysiology .......................................................................... 42 viii  5.2 Visuomotor Adaptation.............................................................................................. 45 5.3 Visuomotor Adaptation- Retention ............................................................................ 47 5.4 Cortical-Cerebellar Neurophysiology and Visuomotor Adaptation Behaviour ............ 49 5.5 Limitations ................................................................................................................ 50 5.6 Implications and Future Directions ............................................................................ 53 5.7 Conclusions ............................................................................................................... 55 References ............................................................................................................................... 56 Appendix ................................................................................................................................. 72 Appendix A: TMS Screening Form ....................................................................................... 72 Appendix B: Visuomotor Adaptation Instructions ................................................................. 73  ix  List of Tables  Table 1: Participant Characteristics ........................................................................................... 17 Table 2: CBI-ratio values at all time-points. .............................................................................. 32 Table 3: Mean RMSE values during Visuomotor Adaptation. ................................................... 35 Table 4: Mean APV Values during Visuomotor Adaptation. ..................................................... 38  x  List of Figures  Figure 1: Transcranial Magnetic Stimulation (TMS). ................................................................ 11 Figure 2: Experimental Procedure. ............................................................................................ 18 Figure 3: Visuomotor adaptation paradigm and neurophysiological assessment......................... 19 Figure 4: CBI Waveforms ......................................................................................................... 22 Figure 5: Experimental Apparatus – Endpoint KINARM Robot Manipulandum........................ 23 Figure 6: Visual targets for visuomotor adaptation task ............................................................. 24 Figure 7: Early and Late Adaptation .......................................................................................... 25 Figure 8: Cortical-cerebellar excitability changes in response to performing a visuomotor adaptation task. ......................................................................................................................... 31 Figure 9: Change in RMSE values during visuomotor adaptation. ............................................. 34 Figure 10: Change in APV Values during Visuomotor Adaptation. ........................................... 37 Figure 11: Change in RMSE values during visuomotor adaptation at retention. ......................... 39 Figure 12: Change in APV values during visuomotor adaptation at retention. ........................... 41  xi  List of Abbreviations  APB: Abductor pollicis brevis  APV: Angle at peak velocity  CBI: Cerebellar inhibition  CCD: Crossed cerebellar diaschisis CS: Conditioned stimulus EMG: Electromyography FM-UL: Fugl-Meyer upper limb assessment ISI: Interstimulus interval  KINARM: Kinesiological instrument for normal and altered reaching movements LTD: Long term depression  M1: Primary motor cortex  MCA: Middle cerebral artery MEP: Motor-evoked potential PM: Pre-motor  RMSE: Root mean square error RMT: Resting motor threshold  TMS: Transcranial magnetic stimulation TS: Test stimulus  VMA: Visuomotor adaptation WMFT: Wolf Motor Function Test  xii  Acknowledgements  I am extremely grateful for the mentorship of my thesis supervisor, Dr. Lara Boyd. Thank you, Lara, for fostering such a positive and enriching lab environment to train in. It has been invaluable learning from you and I have appreciated your encouragement and thought-provoking questions along the way. I would also like to thank my supervisory committee members, Drs. Christine Tipper and Naznin Virji-Babul, I have valued your feedback throughout the thesis process.   To the members of the Brain Behaviour Lab, thank you all for your friendship and collaboration over the past couple of years. I feel very fortunate to have met and worked alongside such a generous and kind group of individuals. Special thank you to Kate Brown, Julia Schmidt, Kate Hayward, and Jason Neva for their contribution and guidance in completion of this project and to Asha Toner for her assistance in participant recruitment.   Words cannot begin to express my gratitude for my parents. You are a constant source of love and inspiration. Without you, this wouldn’t have been possible. Last but not least, thank you to Mike for the city walks, meals made, and endless laughs. I appreciate all that you do.  xiii  Dedication  For Ben1  Chapter 1: Introduction 1.1 General Introduction Across the lifespan, humans have a need to learn complex motor skills that shape their behaviour and their ability to adapt in everyday life. Skilled movements initially require high levels of physical and cognitive resources, but with practice, movements become seemingly effortless. Playing a stringed instrument serves as an example of a complex motor skill that requires fine motor control and bimanual coordination. Acquiring such a skill is attentionally demanding and requires motor adaptation to correct for inaccuracies, i.e. errors. During adaptation, movements are updated on a trial-by-trial basis in response to sensory feedback. When one’s finger misses the string and the note sounds off, the somatosensory and auditory feedback loops are integrated to recalibrate the movement so that a correct note can be played in the future. Once the appropriate motor plans are learned, the brain can then predict which movements should be made without conscious awareness. The process of motor learning is biologically complex, requiring efficient integration of sensory information from the environment to inform, adjust, and coordinate effector patterns necessary to produce the desired movement. A multifaceted neurophysiology is involved, where multiple anatomic pathways including afferent sensory input from body contact with the environment, proprioceptive input from muscles, integration of sensory and motor information at the cerebellum, and motor output from the premotor and motor cortices each play a role. The central nervous system is uniquely able to integrate these processes into coordinated movements to achieve the intended motor sequence. However, when neurological injury within the central nervous system occurs, it can compromise to an individual’s ability to learn and perform motor behaviours. 2  Cerebral infarction, also known as stroke, results in an interruption of cerebral blood flow through the arterial or venous systems resulting in oxygen deprivation and areas of anoxic injury with tissue damage. When the middle cerebral artery (MCA) is injured, the motor cortex, the premotor cortex and pyramidal motor pathways are frequently affected, negatively impacting motor function1,2. Following MCA stroke, motor recovery typically occurs over weeks to months3. Recovery is associated with a reorganization of the uninjured structural and functional brain networks that compensate for tissue damage4. Cerebral reorganization is promoted through rehabilitation programs5–8. Particularly, training of the affected limb after stroke leads to functional reorganization of the motor pathways and improves motor function9–11. However, predicting the outcome of rehabilitation has proven difficult and motor recovery is often incomplete12. Therefore, further work investigating the mechanisms underlying motor learning and adaptation in stroke is required to improve rehabilitation programs and promote motor recovery after stroke.  Predicting motor recovery is inherently challenging because of the variability in size and location of the tissue lesions caused by stroke. Moreover, even when comparing motor recovery in patients with cerebral infarctions within the same vascular territory, the variability in outcomes remains large13. To explain motor recovery after stroke, previous research has focused on hemispheric interactions and corticospinal pathway integrity14–17. Yet, conflicting findings are reported as to which neurophysiological and neuroanatomical substrates influence effective motor function in chronic stroke. Understanding the pathways, networks, interactions, and mechanisms contributing to post-stroke motor recovery is essential to development and optimization of rehabilitation approaches benefitting individuals with stroke. 3  In considering mechanisms and pathways involved in recovery following stroke, an underappreciated factor is the downstream consequence in areas that are anatomically linked but distant from the primary site of cerebral infarction, i.e., diaschisis. Within the context of a stroke in the MCA distribution, there can be diaschisis in the contralateral cerebellum (crossed cerebellar diaschisis; CCD)18. Functional cerebellar inactivation post MCA stroke has been attributed to the disruption of cortical-cerebellar pathways, interfering with connectivity between the primary motor cortex and the cerebellum19. As the cerebellum is essential to motor adaptation20, cortical-cerebellar connectivity after stroke may influence the outcome of rehabilitation programs. To date, research investigating the underlying neurophysiology and changes that occur in cortical-cerebellar circuitry after MCA stroke and how it relates to motor function in chronic stroke and motor adaptation is very limited. Structural connectivity between the motor cortex and cerebellum has been shown to be positively related to motor function after stroke21. However, this physiological association has yet to be explored within the context of motor adaptation in chronic MCA stroke. The application of neurophysiological measures including transcranial magnetic stimulation (TMS) provide an approach to examine the relationship between the motor cortex and the cerebellum in vivo and offer a window to evaluate how these regions interact within the motor adaptation process.  The overall objective of this thesis is to advance understanding of post-stroke cortical-cerebellar networks and their relationship to motor adaptation. Specifically, the thesis will investigate stroke related changes in cortical-cerebellar connectivity, both prior to and during a sensorimotor adaptation task in individuals with chronic stroke. Following is an introductory overview of critical concepts and the rationale for this thesis with its specific aims. 4  1.2 Overview of Motor Learning and Sensorimotor Adaptation Motor learning refers to an improved ability to execute a motor skill through practice and can be divided into motor sequence learning and motor adaptation22. Motor sequence learning refers to the gradual acquisition of movements into well executed behaviour which show lasting improvements over time, whereas motor adaptation refers to fast changes in behaviour in response to a shift in the environment, where performance over time returns to baseline levels23,24. For the purposes of this thesis, I will focus on motor adaptation. Sensorimotor adaptation is the “trial-and-error process of adjusting movement to new demands and calibrating the brain’s prediction of how the body will move while considering costs associated with the new task demand”25. Adaptation requires that the patterns of actions remain the same but changes in terms of one or more parameters (e.g. the pattern of force or direction)25. Sensorimotor adaptation is an essential aspect of motor control across sensory modalities i.e. vision, touch, proprioception, and sound, allowing an individual to maintain flexible control despite ever-changing circumstances. An example of sensorimotor adaptation is the adjustment to newly prescribed progressive lenses, where there is an initial unsettling visual distortion of the environment that occurs with a new prescription but to which the brain rapidly adapts through this process26. Sensorimotor adaptation happens relatively quickly and is made possible through the feed-forward processes; whereby an internal model of motor movements is continually updated online and sent out to the motor regions outputting the motor commands27. Internal models are updated through error-based learning mechanisms23,28,29. Specifically, the adjustment of internal models is based on errors of prediction, defined as the discrepancy between the internal forward model prediction and the actual sensory outcome. Based on the sensory prediction error, the motor command is recalibrated28,30. Deficits in error-based learning 5  mechanisms contribute to motor impairment and may be related to altered neuroanatomic pathways underlying sensorimotor adaptation.  1.3 Neural Substrates for Sensorimotor Adaptation The connections between the cerebral cortex and the cerebellum are essential for sensorimotor adaptation. The cerebellum receives a wide variety of input including information from the vestibular system, proprioceptive information from the spinal cord, and motor information from cortex31. All information travels into the cerebellum via the cerebellar peduncles (superior, middle, and inferior). For the purpose of this thesis, I will focus on the inputs and outputs related to the primary motor cortex (M1)-cerebellar system. Cortical-cerebellar connections are responsible for fine motor control, motor coordination, and motor adaptation32–34. The cerebellum receives input from the primary motor cortex via the cortico-ponto-cerebellar pathway or cerebellar afferent pathway35. The lateral areas of the cerebellar cortex receive this input and project to the dentate nuclei to drive the output of information to the cortex. This component of the loop is referred to as the dentato-thalamo-cortical pathway and is one of the primary efferent projections from the cerebellum that modulates activity in the motor cortex34,36.  In a seminal paper on cerebellar circuitry, Kelly and Strick provided compelling evidence for the presence of reciprocal connections between the cerebellum and the primary motor cortex through the use of transneuronal transport of neurotropic viruses38. Specifically, they mapped the distribution of Purkinje cells in the cerebellum and identified a large proportion of cells projecting to the upper limb area in M1 via portions of the ventrolateral thalamus in a non-human primate38. Taken together, this anatomical loop is commonly referred to as the cortical-cerebellar 6  system, which establishes connections between actions and the contexts in which they are executed in order to produce calibrated, smooth, and organized movements (Figure 1).  With this anatomical circuitry relating the cerebellum to the motor cortex, it can be appreciated how these pathways are functionally relevant to motor adaptation and error-based learning mechanisms. The cerebellum has repeatedly been recognized for its role in motor control20,27,39–41, especially within the context of the forward internal model. Encoding and storage of the internal model is thought to mainly depend on the cerebellum27,42. The internal model in the cerebellum uses a copy of the motor commands sent by the premotor and primary motor cortices to the cerebellum via the cerebellar afferent pathway to predict the expected motor changes29,43. The copy of the motor output allows the cerebellum to monitor motor commands and correct them online44,45,46. Outputs from the dentate nuclei of the cerebellum project back to the primary motor cortex via cerebellar efferent pathway to influence motor control at a relatively high level, perhaps directly influencing motor commands47. The brain then utilizes this information to enhance relationships between motor commands and their consequences48.  Importantly, the cerebellum also accounts for errors. The inferior olive relays sensory and proprioceptive information from the spinal cord to the cerebellum to help guide this process47. The climbing fibers from the inferior olive act as a comparator identifying discrepancies between the actual and predicted sensory consequences and signals errors in the accuracy of the forward internal models44,49. Climbing fibers within the cerebellum code an error signal in response to sensory information and depress Purkinje cells activity in the cerebellum50,51. Cerebellar synaptic plasticity, known as long term depression (LTD), occurs at Purkinje cells and results in disinhibition of the cerebellar efferent pathway to the primary motor cortex and is posited to 7  signal an updating of the internal model27,51. The error signal is used to alter input-output mappings in forward internal models so that subsequent predictions for the same situation can be made more accurately35,52. Evidence of this forward internal model and the importance of the cerebellum in integration of smooth organized movement is well supported by research52. In non-human primates it has been found that cerebellar Purkinje cell activity precedes movement related muscle activity and is likely a representation of an estimate of the coming state of the motor apparatus49,51. Lesion studies confirm the importance of the cerebellum in forward models, as individuals with cerebellar damage often are unable to learn how to use new tools or to compensate for novel visual feedback53. If the cortical-cerebellar circuit is critical to this process and for this internal model to exist, then reversibly disrupting this pathway at different points should affect this process. This is indeed the case, when individuals with essential tremor receive deep brain stimulation that artificially disrupt the ventrolateral thalamus this improves their tremor54. However, they are unable to learn an adaptive reaching task when the stimulator is on and they learn better when the stimulator is off55. In contrast, patients with disease of the basal ganglia showed little or no deficits in adaptation whereas those with cerebellar degeneration show deficits on a visuomotor adaptation task56. For motor control to take place, the internal model must continuously be updated via the aforementioned processes and adapt based on incoming sensory and motor information. Without this adaptive process taking place through the cortical-cerebellar circuit, the motor system would not be able to adjust its online motor commands in response to sensory consequences from the environment, and ultimately would lead to a reduced or inability to adapt to the ever-changing external environment. 8  1.4 The Cortical-Cerebellar Circuit in Stroke Many neurodegenerative diseases and conditions affecting the cerebellum have been investigated in relation to cerebellar motor control and feed-forward mechanisms, yet there has been minimal research investigating the relationship between cortical-cerebellar circuitry and sensorimotor adaptation in stroke. The MCA is the most common arterial occlusion causing stroke and includes superficial branches and the deep lenticulostriate branches31. The superficial branches and deep lenticulostriate branches are important to the motor system as they both supply structures that are critical to movement. Importantly, the deep lenticulostriate branches supply the internal capsule (the genu and anterior limb) which contain cortico-pontine and thalamo-cortical cerebellar fibers, both of which are critical to cortical-cerebellar communication31. Given that these regions are commonly affected after MCA stroke, it would be a plausible speculation that feed-forward mechanisms and sensorimotor adaptation would be affected. Furthermore, there is extensive research recognizing the role of CCD after MCA stroke18,19. Some studies have postulated that the most main downstream pathway underlying CCD is a disruption of the cortico-ponto-cerebellar connections by the infarct, causing deafferentation and metabolic depression of the contralateral cerebellar hemisphere57,58. Yet, the impact of CCD has not been considered when evaluating how an individual relearns motor behaviours after stroke or how it affects functional connectivity between the primary motor cortex and the cerebellum. There is a wealth of research that has been devoted to investigating motor cortex changes post stroke, and minimal attention has been directed towards M1-cerebellar relationships and whether they are adversely impacted by this MCA stroke59–61. Given that the cerebellum is an integral part of the adaptive learning process, then cerebral damage, specifically to areas perfused by MCA should impair motor adaptation as well. Few studies have 9  examined visuomotor adaptation in individuals with stroke and the ones that have looked at populations with injury restricted to specific regions of the brain (i.e.- focal right parietal lesions or cerebellar lesions) to determine their contribution to visuomotor adaptation62,63. However, Shadmehr & Krakauer (2008) have argued that behaviour after an injury is “the sum of the direct effect of a lesion plus its effect on regions connected to the damaged region”29. Furthermore, Shadmehr & Krakauer and others suggest that it is even more important to take neural networks into account in the chronic stage due to cerebral reorganization that takes place beyond the acute phase after a stroke29,64. Thus, making it essential to understand the underlying neurophysiological relationships of distal regions connected to the lesion in order to comprehensively understand motor adaptation post-stroke. 1.5 Assessing Sensorimotor Adaptation Sensorimotor adaptation has been evaluated extensively in a lab setting through the use of a variety of different experimental paradigms65. Numerous studies investigating sensorimotor adaptation have utilized a visuomotor adaptation task to evaluate error-based learning mechanisms66. This task has been validated in young people66, older individuals67, and patient populations with neurological disorders68,69. The visuomotor rotation task is an onscreen-cursor transformation where a discrepancy between hand movement and cursor movement is introduced. In this paradigm, individuals are told to reach from a central target out to peripheral targets on a trial-by-trial basis. Participants must determine how to adjust their hand movement to accommodate for the imposed rotation of the cursor about the central target of the task to reach to peripheral targets as fast and efficiently as possible. The task can be utilized to probe adaptation and error-based learning processes. Although this task has been utilized extensively in a cerebellar stroke population, with cerebellar lesions contributing to impaired adaptation70, it 10  remains unclear whether a more heterogeneous group of individuals with MCA stroke have deficits in adaptation.   1.6 Assessing Neurophysiology using Non-Invasive Stimulation in Humans Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique used to map neurophysiological circuits of the brain71,72. TMS can be used to stimulate targeted regions of the brain by using electromagnetically induced currents (Figure 2). TMS stimulation employs a plastic encased coil, which can be positioned on different regions of the scalp to assess neurophysiological activity of a given region. For instance, a brief pulse of TMS at a sufficient intensity over the hand representation of the left primary motor cortex produces a twitch in the muscles of the right hand. This twitch produces a waveform, termed a motor evoked potential (MEP), that is quantified using electromyography (EMG) (Figure 2). The size of the MEP, typically captured as a peak-to-peak amplitude, serves to measure corticospinal tract excitability. MEP amplitudes are influenced by a variety of external (percent of mean stimulator output, shape and orientation of the coil, technique used), and internal parameters (arousal, time of day, caffeine intake etc.)73.  11   Figure 1: Transcranial Magnetic Stimulation (TMS). An electromagnetic field is generated using a figure-of-eight coil over the surface of cortex. The TMS pulse activates a pool of intracortical interneurons which activate pyramidal neurons of the corticospinal tract and generates a motor response recorded by electromyography (EMG) known as a motor evoked potential (MEP). Figure adapted from Brown et al., 2014.   TMS stimulation can also be delivered with paired-pulses. By combining two pulses in quick succession at known interstimulus intervals (ISIs), TMS can provide information on specific interneuronal circuits. For example, a paired pulse protocol referred to as short intracortical inhibition (SICI), delivers two pulses of TMS 2-5 milliseconds apart and is thought to evaluate intracortical inhibitory circuits within the primary motor cortex74. The first pulse, the conditioned stimulus (CS), activates intracortical inhibitory circuits via excitation of GABAA receptors and leads to a dampening of excitability in response to the second pulse, the test stimulus (TS). Beyond intrahemispheric relationships, TMS can also evaluate regions distal to one another, including transcallosal and interhemispheric relationships.  12  Dual-coil TMS expands on the paired-pulse principles and delivers two pulses over different cortical locations to assess the relationship between two given regions. For example, cerebellar-motor TMS is a valuable method to evaluate the cortical-cerebellar connectivity75. A conditioning stimulus over the cerebellum preceding the test stimulus over the contralateral M1 enables us to study the cerebellar influence on M176. In healthy subjects, a high intensity conditioning pulse over the cerebellum inhibits the amplitude of the test MEP at the motor cortex when it precedes the test stimulus by 5-7 milliseconds relative to trials with the test stimulus alone77. This inhibition mediated through the pathway between the cerebellum and M1 has been termed cerebellar inhibition (CBI). CBI assesses Purkinje cell activity of the cerebellar cortex, quantifying the amount of inhibition of the dentato-thalamo-cortical connection and the observed inhibition at M178,79. This evidence is further supported by prior work indicating that individuals with cerebellar atrophy or lesions to the cerebellum do not show CBI80,81.   In the context of motor adaptation, CBI has been used to better elucidate the role of cortical-cerebellar connectivity during sensorimotor adaptation75,77,79. It is theorised that CBI may be an indirect measure of LTD in the cerebellum and cerebellar LTD is established as a critical cellular mechanism contributing error-based learning and adaptation. Jayaram et al., investigated the role of the cerebellum in young healthy individuals in learning of a locomotor adaptation task and found that early on during adaptation there was an overall decrease in the magnitude of CBI and those that learned the most experienced the greatest reduction in CBI82. There are similar effects in CBI with a visuomotor adaptation task, with CBI decreasing during early adaptation and returning to baseline once individuals had fully adapted to the task77. The release of cerebellar inhibition was posited to indicate a release of the inhibitory influence Purkinje cells possess over the motor cortex82,83. CBI could be a useful diagnostic tool for 13  assessing the excitability of the dentato-thalamo-cortical pathway in MCA stroke as it has been able to differentiate patients with diseases affecting the cerebellar cortex from healthy individuals and may help to provide insight into motor adaptation post-stroke84.  The underlying neurophysiology at baseline and during motor adaptation has yet to be explored in a chronic MCA stroke population creating a unique opportunity to study individuals with chronic MCA stroke to better understand the role that cortical-cerebellar circuitry plays in motor adaptation after stroke. The proposed research will investigate the mechanisms underlying of motor behaviour after chronic MCA stroke; in turn, this information may be employed to improve stroke rehabilitation. 14  Chapter 2: Specific Aims and Hypotheses  Aim 1: To assess excitability of the cortical-cerebellar pathway after MCA stroke and determine whether cerebellar excitability at baseline and during motor adaptation is altered in stroke participants. Hypothesis 1:  After MCA stroke, there will be alterations to the cortical-cerebellar circuit as indicated by reduced CBI at baseline. Further, CBI will decrease less over the course of visuomotor adaptation learning when compared to age-matched controls.  Aim 2: To evaluate sensorimotor adaptation using the visuomotor adaptation task in individuals with MCA stroke and age-matched controls. Sub Aim 2: To determine whether stroke influences retention and re-adaptation on the visuomotor adaptation task.  Hypothesis 2: Individuals with MCA stroke will display impaired adaptation due to reduced connectivity of the cortical-cerebellar circuit as indicated by less improvements in adaptation over the course of the task when compared to age-matched controls. Additionally, individuals with stroke will not retain the adaptation on a follow up assessment 24 hours later.     Aim 3: To evaluate whether the magnitude of cerebellar excitability relates to the magnitude of motor adaptation on a visuomotor adaptation task as has been found with healthy individuals.  Hypothesis 3: A reduction in error scores associated with adaptation motor learning will correlate with greater changes in CBI in both stroke and age-matched controls.   15  Chapter 3: Methods 3.1 Participants Participants were recruited by convenience sampling from the community and local advertisements. We recruited individuals with the following inclusion criteria: a) Chronic MCA stroke: stroke event >6 months previous. Diagnosis confirmed either with review of medical records or through review of neuroimaging (MRI) demonstrating features consistent with a stroke within the MCA territory.  b) Motor ability of affected limb sufficient to grasp and move the handle of the robot endpoint KINARM. c) Ability to evoke a MEP in the APB with stimulation of the ipsilesional motor cortex.  d) Cognitive and attentional ability sufficient to reliably follow test instructions.  e) No contraindication to TMS (Appendix A).  f) Vision sufficient to follow the test procedures.  Additionally, exclusion criteria included the following:  a) Plegic upper limb, contralateral to the MCA stroke.  b) A personal history or a family history of seizure.  c) Any prior history of head injury.  d) Any neurogenerative disease. e) Any neurological or muscular deficits affecting vision or motor control. f) Any unexplained tremor or movement disorder.  g) Medications contraindicated for TMS (Appendix A).  h) Stroke lesion within the cerebellum.  16   Informed consent was obtained from all individuals prior to participation in accordance with the declaration of Helsinki. The University of British Columbia Research Ethics Board approved all aspects of the study design (H16-01928). Thirty individuals participated in the study; fifteen individuals in the chronic phase after their first clinically diagnosed stroke (means±SD: age: 70.93±; 7.75), post-stroke duration (months): 103±76.89; Fugl-Meyer score: 59.68± 8.79; 5 females) and fifteen healthy older individuals (means±SD: age: 66.27± 8.23; 6 females). Two individuals with stroke participated in the study but had to be excluded as they were unable to follow task instructions. There was one individual in the stroke group who participated in the study but whose data was excluded from behavioural analysis due to data loss from technical error. This resulted in an evaluable sample of: a) stroke group: n=14 for CBI analysis and n=13 for KINARM analysis, b) healthy older group: n=15 for CBI analysis and n=15 for KINARM analysis.  Table 1 provides individual data for 29 total evaluable participants.  17   Table 1: Participant Characteristics HC; Healthy Control; PSD: Post-stroke duration; ND: Non-dominant; FM-UL: Fugl-Meyer upper limb; UL: upper limb; WMFT: Wolf-Motor Function Test; CBI: Cerebellar Inhibition.  18  3.2 Experimental Protocol Each participant completed two visits to UBC: the first day consisted of a neurophysiological assessment and a visuomotor adaptation task and the second day consisted of a retention test of the motor adaptation task and baseline functional assessments. An overview of the experimental session can be found in Figure 4.    Figure 2: Experimental Procedure.  Day one consisted of screening for contraindications to TMS and consent to participate in the study as well as the visuomotor adaptation paradigm with neurophysiological assessment (CBI). Day two included a retention test of the visuomotor adaptation paradigm and motor function assessments (FM-UL and WMFT).   3.2.1.1 Functional Assessments  Clinical assessments were performed for the chronic stroke group. The Fugl-Meyer Upper Limb (FM-UL) was performed to evaluate upper limb motor impairment. The FM-UL test consists of 33 items rated on a scale from 0 to 2, with a maximum score of 66 points; a higher score indicates less motor impairment. The FM scale is widely used in clinical and research settings to characterize motor impairment after stroke85,86. The Wolf Motor Function Test rate (WMFT) is a reliable and valid measure of UL function in individuals with stroke87. The test 19  consists of 15 timed movement tasks of the hemiparetic UL. Movement time for each task was used to calculate task rate (WMFT-rate): 60 (s)/time to complete tasks. The rate is calculated as the number of repetitions that are performed within a 60 second time frame. If an individual could not perform the task in 120 s, a mean rate of 0 was given for the task. The average rate of function was then calculated across all tasks, with a faster rate indicating better function.  3.2.1.2 Neurophysiological Assessment  Neurophysiological assessments were collected before and at designated intervals during the visuomotor adaptation task (Figure 5). Figure 3: Visuomotor adaptation paradigm and neurophysiological assessment.  CBI was collected before and after a block of aligned visual feedback consisting of 44 trials (5 epochs). CBI was collected again during a visuomotor adaptation task, with a CBI assessment after 72 trials (8 epochs) and then again after the visuomotor adaptation (VMA) task was completed (144 trials/ 16 epochs). Retention of the visuomotor adaptation task was evaluated 24 hours later (72 trials/ 8 epochs).   3.2.1.3 EMG Recordings and TMS Protocol  Electromyography (EMG) was recorded with two surface EMG electrodes placed over the training abductor pollicis brevis (APB) muscle of the arm used during the visuomotor adaptation task, and one electrode acting as the ground electrode on the back of the hand, with 1 cm diameter circular surface recording electrodes (Covidien, Mansfield, MA). The EMG were 20  recorded from the hand being assessed: the paretic hand in participants with stroke and the non-dominant hand in healthy age matched controls. EMG data were collected and used for online analysis of the MEP and stored for further analysis using Lab Chart software (Lab Chart 8). M1 excitability of the APB muscle representation was determined using a figure-of-eight TMS coil (Magstim 70 mm, Magstim Co., UK) connected to a Magstim 2002 stimulator (Magstim Co., UK). A template brain was used for TMS targeting and position monitoring using Brainsight™ neuronavigation software package (Rogue Research Inc., Montreal, QC, Canada). The ‘hotspot’ for eliciting MEPs in the contralateral APB was found by positioning the coil over the scalp region overlying the hand M1 representation, at a 45-degree angle to the mid-sagittal plane with handle facing backwards in order to produce a posterior to anterior current in the brain71. Once the hot spot was located, we determined the resting motor threshold (RMT) by finding the lowest stimulation required to identify an MEP that is greater than 50 µV in at least five out of ten consecutive trials88. RMT values were used to inform the percentage of stimulation for CBI89. Throughout the study, TMS pulses were delivered at a random rate between 0.15 and 0.2 Hz. 3.2.1.4 Cerebellar Inhibition (CBI) The experimental session involved an assessment of CBI at four-time points: baseline, pre-VMA, early-VMA, and late-VMA (Figure 5). The purpose of having a baseline and pre-visuomotor adaptation session was to assess whether CBI changes in response to reaching movements with aligned feedback or whether it is specific to the visuomotor adaptation task. Early adaptation (early-VMA) and late adaptation (late-VMA) CBI assessments were conducted to assess whether there were changes in cortical-cerebellar neurophysiology over the time course of visuomotor adaptation.   21   CBI was measured for each individual using an established paired pulse paradigm with a protocol similar to previous work75,89,90. Cerebellar stimulation was delivered with a double-cone coil (Magstim 110 mm, P/N 9902-00 Magstim Co., UK). The center of the double cone coil was positioned 3 centimeters lateral to the inion (ipsilateral to the hand being assessed) and the figure-of-eight coil targeting the contralateral M1 APB representation(Figure 3)75,77,83. The double cone coil was placed such that the current would flow downward in the coil, inducing an upward current in the underlying brain tissue82,84,91. The CS was delivered through the double cone coil and the stimulation intensity was set to 100% of RMT, and the TS was delivered with the figure-of-eight coil and with a percentage of maximum stimulator out (%MSO) to elicit a MEP of ~1mV in the APB muscle either 6 or 7 ms later. 100% of RMT was chosen to reduce the likelihood of participant discomfort as previous work has found 100% of RMT elicits consistent CBI without producing cervicomedullary evoked potentials89,92. We collected 10 trials of CBI at two different interstimulus intervals (ISI) (6 and 7 ms) for every time point (baseline, pre-visuomotor adaptation, early adaptation, and late adaptation). These ISIs were chosen based on past work determining that while CBI can be evoked at a variety of ISIs, 6 and 7 ms provide maximal inhibition93. For data analysis, the ISI which elicited the maximal inhibition at baseline and pre-VMA timepoints was used84. Baseline CBI data were analyzed online and in six instances, where participants had RMT thresholds higher than 60% we only collected CBI at one ISI. For the participants where only one ISI was collected, we analysed their baseline CBI online and chose the ISI which evoked greater CBI. During collection of CBI, ten pulses of unconditioned stimuli (TS) were collected. CBI was then calculated as a ratio of the mean MEP amplitude of the CS+TS to the mean TS alone. The presence of CBI is thought to be reflected by 22  ratios below 1.091. These values were used in statistical analyses for assessing changes in cortical-cerebellar excitability over the course of visuomotor adaptation.   (A)   MCA Stroke (B) Healthy Older         Figure 4: CBI Waveforms  Cerebellar Inhibition (CBI) in a representative participant: (A) MCA Stroke, (B) Healthy Older Control. The grey dashed line is the TS MEP when it is delivered alone. The black line displays the MEP waveform when the TS was preceded by a conditioning stimulus CS delivered over the cerebellum. For both individuals, the CS was at 1.0 x RMT and preceded the TS by 6 ms.  3.2.1.5 Data Processing CBI data were processed using a custom MATLAB script (Mathworks, USA). All TMS data was inspected post-hoc and specific trials were discarded if any visible EMG activity 100 ms prior to the test pulse was present. 3.2.1.6 Behavioural Assessment – Visuomotor Adaptation Task 3.2.1.6.1 Experimental Set-up Figure 7 displays the experimental apparatus. Subjects were seated in front of an endpoint kinesiological instrument for normal and altered reaching movements (KINARM) robot manipulandum which allowed for free movement of the upper limb in the horizontal plane 23  (Figure 7) (BKIN Technologies Ltd, Kingston, ON, Canada). The KINARM is outfitted with a TV monitor which displayed the visuomotor adaptation task in the same plane as the participants’ movements when they moved the robot arm. During the visuomotor adaptation task, participants only operated one robot arm with either their paretic arm (stroke participants) or their non-dominant arm (healthy controls) and participants wore a black bib to prohibit viewing of their hands during the task.  Figure 5: Experimental Apparatus – Endpoint KINARM Robot Manipulandum  3.2.1.6.2 Procedure During the visuomotor adaptation task, participants made out and back reaching movements using the endpoint KINARM robot manipulandum, moving a cursor from a central target location out to one of eight targets that appeared in the periphery and back again to the central target. One of eight radial targets were presented and displayed as a small circle from the 24  start region at an angle of 45°, 90°, 135°, 180°, 225°, 270°, or 305° degrees in polar coordinates relative to the start target displayed in the center (Figure 8).  Figure 6: Visual targets for visuomotor adaptation task   Participants performed two different conditions of reaching to visual targets: (1) reaching with aligned feedback (baseline reaching) and (2) reaching with 45° clockwise rotated cursor feedback about the central target (visuomotor adaptation task). Participants were instructed to try and reach to the target and return to the center point as fast and accurately as possible (Appendix B). Accuracy was defined as going in a straight path to the target. Once the participants had reached from the center target out to the peripheral target that appeared, subjects were instructed to wait until the peripheral target disappeared prior to returning to the center target. Cursor feedback of hand position was provided during the reach out to peripheral targets, and not provided on the reach back to center until the hand was within a 2 cm radius of the central target.  The baseline condition consisted of five epochs composed of 40 trials of reaching with aligned visual feedback (i.e. the robot arm position and the cursor acted in a 1:1 fashion) for familiarization. The early and late adaptation blocks consisted of altered visual feedback. For example, during the visuomotor adaptation task, initially the participant would move the robot arm in the direction of the target they want to reach, however, their cursor was heading 45 25  degrees off from where the arm was headed (Figure 9A). Participants had to learn to adapt for this discrepancy (Figure 9B). The early adaptation block contained nine epochs composed of 72 trials and the late adaptation block contained nine epochs of 72 trials (Figure 5). Every epoch contained 8 trials in a pseudorandomized order, with each epoch of eight targets including all eight peripheral target locations. CBI was assessed at baseline, after aligned visual feedback (at pre VMA) after early adaptation, and again after late adaptation (Figure 5).  Adaptation blocks were broken down into an early and late block, the amount of trials included in the early and late block was determined by previous research and was defined as the time whereby errors were still large (i.e.- their movement trajectory was curved) and the late block was defined as the point in which they had begun to plateau in their reaching movements (i.e. – relatively straight movement trajectory). (A)                                                                              (B)  Figure 7: Early and Late Adaptation  A) Early Adaptation: participant reaching to target with rotated cursor feedback, B) Late adaptation: participant adapted to rotated cursor feedback.   26   Prior work in young healthy individuals has identified early adaptation within the range of 50-70 trials and beyond 100 trials, performance begin to plateau66. We opted for 72 trials in our early learning block as older individuals take longer to adapt than healthy young individuals26. Twenty-four hours later, individuals came in for a retention assessment, consisting of 72 trials to determine whether individuals were able to retain the adaptation.  Motor performance was evaluated across adaptation stages and retention by assessing changes across trials in our two primary behavioural outcome measures: root mean square error (RMSE) and angle at peak velocity (APV). RMSE is the average deviation of the resampled trajectory of participant’ movement to the target from the ideal straight trajectory from the central target to the peripheral target94. RMSE reflects the overall error of the movement to the target, with a low RMSE score reflecting greater accuracy in movement toward the peripheral target. The APV captures the point (in degrees) in movement where peak values occur at the highest velocity range achieved by a given subject95. The angle calculated is the angle at their fastest point of movement that is hypothesized to be driven by a prediction made by the cerebellum because the latency of the movement precedes the influence of any peripheral feedback96. Additionally, the rate of adaptation and predicted plateau in performance were evaluated using an exponential curve fitting approach:   E= (RMSE or APVN) = A + Be-aN,  where E(RMSE or APVN) is the expected value of RMSE or APV on trial N. A identifies where the participant has plateaued in performance and a quantifies the rate of adaptation to the point of plateau. The purpose of using the visuomotor adaptation paradigm was to see how adaptation to altered visual feedback influences cortical-cerebellar excitability. 27  3.2.2 Statistical Analysis  Statistical analyses were conducted using SPSS software (SPSS V25, IBM Corporation, Armonk, NY, USA) and Statistica (v 12.0, Statsoft Inc., Dell Software, USA). All variables were tested for assumptions of normality using the Shapiro-Wilk test and was indexed by significance of p<0.00197. If normality of data was not achieved, variables were log transformed for statistical analysis. Effect sizes outlined by standard guidelines were calculated and reported on to inform the strength of the effects98. Mixed model analysis of variance (ANOVA) was used for statistical analysis. Where appropriate, post-hoc testing was performed using Tukey’s Honest Significant Difference (HSD). For all statistical tests, significance level was set at £0.05 and all descriptive statistics are reported as mean ± SEM.  3.2.2.1 Neurophysiological Assessment Cerebellar Inhibition: Baseline Comparisons in Stroke and Healthy Controls To determine whether there were group differences (stroke, healthy older) in baseline cortical-cerebellar excitability (Aim 1) a one-way ANOVA was performed for the neurophysiological measure CBI-ratio at baseline. In the stroke group, bivariate correlations were used to assess associations between WMFT-rate and FM-UL scores with baseline CBI-ratio using Pearson’s (rp) or Spearman’s (rs) for normal and non-normal data, respectively.  Cerebellar Inhibition: Over the Course of Visuomotor Adaptation To assess whether cortical-cerebellar excitability during motor adaptation was altered in stroke participants compared to healthy controls (Aim 1), a two-way mixed model ANOVA was performed with between-subject factor GROUP (2 levels: stroke, healthy older controls), and within-subjects factor of TIME (4 levels: baseline, pre-VMA, early-VMA, late-VMA) for CBI-ratio.  28  3.2.2.2 Behavioural Assessment: Visuomotor Adaptation To determine whether there were group differences (stroke, healthy older) in adaptation during the visuomotor adaptation task (Aim 2), a two-way mixed model ANOVA was performed with between-subject factor GROUP (2 levels: stroke, healthy older controls), and within-subjects factor of TIME (4 levels: early-VMA (epoch 1 and 8), late-VMA (epoch 1 and 8)) for each dependent measure (APV, RMSE).  Predicted Asymptote and Rate of Adaptation To evaluate whether there were group differences (stroke, healthy older) in the rate (a) and predicted asymptote (A) of adaptation during the visuomotor adaptation task, two separate one-way ANOVAs were performed with between-subject factor GROUP (2 levels: stroke, healthy older controls) for each dependent measure (APV, RMSE).  Visuomotor Adaptation: Retention and Re-adaptation    To evaluate whether individuals were able to retain the adaptation task from 24 hours after initial adaptation (Sub Aim 2), a two-way mixed model ANOVA with between-subjects factor GROUP (2 levels: stroke, healthy older controls), and within-subjects factor of TIME (2 levels: early VMA epoch 1 and epoch 1 of retention). To evaluate whether individuals re-adapted at retention, a two-way mixed model ANOVA with between-subjects factor GROUP (2 levels: stroke, healthy older controls), and within-subjects factor of TIME (2 levels: epoch 1 and epoch 8 of retention).  3.2.2.3 Neurophysiological and Behavioural Relationships Simple bivariate correlation analysis was conducted between the change scores of visuomotor adaptation (early-VMA epoch 1 to early-VMA epoch 8) and change scores of CBI (early to late adaptation assessments). These analyses were conducted to evaluate whether the 29  degree of cerebellar excitability relates to the degree of change in adaptation to the visuomotor adaptation task (Aim 3).   30  Chapter 4: Results 4.1 Cerebellar Inhibition: Baseline Comparisons in Stroke and Healthy Controls The one-way ANOVA conducted at baseline identified no significant main effect of group (F(1, 27) = 1.26, p = 0.27). In the stroke group, there were no significant relationships between FM-UL scores (r= 0.37, p= 0.21) or WMFT-rate (r= 0.29, p= 0.32) with CBI-ratio.   4.2 Cerebellar Inhibition: Over the Course of Visuomotor Adaptation The two-way mixed model ANOVA revealed a significant main effect of TIME (F(3, 81) = 5.82, p=0.001, ηp= 0.16, observed power= 0.93). Post-hoc analyses indicated that CBI ratio did not significantly change after reaching with aligned visual feedback, as there was no difference between baseline CBI-ratio and pre-VMA CBI-ratio (p= 0.72).  However, CBI-ratio was significantly higher following the early-VMA compared to pre-VMA (p=0.03). In contrast, CBI-ratio was not different after late-VMA when compared to pre-VMA (p=0.36). 31   Figure 8: Cortical-cerebellar excitability changes in response to performing a visuomotor adaptation task.  Group means displayed for CBI-ratios at each time point. Lower values on the y-axis indicate greater inhibition and values closer to and higher than 100 (dotted horizontal line) indicate less inhibition. Healthy older group data is represented with filled black circles and dotted lines and the stroke group data is represented by hollow squares with solid lines. Asterisks indicate statistical significance (p£0.05). Error bars represent standard error of the mean.     32  Subject ID Baseline Pre-VMA Early-VMA Late-VMA S01 71.01 77.68 80.26 81.42 S02 91.22 49.62 115.24 42.67 S03 126.48 73.37 96.52 62.87 S04 86.08 133.40 148.41 74.48 S05 64.27 61.23 76.99 75.54 S06 56.64 69.73 34.86 89.60 S07 91.19 132.32 99.37 109.70 S08 87.94 78.83 93.41 148.19 S09 94.30 72.51 209.85 183.02 S10 56.96 65.22 81.44 144.13 S11 16.90 53.40 265.73 90.39 S12 53.26 55.31 224.78 144.74 S13 49.04 68.24 57.27 117.51 S14 37.99 79.99 78.19 100.96 HC1 64.51 93.03 126.30 112.87 HC2 69.51 86.36 88.40 53.61 HC3 77.32 56.50 91.95 52.38 HC4 50.68 90.91 75.43 87.74 HC5 78.60 80.79 164.38 52.91 HC6 83.67 78.88 32.51 86.61 HC7 40.16 29.91 158.19 60.15 HC8 97.97 98.11 88.72 212.52 HC9 98.96 136.82 108.33 180.91 HC10 50.62 93.83 60.42 45.90 HC11 91.53 64.46 42.93 101.40 HC12 61.93 93.81 279.24 169.03 HC13 81.74 47.77 98.67 85.59 HC14 34.96 39.90 273.03 59.39 HC15 107.54 151.92 50.19 111.31  Table 2: CBI-ratio values at all time-points.  All values expressed as a ratio. HC; healthy control, S; stroke, VMA; visuomotor adaptation.   33  4.3 Behavioural Assessment: Visuomotor Adaptation 4.3.1 RMSE The two-way mixed model ANOVA detected a significant main effect of TIME (F(1.80, 46.89)=248.79, p= 0.00, ηp= 0.90, observed power= 1.00) and GROUP (F(1, 26)=13.74, p=0.001, ηp=0.346, observed power=0.94), with stroke mean values being greater than age-matched controls. There was no significant TIME x CODE interaction (F(3, 78)= 2.55, p= 0.94, ηp= 0.09, observed power= 0.46). Mauchly’s test indicated that the assumption of sphericity had been violated, c2(5) = 23.37, p= 0.000, therefore degrees of freedom were corrected using Greenhouse-Geisser estimates of sphericity (ε=0.60). Post-hoc analyses revealed differences across mean RMSE values all at time points (all ps <0.002) with values decreasing over time across every all-time points (highest at early-VMA epoch 1, lowest at late-VMA epoch 8) with the exception of early-VMA epoch 8 to late-VMA epoch 1 where there was a significant increase in error (p=0.001).  34   Figure 9: Change in RMSE values during visuomotor adaptation.  Group means displayed for RMSE over the course of visuomotor adaptation. Healthy older group data is represented with filled black circles and dotted lines, and the stroke group data is represented by hollow squares with solid lines. The units of the y-axis is in millimetres. Asterisks indicate statistical significance (p£0.05). Error bars reflect standard error of the mean.  RMSE Predicted Asymptote and Rate: A one-way ANOVA revealed a significant main effect of GROUP (F(1,26)=16.344, p=0.004), with individuals with stroke having a significant higher mean A value when compared to healthy older individuals. There was no difference in a between the two groups (p=0.301).   35  Subject ID Early-VMA  Epoch 1 Early-VMA  Epoch8 Late-VMA  Epoch 1 Late-VMA  Epoch 8 Retention  Epoch 1 Retention  Epoch 8  S1 0.017 0.007 0.008 0.008 0.011 0.008 S2 0.017 0.011 0.010 0.009 0.014 0.010 S3 0.016 0.009 0.012 0.010 0.012 0.007 S4 0.024 0.015 0.014 0.011 0.017 0.010 S5 0.017 0.012 0.010 0.009 0.009 0.008 S6 0.019 0.014 0.015 0.015 0.017 0.010 S7 0.020 0.009 0.012 0.008 0.010 0.007 S8 0.014 0.006 0.008 0.005 0.009 0.004 S9 0.018 0.009 0.009 0.004 0.010 0.008 S10 0.017 0.008 0.009 0.004 0.012 0.004 S11 0.024 0.016 0.015 0.014 0.011 0.009 S12 0.015 0.008 0.010 0.008 0.012 0.008 S13 0.015 0.005 0.008 0.004 0.005 0.004 HC1 0.019 0.004 0.006 0.004 0.008 0.004 HC2 0.013 0.004 0.007 0.003 0.009 0.003 HC3 0.017 0.007 0.008 0.005 0.009 0.004 HC4 0.018 0.006 0.008 0.004 0.011 0.004 HC5 0.016 0.005 0.007 0.003 0.009 0.004 HC6 0.016 0.006 0.009 0.005 0.008 0.005 HC7 0.012 0.009 0.009 0.008 0.012 0.006 HC8 0.017 0.006 0.009 0.004 0.011 0.004 HC9 0.016 0.007 0.010 0.005 0.011 0.003 HC10 0.013 0.008 0.009 0.004 0.011 0.005 HC11 0.019 0.009 0.010 0.005 0.013 0.006 HC12 0.018 0.004 0.007 0.003 0.009 0.003 HC13 0.010 0.007 0.006 0.005 0.007 0.004 HC14 0.015 0.007 0.011 0.006 0.010 0.006 HC15 0.011 0.004 0.007 0.003 0.008 0.003 HC16 0.017 0.006 0.010 0.005 0.009 0.003  Table 3: Mean RMSE values during Visuomotor Adaptation.   Each epoch is an average of 8 trials. HC; healthy control, S; stroke, VMA; Visuomotor adaptation.   36  4.3.2 APV A two-way mixed model ANOVA revealed a significant main effect of TIME (F(1.94, 50.45)=206.08, p= 0.000, ηp= 0.88, observed power=1.00) and main effect of GROUP (F(1, 26)=8.88, p= 0.003, ηp= 0.28, observed power= 0.87), with stroke mean values being greater than age-matched controls. There was no TIME x GROUP interaction (F(3,78)= 2.79, p= 0.072, ηp=0.097, observed power= 0.518). Mauchly’s test indicated that the assumption of sphericity had been violated, c2(5)= 19.192, p= 0.002, therefore degrees of freedom were corrected using Greenhouse-Geisser estimates of sphericity (ε=0.665).  Post-hoc analyses revealed differences across all time points (all ps <0.0001), with values decreasing over time with the exception of early-VMA epoch 8 to late-VMA epoch 1 where there was an increase in APV (p=0.0001).      37   Figure 10: Change in APV Values during Visuomotor Adaptation.  Group means displayed for APV over the course of visuomotor adaptation. Healthy older group data is represented with filled black circles and dotted lines, and the stroke group data is represented by hollow squares with solid lines. The units of the y-axis is in degrees. Asterisks indicate statistical significance (p£0.05). Error bars reflect standard error of the mean.  APV Predicted Asymptote and Rate: A one-way ANOVA revealed a significant main effect of GROUP (F(1,26)= 13.213, p=0.001), with individuals with stroke having significant higher mean A values relative to healthy older controls. There was no difference in a between the two groups (p=0.149).     38  Subject ID Early-VMA  Epoch 1 Early-VMA  Epoch 8 Late-VMA Epoch 1 Late-VMA  Epoch 8 Retention  Epoch 1 Retention  Epoch 8 S1 32.20 15.80 14.13 17.12 18.32 13.13 S2 45.16 22.32 25.04 15.77 21.58 15.95 S3 34.67 21.69 25.78 25.61 33.06 20.59 S4 48.92 35.08 36.07 27.12 45.98 29.16 S5 39.85 18.46 26.02 22.31 23.43 23.18 S6 36.25 29.74 29.25 22.65 35.05 17.27 S7 44.27 25.64 27.28 18.69 27.38 17.72 S8 26.55 16.38 18.42 11.26 23.03 8.72 S9 38.16 13.35 20.78 7.54 23.47 13.03 S10 39.02 20.40 21.30 8.71 31.19 5.31 S11 47.02 29.97 32.39 21.34 28.53 20.54 S12 38.84 18.25 24.27 16.58 19.98 15.86 S13 33.42 11.34 22.23 9.78 13.12 5.16 HC1 37.84 5.88 9.98 8.27 17.40 5.42 HC2 33.27 9.41 16.37 4.20 27.40 4.30 HC3 41.78 14.60 19.74 8.98 24.37 9.32 HC4 38.53 15.87 23.18 10.12 29.57 11.28 HC5 38.24 10.80 13.94 6.91 20.71 5.81 HC6 35.60 14.08 23.84 12.79 20.86 12.44 HC7 29.06 27.73 22.66 24.52 31.69 18.01 HC8 35.23 10.52 17.58 4.17 25.79 7.63 HC9 39.25 15.92 23.61 11.83 28.22 8.40 HC10 29.32 22.94 22.59 9.74 30.26 13.65 HC11 43.16 19.95 25.49 11.45 24.07 10.29 HC12 39.78 9.10 17.62 4.32 25.24 4.56 HC13 38.52 14.78 21.06 11.44 21.40 9.02 HC14 31.35 7.74 15.31 7.07 21.49 5.52 HC15 37.39 13.72 22.04 9.17 22.31 7.85  Table 4: Mean APV Values during Visuomotor Adaptation.   Each epoch is an average of 8 trials. HC; healthy control, S; stroke, VMA; Visuomotor adaptation.   39  4.3.3 Visuomotor Adaptation: Retention 4.3.3.1 RMSE Retention and Re-adaptation To evaluate retention, the two-way mixed model ANOVA was conducted and revealed a significant main effect of TIME (F(1,26)= 110.94, p=0.000, ηp=0.810, observed power=1.00). Post-hoc analysis indicated that RMSE is significantly lower at retention epoch 1 when compared to RMSE values at early-VMA epoch 1 (p=0.000).  To evaluate re-adaptation, the two-way mixed model ANOVA was conducted and revealed a significant interaction between TIME x GROUP (F(1, 26)=9.62, p= .019, ηp= 0.195, observed power=0.677). Post-hoc analysis indicated that RMSE is significantly lower in the age-matched controls when compared to stroke at retention epoch 8 (p=0.003) but not at retention epoch 1 (p= 0.185).  Figure 11: Change in RMSE values during visuomotor adaptation at retention. Group means displayed for RMSE over the course of visuomotor adaptation at retention. Healthy older group data is represented with filled black circles and dotted lines, and the stroke group data is represented 40  by hollow squares with solid lines. The units of the y-axis is in millimetres. Asterisks indicate statistical significance (p£0.05). Error bars reflect standard error of the mean.  4.3.3.2 APV Retention and Re-adaptation To evaluate retention, the two-way mixed model ANOVA was conducted and revealed a significant main effect of TIME (F(1,26)= 74.882, p=0.000, ηp=0.742, observed power=1.00). Post-hoc analysis indicated that APV is significantly lower at retention epoch 1 when compared to APV values at early-VMA epoch 1 (p=0.000).  To evaluate re-adaptation at retention a two-way RM-ANOVA was conducted and disclosed a significant interaction between TIME x GROUP (F(1, 26)=6.387, p= 0.018, ηp= 0.197, observed power=0.682). Post-hoc analysis indicated that APV is significantly lower in the age-matched controls when compared to stroke at retention epoch 8 (p=0.021) but not at retention epoch 1 (p=0.867).      41   Figure 12: Change in APV values during visuomotor adaptation at retention. Group means displayed for APV over the course of visuomotor adaptation at retention. Both groups similarly adapted during epoch 1 at retention, however, re-adaptation was more pronounced in healthy older individuals. Asterisks indicate statistical significance (p£0.05). Error bars reflect standard error of the mean.  4.3.4 Neurophysiological and Behavioural Relationships There was no relationship between CBI-ratio change scores and visuomotor adaptation change scores RMSE (p=0.496) and APV (p=0.594).   42  Chapter 5: Discussion The current work showed, for the first time, in individuals with stroke that cortical-cerebellar excitability changes across practice of a motor adaptation task, similar to patterns in the age-matched healthy controls. CBI decreased from pre-visuomotor adaptation to early adaptation, however later in adaptation when performance was beginning to plateau, CBI increased toward baseline levels. There was no change in CBI in response to non-rotated reaching movements in either group. These findings suggest that participation early during visuomotor adaptation impacts cortical-cerebellar excitability and is specific to rotated visual feedback. Behaviourally, both individuals with chronic stroke and healthy controls improve with practice on the visuomotor adaptation task; however, individuals with chronic stroke perform worse on the task, and this difference continues across practice. Further, at a 24-hour retention test, individuals with chronic stroke do not re-adapt to the visuomotor rotation as efficiently as healthy individuals.  5.1 Cortical-cerebellar neurophysiology In support of prior work in young healthy individuals investigating the relationship between motor adaptation and cerebellar plasticity, we found similar cortical-cerebellar excitability changes (i.e. a reduction in CBI) during the early phase of acquiring a sensorimotor adaptation task in older healthy individuals and individuals with stroke77,82. Functionally, a release in CBI is thought to be important to feed-forward processes of motor adaptation, with the cerebellum signaling that a visual prediction error has occurred in the context of the task at hand82,83,99. In the present thesis, early on during adaptation when the error values are still large due to an inaccurate prediction of the future state of where the body should be within the environment, there is a release in cortical-cerebellar inhibition. This aligns with a previous report 43  by Schlerf et al., 2012 which found that cerebellar excitability changes in response to an abrupt visual rotation of the cursor when the errors are large77. We believe the present data build on these previous findings to support the role of an LTD-like mechanism during early visuomotor adaptation. Importantly, the pattern of CBI shown in the current work suggests that cerebellar neurophysiological mechanisms for adaptation extend into older age and in individuals with chronic stroke. This suggests that there are functionally appropriate connections subserving cortical-cerebellar networks during visuomotor adaptation in those with chronic MCA stroke as well as healthy older individuals.  Results from the current work show that in this sample of participants, chronic MCA stroke does not differentially affect neurophysiological baseline measures of cortical-cerebellar connectivity (CBI) when compared with healthy older individuals. This is in contrast to previous reports of individuals with cerebellar stroke where there is an absence of inhibition of M1 responses to CBI at baseline84. Considering these results within the context of the cortico-cerebellar pathways provides a potential explanation for our findings. When a lesion to the cerebellum occurs, it eliminates or diminishes CBI and is hypothesized to be due to disruption of the cerebellar efferent pathway, or dentato-thalamo-cortical pathway84. Conversely, normal cerebellar inhibition was obtained in patients with a lesion in the middle cerebellar peduncle or pontine nucleus, both of which are members of the cerebellar afferent pathway or cortico-ponto-cerebellar pathway84. Therefore, Ugawa & Iwata (2005) concluded CBI is thought to reflect excitability of the dentato-thalamo-cortical (Figure 1). Crossed cerebellar diaschisis, is known to affect the cortico-ponto-cerebellar pathways after MCA stroke, whereas the evidence is less clear regarding CCD’s effects on the dentato-thalamo-cortical pathway100–103. Therefore, it is possible that the individuals in our MCA stroke group may not have CCD along the dentato-thalamo-44  cortical pathway or affected cerebellar efferent pathways, and thus express normal CBI, similar to the healthy control group. This has yet to be confirmed but is worth exploration using diffusion-based imaging methods.  However, there are some limitations to this interpretation. In assessing the impact of lesions to the efferent cerebellar pathways, Ugawa & Iwata (2005) only included a few individuals with cerebellar stroke in their study and the remainder of their study sample was heterogeneous including individuals with multiple sclerosis, motor neuron disease, Parkinson’s disease, hypothyroidism, and intoxication of anti-epileptic drugs. Whereas for the assessment of afferent pathways, the individuals had lesions resulting solely from cerebral infarction. It is possible that the lack of CBI in the group with damage to the cerebellar efferent pathways is due to neurological deficits from the various conditions as opposed to the lesion itself. Additionally, there is conflicting evidence whether CCD affects only efferent pathways to the cerebellum or whether it affects afferent pathways as well. One recent study found after stroke CCD might affect afferent fibers in individuals with larger lesions affecting the thalamus102. Further study is necessary to identify which neuroanatomical substrates are affected after CCD depending on lesion size and location.  We conducted correlations to assess whether there was a relationship between the WMFT, a motor assessment measure focused on compensation, and CBI as well as a correlation on FM-UL, a motor assessment measure focused on impairment. We expected to see relationships between baseline CBI and the two measures, yet we observed no significant relationship. A majority of the participants with stroke in our sample were quite mild (>55 on the FM-UL)104,105 and both the FM-UL and WMFT test are susceptible to ceiling effects, leaving the participants scores clustered together nearing the ceiling score106. A relationship between the two 45  measures may not be present given that the individuals with stroke has similar scores on both the upper limb motor function assessments and their baseline CBI.  5.2 Visuomotor Adaptation Our observation that motor adaptation is impaired in individuals with chronic stroke when compared to healthy older individuals is consistent with previous studies62,69,107. The stroke group displayed improvements in adaptation reflected by lower RMSE and APV scores over time, although the degree of adaptation was reduced compared with that of controls. We also build on findings that stroke survivors demonstrate improved movement patterns with training post-stroke and the present thesis work suggests that some residual motor adaptive capacity remains108,109.  In our study, we evaluated two measures of visuomotor adaptation, angle at peak velocity and root mean square error. Both measures are used to evaluate accuracy of an individual’s movement during motor adaptation. Angle at peak velocity is thought to be reflective of feed-forward operations of movement, where the cerebellum predicts what future movements should look like based on systematic prediction errors110,111. In our sample, individuals with stroke do appear to adapt and utilize this cerebellar dependent mechanism of feed-forward adaptation as indicated by improvements in their angle at peak velocity values. Stroke participants’ movements became straighter over time as they were able to generate better predictions of how to get to the target with the rotation present. Alternatively, root mean square error is reflective of the discrepancy between the actual movement trajectory and the ideal straight trajectory linking the center target and the peripheral target112. Root mean square error scores take into account feed-forward and feedback mechanisms as it quantifies the degree of correction made over the course of the whole movement and the variability of movement on a trial by trial basis. Stroke 46  participants were able to successfully adjust their movements to accommodate for the manipulated environment and use afferent sensory feedback online to update the accuracy of their movements. Based on these measures it would indicate that individuals with stroke are able to recalibrate their response to visually rotated feedback.  The difference in adaptation ability between the age-matched controls and stroke participants in our sample may be influenced by post-stroke impairments in proprioception. Deficits in upper extremity proprioception are common in individuals with stroke and may affect their motor adaptation efficacy113,114. Proprioceptive estimates of where one’s limb is located in space is needed to coordinate movements across joints and proprioception contributes in compensating for motor performance errors in providing accurate incoming sensory information to inform predictions during motor adaptation115,116. A sufficient discrepancy between visual and proprioceptive feedback regarding hand position must exist in order to effectively recalibrate the felt hand position117. Without adequate information with regard to position sense and sensation, updating of motor programs may result in inaccuracies. While we do not have proprioceptive measures to assess this claim, a future experiment could investigate the contributions of proprioception deficits on visuomotor adaptation after stroke.  The differences in visuomotor adaptation between age-matched controls and stroke in the present thesis could also be due to differences in upper limb dexterity. Dexterity applies to not only the adeptness in which one uses their hands but also encompasses the competence of the use of limbs and body across different tasks. After stroke, loss of dexterity is a common impairment118,119  and continues into the chronic phase120. A loss of dexterity after stroke has been attributed to an inability to appropriately modulate muscle activity according to specific environmental demands resulting in poorer motor function on visuomotor tasks29. The Fugl-47  Meyer Upper Extremity assessment (FM-UL) has been utilized as a measure to assess dexterity in stroke patients and is administered to individuals in both the acute and chronic phase of stroke121. In the present study, all participants in the chronic stroke group with the exception of two participants showed some deficits in dexterity based on their Fugl-Meyer scores. Therefore, it is possible that deficits in dexterity in the current sample may be contributing to the difference seen in visuomotor adaptation efficacy.  Furthermore, it is possible that lesion site plays a role in the impairments seen in visuomotor adaptation in our sample. Stroke lesions in regions commonly affected by MCA stroke such as parietal cortex, primary sensory cortex, and posterior limb of the internal capsule may affect different parts of the visuomotor adaptation process. Posterior parietal cortices are suggested to play an important role in visuomotor remapping, leading to more accurate planning of subsequent movements122. Computational models have been developed and suggest that the cerebellum relays state estimates of the current limb position to the cortex and visuomotor maps are updated in the parietal cortex123,124. There is evidence to suggest that parietal cortices are important for storage of the updated sensorimotor map122,62. For example, Mutha et al. (2011), demonstrated that stroke participants with parietal damage were able to bring their cursor to the intended target but did not improve initial direction over time62. While we cannot be certain that it is damage to the parietal cortices in the current sample that is contributing to impaired visuomotor adaptation, our results indicate regions beyond the cerebellum may be contributing to poorer adaptation when compared to age-matched controls.  5.3 Visuomotor Adaptation- Retention We observed differences in re-adaptation between age-matched controls and stroke on retention day, with the age-matched control group re-adapting to a greater magnitude than the 48  stroke group. These results indicate that individuals with stroke retain the forward model of the adaptation from the day before, however the stroke group continued to make slower and less accurate movements during re-adaptation at retention. Interestingly, accuracy between the two groups during the first epoch of adaptation at retention is similar. A potential process that could help to elucidate our findings is the dual-process of sensorimotor adaptation125. Recently, it has been hypothesized that motor adaptation involves both implicit and explicit strategies, with both processes running in parallel126. Explicit processes involve a cognitive component whereby the participant uses strategies to improve their accuracy in adaptation126. Conversely, implicit processes operate outside an individual’s awareness important role in influencing the refining of the adapted movement and the updating of the internal model127. Explicit processes importantly contribute early on during adaptation (when determining a strategy is important) and it has been proposed that the explicit component may influence initial readaptation125,128. It is possible that individuals with stroke were able to effectively recall the strategy they used to support adaptation (explicit strategy) at retention rather than solely relying on re-exposure to the visual error (implicit strategy). Individuals with stroke may use explicit strategies efficiently as they have to rely on cognitive strategies in movement of their paretic limb and have an increased need to pay attention to action post-stroke, thus having similar initial retention adaptation rates as age-matched controls. In individuals with stroke, activation of cognitive regions of the brain (dorsolateral prefrontal cortex) during an implicit motor learning task have been reported129. However, due to limitations in dexterity, proprioception, or sensorimotor integration, a ceiling effect may have occurred in their ability to adapt over the course of retention when compared to healthy controls.  49  5.4 Cortical-Cerebellar Neurophysiology and Visuomotor Adaptation Behaviour We observed no relationship between the observed change in cerebellar excitability and the amount of learning that occurred. While the cerebellum is a key contributor to sensory error-based learning, there is quite an extensive system underlying motor adaptation and many of the related nodes of the adaptation network may be influencing the adaptation process. Although CBI might not be different between the two groups, adaptation behaviour could relate to cerebellar-premotor interactions or sensorimotor integration in other regions. For instance, cerebello-thalamo-cortical afferent pathways travel to the primary motor cortex as well as the premotor and posterior parietal areas of the cerebral cortex130. Evidence is mounting that the cerebellum can influence control of movement from areas beyond M1 and it is likely through interactions with premotor cortical areas and regions of the parietal cortex131,132. Therefore, it is plausible that interactions between premotor (PM) and parietal regions change in response to visuomotor adaptation processes. Both parietal cortices and premotor cortices play critical roles in goal-oriented reaching actions, movement selection, and movement planning133,134.  As previously mentioned, lesions to the parietal cortex causes impairments in motor adaptation62. Similarly, PM cortex has been hypothesized to play a role in the feedback process of adaptation as it is thought to receive peripheral input about the ongoing movement135,136,137. Therefore, differences in plasticity between age-matched controls and stroke during visuomotor adaptation may lie in alternative networks such as cerebellar-parietal networks. Further research is needed to investigate cerebellar-cortical networks beyond M1 to further our understanding of the physiological neuroplastic changes underlying visuomotor adaptation, both in young healthy individuals and clinical populations. Lastly, cerebellar to cortical relationships may be best evaluated using an anterior-posterior current in the brain as it activates interneuronal circuitry in 50  the motor cortex that is specifically influenced by the cerebellum149. Hamada et al., 2014 found that excitability changes of anterior-posterior sensitive circuits depends on cerebellar activity and repetitive stimulation of the anterior-posterior circuits selectively modulates motor adaptation149. It would also be of interest to assess cortical-cerebellar relationships during visuomotor adaptation using an anterior-posterior current to see if anterior-posterior sensitive circuits are altered in individuals with stroke relative to healthy older individuals. Secondly, individuals with stroke and healthy older individuals both displayed similar CBI at baseline and over the course of adaptation. It is possible that a relationship may not exist due to the lack of variability in CBI scores. Without variability in scores on one measure, it would be difficult to find a relationship with a second measure or variable. Future work should investigate related cerebellar networks to disentangle the underlying neurophysiology of adaptation after MCA stroke. 5.5 Limitations Several limitations must be taken into account when interpreting the results of this study. First, the group of stroke participants that enrolled in this study had very mild motor symptoms with almost every individual in the stroke group having FM-UL scores higher than 55. Due to the nature of the task on the KINARM (they needed to be able to grasp and independently use their paretic arm with accuracy) and the requirements for assessing neurophysiology using TMS, we were limited to relatively well-recovered individuals. In order to utilize CBI as measure of cortical-cerebellar excitability, it requires that an MEP can be evoked from the ipsilesional hemisphere. Obtaining MEPs from the ipsilesional hemisphere has been shown to predict upper limb recovery after stroke, with individuals with severe stroke usually having no upper limb MEP138,139. Individuals with stroke in the present study were included if they had a stroke within the middle cerebral arterial distribution, however, knowledge of their precise lesion location was 51  limited. Lesion location may play a role in the response seen in neurophysiological measures as CBI responses may be dependent on whether the dentato-thalamo-cortical pathway is affected. Regardless, our primary interest was to have broad applicability and investigate whether a heterogeneous group of individuals diagnosed with MCA stroke displayed differences in cortical-cerebellar excitability when compared to controls, not to investigate the contribution of specific lesion locations to cortical-cerebellar excitability during the adaptation process.  Secondly, we limited our investigation of CBI to two ISIs. We assessed two different ISI (6 ms and 7 ms) as they have been found to evoke CBI140. However, some studies describe CBI at 5 ms ISI141. Thus, it is possible that different ISIs may have evoked greater inhibition of MEPs as prior work has found inhibition is greatest between 5-7 ms and it cannot be ruled out that either group may have expressed greater CBI at 5 ms91. CBI has been reported to be uncomfortable as some individuals cannot tolerate the stimulation at higher intensities, so we opted for the assessment of two ISIs to reduce participant burden. Nevertheless, we were still able to evoke CBI with most individuals exhibiting CBI at 6 ISI and fewer exhibiting CBI at 7 ISI.  Another logistical limitation was the number of times throughout the visuomotor adaptation process that we were able to assess CBI. Given that we only assessed CBI at four-time points (two during the adaptation process), it is possible that we might be missing variability in neurophysiology as individuals may adapt to the visuomotor adaptation task at different rates. However, our decision to assess cortical-cerebellar excitability at fewer time points was to minimize disruption to the adaptation process.  Additionally, TMS is evaluated through quantification of a motor response peripherally, whether that be measured through muscles of the hand, arm, or leg. For the present study, we 52  opted for the APB, a muscle which assists primarily with abduction of the thumb. Changes in the motor cortex are all based on evaluation of the resting target muscle, in this case APB142,71.  However, the visuomotor adaptation task on the KINARM requires hand involvement to manoeuver the robot arm and is not exclusive to movement of the thumb. Therefore, our evaluation of cortical-cerebellar excitability is relatively non-specific. However, Spampinato and colleagues (2017) demonstrated CBI changes during a visuomotor adaptation task for not only the effector involved but the ipsilateral foot representation as well, which is indicative of interlimb transfer of the adaptation78. They posit that neurophysiologically, this may be mediated by overlapping cerebellar representations143. Given that the thumb and hand representation are very closely situated, it is likely that they are activated together during the task and both the hand and thumb are being used concurrently in our study.   When evaluating our healthy older control data compared to our stroke data, it is important to consider one key element of the study design. Studies investigating motor learning and adaptation often compare the affected upper limb in individuals with stroke to the non-dominant upper limb in healthy older controls to control for handedness144,145. In the present study, we proceeded accordingly. However, recent work by Schlerf et al. (2015), found that in young healthy right-handed individuals, there was greater inhibition at the motor cortex from CBI when assessing the right cerebellum rather than the left cerebellum146. Our age-matched control group was predominantly right handed (n=14), however we evaluated their non-dominant hand. Furthermore, in our stroke group, many individuals still used their dominant hand (right hand) from prior to the stroke (n=8). Therefore, the amount of inhibition collected from CBI measures may be influenced by handedness and this must be considered when interpreting the current results.  53   Lastly, our sample size, although comparable to other studies investigating neurophysiology using TMS in stroke samples, is a primary limitation. Our study could have benefitted from a larger sample size with more representation from individuals with moderate stroke. However, this is a limitation of many studies investigating stroke samples using TMS. Nevertheless, the current study provides important information on cortical-cerebellar neurophysiology in a group of individuals with mild stroke and may act as a preliminary investigation for moving forward in larger stroke samples.   5.6 Implications and Future Directions A key scientific and clinical challenge in stroke rehabilitation is understanding how an individual might recovery after stroke and factors that may influence their recovery. The field of stroke rehabilitation is transitioning from a “one size fits all approach” to utilizing biomarkers to help in stratifying patients into the right treatment plan12,147. It is important that we explore how neurophysiological networks function after commonly occurring strokes, such as an MCA stroke, in order to begin to understand how to stratify patients into the appropriate treatment. Understanding mechanisms of plasticity of the cortical-cerebellar pathways after MCA stroke provides insight into understanding how functional networks communicate during motor tasks and whether the cerebellum is an appropriate target for stimulation for influencing motor recovery. The research presented in this thesis contributes to a relatively new line of inquiry investigating cortical-cerebellar relationships in chronic MCA stroke. Additionally, the present thesis furthers our understanding of the neurophysiology of visuomotor adaptation by extending investigation into those with chronic MCA stroke.   Despite the contribution of this thesis work to our knowledge base, many questions related to the effect of stroke on cortical-cerebellar function and motor adaptation remain 54  unanswered. The neurophysiological experiment conducted in this thesis research utilized TMS techniques (CBI) to explore the effects of stroke on cortical-cerebellar connectivity. Further work utilizing diffusion tensor imaging, a reflection of white matter integrity, could evaluate whether efferent and afferent cortical-cerebellar pathways remain intact after an MCA stroke. If afferent dentato-thalamo-cortical pathway integrity is similar to that of controls, it would provide support for our findings that this pathway is functional and intact after MCA stroke and may be utilized to modulate other connected cortical areas. Additionally, it would provide further incentive to investigate cerebellar-premotor cortex and cerebellar-parietal cortex excitability to determine whether these relationships are affected and relate to motor adaptation measures. If these networks are affected, cerebellar-cortical modulation may be able to target these areas and enhance residual motor functions in chronic MCA stroke.   In addition to furthering our understanding of chronic stroke, it is necessary that we begin to investigate the acute phase to understand changes in cerebellar excitability early on. Crossed cerebellar diaschisis has been reported upon in the subacute stages of stroke148 and can serve as an indicator of functional impairments following stroke101. In the present thesis, we did not have measures of CCD. In future work, it would be of great interest to identify those with CCD and investigate whether CBI values correspond with the amount of diaschisis present. Both could be viable biomarkers for predicting recovery and may identify a cohort of individuals who may benefit from cerebellar stimulation.   The last future research direction that will be proposed relates to the findings from the behavioural component of the present thesis work. As previously discussed, we found differences between individuals with stroke and age-matched controls in their ability to adapt on a visuomotor adaptation task and this may be attributed to deficits in proprioception within the 55  stroke group. Determining whether visuomotor adaptation is mediated by proprioception would aid in better understanding what is driving the deficits seen in individuals with stroke. One study demonstrated that proprioception was enough to guide movement during a visuomotor rotation task in young healthy individuals. Similarly, future work could eliminate online visual feedback on a visuomotor adaptation task and evaluate whether individuals with stroke are able to adapt in the absence of online cursor feedback. Additionally, an interesting study might evaluate whether proprioceptive training enhances visuomotor adaptation efficacy post-stroke. 5.7 Conclusions The aim of the present thesis was to explore the contribution of cortical-cerebellar mechanisms to visuomotor adaptation after chronic MCA stroke. Efferent cerebellar-M1 pathways were examined to determine their impact on post-stroke motor control. Cerebellar-M1 pathways remain plastic after chronic stroke as indicated by the presence of cortical-cerebellar excitability changes during visuomotor adaptation. In individuals with chronic stroke, the capacity for visuomotor adaptation is present yet diminished when compared to healthy older controls. Cumulatively, this work highlights the presence of connectivity between cerebellar and motor regions that may be capitalised upon for improving motor adaptation in chronic MCA stroke. 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(2014) Two distinct interneuron circuits in human motor cortex are linked to different subsets of physiological and behavioural plasticity. Journal of Neuroscience, 34(38).                  72  Appendix Appendix A: TMS Screening Form    BRAIN BEHAVIOR LAB TRANSCRANIAL MAGNETIC STIMULATION (TMS) SCREENING FORM  Below is a questionnaire used to exclude participants considered not suitable for transcranial magnetic stimulation (TMS).  This information, as well as your identity, will be kept confidential.   PLEASE COMPLETE FORM BELOW: Participant Code: ___________________________________________  Please CIRCLE ONE: Neurological or Psychiatric Disorder YES NO Multiple Sclerosis YES NO Head Trauma YES NO Depression YES NO Stroke YES NO Clinical Depression YES NO Brain surgery YES NO Treatment with amitryptiline and haloperidol YES NO Metal in cranium YES NO Implanted medication pump YES NO Brain Lesion YES NO Intracranial Pathology YES NO Pacemaker YES NO Albinism YES NO History of seizure YES NO Intractable anxiety YES NO Family history of epilepsy YES NO Pregnant YES NO History of epilepsy YES NO Headaches or Hearing problems YES NO Intracorporal electronic devices YES NO Family History of Hearing Loss YES NO Intracardic lines YES NO Other medical conditions YES NO If you answered “yes” to any of the above questions, please provide details below. __________________________________________________________________________________________________________________________________________________________________________  73  Appendix B: Visuomotor Adaptation Instructions  KINARM Visuomotor Adaptation Script: For this task, the goal is to reach from the center target out to the targets that appear at the periphery. I want you to reach as fast and as accurately as possible. What I mean by accurately is that I want you to reach straight to the target. The fastest way to get to the target is to move in a straight path to the target. Once you have reached the target by moving as fast and accurately as possible, wait for the target to disappear before you return to center. Once you return to center, another peripheral target will appear and the same rules apply.   Reminder messages:  Please move as fast as you can by moving in a straight path toward the target. Please wait for the target to disappear before you start to move to return to center.     

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