Health and Social Development, Faculty of (Okanagan)
Health and Exercise Sciences, School of (Okanagan)
DSpace
UBCO
Magnuson, Justine
Doctor of Philosophy - PhD
University of British Columbia
The overall aim of my dissertation was to assess cortical and neuromuscular responses to both mental and physical fatigue. My first two studies showed that mental fatigue, induced by either a prolonged cognitively demanding task or sleep deprivation, resulted in slowed ongoing neural activity, less efficient evoked cortical responses, and worse accuracy on a complex reaching task. Study #1 also showed that after acute mental fatigue is induced, motor task performance recovers by ~40 minutes post-task, whereas slowed ongoing cortical activity and increased ratings of fatigue are still present until at least 60 minutes post-task, suggesting a different time course of recovery for behavioural and cortical responses. Study #2 showed that, in addition to slowed and impaired inhibitory control with sleep deprivation, task performance during an emotional inhibitory control task was even worse, suggesting interference of emotion processing on response inhibition. Study #3 assessed how two types of fatiguing submaximal contractions (matched-torque vs. matched-electromyographic activity, EMG) affected voluntary activation (VA), corticomuscular and intermuscular coherence, and motor unit discharge rates. I found higher coherence, worse VA, and increased motor unit discharge rates in response to the matched-torque task; however, during the matched-EMG task, when motoneuron pool output was maintained, VA and coherence scores were unchanged and motor unit discharge rates decreased. This suggests different compensatory strategies to maintain task performance during a matched-torque vs. matched-EMG fatiguing protocol. Lastly, given the limited understanding of antagonist motor unit behaviour, study #4 aimed to examine the recruitment patterns of motor units when acting as an agonist vs. antagonist. Surprisingly, I found no antagonist motor unit recruitment in most participants during ramped isometric contractions to MVC, despite appreciable surface-EMG activity. This study showed that motor units first recruited during an agonist contraction were seldom among those first recruited during an antagonist contraction. Overall, this dissertation provides important new insight into the cortical and motor responses to both mental and physical fatigue, and advances our understanding of motor unit behaviour.
Neural and motor responses to mental and physical fatigue by Justine Magnuson M.Sc., Simon Fraser University, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Kinesiology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) January 2023 © Justine Magnuson, 2023 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Neural and motor responses to mental and physical fatigue submitted by Justine Magnuson in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Chris McNeil, School of Health and Exercise Sciences Supervisor Dr. Brian Dalton, School of Health and Exercise Sciences Supervisory Committee Member Dr. Paul Van Donkelaar, School of Health and Exercise Sciences Supervisory Committee Member Dr. Sarah Kraeutner, Irving K. Barber Faculty of Arts and Social Sciences University Examiner Dr. Anita Christie, Western University External Examiner iii Abstract The overall aim of my dissertation was to assess cortical and neuromuscular responses to both mental and physical fatigue. My first two studies showed that mental fatigue, induced by either a prolonged cognitively demanding task or sleep deprivation, resulted in slowed ongoing neural activity, less efficient evoked cortical responses, and worse accuracy on a complex reaching task. Study #1 also showed that after acute mental fatigue is induced, motor task performance recovers by ~40 minutes post-task, whereas slowed ongoing cortical activity and increased ratings of fatigue are still present until at least 60 minutes post-task, suggesting a different time course of recovery for behavioural and cortical responses. Study #2 showed that, in addition to slowed and impaired inhibitory control with sleep deprivation, task performance during an emotional inhibitory control task was even worse, suggesting interference of emotion processing on response inhibition. Study #3 assessed how two types of fatiguing submaximal contractions (matched-torque vs. matched-electromyographic activity, EMG) affected voluntary activation (VA), corticomuscular and intermuscular coherence, and motor unit discharge rates. I found higher coherence, worse VA, and increased motor unit discharge rates in response to the matched-torque task; however, during the matched-EMG task, when motoneuron pool output was maintained, VA and coherence scores were unchanged and motor unit discharge rates decreased. This suggests different compensatory strategies to maintain task performance during a matched-torque vs. matched-EMG fatiguing protocol. Lastly, given the limited understanding of antagonist motor unit behaviour, study #4 aimed to examine the recruitment patterns of motor units when acting as an agonist vs. antagonist. Surprisingly, I found no antagonist motor unit recruitment in most participants during ramped isometric contractions to MVC, despite appreciable surface-EMG activity. This study showed that motor units first recruited during an iv agonist contraction were seldom among those first recruited during an antagonist contraction. Overall, this dissertation provides important new insight into the cortical and motor responses to both mental and physical fatigue, and advances our understanding of motor unit behaviour. v Lay Summary The purpose of my dissertation was to better understand the effects of fatigue on the human cognitive and neuromuscular systems. To accomplish this, across three studies, I induced either mental or physical fatigue through cognitively or physically-demanding tasks, or sleep deprivation. I then used various techniques, including electroencephalography (EEG), electromyography (EMG), transcranial magnetic stimulation (TMS), and cognitive and motor assessments to examine the effect of this induced fatigue on both neural and motor function. A surprising finding from my third study, which examined the effects of physical fatigue on neuromuscular function and motor unit discharge rates, led me to explore antagonist motor unit activity during simple ramped, isometric contractions for my final study. Overall, my dissertation provides new insight into the cortical, neuromuscular, and motor responses to both mental and physical fatigue, and improves our understanding of antagonist motor unit behaviour. vi Preface The University of British Columbia’s Clinical and/or Behavioural Research Ethics Board (CREB/BREB) granted ethics approval for all original research studies presented in this thesis. Chapter 1: Written by Justine Magnuson and edited by Dr. McNeil Chapter 2: Includes study #1 of my dissertation. BREB ethics approval number: H19-01091. This chapter was published in Biological Psychology: Magnuson, J.R., Doesburg, S.M., McNeil, C.J. (2021). Development and recovery time of mental fatigue and its impact on motor function. Biological Psychology. Published April, 2021. Justine Magnuson conceptualized study protocol, led data collection, analyzed all data, drafted the manuscript and created all tables and figures. Dr. McNeil assisted with protocol conceptualization and edited the manuscript, and all authors approved the final version. Chapter 3: Includes study #2 of my dissertation. CREB ethics approval number: H19-01274. This chapter was published in Behavioural Brain Research: Magnuson, J.R., Kang, H., Dalton, B., McNeil, C.J. (2022). Neural effects of sleep deprivation on inhibitory control and emotion processing. Behavioural Brain Research. Published May 24, 2022. Study conceptualization was performed by Justine Magnuson, Hogun Kang, Dr. Dalton and Dr. McNeil. Data collection was completed by Justine Magnuson and Hogun Kang. Justine Magnuson analyzed all data, created tables and figures, and drafted the manuscript. Dr. McNeil and Dr. Dalton edited the manuscript. All authors approved the final version. Chapter 4: Includes study #3 of my dissertation. CREB ethics approval number: H19-03310. Justine Magnuson and Dr. McNeil conceptualized the study. Justine Magnuson and Christina Bruce completed data collection. Justine Magnuson performed all data analyses, drafted the vii manuscript, and produced all tables and figures. Dr. McNeil and Dr. Dalton edited the manuscript. Chapter 5: Includes study #4 of my dissertation. CREB ethics approval number: H19-03310. This chapter has been submitted to Medicine & Science in Sports & Exercise (MSSE-S-22-00916). Justine Magnuson, Dr. McNeil, and Dr. Dalton conceptualized the study. Justine Magnuson led data collection, performed all data analyses, drafted the manuscript, and produced tables and figures. Dr. McNeil and Dr. Dalton edited the manuscript. All authors approved the final version. Chapter 6: Written by Justine Magnuson and edited by Dr. McNeil. Publications produced from the original work presented in this dissertation: Peer-reviewed publications: 1. Magnuson, J.R., Doesburg, S.M., McNeil, C.J. (2021). Development and recovery time of mental fatigue and its impact on motor function. Biological Psychology. Doi: https://doi.org/10.1016/j.biopsycho.2021.108076. Published April, 2021. (Chapter 2). 2. Magnuson, J.R., Kang, H., Dalton, B., McNeil, C.J. (2022). Neural effects of sleep deprivation on inhibitory control and emotion processing. Behavioural Brain Research. Doi: https://doi.org/10.1016/j.bbr.2022.113845. Published May 24, 2022. (Chapter 3). Conference presentation (bold text indicates presenting author): 1. Justine Magnuson, Sam Doesburg, Chris McNeil. Investigating the development and recovery time of mental fatigue and its effect on cognitive and motor function. Canadian Society for Exercise Physiology Annual General Meeting. Kelowna, BC, Canada. November, 2019. [Poster Presentation]. (Chapter 2). 2. Justine Magnuson, Hogun Kang, Brian Dalton, Chris McNeil. Neural effects of sleep deprivation on inhibitory control. Canadian Society for Exercise Physiology Annual General Meeting. Virtual platform. October, 2020. [Poster Presentation]. (Chapter 3). 3. Justine Magnuson, Hogun Kang, Brian Dalton, Chris McNeil. Neural effects of sleep viii deprivation on inhibitory control and emotion processing. Graduate Research Day, University of British Columbia. Virtual platform. May 2021. [Oral Presentation]. (Chapter 3). 4. Justine Magnuson, Christina Bruce, Brian Dalton, Chris McNeil. Corticomuscular coherence and neuromuscular function with two submaximal fatiguing tasks. Motoneuron Meeting, Banff, Canada. June, 2022. [10-minute Oral Presentation]. (Chapter 4). 5. Justine Magnuson, Brian Dalton, Chris McNeil. Intramuscular recordings of biceps and triceps brachii motor unit activity during ramped, isometric contractions performed as an agonist or antagonist muscle. International Society for Electrophysiology and Kinesiology Congress, Quebec City, Canada. June, 2022. [10-minute Oral Presentation]. (Chapter 5). 6. Justine Magnuson, Brian Dalton, Chris McNeil. Motor unit discharge rates during a submaximal fatiguing contraction with a constant level of integrated electromyographic activity. Society for Neuroscience Conference. San Diego, CA, USA. November, 2022 [Poster Presentation]. (Chapter 4). ix Table of Contents Abstract ....................................................................................................................................................... iii Lay Summary .............................................................................................................................................. v Preface ......................................................................................................................................................... vi Table of Contents ....................................................................................................................................... ix List of Tables .............................................................................................................................................. xii List of Figures ........................................................................................................................................... xiii List of Abbreviations ................................................................................................................................. xv Acknowledgements ................................................................................................................................... xvi Chapter 1: Introduction .............................................................................................................................. 1 1.1 Mental fatigue ............................................................................................................................ 1 1.1.1 Mental fatigue and behaviour ............................................................................................. 1 1.1.1.1 Electroencephalography (EEG) ....................................................................................... 2 1.1.2 Mental fatigue and EEG outcomes ..................................................................................... 3 1.1.3 Sleep Deprivation ................................................................................................................ 5 1.2 Muscle fatigue ........................................................................................................................... 6 1.2.1 Peripheral fatigue ................................................................................................................ 6 1.2.2 Central fatigue ..................................................................................................................... 6 1.2.3 Task dependency ................................................................................................................. 8 1.2.4 Perceived and performance fatigability .............................................................................. 9 1.2.5 Corticomuscular coherence and muscle fatigue ................................................................ 10 1.2.6 Individual motor unit activity and muscle fatigue ............................................................ 11 1.2.7 Antagonist muscle activity and muscle fatigue ................................................................. 12 1.3 Antagonist motor unit activity ................................................................................................. 12 1.4 Dissertation objectives ............................................................................................................. 14 Chapter 2: Development and recovery time of mental fatigue and its impact on motor function .... 17 2.1 Introduction ............................................................................................................................. 17 2.2 Methods ................................................................................................................................... 20 2.2.1 Participants ........................................................................................................................ 20 2.2.2 Experimental setup ............................................................................................................ 20 2.2.3 Experimental tasks ............................................................................................................ 21 2.2.4 Experimental procedures ................................................................................................... 22 2.2.5 Data analysis ..................................................................................................................... 23 2.2.6 Statistical analysis ............................................................................................................. 25 2.3 Results ..................................................................................................................................... 26 2.3.1 Development of mental fatigue ......................................................................................... 26 2.3.1.1 Rating of perceived fatigue and aversion ................................................................. 26 2.3.1.2 EEG frequency power .............................................................................................. 26 2.3.1.3 Task performance ..................................................................................................... 27 2.3.2 Recovery of mental fatigue ............................................................................................... 28 2.3.2.1 EEG frequency power during rest ............................................................................ 28 2.3.2.2 ERP amplitude during the go/nogo task ................................................................... 29 2.3.2.3 Task performance on the go/nogo task ..................................................................... 30 2.3.2.4 Motor function .......................................................................................................... 31 2.3.2.5 Rating of perceived fatigue ...................................................................................... 32 2.4 Discussion ................................................................................................................................ 32 2.4.1 Development of fatigue (N-back task) ............................................................................. 32 2.4.2 Recovery of mental fatigue (resting state, go/nogo, motor tasks) .................................... 34 2.4.3 Conclusion ........................................................................................................................ 37 Chapter 3: Neural effects of sleep deprivation on inhibitory control and emotion processing ......... 38 3.1 Introduction ............................................................................................................................. 39 x 3.2 Methods ................................................................................................................................... 42 3.2.1 Participants ........................................................................................................................ 42 3.2.2 Experimental setup ............................................................................................................ 43 3.2.3 Experimental tasks ............................................................................................................ 43 3.2.4 Experimental procedures ................................................................................................... 44 3.2.5 Data analysis ..................................................................................................................... 45 3.2.6 Statistical analysis ............................................................................................................. 46 3.3 Results ..................................................................................................................................... 47 3.3.1 Ongoing low-frequency activity ....................................................................................... 47 3.3.2 Go/nogo tasks- accuracy and reaction time ...................................................................... 49 3.3.3 Go/nogo tasks- ERP amplitude differences ...................................................................... 50 3.3.4 KINARM object hit and avoid task .................................................................................. 53 3.4 Discussion ................................................................................................................................ 54 3.4.1 Considerations ................................................................................................................... 58 3.4.2 Conclusion ........................................................................................................................ 59 Chapter 4: Cortical and neuromuscular responses to matched-torque and matched-EMG fatiguing contractions ................................................................................................................................................ 61 4.1 Introduction ............................................................................................................................. 62 4.2 Methods ................................................................................................................................... 64 4.2.1 Participants ........................................................................................................................ 64 4.2.2 Experimental setup ............................................................................................................ 64 4.2.3 Experimental procedures ................................................................................................... 67 4.2.4 Data analysis ..................................................................................................................... 68 4.2.5 Statistics ............................................................................................................................ 69 4.3 Results ..................................................................................................................................... 71 4.3.1 Rating of perceived effort ................................................................................................. 71 4.3.2 Torque/iEMG during fatiguing contraction ...................................................................... 72 4.3.3 Maximal voluntary contraction torque .............................................................................. 73 4.3.4 Voluntary activation .......................................................................................................... 75 4.3.5 Coherence .......................................................................................................................... 75 4.3.6 Power ................................................................................................................................ 77 4.3.7 Motor unit discharge rate .................................................................................................. 80 4.4 Discussion ................................................................................................................................ 84 4.4.1 Rating of perceived effort, maximal voluntary contraction torque, and voluntary activation ........................................................................................................................................ 85 4.4.2 Corticomuscular and intermuscular coherence and power ............................................... 86 4.4.3 Motor unit discharge rate .................................................................................................. 88 Chapter 5: The orderly recruitment of motor units is modified when a muscle is acting as an antagonist ................................................................................................................................................... 90 4.1 Introduction ............................................................................................................................. 91 5.2 Methods ................................................................................................................................... 93 5.2.1 Participants ........................................................................................................................ 93 5.2.2 Experimental setup ............................................................................................................ 93 5.2.3 Experimental procedures ................................................................................................... 94 5.2.4 Data analysis and statistics ................................................................................................ 95 5.3 Results ..................................................................................................................................... 96 5.3.1 Agonist activity: ................................................................................................................ 96 5.3.2 Antagonist activity during elbow extensions: ................................................................... 99 5.3.3 Antagonist activity during elbow flexions: ..................................................................... 100 5.4 Discussion .............................................................................................................................. 100 Chapter 6: Conclusion ............................................................................................................................ 106 xi 6.1 Effects of mental fatigue on cognitive and motor function ................................................... 106 6.1.1 Conclusions ..................................................................................................................... 106 6.1.2 Strengths .......................................................................................................................... 107 6.1.3 Limitations ...................................................................................................................... 107 6.2 Effects of sleep deprivation on cognitive and motor function ............................................... 108 6.2.1 Conclusions ..................................................................................................................... 108 6.2.2 Strengths .......................................................................................................................... 108 6.2.3 Limitations ...................................................................................................................... 109 6.3 Effects of physical fatigue on task performance and neuromuscular outcomes .................... 110 6.3.1 Conclusions ..................................................................................................................... 110 6.3.2 Strengths .......................................................................................................................... 111 6.3.3 Limitations ...................................................................................................................... 111 6.4 Antagonist motor unit behaviour ........................................................................................... 112 6.4.1 Conclusions ..................................................................................................................... 112 6.4.2 Strengths .......................................................................................................................... 112 6.4.3 Limitations ...................................................................................................................... 113 6.5 Future directions .................................................................................................................... 113 6.6 Summary ................................................................................................................................ 115 Bibliography ............................................................................................................................................ 117 xii List of Tables Table 2.1 Mean and standard deviation values for response accuracy and reaction time across the 60-minute N-back task .................................................................................................................. 27 Table 2.2 Mean and standard deviation values for response accuracy and reaction time during the go/nogo task initiated before or after the 60-minute N-back task. ............................................... 31 Table 2.3 Mean and standard deviation values for motor function tasks initiated before or after the 60-minute N-back task. ........................................................................................................... 32 Table 3.1 Event related potential amplitudes (mean ± standard deviation) of the N2, P3, and N170 components during the shape and face go/nogo (GNG) tasks. Go/happy and nogo/angry trials are presented. ....................................................................................................................... 51 Table 3.2 Key parameters of the Object Hit and Avoid (OHA) task during the control and sleep deprivation conditions. .................................................................................................................. 54 Table 5.1 Single MU data recorded from biceps brachii of each participant. ............................. 99 Table 5.2 Single MU data recorded from triceps brachii of each participant. ............................. 99 xiii List of Figures Figure 1.1 A waveform showing multiple early and late ERP components....................................2 Figure 2.1 Timeline of mental fatigue experimental procedures .................................................. 23 Figure 2.2 Mean frequency activity recorded at the Pz electrode across 15-minute blocks of the 60-minute N-back task. ................................................................................................................. 27 Figure 2.3 Mean resting frequency activity recorded at the Pz electrode across a 3-minute period initiated before, immediately after, 30 minutes after and 60 minutes after performance of the N-back task. ....................................................................................................................................... 28 Figure 2.4 Mean event related potential waveforms recorded at the Cz electrode during the ~7-minute go/nogo task initiated: a) before; b) ~3 minutes after; c) ~33 minutes after; and d) ~63 minutes after performance of the N-back task. ............................................................................. 30 Figure 3.1 Topographical plot of the log-transformed resting-state theta activity for the control (left) and sleep deprivation (right) conditions. ............................................................................. 48 Figure 3.2 Topographical plot of the log-transformed resting-state alpha activity for the control (left) and sleep deprivation (right) conditions. ............................................................................. 48 Figure 3.3 Go (G), no-go (NG) and overall (O) accuracy as well as reaction time results for the shape and emotional go/nogo tasks during the control and sleep deprivation conditions. ........... 50 Figure 3.4 Mean event-related potential waveforms recorded at the Cz electrode during the (A) shape go/nogo task and (B) emotional go/nogo task for the control and sleep-deprivation conditions.. .................................................................................................................................... 52 Figure 3.5 Mean event-related potential waveforms recorded at an average of the P7 and P8 electrodes during the emotional go/nogo task for the control and sleep deprivation conditions. . 53 Figure 4.1 Protocol timeline showing the 10-minute fatiguing contraction. ................................ 68 Figure 4.2 Rating of perceived effort (RPE) scores for matched-torque (filled circles) and matched-EMG (open circles) protocols. ....................................................................................... 72 Figure 4.3 MVC torque and VA after fatigue. .............................................................................. 74 Figure 4.4 CMC and IMC during fatigue. .................................................................................... 77 Figure 4.5 Cz, BB and TB alpha and beta power during fatigue. ................................................. 80 Figure 4.6 Discharge rates of all motor units during fatigue... ..................................................... 82 Figure 4.7 Discharge rates of continuously on (CO) motor units during fatigue.. ....................... 82 Figure 4.8 Discharge rates of all motor units per participant during matched-torque fatigue... ... 82 xiv Figure 4.9 Discharge rates of all motor units per participant during matched-EMG fatigue.. ..... 82 Figure 5.1 Experimental procedures timeline for the elbow flexion (EF) and elbow extension (EE) contractions.. ........................................................................................................................ 95 Figure 5.2 Representative traces of single MUs that were recruited during both agonist (left) and antagonist (right) contractions from biceps (A) and triceps brachii (B). ...................................... 97 Figure 5.3 Single participant data showing four triceps brachii (TB) intramuscular recordings before, during, and after a ramped MVC of the elbow flexors.. ................................................. 103 xv List of Abbreviations Ag-AgCl Silver-silver chloride CDC Centres for disease control and prevention CMC Corticomuscular coherence EEG Electroencephalography EMG Electromyography EOG Electrooculography ERP Event related potential ERT Estimated resting twitch GNG Go/nogo iEMG Integrated electromyography IMC Intermuscular coherence MEP Motor evoked potential MVC Maximal voluntary contraction MU Motor unit MUDR Motor unit discharge rate OHA Object hit and avoid PSQI Pittsburgh sleep quality index ANOVA Analysis of variance RMS Root mean square RPE Rating of perceived effort TMS Transcranial magnetic stimulation VA Voluntary activation xvi Acknowledgements I am incredibly grateful for the support that I have received from many individuals throughout my PhD. To my supervisor, Dr. Chris McNeil: thank you for your consistent guidance, wisdom, care, and patience. You are always willing to put in the time to help us perform the best research we can and to mentor us not only as researchers but also as humans. I am grateful for all the ways you have helped to prepare me for my future, including the many research and teaching opportunities that you have provided for me. I have learned so much from being your student and could not have asked for a better supervisor. To my committee members: Dr. Brian Dalton, thank you not only for your insight and ideas on ways to improve our research studies, but also for being like a second supervisor to me and someone I can always count on. Dr. Paul Van Donkelaar, thank you for your encouragement and important contributions and ideas that have helped to shape my studies and research path. A special thank you to my examiners, Dr. Anita Christie and Dr. Sarah Kraeutner, for agreeing to spend many hours reviewing my thesis. I am thankful for the opportunity to receive feedback from each of you. To my lab mates: Alex Y, Nique, Alex P, Hogun, Matt, Lisa, Paige, Quinn, Megan, Bryce, you have all made these years so fun and memorable. I feel incredibly blessed to have had each of you as labmates and friends and am excited to pursue future goals alongside you kind and brilliant people! To my family: to say I’m thankful for your support/prayers is an understatement. Ben, mom, dad, grandma, grandpa, all my siblings, you never fail to motivate and build me up, and provide the exact kind of love and support that only you could have known that I needed. 1 Chapter 1: Introduction Chapter 1 of this dissertation provides a general, non-comprehensive overview of the topics of mental fatigue (Section 1.1), muscle fatigue (Section 1.2), and antagonist motor unit recruitment (Section 1.3). Section 1.4 provides a short description of the aims and hypotheses of each of my dissertation studies. 1.1 Mental fatigue Mental fatigue is a decline in ability and efficiency of mental activities (Ishii et al., 2014), and can be induced by prolonged performance of a cognitively demanding task (Boksem et al., 2005; Langner et al., 2010). 1.1.1 Mental fatigue and behaviour Mental fatigue is often measured via accuracy and/or reaction time scores, perceived rating scales, and other behavioural outcomes. Studies have found reduced reaction time and/or increased error rates in response to tasks that involve cognitive processes, including working memory, delay of gratification, inhibition, attention, planning, and flexibility (Boksem et al., 2005; Möckel et al., 2015). The type of cognitive task plays an important role in the behavioural outcomes of fatigue. For example, Shigihara et al., showed higher error rate during a simple working memory task (0-back) compared to a more complex working memory task (2-back), both of which lasted for 30 minutes (2013). They suggested that the poorer performance on the 0-back task was a result of increased boredom and sleepiness. It is likely that motivation plays a large role in task performance and cortical activity with mental fatigue. Although many behavioural studies have been used to determine changes in reaction time and accuracy with the onset of mental fatigue, 2 objective markers, including the neural correlates of mental fatigue, are less concrete (Dorrian et al., 2000; Langner et al., 2010; Scheffers et al., 1999; Smith et al., 2002). 1.1.1.1 Electroencephalography (EEG) EEG can be used to measure objective, neural responses to conditions such as mental fatigue. EEG is an excellent tool for assessing cortical activity because of its high temporal resolution. Specifically, EEG can be used to measure event related potentials (ERPs), which reflect the summed activity of postsynaptic potentials caused by the simultaneous firing of many pyramidal neurons in response to a sensory, motor or cognitive event (Sur & Sinha, 2009). ERP components that are evoked soon after an event has occurred are referred to as sensory or exogenous, whereas later ERPs reflect cognitive or endogenous information processing (Figure 1.1) (Luck, 2014). ERPs are quantified according to their amplitude and latency, whereby amplitude reflects the neural resources recruited to process the stimuli, and the latency of the ERP reflects neural processing speed (Luck, 2014). Figure 1.1 A waveform showing multiple early and late ERP components (Luck, 2014) In addition to being able to measure evoked potentials, EEG can be used to assess ongoing oscillatory activity in the brain, which is highly useful for interpreting cortical activity 3 and neural networks at rest. These oscillations are produced via a balance between excitatory and inhibitory processes in the brain (Schomer and Lopes da Silva, 2017). Specifically, they can be elicited from excitatory-excitatory, excitatory-inhibitory or inhibitory-inhibitory neural interactions via feedback activity to ensure synchronous firing (Cohen, 2014). The ongoing frequency bands of interest in brain research are delta (1-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (15-35Hz) and gamma (30-60 Hz). Delta rhythms are present in deep sleep and are prominent in frontocentral brain regions, whereas theta activity is common during states of drowsiness, inward focused attention and early sleep (Nayak & Anilkumar, 2020). Alpha power is dominant in posterior brain regions and is common during passive attention, relaxation and eyes-closed resting state (Nayak & Anilkumar, 2020). Alpha activity over the motor cortex is termed mu activity and is attenuated with movement or motor imagery, or somatosensory stimulation (Nayak & Anilkumar, 2020). Beta frequency activity reflects active, external attention and processing related to steady motor movements and is most prominent over frontal and central regions (Nayak & Anilkumar, 2020). Lastly, gamma power reflects states of concentration and sensory perception and integration of multiple different cortical regions (Nayak & Anilkumar, 2020). 1.1.2 Mental fatigue and EEG outcomes EEG studies show reduced late ERP component amplitudes with mental fatigue (Hopstaken et al., 2015; Kato et al., 2009; Möckel et al., 2015). For example, studies have shown reduced amplitude of the N2 ERP component, which reflects conflict monitoring and error detection processes associated with the anterior cingulate cortex (Mockel et al., 2015; Kato et al., 2009). Thus, mental fatigue may slow down or reduce the cognitive processes/control occurring in the anterior cingulate cortex. These studies also showed reduced amplitude and/or prolonged 4 latency of the P3 component, which reflects attention and processes related to the actual inhibition of a planned motor response. Altogether, it is possible that impaired/reduced neural processing of stimuli due to fatigue may result in worse motor execution and inhibition due to reduced top-down attention/processing (Boksem et al., 2005). Boonstra and colleagues also found reduced early N1 (reflecting sensory processing) amplitude with mental fatigue (2007). Studies have also shown larger early ERP amplitudes for irrelevant compared to relevant stimuli, suggesting that subjects were less able to ignore irrelevant stimuli at the sensory level (Boksem et al., 2005). With respect to EEG waveforms unrelated to a particular stimulus, some studies have shown a decrease in low frequency (alpha) power with mental fatigue (Ishii et al., 2013; Tanaka, Shigihara, Ishii, et al., 2012), as well as migration of alpha activity from anterior to posterior brain regions (Barwick et al., 2012). In a recent review of the effects of mental fatigue on brain function, it was determined that the majority of studies report modest increases in alpha power (8-12 Hz) (Boksem et al., 2005; Lim et al., 2013) and strong increase in theta power (4-8 Hz) (Boksem et al., 2005) with fatigue, showing a more ‘inhibitory’ neural state and slowing of neural processing/communication between regions of the cortex (Tran et al., 2020). In conclusion, it is likely that mental fatigue significantly impacts the performance monitoring/attentional top-down aspects of inhibitory control (Tran et al., 2020). Attention during inhibitory control tasks would thus become more bottom-up or sensory driven (Boksem et al., 2005). Despite these interesting findings, the recovery time of this mental fatigue state after performance of a cognitively demanding task has received surprisingly little attention. 5 1.1.3 Sleep Deprivation In addition to performing a prolonged cognitively-demanding task, mental fatigue can be induced via sleep deprivation. According to the Center for Disease Control and Prevention survey in 2014, only 65% of individuals reported a healthy sleep duration and 83.6 million adults were sleeping less than 7 hours per 24 hours. Sleep deprivation is prevalent not only in certain occupations such as those involving night shifts, but also in many clinical populations as well as the general population. Results across multiple studies suggest that sleep deprivation is likely to result in reduced attention and arousal, thus negatively affecting sensory processing as well as higher cognitive processes (Lim & Dinges, 2010). In experimental settings, tasks that require inhibiting a prepotent response such as the go/nogo (GNG) task, and those that involve interference from previous stimuli are particularly difficult while sleep deprived (Lim & Dinges, 2010). Across most of the existing literature, studies suggest that the prefrontal cortex is the primary region affected by sleep deprivation (Bobić et al., 2016; Boonstra et al., 2007). This region connects perceptual, motor, and limbic regions within the brain and is essential for top-down executive processing and attention (Boonstra et al., 2007). Specifically, reduced cortical excitation or impaired prefrontal cortex functioning can negatively affect working memory, inhibition and emotion regulation in a sleep-deprived state (Talebi et al., 2011). However, the interplay between these impairments, specifically inhibition and emotion regulation, has not been assessed. Similar to mental fatigue induced by a cognitive task, one review suggested that N1 (reflecting bottom-up sensory processing) and P3 (reflecting detection of unexpected or target stimuli) ERP component amplitudes are reduced as a result of sleep deprivation (Boonstra et al., 6 2007). Further, this review reported a reduction in alpha activity and an increase in theta activity after sleep deprivation, and suggested that this shift to lower frequencies may provide an index of sleep propensity in a sleep-deprived state (Boonstra et al., 2007; Talebi et al., 2011). 1.2 Muscle fatigue Traditionally, muscle fatigue is defined by a reduction in a person’s ability to exert force or power in response to a voluntary effort (Bigland-Ritchie et al., 1995). Muscle fatigue can be separated into its peripheral and central components, whereby peripheral fatigue is force or torque (torque hereafter) loss caused by changes at or distal to the neuromuscular junction, and central fatigue refers to a failure of the central nervous system to maximally drive the muscle (Gandevia, 2001). 1.2.1 Peripheral fatigue Peripheral fatigue is evidenced primarily by a reduction in the peak torque of the response evoked by a single external stimulus to a peripheral nerve or muscle belly (i.e., the resting twitch). This reduction occurs due to impairments in excitation-contraction coupling, calcium sensitivity, and cross-bridge attachment/detachment resulting from increased metabolites and altered ion concentrations (Taylor et al., 2016). For example, if the excitation-contraction process becomes less effective, then less calcium will be released from the sarcoplasmic reticulum, and thus, fewer binding sites will be open for the actin and myosin to form a cross-bridge, which will result in lower twitch torque. 1.2.2 Central fatigue Central fatigue is most commonly quantified using the interpolated twitch technique to assess voluntary activation (VA), which is a measure of a person’s ability to maximally drive their 7 muscle (Merton, 1954). This technique involves comparing the increment in torque evoked by stimulation during a maximal voluntary contraction (MVC) relative to the increment in torque evoked by the same stimulation delivered to the peripheral nerve or muscle belly during relaxation after the MVC (resting twitch, RT) (equation 1; McKenzie et al., 1992). If additional torque is induced by the stimulation (a superimposed twitch, SIT), this indicates that some of the motor units (MUs) were not recruited or were not discharging at an optimal rate to produce a maximal torque output (Belanger & McComas, 1981). Voluntary Activation (%) = 31 − SITRT: × 100% (1) VA can be assessed using transcranial magnetic stimulation (TMS) delivered to the motor cortex to provide a more specific location of possible suboptimal drive to the muscle (i.e., at or above the motor cortex vs. at or above the site of peripheral stimulation) (Todd et al., 2003). However, TMS evokes a small torque response at rest, one which is not representative of the actual capacity of the muscle. Thus, an estimated resting twitch (ERT) is obtained, whereby stimulation is applied during brief contractions at 100, 75, and 50% MVC torque and SIT torques are plotted against voluntary torque (Todd et al., 2003). The value at the y-intercept of this regression line is the ERT, which is used in the VA equation in place of the resting twitch value obtained from peripheral stimulation (equation 2). Voluntary Activation (%) = 31 − SITERT: × 100% (2) Although a reduction in VA is the gold standard measure of central fatigue, other indicators of impaired neural drive could include reduced torque steadiness and MU discharge rates (Marsden et al., 1983), as well as ratings of perceived fatigue during submaximal efforts 8 that have increased disproportionately relative to the drop in maximal torque generating capacity (McNeil et al., 2011). Mechanisms underlying central fatigue are not well understood, mainly due to limitations in human assessment techniques. Broadly speaking, reductions in cortical and motoneuronal excitability as well as increased muscle afferent feedback may be responsible for the torque loss due to central fatigue (McNeil et al., 2009, 2011; Taylor et al., 2016). Group III/IV muscle afferents are considered to play a key role in the development of central fatigue. Specifically, group III/IV afferent activity increases with fatiguing exercise, and impairs supraspinal drive, as shown through studies of VA with TMS (Kennedy et al., 2014; Sidhu et al., 2014). A change to intrinsic properties of motoneurons due to repetitive activation is also a likely mechanism responsible for torque loss that is caused by the central nervous system (Gandevia et al., 1999; McNeil et al., 2011; Rossi et al., 2012). 1.2.3 Task dependency The task by which fatigue is induced can alter the physiological mechanisms that limit performance (Bigland-Ritchie et al., 1995). For example, central fatigue accounts for a greater proportion of the torque loss with sustained submaximal compared to maximal tasks (Smith et al., 2007; Søgaard et al., 2006; Thomas et al., 2014). Within the context of a submaximal isometric task, neuromuscular fatigue can be modulated not only by contraction intensity and duration, but also by whether the task is matched for torque or EMG. During a matched-torque fatiguing protocol, the nervous system will increase motoneuron pool output to compensate for impairments at spinal and peripheral levels. During a matched-EMG protocol, the motoneuron pool output is maintained throughout the contraction, allowing for more accurate assessments of measures such as neural excitability with 9 fatigue. Despite this advantage to a matched-EMG fatiguing task, nothing is known about how brain-muscle or muscle-muscle interactions, or MU discharge rates are affected during such a protocol. 1.2.4 Perceived and performance fatigability Given the differences across tasks between central and peripheral drivers of torque loss, and the tightly connected nature of central and peripheral components in the human neuromuscular system, Enoka and Duchateau recently recommended changing the terminology away from central vs. peripheral fatigue to perceived vs performance fatigability (2016). Psychological factors such as attention, anxiety, motivation, discomfort, stress, and prior experiences may all also influence our levels of fatigue (Hureau et al., 2018). Thus, obtaining measures of perceived fatigability may lend to a better understanding of changes that are occurring due to reduced motivation and mental fatigue, which likely influences task performance through dopaminergic modulation at the striatum (Tanaka & Watanabe, 2012). Specifically, higher dopamine release results in greater motivation. Although my dissertation did not directly assess the relationship between mental and physical fatigue, it is important to note that dopamine release/motivation affects both types of fatigue and common neural networks are likely shared between the cortical inhibitory/excitatory systems that underly both mental and physical fatigue (Ishii et al., 2014; Tanaka & Watanabe, 2012). Despite the recommendations of Enoka and Duchateau, for the purpose of my dissertation, I will refer to muscle fatigue via central and peripheral fatigue to provide further specificity of neuromuscular adaptations occurring with fatiguing exercise, with a special emphasis on central fatigue. 10 1.2.5 Corticomuscular coherence and muscle fatigue As previously mentioned, mechanisms underlying central fatigue in humans are not well understood. Most notably, little is known about cortical adaptations with fatigue, possibly partly due to the limited application of brain imaging techniques to study muscle fatigue. To target cortical responses to fatiguing exercise, one can use combined EEG and EMG assessments to measure the connection between the motor cortex and the activated muscle. Specifically, frequency activity measured at the motor cortex and the muscle can be compared using a technique called corticomuscular coherence (CMC). CMC reflects conduction of electrical potentials through pyramidal pathways from the brain to the peripheral musculature and back (Schulz et al., 2014). CMC values in the beta frequency range may be present during static torque production (Liu et al., 2019), whereas coherence in the alpha frequency range has been identified during precise finger movements, and likely reflects ascending or afferent feedback interactions between the cortex and peripheral musculature (Liu et al., 2019; Mehrkanoon et al., 2014). Further, low CMC in the alpha band is associated with reduced corticospinal excitability (motor evoked potential (MEP) amplitude) (Schulz et al., 2014). The effect of muscle fatigue on beta-band coherence is unclear because there are reports of both increases (McManus et al., 2016; Tecchio et al., 2006; Ushiyama et al., 2011) and decreases (Yang et al., 2009, 2010). An increase in coherence may act as a compensatory mechanism such that enhanced beta-band oscillatory activity between the muscle and cortex allows for performance of a sustained isometric contraction (Ushiyama et al., 2011). In contrast, another group employing elbow flexion (Yang et al., 2009) and handgrip (Yang et al., 2010) fatiguing tasks showed a reduction in beta-band CMC with fatigue. Yang and colleagues suggested that this reduced coherence reflects fatigue-related increases in inhibitory drive to 11 output centers or shifting of primary output centers in the brain (2009). Given these contradicting findings, I decided to assess the effects of muscle fatigue on CMC in a controlled setting alongside supporting measures, including VA (to assess central fatigue) and individual MU activity to assess specific neural strategies being employed by the central nervous system. 1.2.6 Individual motor unit activity and muscle fatigue Although it is possible to measure overall motoneuron pool activity with EEG and surface EMG, intramuscular EMG or high-density surface EMG is needed to record individual MU properties to gain insight into specific MU recruitment strategies. One measurable outcome of the MU is its discharge rate. It was initially believed that, in the context of fatigue, MU discharge rates decrease to match the contractile slowing of the muscle to maximize torque production with minimal neural drive, a concept known as the ‘muscle wisdom’ hypothesis (Jones et al., 1979; Bigland-Ritchie & Jones, 1986). However, later work did not support the hypothesis, making it unlikely that torque production is highest with reduced discharge rates during fatigue (Fuglevand & Keen, 2003). Dalton and colleagues also found that the delayed recovery of reduced MU discharge rates in an older population after a fatiguing task of the soleus did not match the quick recovery of contractile properties of the muscle (2010). Owing to conflicting reports across studies, it is not clear how MU discharge rates should behave during a matched-torque contraction. Modulation of MU discharge rates with fatigue can stem from a variety of factors including changes in afferent feedback, descending drive, and intrinsic motoneuron properties (Taylor et al., 2016). The type of contraction likely plays a large role in the modulation of MU discharge rate with fatigue. For example, discharge rates decreased quickly during a sustained MVC (Bigland-Ritchie et al., 1986), but also decreased during a 12 ramped contraction to 50% MVC torque (Carpentier et al., 2001) or a sustained contraction at 66% MVC torque (Kuchinad et al., 2004). In contrast, discharge rates were reported to increase during sustained 20-30% MVC torque contractions (Griffin et al., 2001; Kuchinad et al., 2004; Maton & Gamet, 1989), or increase, decrease, or remain stable at ~20% MVC torque (Adam & De Luca, 2005). With simulated data generated by a MU behaviour model for muscle fatigue, MU discharge rates increased during a sustained contraction at 20% MVC torque (Potvin & Fuglevand, 2017). 1.2.7 Antagonist muscle activity and muscle fatigue During fatigue, the central nervous system may also regulate and modify the activity of antagonist as well as agonist muscles (Wang et al., 2015). Therefore, studying CMC in relation to both agonist and antagonist muscles and intermuscular coherence (IMC) between agonist and antagonist muscles may aid our understanding of the mechanisms behind sustained joint torque output and stability under specific fatiguing conditions (Wang et al., 2015). 1.3 Antagonist motor unit activity Only two studies have assessed individual MU activity in the antagonist muscle during a voluntary contraction (De Luca & Mambrito, 1987; Reece & Herda, 2021). In the study by De Luca and Mambrito (1987), individual MUs were recorded using intramuscular needle EMG electrodes in only three participants. For contractions up to 60% MVC torque, antagonist co-activation occurred only when the participants were provided with an instantaneous torque trace or when quick compensatory torque was required, but not when they could see the entire torque trace that they were to follow (De Luca & Mambrito 1987). In contrast to these findings, Reece and Herda (2021) reported recruitment of triceps brachii MUs at only 10% MVC torque during 13 ramped trapezoidal elbow flexion contractions with a total duration of 30 seconds. The authors also reported that MU control characteristics, including MU discharge rate vs. recruitment threshold and MU action potential amplitude vs. recruitment threshold, were similar between antagonist and agonist units. However, it is important to note that the findings from Reece and Herda were based on decomposition of a 5-pin surface EMG array, which, unlike intramuscular EMG, is highly influenced by amplitude cancelation and much less sensitive to detecting smaller MU action potentials (Haynes & Kim, 2021). Importantly, neither the DeLuca and Mambrita nor Reece and Herda studies had participants perform contractions when the antagonist muscle acted as an agonist, and, thus, could not indicate whether the MUs recruited during an antagonist contraction matched those recruited during agonist contractions. Despite a strong understanding of agonist MU behaviour, including the principle that MUs are recruited in order of size (Henneman, 1957), we currently have a very poor understanding of individual antagonist MU behaviour. Our limited knowledge of antagonist MU behaviour is concerning given the perceived need of some level of co-activation (simultaneous activation of agonist and antagonist muscles) for proper contractile function at a joint (Baratta et al., 1988; De Luca & Mambrito, 1987). However, too much co-activation is inefficient and has been shown to be higher in various clinical populations, such as individuals who have had a stroke (Knutsson & Richards, 1979; Lamontagne et al., 2000). Agonist muscle activation occurs alongside antagonist muscle inhibition/relaxation (reciprocal inhibition), and the relative amount of co-activation vs. inhibition is dependent on the integration of multiple different inputs including descending drive, muscle afferent activity (i.e., Ia, II, Ib and III/IV) and Renshaw cell activity. During an MVC, co-activation levels obtained from surface EMG recordings are ~10-20%; i.e., the antagonist muscle produces 10-20% of its maximal activity when acting as an agonist (Bazzucchi et al., 14 2006; Klein et al., 2001; Maeo et al., 2013). Despite the acceptance of antagonist muscle co-activation from surface EMG data, specific discharge pattern characteristics (i.e., recruitment order, discharge rates, etc.) of individual antagonist MUs are not well known. 1.4 Dissertation objectives Overall, the main aims of my dissertation were to assess the effects of both mental and physical fatigue on cortical and neuromuscular function, and to examine antagonist muscle activity both within and outside the context of fatigue. Study 1: No study had assessed functional and neural markers of mental fatigue underlying motor control, and tracked these responses through a recovery period after mental fatigue had been induced. Thus, in order to gain a more complete picture of the neural processes and their functional outcomes underlying fatigue, I used questionnaires, EEG, and motor assessments to determine how an hour-long cognitively-demanding task affected perceived fatigue, task aversion ratings, neural frequency activity, ERP amplitudes, and motor functional outcomes. I also assessed how each of these variables recovered in the hour following this cognitively-demanding task. I hypothesized that mental fatigue would result in increased alpha and theta frequency activity, slower reaction time and reduced accuracy on an inhibitory control task, reduced ERP amplitude, as well as impaired fine and gross motor function. I also predicted that these impairments would be present until at least 30 minutes after the end of the fatiguing task. 15 Study 2: Given the little information known about neural underpinnings of sleep deprivation, especially as they relate to emotion processing and inhibitory control, I assessed the outcome of 24 hours of sleep deprivation on behavioural and neural responses to emotional inhibitory control tasks. I expected that sleep deprivation would result in less efficient neural processing, reflected by reduced ERP component amplitudes and increased alpha and theta activity, as well as accuracy impairments, and delayed response times on all inhibitory motor control tasks. Study 3: Overall, studies assessing cortical mechanisms of physical fatigue show conflicting findings, including differences in neural low-frequency power and beta band coherence between the cortex and the innervated muscle. Thus, cortical mechanisms of fatigue and their relation to functional outputs of fatigue (VA and MVC torque loss) and specific neural recruitment strategies need further exploration. For my third study, I assessed the effect of two types of fatiguing contractions on perceived exertion, VA, CMC and IMC, and MU discharge rates. One of the fatiguing contractions required the participants to match a torque target, while the other required them to maintain a given integrated EMG value, allowing for differing levels of motoneuron pool output to be studied in the context of fatigue. I predicted that the matched-torque contraction would lead a greater reduction of VA and MVC torque, but greater increases of CMC and IMC compared to the matched-EMG contraction. Due to the conflicting findings for MU discharge rate during a matched-torque contraction, and the lack of studies assessing MU discharge rates during a matched-EMG fatiguing contraction, I did not form a hypothesis. 16 Study 4: Given the lack of research aimed at identifying individual MU discharge characteristics in the antagonist muscle, and virtually no triceps brachii MU activity during the sustained, submaximal elbow flexion contractions of Study 3, I chose to use intramuscular EMG recordings to examine MU behaviour of biceps brachii and triceps brachii during ramped elbow flexor and elbow extensor contractions. Based on surface EMG co-activation results and the Reece and Herda (2021) study reporting low-threshold triceps brachii MU recruitment during an elbow flexion contraction, I hypothesized that antagonist MU activity would be recorded during the early stages of the agonist contraction (starting at ~10% MVC), and that it would increase throughout the contraction, alongside surface EMG measures of co-activation. 17 Chapter 2: Development and recovery time of mental fatigue and its impact on motor function Justine R. Magnuson, Sam M. Doesburg and Chris J. McNeil Published: Magnuson, J. R., Doesburg, S. M., & McNeil, C. J. (2021). Development and recovery time of mental fatigue and its impact on motor function. Biological psychology, 161, 108076. Abstract: Mental fatigue is commonplace but there is limited understanding of the neural underpinnings of its development, the time course of its recovery, and its impact on motor function. Hence, this study used electroencephalography and motor assessments to investigate the development and recovery of mental fatigue. Twenty participants performed a 60-minute N-back task, with neural and motor function assessments prior to, and beginning at 0, 30 and 60 minutes post-task performance. Alpha activity and ratings of perceived fatigue and task aversion increased by 30-45 minutes of the N-back task and remained elevated until at least 60 minutes post-task. Motor skills were impaired at ~10-15 minutes post-task but recovered at ~40-45 minutes. Using a unique combination of measures, our results suggest that attentiveness and, possibly, selectiveness in inhibiting irrelevant information are impaired at the neural and behavioural level after an acute mentally-fatiguing task. Notably, recovery time differed for neural and functional measures. 2.1 Introduction High demands on neural and cognitive systems can lead to a state of ‘mental fatigue’, commonly defined as a decline in cognitive performance. Mental fatigue often leads to reduced attentiveness, distractibility, and impaired information processing (Boksem et al., 2005; Wascher 18 & Getzmann, 2014). This decline in neural function can result in increased aversion to a task, impaired problem solving and task performance skills, decreased tolerance of risk and effort, and increased numbers of errors or accidents (Yazdi & Sadeghniiat-Haghighi, 2015). Many studies use only impairments in task performance as an indicator of mental fatigue; however, neural activity, as measured by EEG, provides a more direct index of altered cognitive processes. EEG activity is often characterized by the power spectrum of neural activity, with each band reflecting specific information processing in the central nervous system (Tanaka et al., 2012). For example, the alpha (8-12 Hz) and theta (4-8 Hz) bands likely reflect inhibitory activity induced by GABAergic neurons (Jones et al., 2000; Ujfalussy & Kiss, 2006). Increases in alpha and theta frequencies from a non-fatigued to a mentally-fatigued state have been identified (Boksem et al., 2005; Barwick et al., 2012; Tanaka et al., 2012). According to the alpha inhibition theory, an increase in alpha activity occurs in spatially irrelevant regions of the brain in order to inhibit activity in those regions, whereas low alpha activity indicates active neuronal processing (Klimesch et al., 2007). To further support this theory, combined functional magnetic resonance imaging and EEG studies also show a negative correlation between activation of brain regions and alpha activity (Goldman et al., 2002; Yuan et al., 2010). It is possible that, during fatigue, less efficient neural processes result in greater alpha activity in an effort to inhibit irrelevant regions. Further, anteriorization of alpha activity is reported to occur with mental fatigue (Barwick et al., 2012). Overall, increased alpha activity as well as an anteriorization of alpha activity may result in reduced or compensatory neural processing during fatigue. Increased theta activity might reflect encoding of new information related to response monitoring, control of memory and error processing (Klimesch, 1999). 19 In addition to frequency activity, neural processes with EEG are commonly characterized by the amplitude and latency of an event related potential (ERP). Individual ERP components reflect distinct cognitive processes. For example, N2 is a negative component that peaks ~200-300ms post-stimulus and reflects conflict monitoring, whereas P3 is a positive component that peaks ~300-500ms post-stimulus and reflects later-stage cognitive processes related to inhibition of the motor response in the premotor cortex (Donkers and Van Boxtel, 2004; Folstein and Van Petten, 2008). The P3 component is also proportional to the available attentional resources (Guo et al., 2016; Hillyard et al., 1973). Inhibitory control tasks, such as the GNG task, have found reduced or inefficient neural resource allocation (smaller N2 and P3 amplitudes) with mental fatigue (Boksem et al., 2005; Hopstaken et al., 2015; Kato et al., 2009; Murata et al., 2005). The functional manifestation of these neural inefficiencies can be an impairment of task performance (e.g., increased number of errors or slowed reaction time) (Lim et al., 2010; Murata et al., 2005; Schellekens et al., 2000; Tanaka et al., 2009). Tasks that measure speed-accuracy trade-off in goal-directed movements or manual dexterity can be used to assess the impact of mental fatigue on coordination of gross and fine voluntary movements (Rozand et al., 2015; Yancosek & Howell, 2009). To our knowledge, no studies have employed these behavioural measures in conjunction with EEG assessments and perceptions of fatigue to investigate, across three domains, the persistence of mental fatigue after an acute cognitively-demanding task. Data related to the time course of mental fatigue and its impact on various motor functions could have many important applications; e.g., they could inform workplace policies to optimize efficiency and minimize risk. Hence, the purpose of this study was to characterize the effects of a cognitively-demanding task by using a novel combination of measurement tools (functional, neural, and 20 perceptual) across an extended time frame (more than 1 hour post-task). Throughout the task, we expected mental fatigue to increase as indicated by: a) increased alpha and theta frequency activity; b) slower reaction time and reduced accuracy; and c) increased perceived fatigue and aversion to task ratings. Further, we expected mental fatigue to persist for at least 30 minutes post-task, as indicated by: a) increased perceived ratings of fatigue; b) increased alpha and theta activity; c) reduced N2 and P3 amplitude; and d) impaired fine and gross motor function. 2.2 Methods 2.2.1 Participants Data were collected from 20 healthy participants (12 female) between the ages of 21 and 31. Participants gave written informed consent prior to testing, which took place during a single session of ~2.5 hours. The experiment began between 8:00-10:00am and participants reported that they had slept for 6-10 hours the night before testing. All participants refrained from consuming caffeinated beverages the morning of the experiment. The study protocol was approved by the Behavioural Research Ethics Board of the University of British Columbia (H19-01091) and conformed to the Declaration of Helsinki, except for registration in a database. Exclusion criteria for the study were: a recent head injury (concussion); medications with side effects of dizziness, lack of motor control, or slowed reaction time; a myopathic condition or motor neuropathy. 2.2.2 Experimental setup Participants sat in front of a KINARM End-Point Lab (BKIN Technologies Ltd., Kingston, Canada) and had a table with a laptop positioned on their left. EEG data were acquired using the 8-channel Enobio EEG system (Neuroelectrics, Barcelona, Spain) and electrode gel (SignaGel; 21 Parker Laboratories, Fairfield, NJ). Brain activity was recorded via electrodes at the Fz, Cz, P3, Pz, P4 and Oz positions, whereas the electrooculogram (EOG) was monitored with electrodes placed above and beside the left eye. A reference electrode was placed on the right ear lobe. EEG and EOG activity were sampled at 500 Hz. 2.2.3 Experimental tasks Resting state EEG: Ongoing neural frequency activity was assessed from EEG recordings taken at rest. For these recordings, participants were required to sit still while focusing their gaze on a fixation cross for 3 minutes. Go/nogo: To assess inhibitory control, participants completed a computerized GNG task. Stimuli were presented in the centre of a laptop computer screen (12.3 × 6.9 inch display), which was positioned ~60 cm from the participant’s eyes. Participants were instructed to press the left touchpad key as quickly as possible when they observed a coloured square on the screen but not respond when they observed a black square. Unbeknownst to the participants, but knownst to the experimenters, black squares appeared in 20% of the 300 trials. N-back: In this working memory task used to induce mental fatigue over the course of 1 hour, participants were instructed to press the left touchpad key when the stimulus (letter) presented in the centre of the laptop computer screen matched the stimulus from 2 steps earlier (i.e., a 2-back 22 task). A stimulus match appeared in 25% of the trials. Participants were given no instructions on the preferred speed of response or the percentage of stimulus matches. Object Hit and Avoid: To assess aspects of executive function (e.g., processing speed, attention, inhibitory control, etc.) and gross motor function, participants completed the KINARM Object Hit and Avoid task. For this bimanual task, participants grasped handles linked to robotic motors and, with their hands hidden from view, performed movements in the horizontal plane in response to targets presented on an augmented reality display. Participants directed the virtual representation of their hands to hit two target objects, while avoiding six distractor objects. The duration of the task was ~2.5 minutes and included 200 target and 100 distractor objects. Pegboard: Fine motor skills were evaluated using the Purdue Pegboard task (Lafayette Instrument Co., Lafayette, IN), which required the participant to place cylindrical metal pegs into a pegboard consisting of two parallel rows of 25 holes. Participants had 30 seconds to place as many pegs in the holes as possible and completed trials using either their dominant hand or both hands. 2.2.4 Experimental procedures After the participant was seated and instrumented for EEG recordings, they received instructions about each task and completed practice trials (8, 10, 2, and 3 for the GNG, N-back, Object Hit and Avoid, and pegboard tasks, respectively). To obtain pre-fatigue data, participants completed a comprehensive test block, which included 3 minutes of resting state EEG, ~7 minutes of the GNG task, ~2.5 minutes of the Object Hit and Avoid task, ~2 minutes of the 23 pegboard task and a rating of perceived mental fatigue using an 11-point Borg scale (Borg 1990). The participant then performed 60 minutes of the N-back task as the fatiguing portion of the experiment. At 15-minute intervals during the N-back task, a short pause (~30 seconds) was provided and, using the same Borg scale, participants rated their level of mental fatigue and aversion to the task. To conclude, comprehensive test blocks were initiated at 0, 30 and 60 minutes after completion of the N-back task. During the recovery period, participants remained seated and did not engage in conversation. See Figure 2.1 for a depiction of the procedures. 2.2.5 Data analysis EEG and EOG activity were analyzed off-line using Matlab software (version 2016b). EEG data were filtered after data collection using a 0.5–30 Hz, 4th-order Butterworth bandpass filter. At rest and during the N-back task, fast Fourier transform power calculations were made within 2-second epochs with a 10% overlap of the continuous EEG data (Levy, 1987; Wascher et al., 2014). These power values were then averaged across epochs for each frequency band. Segments containing significant muscle movements, eye movements and eye blinks, indicated by a z-value of >4 in the EEG (Fz, Cz, P3, Pz, P4, Oz) and EOG channels were discarded and EEG at rest (~3 minutes) Go/nogo(~7 minutes)Hit and avoid (~2.5 minutes)PP(~2 minutes)N-back (60 minutes)EEG at rest (~3 minutes) Go/nogo(~7 minutes)Hit and avoid (~2.5 minutes)PP (~2 minutes)EEG at rest (~3 minutes) Go/nogo(~7 minutes)Hit and avoid (~2.5 minutes)PP(~2 minutes)EEG at rest (~3 minutes) Go/nogo(~7 minutes)Hit and avoid (~2.5 minutes)PP(~2 minutes)30 minutes post-task 60 minutes post-task 0 minutes post-task Pre-task Figure 2.1 Timeline of mental fatigue experimental procedures 24 replaced by cubic interpolation between segments (Jansen et al., 2012). Visual inspection of the data was used to check for flat lines. Power values within the frequency bins of 4-8 Hz and 8-12 Hz were used to calculate theta and alpha power, respectively. Relative alpha and theta power were used to ensure homogeneous data processing (Barwick et al., 2012). Alpha power was calculated separately from the Fz, Cz, Pz, and Oz electrode locations to examine a potential migration effect, and theta power was calculated from the Fz electrode location. ERP component epochs were locked to the onset of the go and nogo stimuli in the GNG task. Stimulus-locked group average ERP amplitude values were calculated for accurate trials, with a 200 ms pre-stimulus baseline correction. The Cz electrode was used to measure mean amplitude of the N2 and P3 go and nogo components (Vuillier et al., 2016). Based on the extant literature and visual analysis of the individual and group averaged ERPs, the latency windows of maximal amplitude that were used to calculate the mean amplitude of the N2 and P3 components were 200-300ms and 300-600ms respectively. Conventional accuracy scores for the GNG and N-back tasks were assessed as the average between the target trial accuracy and the distractor trial accuracy. D-prime (z(Hit) minus z(False Alarm)) values for the N-back and GNG tasks were also presented as a measure of trial sensitivity/discriminability. For the Object Hit and Avoid task, participants were scored on the number of target and distractor shapes they hit. Lastly, for the pegboard task, participants were scored based on the number of pins placed in 30 seconds. Each sub-task (dominant hand and both hands) was measured three times in each block of testing to obtain a reliable index of manual dexterity. EEG frequency data (alpha and theta power), and reaction time values were log-transformed prior to statistical analyses. 25 2.2.6 Statistical analysis All statistical analyses were performed with IBM SPSS Statistics (SPSS Inc., Chicago, IL, USA). One-way repeated measures ANOVAs were used to assess the effect of time (0-15, 15-30, 30-45 and 45-60 minutes from the beginning of the N-back task) on perceived fatigue, aversion to the task, reaction time, accuracy, and theta power. To assess alpha power and test for an anteriorization effect with time, a two-way repeated measures ANOVA was used, with time and location (Fz, Cz, Pz, Oz) as within-subjects factors. To assess the recovery profile of mental fatigue (before, immediately after, 30 minutes after, and 60 minutes after the N-back task), one-way repeated measures ANOVAs were used for perceived fatigue, theta power at rest, reaction time and accuracy on the GNG task, Object Hit and Avoid scores, and pegboard scores. A two-way repeated measures ANOVA (within-subjects factors of time and location) was used for alpha power at rest. Lastly, two-way repeated measures ANOVAs, with time and trial (go vs. nogo) as within-subjects factors, were used for N2 amplitude and P3 amplitude. Participants were excluded from statistical analyses if more than 25% of the EEG data were contaminated by motion artifacts (Luck, 2012). As such, one participant in the N-back analysis and two participants in the resting state analyses were excluded. Additionally, one participant was removed from the N-back frequency analysis due to a malfunction in two of the electrodes during testing and one participant was omitted from the perceived fatigue analysis before and after N-back task performance due to data loss. To summarize, n = 20 for all analyses other than N-back and resting state data (n = 18) as well as perceived fatigue (n = 19). For all ANOVAs, the Greenhouse-Geisser correction method was used for a violation in sphericity. If there was a significant main or interaction effect, the results of paired t-tests were compared to determine when a degradation in performance could be seen. The level of statistical significance 26 for all main and interaction effects was set at p ≤ 0.05. To account for the three comparisons across the four time points of fatigue and recovery analyses, the level of significance was set at p ≤ 0.016 for post-hoc paired samples t-tests. Data are reported as mean ± standard deviation. 2.3 Results 2.3.1 Development of mental fatigue 2.3.1.1 Rating of perceived fatigue and aversion During the 60-minute N-back task, participants reported increased perceived fatigue (F(2,31) = 83.57, p < 0.01) and increased aversion to the task (F(1,28) = 22.31, p < 0.01). Post-hoc testing indicated that ratings of perceived fatigue and task aversion were higher for Blocks 2, 3 and 4 (15-30, 30-45, and 45-60 minutes, respectively) compared to Block 1 (0-15 minutes). Specifically, perceived fatigue increased from a score of 3.1 ± 1.2 at the end of Block 1 to a score of 7.5 ± 2.0 at the end of Block 4, whereas task aversion increased from 4.3 ± 2.4 to 7.9 ± 1.7. 2.3.1.2 EEG frequency power During the fatiguing N-back task, alpha power had a main effect of time (F(3,51) = 13.07, p < 0.01) but neither a main effect of electrode location (F(3,51) = 0.34, p = 0.73) nor a time × electrode location interaction (F(9,153) = 0.89, p = 0.46) (Figure 2.2). Paired samples t-tests showed a significant increase in alpha power at Block 3 compared to Block 1 (t(17) = -2.92, p < 0.01); as well as for Block 4 compared to Block 1 (t(17) = -2.76, p = 0.013). For theta power, there was no main effect of time (F(3, 51) = 1.79, p = 0.16) (Figure 2.2). 27 2.3.1.3 Task performance The behavioural results of the N-back task are shown in Table 2.1. No effect of time was found for conventional accuracy (F(3,57) = 0.32, p = 0.81), d-prime scores (F(3,57) = 0.36, p = 0.78), or reaction time on correct target trials (F(3,57) = 1.15, p = 0.34). Table 2.1 Mean and standard deviation values for response accuracy and reaction time across the 60-minute N-back task Time during N-back Accuracy (d’) Reaction time (ms) 0-15 minutes (Block 1) 2.36 ± 0.51 630.72 ± 91.30 15-30 minutes (Block 2) 2.41 ± 0.59 620.62 ± 88.24 30-45 minutes (Block 3) 2.39 ± 0.53 612.45 ± 78.18 45-60 minutes (Block 4) 2.46 ± 0.61 611.15 ± 78.23 Figure 2.2 Mean frequency activity recorded at the Pz electrode across 15-minute blocks of the 60-minute N-back task. 28 2.3.2 Recovery of mental fatigue 2.3.2.1 EEG frequency power during rest A main effect of time was identified for alpha power (F(3,51) = 21.29, p < 0.01) but there was not an effect of electrode location (F(3,51) = 0.21, p = 0.89) or a time × electrode location interaction (F(9, 153) = 1.12, p = 0.35) (Figure 2.3). Paired samples t-tests showed significantly greater alpha power 30 minutes after the task (t(17) = -4.50, p < 0.01), and 60 minutes after the task (t(17) = -4.18, p < 0.01) compared to before the task. However, no significant difference was found for alpha power 0 minutes after the task (t(17) = -2.15, p = 0.046) compared to before the task. There was also a main effect of time for theta power (F(3, 51) = 4.83, p < 0.01) (Figure 2.3). Paired samples t-tests showed significantly greater theta power only at 60 minutes after the task (t(17) = -4.02, p < 0.01) compared to before the task. Figure 2.3 Mean resting frequency activity recorded at the Pz electrode across a 3-minute period initiated before, immediately after, 30 minutes after and 60 minutes after performance of the N-back task. 29 2.3.2.2 ERP amplitude during the go/nogo task There were no main effects of time (F(2,29) = 0.63, p ≥ 0.50) but main effects of trial for both the N2 (F(1,19) = 22.70, p < 0.01) and P3 components (F(1,19) = 41.64, p < 0.01), indicating significantly larger amplitude for the nogo trials compared to the go trials across all time points (Figure 2.4). There was also a time × trial interaction for the P3 component (F(2,32) = 13.08, p < 0.01). However, following post-hoc testing, the disparity in P3 amplitude for go vs. nogo trials was not different from pre-fatigue values for the testing blocks started at ~3 minutes (t(19) = 1.60, p = 0.13), ~33 minutes (t(19) = 2.13, p = 0.047), or ~63minutes (t(19) = 0.014, p = 0.99) after task performance. 30 Figure 2.4 Mean event related potential waveforms recorded at the Cz electrode during the ~7-minute go/nogo task initiated: a) before; b) ~3 minutes after; c) ~33 minutes after; and d) ~63 minutes after performance of the N-back task. 2.3.2.3 Task performance on the go/nogo task The behavioural results of the GNG task are shown in Table 2.2. No main effect of time was found for the conventional accuracy (F(3,57) = 1.59, p = 0.20), d-prime (F(3,57) = 1.40, p = 0.25), or reaction time (F(3,57) = 1.55, p = 0.21) scores. 31 Table 2.2 Mean and standard deviation values for response accuracy and reaction time during the go/nogo task initiated before or after the 60-minute N-back task. Time relative to N-back Accuracy (d’) Reaction time (ms) Before 3.51 ± 0.79 318.31 ± 43.58 ~3-10 minutes after 3.58 ± 0.87 326.18 ± 33.13 ~33-40 minutes after 3.39 ± 0.85 311.69 ± 28.19 ~63-70 minutes after 3.22 ± 0.96 317.98 ± 29.90 2.3.2.4 Motor function All Object Hit and Avoid and pegboard scores are shown in Table 2.3. A main effect of time was found for the number of distractor shapes hit during the Object Hit and Avoid task (F(3,54) = 3.84, p < 0.05). Using a corrected p value of ≤ 0.016 to reflect three comparisons, paired samples t-tests showed that, compared to the pre-fatigue value, significantly more distractor shapes were hit during the testing block started ~10 minutes after performance of the N-back task (t(19) = -2.87, p < 0.01) but not ~40 minutes (t(19) = -2.11, p = 0.048) or ~70 minutes (t(19) = -2.37, p = 0.029) after the task. A main effect of time was found for the number of target shapes hit during the Object Hit and Avoid task (F(3,54) = 3.81, p < 0.05), with post-hoc analysis revealing an increase for the block initiated at ~70 minutes post-task compared to pre-task (t(19) = -2.98, p < 0.01). Scores on the pegboard task showed a main effect of time for trials with the dominant hand (F(3,54) = 15.78, p < 0.01) and both hands (F(3,54) = 5.73, p < 0.01). Paired samples t-tests showed that participants placed significantly fewer pegs with their dominant hand (t(19) = 4.84, p < 0.01) and with both hands (t(19) = 2.64, p < 0.016) in the testing block directly after (at ~12 minutes) compared to before the N-back task. 32 Table 2.3 Mean and standard deviation values for motor function tasks initiated before or after the 60-minute N-back task. Asterisks indicate values significantly different to those prior to the fatiguing task. Object hit and avoid task Pegboard task Time relative to N-back Target hits (%) Distractor hits (%) Dominant hand (pegs placed) Both hands (pegs placed) Before 77.7 ± 8.3 4.4 ± 3.2 16.1 ± 2.1 12.8 ± 1.9 ~10-15min after 76.2 ± 7.2 7.2 ± 5.0* 14.8 ± 1.9* 12.4 ± 1.7* ~40-45min after 77.5 ± 8.3 6.2 ± 3.7 15.7 ± 1.8 12.8 ± 1.5 ~70-75min after 80.2 ± 7.4* 5.7 ± 3.9 16.3 ± 1.8 13.0 ± 1.5 2.3.2.5 Rating of perceived fatigue Participants reported increased mental fatigue (F(2,41) = 29.34, p < 0.01) after the N-back task, compared to the pre-task ratings. This effect was significant for testing blocks that began immediately after the task (t(18) = -7.94, p < 0.01), 30 minutes after the task (t(18) = -5.90, p < 0.01), and 60 minutes after the task (t(18) = -4.16, p < 0.01). 2.4 Discussion An increase in the level of fatigue during the N-back task was supported by increases in alpha power activity, aversion to the task and perceived fatigue. A slow (at least 60 minute) recovery from fatigue was supported by some (alpha and theta activity as well as perceived fatigue) but not all variables (ERP amplitude as well as gross and fine motor function). Although there is evidence for a mentally-fatigued state during and after performance of a 60 minute N-back task, not all neural and functional variables are affected to the same degree. 2.4.1 Development of fatigue (N-back task) Perceived fatigue and aversion to the task increased significantly from Block 1 (0-15 minutes) to Block 2 (15-30 minutes) and remained elevated for the duration of the task. However, alpha power was not elevated relative to Block 1 until Block 3 (30-45 minutes). These results suggest 33 that mental fatigue, as determined by both subjective ratings of fatigue and neurophysiological indicators, can be achieved within 45 minutes on an N-back task (Ishii et al., 2013; Shigihara et al., 2013; Tanaka, Shigihara, Ishii, et al., 2012). Multiple studies support our finding of increased alpha activity during cognitively-demanding tasks (Boksem et al., 2005; Lim et al., 2013; Wascher et al., 2014; Zhao et al., 2012). Greater alpha activity may reflect an increased effort to maintain an alert state via inhibition of cortical regions that are not responsible for task performance, as is required during an eyes-open rest condition or during performance of an N-back task (Klimesch, 1999). An anterior shift of alpha activity has been shown to indicate a state of drowsiness (Lal & Craig, 2000) and has been proposed as an index of mental fatigue (Barwick et al., 2012; Trejo et al. 1995). However, Barwick and colleagues (2012) showed an increase in alpha activity across frontal, central, parietal and occipital regions following a fatiguing Stroop Interference task, with the greatest increase observed over the parietal region, rather than the expected frontal regions. The cognitively demanding Stroop Interference task is sensitive to fatigue effects such as declines in alertness or vigilance (Barwick et al., 2012). It is possible that the anteriorization of alpha activity reflects a state of tiredness or drowsiness, whereas an increase in alpha activity in posterior regions reflects mental fatigue due to a cognitively demanding task such as the 2-back. This may help to explain the increase in alpha activity across parietal regions in the current study but lack of anteriorization. No accuracy or reaction time changes were found throughout the 60-minute 2-back task in the current study. These results align with the data of Shigihara and colleagues (2013), which showed no significant changes in reaction time or accuracy, other than reduced accuracy during one 5-minute section in the first half of a 30-minute 2-back task. These results are also supported 34 by Hopstaken and colleagues (2015), who reported no increase in reaction time during a 2-hour 2-back task. However, they did find reduced d-prime scores. Thus, it is possible that 60 minutes is not long enough to elicit behavioural changes, despite changes in neural activity. More specifically, it is possible that neural mechanisms are compensating for the fatigue that is occurring, and thus, maintained behaviour was possible. 2.4.2 Recovery of mental fatigue (resting state, go/nogo, motor tasks) The recovery of fatigue was investigated using: a) the participant’s alpha and theta power at rest; b) accuracy and reaction time on a GNG task; c) P3 and N2 amplitudes in go vs. nogo trials; d) manual dexterity and gross motor function; and e) perceived ratings of fatigue. Interestingly, the resting alpha and theta power values immediately after the N-back task were not different to the pre-fatigue values. This finding appears to conflict with previous studies (Barwick et al., 2012; Boksem et al., 2005; Tanaka, Shigihara, Ishii, et al., 2012) and the reason for this discrepancy is not clear. It is possible that the N-back task was so mentally taxing that participants made little effort to maintain attention on the fixation cross during the rest period immediately after task completion. However, there was increased neural activity at 30 and 60 minutes post-task (alpha vs. alpha and theta, respectively). As far as we are aware, this is the first study to assess frequency activity this far into the recovery period following a mentally-fatiguing task; hence, we cannot support or refute published findings. These results may indicate that individuals were continuing to fatigue following completion of the task but could also reflect an influence of sleepiness or underarousal due to diurnal variation and/or the sedentary nature of the recovery period; however, the latter explanations seem unlikely for three reasons. First, it is unlikely that variations in time of day affected our results, given that Higuchi and colleagues (2001) reported reduced sleepiness and improved reaction time and error rates from 8 to 11am, 35 results that contrast our findings across a similar time frame. Thus, based on the data Higuchi and colleagues (2001), the circadian rhythm may even have blunted the task-induced impairments. Second, the testing blocks started 0, 30, and 60 minutes post-task lasted ~15 minutes, so only minutes 15-30 and 45-60 were spent sitting quietly. Third, under arousal commonly leads to a vigilance decrement (e.g., slowed reaction time or increased error rates) with tasks requiring sustained attention (Haubert et al., 2018; Pattyn et al., 2008). We did not observe slowed reaction time or reduced accuracy during the fatiguing N-back task or the GNG task of the recovery period. The lack of behavioural impairments suggests that the increases in neural activity are more likely due to other cognitive effects of the task, rather than reduced vigilance/underarousal. Specifically, the increases in alpha and theta activity might indicate a compensatory mechanism in order to maintain behavioural performance and attention/arousal (Klimesch, 1999; Tanaka, Shigihara, Funakura, et al., 2012; Trejo et al., 2015; Wascher et al., 2014). While behavioural and EEG frequency data provide meaningful information about one’s level of attentiveness and neural state, respectively, ERP results can provide important information on the efficiency of these neural processes resulting in a given behavioural response. The P3 component has been related to many cognitive functions, including decision making, working memory, orienting, executive function, as well as motor and somatosensory functions (Barry et al., 2020). Further, studies have shown that the P3 component amplitude is highly related to attentional resources (Herrmann & Knight, 2001; Sur & Sinha, 2009). More specifically, increases or decreases in this later-stage ERP component reflect changes in top-down attentional processes, rather than stimulus-driven bottom-up attentional processes, as reflected in early ERP components (Corbetta & Shulman, 2002). Specific to the GNG task, a 36 large difference in P3 component amplitude between the go and nogo trials (i.e., nogo > go) indicates a more effective selection process, such that fewer neural resources are devoted to attending to irrelevant stimuli. A reduction in the efficiency of this top-down selective attention could result in a decreased ability of subjects to focus their attention on task-relevant items, resulting in reduced accuracy. However, we did not find reduced accuracy scores post-task compared to pre-task despite a reduction in the go/nogo difference for the P3 component, as indicated by an interaction effect of time × trial. Therefore, the P3 data could reflect increased neural resources recruited for processing irrelevant stimuli in order to maintain similar accuracy scores in the presence of fatigue. No main or interaction effects were found for the N2 component amplitude. Thus, the reduced selectivity or flexibility, evidenced by reduced P3 go/nogo amplitude difference with increased time, may reflect selectivity impairments specific to motor-related processes, as opposed to conflict-related impairments that would be evidenced from changes in N2 amplitude (Bruin & Wijers, 2002; Burle et al., 2004; J. L. Smith et al., 2008). Results from the Object Hit and Avoid task performed ~10 minutes after the N-back task showed that, although participants hit a similar percentage of targets, they hit significantly more distractor shapes compared to pre-task. These findings suggests that although participants were able to respond to relevant stimuli, they had a more difficult time avoiding distracting or irrelevant stimuli. A fatigue-induced inability to avoid attending to distracting stimuli would increase risk in activities such as driving due to a lack of selective processing or a more automated behavioural pattern (Boksem et al., 2005). Of note, during the Object Hit and Avoid task of last testing block (~70 minutes post-task), participants hit more targets compared to pre-fatigue, possibly indicating a learning effect, which could contribute to an underestimation of the 37 influence of the fatigue task on the earlier recovery time points. Scores for the pegboard task (dominant hand and both hands) were lower during the first post-task testing block compared to the pre-task block. This impairment of fine motor skills has clear implications for tasks that require a high level of manual dexterity. 2.4.3 Conclusion This study is the first to simultaneously assess changes in alpha and theta activity, ERP component amplitude, perceived fatigue, as well as motor function both during and after a mentally-fatiguing task. Subjective (increased ratings of perceived fatigue and task aversion) and objective (increased alpha power) data indicate that mental fatigue was induced after 30-45 minutes of N-back task performance. Further, perceived fatigue as well as alpha and theta power were elevated for at least 60 minutes post-task. Inefficiencies in neural mechanisms underlying the processing of relevant and irrelevant information were also identified by the time × trial interaction effect on the P3 component. Gross and fine motor skills were impaired for at least 15 minutes post-task; however, these functional measures had a more rapid recovery than perceived fatigue as well as alpha and theta power. These novel findings of a differential time course of recovery for neural activity and functional outputs following an acute mentally-fatiguing task show the importance of using multiple objective neural assessments to determine one’s level of fatigue. h 38 Chapter 3: Neural effects of sleep deprivation on inhibitory control and emotion processing Justine R. Magnuson, Hogun J. Kang, Brian H. Dalton, Chris J. McNeil Published: Magnuson, J. R., Kang, H. J., Dalton, B. H., & McNeil, C. J. (2022). Neural effects of sleep deprivation on inhibitory control and emotion processing. Behavioural Brain Research, 426, 113845. Abstract: Sleep deprivation is commonplace and impairs memory, inhibition, cognitive flexibility and attention. However, little is known about the neurophysiological impact of sleep deprivation in the context of go/nogo (GNG) task performance and emotion processing. To address this knowledge gap, 12 females performed two computerized GNG tasks (shapes; emotional facial expressions) and an object hit and avoid (OHA) task after a night of typical sleep and 24 hours without sleep. Electroencephalographic (EEG) recordings were taken during a 3-minute eyes-open resting period as well as during GNG task performance. Resting EEG power in the theta band was 33% higher for the sleep-deprived than control condition (p < 0.05), whereas alpha activity was unchanged. When sleep deprived, participants had ~6% slower response times (go trials) and made ~7% more total errors during GNG tasks (p < 0.05). Reaction time and overall accuracy were ~25% and ~9% worse for the emotional compared to shape GNG task (p < 0.05), respectively, which suggests interference of emotion processing on task performance. Smaller differences in amplitude between go and no-go trials for the N2 and both the N2 and P3 event-related potential components were found during sleep deprivation for the emotional and shape GNG tasks, respectively (p < 0.05). No changes to the N170 component were found. Lastly, participants hit more distractors during the OHA when sleep deprived (p < 0.05). Altogether, 39 these results indicate sleep deprivation slows neural processing and impairs inhibitory task performance, possibly due to a more bottom-up, stimulus-driven approach to inhibiting motor responses. 3.1 Introduction Sleep deprivation leads to impaired indices of executive function, including creativity, planning, verbal fluency, and driving performance (Boonstra et al., 2007). Further, electroencephalography (EEG) recordings from the brain often show an increase in resting theta (4-8Hz) power for a sleep-deprived compared to control state (Cajochen et al., 1995; Finelli et al., 2000). A shift from higher frequency activity toward theta activity, especially in the frontal cortex (a region that is highly important for regulating top-down processing and attention) is suggested to reflect slowing of neural processing and has been correlated with impaired performance on inhibitory and visuomotor control tasks (Bernardi et al., 2015). Although commonly studied, the effect of sleep deprivation on alpha activity is unclear, with evidence of increased (Boonstra et al., 2005) or decreased alpha activity in frontal cortical regions (Verweij et al., 2014). High alpha activity may indicate suppression of task irrelevant brain regions or overall reduced brain activity, whereas low alpha activity has been shown to reflect active neuronal processing (Goldman et al., 2002; Klimesch et al., 2007). Another change to the EEG signal observed with sleep deprivation is reduced later-stage event-related potential (ERP) component amplitudes (Gosselin et al., 2019; Jin et al., 2015; Liu et al., 2015; Qi et al., 2010; Renn & Cote, 2013). The N2 and P3 ERP components reflect top-down attention-related neural processing. Specifically, the N2 depicts conflict detection or monitoring and plays an important role in the inhibition of a planned motor response, with increased N2 amplitude correlated with more successful response inhibition (Folstein & Van 40 Petten, 2008). The P3 represents attention-related, evaluative and updating mechanisms (Huster et al., 2013) and may also reflect inhibitory processes related to the actual inhibition of the motor response (Smith et al., 2008). Thus, these components are elicited during inhibitory control tasks whereby participants must inhibit a prepotent response. Sleep deprivation leads to a reduced P3 amplitude during various inhibitory control tasks (Boonstra et al., 2007; Jin et al., 2015; Liu et al., 2015; Qi et al., 2010). The influence of sleep deprivation on the N2 component is unclear as there are reports that amplitude is reduced (Qi et al., 2010; Renn & Cote, 2013), latency is increased (Jin et al., 2015), or there are no changes to amplitude or latency (Liu et al., 2015). Although studies have assessed the effects of sleep deprivation on impulsivity and inhibitory tasks such as the GNG task, no study has assessed neural indicators of inhibitory control to emotional face stimuli in a sleep-deprived state. Evaluation of the influence of sleep deprivation on emotion processing is important because an ability to regulate emotional responses and extract emotional content from facial expressions is necessary for positive human interaction. An emotional GNG task not only provides information on inhibitory control but also on one’s ability to process emotional stimuli and any interference that these stimuli have on response inhibition (Connell et al., 2020; Schulz et al., 2007). Despite the lack of literature on neural responses to an emotional GNG task after sleep deprivation, there is evidence of altered neural activity in the prefrontal and anterior cingulate cortices in response to emotional stimuli (including objects and scenes) (Gujar et al., 2011; Yoo et al., 2007; J. Zhang et al., 2019b). It has also been reported that sleep deprivation led to increased amplitude of the N170 ERP component (Cote et al., 2014), which reflects the perception/structural encoding of facial features (Cote et al., 2019; Eimer, 2012). Thus, there may be important neural consequences of sleep deprivation 41 on impulse/inhibitory control in the context of emotional face processing. However, no study has assessed the effect of emotion processing on response inhibition. There is plenty of evidence that indicates basic motor tasks (e.g., simple reaction time) are impaired in a sleep deprived state (Cain et al., 2011; Lorenzo et al., 1995; Van Den Berg & Neely, 2006); yet, very few studies have assessed the effects of sleep deprivation on complex motor-related tasks (Skurvydas et al., 2020; Wickens et al., 2015). Recently, Skurvydas and colleagues (2020) reported that 24 hours of no sleep did not affect performance on a speed-accuracy movement task of the dominant arm. While this task evaluated aspects of visuospatial attention, rapid decision-making, visuomotor control and cognitive processing, greater insight could be gleaned by the Object Hit and Avoid (OHA) task of the KINARM, which assesses these attributes under more complex conditions as well as inhibitory control and bilateral arm coordination (Bourke et al., 2016; Mang et al., 2018). Impairments on such a task would provide insight into one’s ability to avoid distracting information and focus on relevant cues. This is the first study of sleep deprivation to assess the neurophysiological underpinnings of emotion processing and any interference on response inhibition. Further, this study examines a myriad of attributes measured by the OHA task, including combining motor and cognitive assessments into a single task. This is rarely accomplished by other tasks and has important consequences for situations such as driving that require rapid and complex motor decisions (Bourke et al., 2016). These measures were also supplemented with traditional EEG indices, including neural frequency activity at rest and responses to a shape GNG task, to provide a meaningful objective assessment of the effects of sleep deprivation on sensorimotor and cognitive skills. Specifically, measuring the effects of sleep deprivation via ERP responses (e.g., N2, P3 and N170 components) can provide a more comprehensive assessment of possible neural 42 changes in response to a lack of sleep, including the mechanisms underlying our ability to detect conflict and inhibit prepotent motor responses with and without the added cognitive demand of processing emotions as detected through the N170. Further, assessing neural frequency at rest provides an ongoing, non-task related measure of neural state. We expected that sleep deprivation would result in less efficient neural processing, reflected by reduced ERP component amplitudes and increased alpha and theta activity, as well as accuracy impairments and delayed response times on all inhibitory motor control tasks. 3.2 Methods 3.2.1 Participants Data were collected from 12 healthy female participants between 18 and 27 years of age (22.8 ± 2.7). Participants gave written informed consent prior to testing, which took place during two randomized, single sessions of ~45 minutes each. Testing was scheduled to begin ~1 hour after a participant’s typical wake time and the start time (6:00-9:00am) was matched for the two sessions. All participants reported a stable, long-term sleep pattern with an average of 6-8 hours per night (e.g., 6.6 ± 0.8 for the three nights before control testing). Further, with a score of 4.8 ± 1.4 on the Pittsburgh sleep quality index (PSQI), on average, participants had good sleep quality (PSQI < 5) over the month prior to the first session. For the sleep deprivation condition, participants reported to the testing site in the evening of the day before data collection. During the sleepless night, participants only engaged in sedentary activity and were monitored by study investigators. All participants refrained from consuming caffeinated beverages for at least 12 hours prior to each session. The order of sleep deprivation and control test sessions were randomized across participants with five participants starting with the sleep deprivation session. The study protocol was approved by the Clinical 43 Research Ethics Board of the University of British Columbia (H19-01274) and conformed to the Declaration of Helsinki, except for registration in a database. Exclusion criteria for the study were: a recent head injury (concussion); medications with side effects of dizziness, lack of motor control, or slowed reaction time; a myopathic condition or motor neuropathy; a sleep disorder (e.g., insomnia). 3.2.2 Experimental setup For the electroencephalograhy (EEG) tests, participants sat in a windowless room with a laptop computer (12.3 × 6.9-inch display) positioned ~60 cm in front of their face. The EEG data were acquired using the 8-channel Enobio EEG system with NG geltrodes (NE010WF and NE032, respectively; Neuroelectrics, Barcelona, Spain) and electrode gel (SignaGel; Parker Laboratories, Fairfield, NJ). Brain activity was recorded via NG geltrodes (Ag/AgCl; 12mm diameter) at the Fz, Cz, P3, Pz, P4 and Oz positions and the electrooculogram (EOG) was monitored with Ag/AgCl electrodes (24mm diameter; NE025; Neuroelectrics) placed above and beside the left eye. A reference electrode was placed on the right ear lobe. The EEG and EOG activity were sampled at 500 Hz. For executive and gross motor function assessment, participants stood in front of a KINARM End-Point Lab (BKIN Technologies Ltd., Kingston, Canada). 3.2.3 Experimental tasks Ongoing neural frequency activity was assessed from EEG recordings taken at rest, which required participants to sit still while focusing their gaze on a fixation cross for 3 minutes. To assess inhibitory control, participants completed two computerized GNG tasks (the first with shapes and the second with pictures of emotional faces obtained from the facial affect database of the Paul Ekman Group). See supplemental materials for images of task stimuli. Stimuli were presented in the centre of the laptop computer. During the shape GNG task, participants were 44 required to press the left touchpad when they observed a coloured square, and not respond when they observed a black square. For the emotional GNG task, participants were instructed to press the left touchpad key as quickly as possible when they observed a happy face on the screen but not respond when they observed an angry face. Angry faces were selected as no-go trials because of the practical implications of potentially failing to inhibit a response to stimuli that could be considered threatening in a sleep-deprived state. Unbeknownst to the participants, black squares/angry faces appeared in 20% of the 300 trials. For both tasks, colour/black squares and happy/angry faces were presented for 500ms, with a trial length ranging from 1500-1800ms. A fixation cross was presented in the center of the computer screen when the shape/face disappeared. A short latency jitter was introduced between trials to prevent alpha entrainment. Each task was continuous and lasted ~8.5 minutes. For the executive and gross motor function assessments, participants completed the standardized OHA task in the pre-packaged KINARM software. Participants stood such that a large monitor showed the virtual representation of their hands (and the objects during the task) but hid the movements of their hands and the robotic arms of the KINARM. During the OHA task, participants moved the handles of the robotic arms so that the virtual representation of their hands would hit two target objects but avoid six distractor objects. The task included 200 target and 100 distractor objects. 3.2.4 Experimental procedures Participants completed the PSQI and Piper Fatigue Scale at the beginning of their first test session, with the latter scale also completed at the start of the second session. The Piper Fatigue Scale evaluates a participant’s perceived fatigue from 1-10 (mild-severe), based on behavioural, cognitive, sensory and mood-related sensations (Piper et al., 1998), and has been 45 shown to have high construct validity (Machado et al., 2021). Similarly, the PSQI has been shown to have excellent test-retest reliability (Lu et al., 2014). For both the control and sleep deprivation condition, the participant received instructions about each task and completed practice trials (8 and 2 for the GNG and OHA tasks, respectively). The participant then completed the 3-minute resting state assessment, followed by the shape and emotional GNG tasks, and finally the OHA task. 3.2.5 Data analysis Analyses of EEG and EOG data were completed off-line using Matlab software (version 2016b). To begin, a 4th-order Butterworth bandpass filter (0.5–30 Hz) was applied to the EEG data. Next, segments containing significant muscle movements, eye movements and eye blinks, indicated by a z-value of >4 in the EEG (Fz, Cz, P7, Pz, P8, Oz) and EOG channels were discarded and replaced by cubic interpolation between segments (Jansen et al., 2012; Magnuson et al., 2021). Relative theta and alpha power were calculated as average amplitude, squared, across the resting-state recording within the ranges of 4-8 Hz and 8-12 Hz, respectively. These data were log-transformed prior to running statistical analyses. For the GNG tasks, the mean amplitudes of ERP components were calculated for accurate trials. Data were locked to the stimulus onset and a 200-ms pre-stimulus baseline correction was applied (Luck, 2014). The N2 and P3 components were measured from the Cz electrode (Vuillier et al., 2016) and, for the emotional GNG task, the N170 component was also measured from an average of P7 and P8 electrode activity. Electrode locations were chosen a priori, based on previous literature (Bokura et al., 2001; Eimer, 2012; Luck, 2014). The latency windows of maximal amplitude that were used to calculate the mean amplitude of the N2, P3, and N170 components were 200-300ms, 300-600ms, and 160-220ms respectively. These latency windows were chosen based on previous literature 46 (Eimer, 2012; Luck, 2014), as well as visual inspection of the averaged waveform constructed using data from all participants. The average number of correct trials used in the ERP analyses for the shape GNG task were 229 ± 4 (go trials) and 66 ± 6 (nogo trials) during the control test day and 215 ± 26 (go trials) and 59 ± 10 (nogo trials) during the sleep deprivation test day. For the face GNG task, 223 ± 23 (go trials) and 54 ± 10 (nogo trials) were used on the control day, and 200 ± 42 (go trials) and 53 ± 12 (nogo trials) on the sleep deprivation test day. For each GNG task, response accuracy scores for the go and no-go trials were independently assessed, as well as averaged for a single accuracy score. Go and no-go trials were assessed independently to isolate attention/response selection and inhibitory control aspects of the GNG task, respectively. However, go and no-go trials were also averaged together in order to gain a more accurate depiction of overall task performance. Reaction time on the correct go trials was also assessed during the GNG tasks. For the OHA task, participants were scored on the number of target and distractor shapes they hit, total objects hit, distraction proportion (distractors hit divided by total objects hit), and hand speed (averaged across both hands). 3.2.6 Statistical analysis All statistical analyses were performed with IBM SPSS Statistics (SPSS Inc., Chicago, IL, USA). Paired t-tests were used to assess the effects of sleep on theta power, OHA scores, and the difference in amplitude between go and no-go trials for the N170 component during emotional GNG task. Two-way repeated measures ANOVAs, with condition (control vs. sleep deprivation) and task (emotional vs. shape) as within-subject factors, were used to assess changes in GNG reaction time, accuracy, as well as the ERP difference waves (amplitude difference between go and nogo trials) for the N2 and P3 components. For GNG accuracy, we ran three separate two-way repeated measures ANOVAs: one for go trials; one for no-go trials; and one for the 47 averaged score of all trials. The shape GNG task was used as a ‘control’ so that when compared against the emotional GNG task, we can assess interference that emotional stimuli might have on performance of an inhibitory control task (MacNamara et al., 2018). We were prepared to exclude participants if more than 25% of their EEG data were contaminated by motion artifacts (Luck, 2014); however, data rejection due to artifact was minimal (10.3% for the resting state data; 5.1% for the shape GNG data; 4.3% for the emotional GNG data), so we did not need to exclude anyone due to noisy data. One participant did not complete the emotional GNG task and, due to equipment malfunction, one participant did not have useable data for the shape GNG task on the control day; thus, these participants were excluded from analyses involving the emotional and shape GNG tasks, respectively. A two-way repeated measures ANOVA was also used to assess alpha power and test for an anteriorization effect with sleep deprivation, with condition (control vs. sleep deprivation) and location (Fz, Cz, Pz, Oz) as within-subjects factors. For all ANOVAs, the Greenhouse-Geisser correction method was used for violations in sphericity. The level of statistical significance for all main and interaction effects was set at p ≤ 0.05. Sensitivity analysis calculations showed that we would have 80% power to detect effect sizes of η!" = 0.162 for the repeated measures ANOVAs and effect sizes of d = 0.766 for our paired-samples t-tests. 3.3 Results 3.3.1 Ongoing low-frequency activity The paired-samples t-test showed higher theta activity at rest during the sleep deprivation (2.68 ± 1.30 nV2) compared to the control (2.05 ± 0.90 nV2) condition (t(11) = −2.237, p = 0.047, d = 48 0.65) (Figure 3.1). No main effect of condition (F(1,11) = 0.048, p = 0.831, 𝜂#" = 0.004) or condition × location interaction effect (F(3,33) = 1.076, p = 0.373, 𝜂#" = 0.304) was found for ongoing alpha activity at rest (Figure 3.2). There was, however, a main effect of location (F(3,33) = 4.236, p = 0.038, 𝜂#" = 0.702), with the highest alpha power recorded from posterior electrodes (control: 2.26 ± 1.69 nV2; sleep deprivation: 2.60 ± 1.59 nV2). Figure 3.1 Topographical plot of the log-transformed resting-state theta activity for the control (left) and sleep deprivation (right) conditions. Yellow indicates higher power values. Resting state theta activity was higher for the sleep deprivation compared to the control condition at the Fz electrode site (* p < 0.05). Figure 3.2 Topographical plot of the log-transformed resting-state alpha activity for the control (left) and sleep deprivation (right) conditions. Yellow indicates higher power values. 49 3.3.2 Go/nogo tasks- accuracy and reaction time Accuracy and reaction time data are presented in Figure 3.3. Main effects of condition (F(1,9) = 16.569, p = 0.003, 𝜂#" = 0.648) and task (F(1,9) = 10.597, p = 0.010, 𝜂#" = 0.541) were found for the averaged GNG accuracy score, but there was no condition × task interaction (F(1,9) = 0.862, p = 0.378, 𝜂#" = 0.087). A main effect of condition (F(1,8) = 8.256, p = 0.021, 𝜂#" = 0.508) was found for go trials, but there was no effect of task (F(1,8) = 4.074, p = 0.079, 𝜂#" = 0.336) or an interaction (F(1,8) = 0.057, p = 0.818, 𝜂#" = 0.007). In contrast, no-go trials had a main effect of task (F(1,8) = 6.798, p = 0.031, 𝜂#" = 0.459) but not condition (F(1,8) = 4.658, p = 0.063, 𝜂#" = 0.368), and had no interaction (F(1,8) = 0.249, p = 0.631, 𝜂#" = 0.030). Reaction time for correct go trials had main effects of condition (F(1,9) = 8.526, p = 0.017, 𝜂#" = 0.486) and task (F(1,9) = 177.483, p < 0.001, 𝜂#" = 0.952), without an interaction (F(1,9) = 1.678, p = 0.227, 𝜂#" = 0.157). To summarize these findings, compared to the control condition, sleep deprivation had lower overall and go trial accuracy and slower reaction times for the shape and emotional GNG tasks. Overall accuracy, no-go trial accuracy, and reaction times were worse for the emotional compared to shape GNG task for the control and sleep deprivation conditions. 50 Figure 3.3 Go (G), no-go (NG) and overall (O) accuracy as well as reaction time results for the shape and emotional go/nogo tasks during the control and sleep deprivation conditions. Sleep deprivation had lower go and overall trial accuracy as well as slower reaction times for the shape and emotional GNG tasks (* p < 0.05). No-go and overall trial accuracies were also worse and reaction times were slower for the emotional compared to shape GNG task for the control and sleep deprivation conditions († p < 0.05). 3.3.3 Go/nogo tasks- ERP amplitude differences The difference in N2 component amplitude between go and no-go trials had a main effect of condition (F(1,9) = 27.333, p = 0.001, 𝜂#" = 0.752) and task (F(1,9) = 32.133, p < 0.001, 𝜂#" = 0.781), but no condition × task interaction (F(1,9) = 2.354, p = 0.159, 𝜂#" = 0.207). ERP means and standard deviations are presented in Table 3.1. 51 Table 3.1 Event related potential amplitudes (mean ± standard deviation) of the N2, P3, and N170 components during the shape and face GNG tasks. Go/happy and nogo/angry trials are presented. ERP variables Control (𝜇V) Sleep deprivation (𝜇V) Shape GNG task N2-go amplitude 0.07 ± 3.52 1.19 ± 2.50 N2-nogo amplitude -6.03 ± 5.23 -2.79 ± 3.27 P3-go amplitude 5.91 ± 2.86 5.61 ± 2.88 P3-nogo amplitude 10.62 ± 3.28 6.53 ± 2.66 Face GNG task N2-go amplitude -7.00 ± 4.61 -5.29 ± 4.57 N2-nogo amplitude -7.96 ± 5.50 -3.63 ± 4.79 P3-go amplitude 1.29 ± 3.82 0.86 ± 3.45 P3-nogo amplitude 5.62 ± 4.67 6.47 ± 4.75 N170-happy amplitude 0.17 ± 2.64 -0.40 ± 15.6 N170-angry amplitude -0.54 ± 2.84 0.71 ± 5.13 A smaller N2 GNG amplitude difference for the sleep deprivation compared to the control condition was found for both the shape (Figure 3.4a) and emotional (Figure 3.4b) GNG tasks. Additionally, GNG amplitude difference was smaller for the emotional task compared to the shape task for both the control and sleep deprivation conditions. GNG amplitude difference of the P3 component had a condition × task interaction (F(1,9) = 7.174, p = 0.025, 𝜂#" = 0.444), but no main effect of condition (F(1,9) = 0.986, p = 0.347, 𝜂#" = 0.099) or task (F(1,9) = 0.277, p = 0.611, 𝜂#" = 0.030). Specifically, the GNG amplitude difference was smaller in the sleep-deprived compared to the control state for the shape task (t(10) = 5.680, p < 0.001, d = 1.713), but not different between conditions for the emotional task (t(10) = 0.290, p = 0.777, d = 0.088). During the emotional task, the difference in N170 component amplitude between go and no-go 52 trials was not different between the control and sleep deprivation conditions (t(10) = 1.082, p = 0.308, d = 0.306) (Figure 3.5). Figure 3.4 Mean event-related potential waveforms recorded at the Cz electrode during the (A) shape go/nogo task and (B) emotional go/nogo task for the control and sleep-deprivation conditions. GNG N2 amplitude difference was smaller for the sleep deprivation condition compared to control (* p < 0.05) and for the emotional task compared to the shape task († p < 0.05). A condition by task interaction was found for the P3 GNG amplitude difference, indicating smaller difference in the sleep deprivation condition for the shape task but not different between conditions for the emotional task (‡ p < 0.05) GNG N2 amplitude difference was smaller for the sleep deprivation condition compared to control (* p < 0.05) and for the emotional task compared to the shape task († p < 0.05). A condition by task interaction was found for the P3 GNG amplitude difference, indicating smaller difference in the sleep deprivation condition for the shape task but not different between conditions for the emotional task (‡ p < 0.05). 53 3.3.4 KINARM object hit and avoid task Although participants hit relatively few (<7%) distractor shapes during each session, the number was 50% higher for the sleep-deprived compared to control condition (t(10) = −2.994, p = 0.013, d = 0.903) (Table 3.2). Conversely, participants hit most (~77 to 80%) of the target shapes during each session, with no difference between conditions (t(10) = 1.648, p = 0.130, d = 0.497) (Table 3.2). There was also no difference between the sleep deprivation and control condition for the total number of objects hit (t(10) = 0.624, p = 0.547, d = 0.188), distractor proportion (t(10) = −1.661, p = 0.128, d = 0.501), or hand speed (t(10) = -0.175, p = 0.865, d = 0.053) (Table 3.2). Figure 3.5 Mean event-related potential waveforms recorded at an average of the P7 and P8 electrodes during the emotional go/nogo task for the control and sleep deprivation conditions. 54 Table 3.2 Key parameters of the Object Hit and Avoid (OHA) task during the control and sleep deprivation conditions. Asterisks indicate a difference between control and sleep deprivation (p < 0.05). 3.4 Discussion The purpose of this study was to assess the effects of sleep deprivation on resting brain activity, emotion processing, neural and behavioural responses to inhibitory control tasks, as well as executive and motor function. Sleep deprivation increased low-frequency brain activity and also resulted in worse accuracy scores, prolonged reaction times, and less efficient brain responses during GNG tasks. Irrespective of testing day, overall response accuracy and reaction time were worse for the emotional than shape GNG task. Sleep deprivation also impaired executive and gross motor function, as participants hit more distractor objects during the OHA task. In the present study, theta activity was ~33% higher after 24 hours of sleep deprivation than the control condition. Bernardi and colleagues (2015) hypothesized that theta waves represent an inactive, “OFF” state for neural activity, or a form of local sleep. They reported increased global theta power after 24 hours of sleep deprivation, as well as a positive correlation between theta activity and impairments on psychomotor vigilance and GNG tasks. Sleep deprivation has also been reported to increase the power density ratio between theta and beta bands (Zhang et al., 2019a). Thus, our results are in alignment with these previous findings and support an increase of low (4-8 Hz) frequency activity when sleep deprived. This finding, paired OHA variable Control Sleep deprivation Targets hit (# out of 200) 159.3 ± 18.1 153.5 ± 17.9 Distractors hit (# out of 100) 4.6 ± 4.3 6.9 ± 4.4 * Total objects hit (# out of 300) 164.0 ± 19.2 160.5 ± 17.4 Distractor proportion (%) Hand speed (m/s) 2.8 ± 2.0 0.27 ± 0.077 4.4 ± 2.7 0.27 ± 0.067 55 with the absence of an effect of sleep deprivation on alpha activity, may suggest weaker/less active top-down prefrontal regulation of subcortical areas, which could have important consequences for both the shape and emotional GNG tasks (Zhang et al., 2019a). Behavioural studies report impaired inhibitory control in sleep-deprived individuals, including greater difficulty withholding an inappropriate response (Anderson & Platten, 2011; Drummond et al., 2005). In other words, participants became more impulsive with a lack of sleep. In our study, sleep deprivation led to lower overall task accuracy on both GNG tasks. Data from the shape GNG task provide important information about the overall inhibitory task performance abilities of individuals in a sleep-deprived state; however, data from the emotional GNG task are particularly meaningful because so little is known about the effect of sleep deprivation on emotional interference/biasing of responses to an inhibitory control task (K. P. Schulz et al., 2007). Our findings of lower overall accuracy and slowed reaction times for the emotional compared to the shape GNG task suggests an emotional biasing caused by processing emotions, making task performance more difficult. However, the lack of a condition × task interaction suggests this interference was not exacerbated by sleep deprivation. In the context of the emotional GNG task, slower reaction time and lower overall accuracy for sleep deprivation compared to control suggests slower processing and reduced selectivity/enhanced reactivity to negative stimuli (nogo/angry trials) (Gujar et al., 2011). The independent analysis of go and no-go trials showed impaired accuracy on go trials in a sleep-deprived state for both tasks, but no main effect of condition for the no-go trials (p = 0.063), which could be interpreted to mean that inhibitory control itself is not impaired with sleep deprivation. However, poorer performance of go trials could contribute to an underestimation of the impairment of no-go trial performance. That is, slower reaction time and lower accuracy for go trials suggests poorer sustained attention 56 (Bauer et al., 2020; Smallwood, 2013), which means accuracy for no-go trials may be artificially high due to lapses of attention rather than true response inhibition. Thus, in addition to reductions of go trial and overall task performance, we believe it is likely that sleep deprivation also led to impairments related specifically to inhibiting a motor response. In order to shed light on the neural mechanisms underlying the disparities for accuracy and reaction time across conditions and tasks, we assessed the difference in amplitude between go and no-go trials for later-stage ERP components (N2 and P3 for the shape task; N2, P3, and N170 for the emotional task). We found a smaller N2 go vs. no-go amplitude difference for the emotional compared to the shape GNG task, as well as for the sleep-deprived compared to the control condition for both tasks. For P3, go vs. no-go amplitude difference was smaller for sleep deprivation than control for the shape task but not different between conditions for the emotional task. Finally, N170 amplitude did not differ between conditions for the emotional GNG task. Compared to the shape task, the emotional task had worse overall and no-go trial accuracy, slower reaction time, and a smaller N2 go vs. no-go amplitude difference, which suggests increased competition for resources due to additional demands of processing emotional content in the emotional GNG task. It is possible that individuals devoted more cortical resources to the perceptual processing of emotions than conflict monitoring, resulting in a smaller N2 go vs. no-go amplitude difference and impaired inhibitory control (Yang et al., 2014; Zhang et al., 2016). Specifically, in a GNG task involving emotion processing, emotional and motor response inhibition may coexist and coactivate brain regions such as the anterior cingulate cortex. It is also possible there is reduced functional connectivity between the amygdala and prefrontal brain regions, which are known to mediate cognitive control against emotional distraction (Chuah et al., 2010). N2 go vs. no-go amplitude difference was smaller in the sleep-deprived than control 57 state for both GNG tasks, without a condition × task interaction, which suggests less efficient differentiation of relevant shape/emotional stimuli in the context of motor inhibition; i.e., general impairments in conflict monitoring. Behaviourally, for the emotional GNG task specifically, sleep deprivation may lead to an impaired ability to accurately identify and select appropriate responses to an inhibitory control task in the context of a threatening or angry stimulus. A large P3 amplitude difference for go vs. no-go trials indicates more effective/selective attention and a devotion of conflict-monitoring processes to task-relevant items (i.e. fewer neural resources are devoted to attending to irrelevant stimuli). Hence, the smaller P3 go vs. no-go amplitude difference for sleep deprivation compared to control for the shape GNG task, in addition to lower go trial and overall accuracy scores, suggests less efficient neural processing/attention, or more bottom-up processing. This inefficiency is possibly due to reduced prefrontal cortex activation of the cholinergic system, effectively reducing or fragmenting the P3 component (Bobić et al., 2016; T. W. Boonstra et al., 2007). Other studies also report reduced P3 amplitude during a shape or number GNG task after sleep deprivation (Jin et al., 2015; Liu et al., 2015; Qi et al., 2010). The finding that sleep deprivation led to a smaller go vs. no-go P3 amplitude difference for the shape but not emotional GNG task may indicate that, even in a sleep-deprived state, considerable neural resources were committed to the emotional task. As overall accuracy and reaction time were worse for the emotional than shape GNG task but there were no condition × task interactions, perhaps the high resource allocation during sleep deprivation prevented a further decay in performance relative to the shape GNG task. The sensitivity of the N170 component to the emotional content of facial expressions is not well known; however, it is suggested that the N170 amplitude will be much larger in response to negative/angry (no-go) trials compared to happy (go) trials (Hinojosa et al., 2015). 58 Interestingly, N170 go vs. no-go amplitude difference during the emotional GNG task was not affected by sleep deprivation, which does not align with a report of increased amplitude for fearful and angry faces when perceptual difficulty increased after 31.5 hours of sleep deprivation (Cote et al., 2014). This disparity between findings may be due to differences in the task (categorization of emotional expressions vs. emotion perception in the context of response inhibition) or the duration of sleep deprivation (31.5 vs. 24 hours). For the OHA task, participants hit more distractor objects during the sleep-deprived than control state. Together with ERP data from the GNG tasks, this impairment of executive motor function supports the idea that sleep deprivation results in functional impairment of the prefrontal cortex (Skurvydas et al., 2020). A more bottom-up or reactive/automated response, as indicated by the reduced ability to avoid distracting stimuli in a sleep deprived state, could have negative consequences during performance of tasks such as driving. The OHA task measures many skills (Bourke et al., 2016; Mang et al., 2018), so the lack of a difference between conditions for target hits, total hits, distractor proportion, and hand speed indicates that some cognitive and sensorimotor functions are not impaired by 24 hours of sleep deprivation. 3.4.1 Considerations According to the Centers for Disease Control and Prevention (CDC), at least 7 hours of sleep is required for optimal health and well-being (CDC, 2011); thus, although sleep duration the night before the control session (6.6 ± 0.8 hours) was reported as typical for our participants (undergraduate students), it may not be typical of the general population. Further, sleep duration and quality on the nights prior to testing were measured based on self-reports, which are less accurate than physiological measures (e.g., actigraphy). Another consideration is that the emotional GNG task compared angry faces to happy rather than neutral faces. This was done to 59 test emotional differentiation (angry vs. happy faces) rather than basic emotional processing (angry vs. neutral faces). However, this potentially lessened any effects related to heightened reactivity and neural processing of angry faces that might be seen in a sleep-deprived vs. rested state. The order of GNG tasks (shape then emotional) could have resulted in increased mental fatigue during the emotional GNG task; however, we believe this is unlikely given the relatively short duration of each task (~8.5 minutes) and break between tasks. Our sample size of 12 individuals was relatively small and included only females. To address the small sample size, a sensitivity analysis calculation was performed, with the result that we were underpowered to detect small effects (see Statistical analysis section). Further, the relatively low power increases the likelihood of inflated effect sizes for significant effects. With respect to only female participants, this study was part of a larger project that had neuromuscular measures known to have sex-based differences. As such, due to the challenges of conducting sleep deprivation research, we elected to test only one sex and, owing to underrepresentation in studies of human physiology, we decided to study females. Although we are unaware of sex-based differences for any of the measures taken in the current study, additional research should be conducted to determine whether sleep loss negatively impacts males to a similar degree. 3.4.2 Conclusion This is the first study to concurrently measure both neural and behavioural responses to two types of GNG tasks, ongoing brain activity at rest, as well as sensorimotor and cognitive function during a complex motor task to obtain a comprehensive understanding of the neural mechanisms that underlie impaired task performance with sleep deprivation. Go trial and overall task performance was worse for both GNG tasks in a sleep-deprived than control condition, and the EEG data suggest less efficient differentiation of relevant task stimuli in the context of motor 60 inhibition. Moreover, no-go and overall task performance was worse and neural efficiency was lower during the emotional compared to the shape GNG task, suggesting an imbalance between increased emotional processing and less effective prefrontal inhibitory processing and regulation. Results from the OHA task indicate that individuals are less able to inhibit/avoid making a response to distracting information in a sleep-deprived than rested state. When considered with the higher theta activity at rest, these results suggest that sleep-deprived individuals have slower neural processing, and are more likely to respond inappropriately to both relevant and distracting emotional or non-emotional information. 61 Chapter 4: Cortical and neuromuscular responses to matched-torque and matched-EMG fatiguing contractions Justine R. Magnuson, Christina D. Bruce, Brian H. Dalton, Chris J. McNeil Abstract: During a sustained, submaximal matched-torque fatiguing contraction, neural drive is increased to overcome contractile impairments and maintain the requisite muscle output. As many evoked indices of neural excitability are influenced by the relative activation of the motoneuron pool, it can be difficult to isolate fatigue-related changes to excitability. For this reason, it is becoming increasingly common to investigate fatigue using a sustained contraction with constant output from the motoneuron pool; i.e., matched electromyographic (EMG) activity. However, it is unknown how motor unit (MU) behaviour and coherence between the cortex and active muscles are modulated as fatigue develops during a matched-EMG contraction. On two separate visits, 16 participants performed a sustained 10-min isometric elbow flexion contraction at 20% maximal voluntary contraction (MVC) torque or at the level of integrated biceps brachii EMG recorded at 20% MVC torque. Four fine-wire electrode pairs were inserted into biceps brachii to record single MU activity. Electroencephalographic and surface EMG recordings were also obtained from the somatosensory cortex and biceps and triceps brachii, respectively. Corticomuscular (CMC) and intermuscular coherence (IMC), voluntary activation (VA), torque/EMG steadiness and MU discharge rates were assessed throughout the protocol. For the matched-torque contraction only, CMC, IMC, and MU discharge rates increased by task termination, while torque steadiness and VA were reduced. These findings suggest that during a matched-torque contraction, the nervous system may enhance rhythmic drive and increase MU discharge rate to maintain torque production at the cost of torque steadiness. However, during a 62 matched-EMG contraction, when motoneuron pool output is clamped, the nervous system may prefer other strategies to perform the fatiguing contraction, such as MU rotation. 4.1 Introduction Physiological fatigue is often described via a torque-fatigability relationship, such that during exercise, torque and endurance time seem to exert interactive effects on each other (Enoka & Stuart, 1992). Impairments to maximal torque generating capacity induced by submaximal fatiguing contractions are attributed to changes within the central nervous system as well as the muscle (Hunter et al., 2006; Smith et al., 2007; Søgaard et al., 2006; Taylor et al., 1996). Experimentally, central fatigue is evidenced by a decrease in voluntary activation (VA) (Gandevia, 2001), and is likely mediated by a myriad of events, including changes in cortical and motoneuronal excitability as well as muscle afferent feedback (McNeil et al., 2009, 2011; Taylor et al., 2016). However, the mechanisms underlying these changes remain largely unknown. To more directly examine neural changes underlying voluntary control of the muscle in the context of fatiguing exercise, electroencephalography (EEG) and electromyography (EMG) can be used in combination to assess descending cortical oscillatory drive and ascending feedback through a measure called corticomuscular coherence (CMC) (Héroux & Gandevia, 2013; Liu et al., 2019). CMC provides information on the specific oscillations transmitted by corticospinal axons between the motor cortex and muscle (Baker, 2007), which may relate to the control of torque (torque steadiness) (Kristeva-feige, 2002) and/or modulation of fatigue (Wang et al., 2015; Yang et al., 2009). Similarly, it is possible to measure common frequency activity between muscles using a measure called intermuscular coherence (IMC). As fatigue develops, the central nervous system may increase common input to different muscles involved in the 63 contraction in order to maintain torque production (McManus et al., 2016; Tecchio et al., 2006; Ushiyama et al., 2011). Despite the usefulness of the CMC measure in assessing the functional connection between the cortex and muscles, it does not directly measure motor unit (MU) behaviour such as MU discharge rates, a primary determinant of torque production. Intramuscular EMG recordings are the gold-standard technique to measure MU characteristics, including recruitment threshold, discharge rate, etc. Even with intramuscular recordings of single MUs, it is unclear what happens to MU discharge rate during a fatiguing submaximal protocol because the data are equivocal. That is, when torque is maintained, multiple studies have reported a decrease (Bigland-Ritchie et al., 1986; Carpentier et al., 2001; Garland et al., 1994) or increase in discharge rate (Griffin et al., 2001; Kuchinad et al., 2004; Maton & Gamet, 1989), whereas at least one reported an initial decrease then increase with fatigue (Adam & De Luca, 2005). It is not known how discharge patterns of individual MUs are affected by a matched-EMG fatiguing contraction. The main aim of this study was to assess CMC, IMC, individual MU discharge rates, and VA after two types of sustained isometric tasks. One fatiguing protocol required the participant to match a torque target, leading to increased motoneuron pool output to compensate for impaired function at spinal and peripheral levels. The second protocol involved sustaining the level of integrated EMG recorded at the torque target of the first protocol, allowing for maintained output of the motoneuron pool throughout the task. Although the latter protocol has been used to assess neural excitability without the confound of increasing motoneuron pool output, no attempt has been made to record single MU discharge rates or CMC/IMC. By applying both types of protocols, I assessed, for the first time, important macroscopic neural mechanisms and motoneuron discharge strategies that are at play during a matched-torque vs. 64 matched-EMG task. As evidence of increased fatigue with time on task, I expected to see increased IMC and CMC, and reduced MVC torque and VA for both fatiguing protocols. I hypothesized that, compared to the matched-EMG protocol, the matched-torque protocol would elicit: a) greater ratings of perceived effort (RPE), b) greater CMC and IMC, c) greater impairment of maximal strength, and d) a greater decline in VA from pre- to post-fatigue. Because of the absence of (matched-EMG) or conflicting nature of (matched-torque) findings for MU discharge rates during fatigue, I did not form a hypothesis for the intramuscular EMG data. 4.2 Methods 4.2.1 Participants Eighteen healthy individuals volunteered for the study; however, the presented data are from 16 participants because two males were unable to complete the 10-min matched-torque contraction. Of these 16 participants, 8 were female (age: 28.0 ± 4.3y; height: 166.1 ± 6.1cm; mass: 59.0 ± 4.2kg) and 8 were male (age: 33.5 ± 8.6y; height: 179.1 ± 4.9cm; mass: 76.1 ± 10.4kg). The two fatiguing contractions were performed no closer than 48 hours apart and were completed at approximately the same time of day. A TMS screening questionnaire was also completed by participants prior to data collection to ensure eligibility. Participants gave written informed consent prior to testing, and all procedures were approved by the local institutional clinical research ethics board (H19-03310). 4.2.2 Experimental setup Participants were seated with their dominant arm secured to an isometric myograph used to record elbow flexor torque via a linear strain gauge (SBO300; transducer Techniques, Temecula, CA). Torque signals were sampled at 1,000Hz. The EEG activity was recorded from an 8-65 channel system (Enobio; Neuroelectrics, Barcelona, Spain) with electrodes placed over C3, C1, Cz, C2, C4 and Pz positions. The last two channels recorded from adhesive electrodes placed above and beside the left eye to capture EOG activity (eye movements and blinks). EEG and EOG activity were sampled at 500Hz and stored to the computer using Neuroelectric’s NIC2 software. A reference electrode was placed over the right earlobe. As a measure of global muscle activation of biceps and triceps brachii, surface EMG data were collected via self-adhesive Ag-AgCl electrodes (10mm diameter, Cleartrace, Conmed, Utica, NY) arranged in a bipolar fashion over each muscle belly (centre-to-centre spacing of 2cm). Activity of individual MUs was recorded using fine-wire electrodes inserted into the muscle belly of both the long and short heads of biceps brachii (i.e., in total, there were three or four pairs of fine-wire electrodes). Surface EMG signals were pre-amplified (×100), band-pass filtered (10-1,000Hz) and sampled at 2,000Hz using CED 1902 amplifiers (Cambridge Electronic Design Ltd., Cambridge, UK), whereas intramuscular EMG signals were pre-amplified (×1,000), band-pass filtered (10-10,000Hz) and sampled at 20,000Hz. For the matched-EMG protocol, the surface EMG signal from biceps brachii was also rectified and integrated using a 100-ms time constant (NeuroLog System NL124A module; Digitimer, Welwyn Garden City, UK). Torque and EMG signals were stored to a computer using a 16-bit A/D converter (CED 1401 Plus) and Spike2 software (version 8; Cambridge Electronic Design). A computer monitor was used to provide visual feedback of the elbow flexor torque on the matched-torque day as well as the surface EMG signals on the matched-EMG day. Participants also had a clearly visible graphic of a 0-10 Borg Scale placed in front of them, which was used to assess the rating of perceived effort (RPE) needed to maintain the target, not rate discomfort or pain in biceps brachii. 66 Brachial plexus stimulation (BPS): To record the maximal compound muscle action potential (Mmax) in biceps and triceps brachii, a constant current stimulator (DS7AH, Digitimer) was used to deliver single, square-wave electrical stimuli (200μs pulse width, ≤400V) to the brachial plexus at Erb’s point. The cathode and anode (Cleartrace electrodes) were placed in the supraclavicular fossa and over the acromion, respectively. Transcranial magnetic stimulation (TMS): To assess elbow flexor voluntary activation, a circular coil (13.5cm outside diameter), attached to a Magstim 2002 stimulator (Magstim, Whitland, UK), was placed over the vertex (middle of the head). The stimulus intensity was set to evoke the largest elbow flexor superimposed twitch torque during a brief (~2s) contraction at 50% of maximal torque, while maintaining the amplitude of the triceps brachii motor evoked potential to ≤15% Mmax. The selected TMS intensity was applied again during a brief MVC to ensure that the triceps brachii MEP remained below 15% Mmax. If this was not the case, the TMS intensity was turned down until this threshold was met. Intramuscular needle EMG: Indwelling EMG electrodes were custom-made by the investigators using 44-gauge, insulated fine wire (0.0508mm diameter; California Fine Wire Company, Grover Beach, CA) with the insulation removed from the tips. Each pair (three or four) of recording fine wires was inserted into biceps brachii muscle belly using a hypodermic needle (Becton Dickinson or Exel 25-30 G 1.5”). Thus, the custom-made electrodes were inserted into the muscle in a bipolar 67 arrangement (recording area distance: ~2-5mm) and remained fixed in the initial position for the duration of the experiment, once the hypodermic needle was removed. 4.2.3 Experimental procedures Participants attended two sessions of approximately 1 hour, with the order of sessions randomized across participants (eight participants completed the matched-torque contraction first). To begin data collection, Mmax was recorded from the relaxed biceps and triceps brachii. Stimulus current was increased incrementally for successive stimuli until the peak-to-peak amplitude of the compound muscle action potentials reached a plateau. Participants then performed two brief (~2s) MVCs to establish peak elbow flexor torque. Strong verbal encouragement and visual feedback of the torque were provided during all MVCs, and at least 90s of rest separated each contraction. Following the MVCs, a 50% MVC torque target line was set and participants were asked to perform brief (~2s) contractions to this line to set the TMS intensity. Once the TMS intensity was set and confirmed during a brief MVC (to ensure that the triceps brachii MEP remained ≤15% Mmax), baseline VA was recorded by delivering TMS during three series of brief (~2s) contractions at 100, 75 and 50% MVC torque. At least 90s of rest between series and ~5s rest between contractions was given. To set the target for the matched-torque fatigue protocol, a line was set at 20% MVC torque. For the matched-EMG session, an additional target line was set at the level of integrated EMG (iEMG) produced at 20% MVC torque (henceforth referred to as 20% iEMG). To induce fatigue, participants were asked to maintain the torque or EMG target for 10min and report their RPE at the start of the contraction and end of each minute. The VA sequence (contractions at 100, 75 and 50% MVC torque) was repeated immediately following and 5min after the fatiguing protocol (Figure 4.1). 68 The EEG as well as surface and intramuscular EMG recordings were sampled throughout the entire 10-minute fatiguing protocol. 4.2.4 Data analysis The EEG and EMG data were synchronized using an external TTL pulse and analyzed off-line using Matlab (version 2021a). To calculate overall power, CMC, and IMC, surface EMG data were digitally bandpass filtered from 10-250Hz (Kenville et al., 2020; Laine et al., 2015; Schoffelen et al., 2011). EMG signals were then rectified, as recommended for coherence analyses (Boonstra & Breakspear, 2012; Farina et al., 2013). Prior to coherence and power analyses, EEG data were digitally bandpass filtered between 0.5-100Hz (Kenville et al., 2020). EEG power was calculated as the squared amplitude of the signal in both the alpha (8-12Hz) and beta (15-35Hz) frequency ranges. CMC and IMC values between 0 and 1 (0 indicating no Figure 4.1 Protocol timeline showing the 10-minute fatiguing contraction. Voluntary activation (VA) was obtained at baseline, immediately post-fatigue (T0), and 5 minutes post-fatigue (T5). 69 coherence and 1 indicating a perfect linear relationship) were calculated using the mscohere function in Matlab, with segments of 2 seconds and 50% overlap (Laine et al., 2015; Laine & Valero-Cuevas, 2017). Torque and iEMG steadiness were also measured for the matched-torque and matched-EMG contractions, respectively. Steadiness was quantified using the coefficient of variation (CV) formula, and values were obtained for the first and last minutes of the contraction from the torque channel for the matched-torque day and the iEMG channel for the matched-EMG day. The root mean square (RMS) amplitude of the biceps and triceps brachii surface EMG signals was also obtained throughout the fatiguing protocol and mean values were obtained at the beginning and end of each contraction type. For the intramuscular recordings, MU action potentials were discriminated based on a template-matching algorithm, using Plexon Offline Sorter (Plexon Inc, Dallas, Texas, USA, version 4.6.1) software. Custom-built Matlab scripts were used to assess the average rate of discharge over the first and last minute of the contraction as well as the spike timing of distinct MUs throughout the contraction. VA prior to and after the fatiguing contraction was calculated with the following equation: VA (%) = [1 – (superimposed twitch during an MVC ÷ estimated resting twitch)] × 100. The estimated resting twitch (ERT) was calculated as the y-intercept of the regression line between superimposed twitch torque at 50, 75, and 100% MVC torque plotted against percentage of peak MVC torque. Pre-fatigue values for the ERT and VA were based on an average of three sequences, whereas the values during recovery were from a single sequence at each time point. 4.2.5 Statistics All statistical analyses were performed with IBM SPSS Statistics (Version 27, Chicago, IL). Pre-fatigue MVC torque and VA were compared between the two protocols using a paired-samples t-70 test. To test for differences across protocols during and after the fatiguing tasks, a three-way repeated-measures analysis of variance (ANOVA) was used for all variables with Protocol, Time, and Sex as main factors. Apart from RMS EMG at 20% MVC torque, alpha and beta power at the Cz electrode location and firing rates of continuously active MUs (discharging at least 75% of the time), no variables had a significant interaction involving sex or a main effect of Sex (p ≥ 0.067). Due to the large number of variables, in the interest of avoiding an overly long Results section, Sex-based statistical outcomes are only reported for those four variables that had a significant interaction involving sex. For these variables with Sex-based interactions, two-way ANOVAs with factors of Time and Protocol were run separately for females and males. For all other variables, data were pooled across all participants for statistical analyses. For VA and MVC torque, Time had three levels (pre-fatigue, 0 min post-fatigue and 5 min post-fatigue), for beta and alpha power, CMC and IMC and MU discharge rate, Time had two levels (averaged values across the first and last minute of the fatiguing protocol), and finally, Time had 11 levels for RPE scores (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 minutes from protocol onset). If a main effect of Protocol or Protocol by Time interaction was found for RPE, VA, or MVC torque, one-way ANOVAs for each protocol were run separately, followed by paired t-tests to determine the effect of Time within each protocol and differences across protocols. For beta and alpha power, CMC, IMC, and MU discharge rates (i.e., when Time had only two levels), separate paired t-tests were run for each protocol in the case of a significant Protocol by Time interaction. All data were averaged per participant prior to statistical analyses. When a two-way ANOVA had only a main effect of Time, data were pooled for the two protocols and paired t-tests were used to determine when a degradation in performance occurred. All effect sizes are presented and the level of statistical significance was set at p ≤ 0.05 and adjusted for violations in sphericity. 71 Bonferroni correction was applied to each t-test to correct for the issue of multiple comparisons. Data are presented as mean ± standard deviation. 4.3 Results 4.3.1 Rating of perceived effort RPE was higher for the matched torque protocol compared to the matched-EMG protocol as evidenced by the main effect of Protocol (F(1,15) = 7.049, p = 0.018, ηP2 = 0.320). A main effect of Time (F(10,150) = 135.162, p < 0.001, ηP2 = 0.900) and a Protocol by Time interaction (F(10,150) = 14.674, p < 0.001, ηP2 = 0.495) were also found. Post-hoc tests showed that RPE was significantly higher for the matched-torque compared to matched-EMG protocol by 6 minutes into the task (p < 0.011), and remained elevated for the duration of the protocol (Figure 4.2). A main effect of Time was found for both the matched-torque (F(10,150) = 145.598, p < 0.001, ηP2 = 0.907) and matched-EMG (F(10,150) = 73.155, p < 0.001, ηP2 = 0.830) protocols. For both protocols, post-hoc t-tests showed that RPE was higher compared to baseline after only one minute (p < 0.004). 72 4.3.2 Torque/iEMG during fatiguing contraction A Protocol by Time interaction (F(1,14) = 231.389, p < 0.001, ηP2 = 0.943), as well as main effects of Time (F(1,14) = 218.769, p < 0.001, ηP2 = 0.940) and Protocol (F(1,14) = 28.360, p < 0.001, ηP2 = 0.669) were found for submaximal torque. Post-hoc analyses showed that torque values did not change from the beginning to the end of the matched-torque protocol (t(15) = 2.107, p = 0.052, d = 0.527); however, they decreased from the beginning (100.1 ± 13.3% pre-fatigue) to the end (56.0 ± 19.1% pre-fatigue) of the matched-EMG contraction (t(15) = 15.858, p < 0.001, d = 2.471). Sex-based interactions were found for RMS EMG values at 20% MVC torque, thus, females and males were analyzed separately. Both females and males had a Protocol by Time interaction (females: F(1,7) = 23.846, p < 0.001, ηP2 = 0.773; males: F(1,7) = 31.809, p < 0.001, ηP2 = 0.820), as well as main effects of Time (females: F(1,7) = 25.328, p = † * Figure 4.2 Rating of perceived effort (RPE) scores for matched-torque and matched-EMG protocols. RPE scores were higher than baseline by 1-minute after the start of the task for both protocols (*, p < 0.05) and greater during the matched-torque compared to the matched-EMG contraction from 6-10 minutes (†, p < 0.05). 73 0.002, ηP2 = 0.827; males: F(1,7) = 33.511, p = 0.002, ηP2 = 0.827) and Protocol (females: F(1,7) = 24.321, p = 0.002, ηP2 = 0.777; males: F(1,7) = 42.410, p < 0.001, ηP2 = 0.858). RMS EMG was significantly higher at the end (181.1 ± 44.5% pre-fatigue) compared to the beginning (105.1 ± 11.9% pre-fatigue) of the matched-torque contraction (t(7) = -4.992, p = 0.002, d = -1.765), but were not different between the beginning (99.0 ± 1.7% pre-fatigue) and end (100.1 ± 3.8% pre-fatigue) of the matched-EMG protocol (t(7) = -0.624, p = 0.553, d = -0.220) for females. Similarly, males had higher RMS EMG at the end (250.4 ± 69.6% pre-fatigue) compared to the beginning (107.1 ± 7.7% pre-fatigue) of the matched-torque contraction (t(7) = -5.730, p < 0.001, d = -2.026), but no difference between the beginning (99.8 ± 2.9% pre-fatigue) and end (100.2 ± 6.1% pre-fatigue) of the matched-EMG contraction (t(7) = -0.191, p = 0.854, d = -0.067). Torque steadiness decreased from the beginning (2.9 ± 1.3% CV) to the end (5.3 ± 2.2% CV) of the matched-torque fatiguing contraction (t(15) = -4.441, p < 0.001, d = -1.110); however, iEMG steadiness was not significantly different from the beginning (16.2 ± 3.5% CV) to the end (17.5 ± 4.0% CV) of the matched-EMG protocol (t(15) = -1.735, p = 0.103, d = -0.434). 4.3.3 Maximal voluntary contraction torque Pre-fatigue MVC torque was significantly higher for the matched-torque (58.9 ± 20.3N⸱m) compared to matched-EMG (55.5 ± 19.9N⸱m) test day (t(15) = 3.030, p = 0.008, d = 0.757). When data from the fatigue and recovery periods were normalized to the pre-fatigue value, MVC torque showed a Protocol by Time interaction (F(2,28) = 6.525, p = 0.005, ηP2 = 0.318), a main effect of Protocol (F(1,14) = 15.561, p = 0.001, ηP2 = 0.526), and a main effect of Time (F(2,28) = 38.934, p < 0.001, ηP2 = 0.736). Immediately after protocol termination (T0), the reduction of MVC torque was greater for the matched-torque (32.9 ± 17.0%) than matched-EMG contraction (20.6 ± 18.6%) (t(15) = -2.662, p = 0.018, d = -0.666), a difference that persisted at T5 (t(14) = -74 3.450, p = 0.004, d = -0.891). There was a main effect of Time for the matched-torque day (F(2,30) = 56.255, p < 0.001, ηP2 = 0.789), with MVC torque lower than baseline at both T0 (t(15) = 7.733, p < 0.001, d = 1.933) and T5 (t(15) = 4.659, p < 0.001, d = 1.165) (Figure 4.3a). In contrast, although a main effect of Time was also found for the matched-EMG day (F(2,28) = 14.054, p < 0.001, ηP2 = 0.501), MVC torque was impaired at T0 (t(15) = 4.430, p < 0.001, d = 1.108), but not different to baseline at T5 (t(14) = 1.114, p = 0.284, d = 0.288). Impairment of MVC torque was greater for the matched-torque compared to matched-EMG protocol both at T0 (t(15) = -2.662, p = 0.018, d = -0.666) and T5 (t(14) = -3.450, p = 0.004, d = -0.891). A)† † † B)Figure 4.3 (A) MVC torque as a % pre-fatigue for the matched-torque (light grey) and matched-EMG (dark-grey) fatiguing contractions at task completion and 5-minutes post-contraction. At both time points, the reduction of MVC torque was greater for the matched-torque than matched-EMG contraction (†, p < 0.05). MVC torque was lower than baseline at 0 min post task for both protocols, and at 5 min post task for the matched-torque protocol (*, p < 0.05). (B) Voluntary activation (VA) scores at baseline, 0 min post task and 5 min post task are reported. VA scores were lower than baseline at 0 min post task for the matched-torque contraction (*, p < 0.05), and significantly lower than the matched-EMG contraction at 0 min post task (†, p < 0.05). 75 4.3.4 Voluntary activation Pre-fatigue VA scores were not different for the matched-torque (94.4 ± 4.7%) and matched-EMG (93.9 ± 5.0%) days (t(15) = 0.548, p = 0.592, d = 0.137) (Figure 4.3B). However, a Protocol by Time interaction (F(2,22) = 4.652, p = 0.021, ηP2 = 0.297), main effect of Protocol (F(1,11) = 6.436, p = 0.028, ηP2 = 0.369) and a main effect of Time (F(2,22) = 5.824, p = 0.009, ηP2 = 0.346) were found across fatigue and recovery. Post-hoc analyses showed that VA scores were significantly lower for the matched-torque contraction compared to the matched-EMG contraction at T0 (t(12) = -2.272, p = 0.042, d = -0.630) but not T5 (t(13) = -1.009, p = 0.331, d = -0.270). The matched-torque protocol had a main effect of Time (F(2,26) = 10.371, p < 0.001, ηP2 = 0.444), with VA scores lower than pre-fatigue at T0 (t(13) = 3.600, p = 0.003, d = 0.962), but not at T5 (t(15) = 1.581, p = 0.135, d = 0.395). A main effect of Time was not found for the matched-EMG day (F(2,22) = 1.239, p = 0.309, ηP2 = 0.101). 4.3.5 Coherence There was no main effect of Protocol (F(1,15) = 0.235, p = 0.635, ηP2 = 0.015) or Time (F(1,15) = 2.754, p = 0.118, ηP2 = 0.155) for coherence between the somatosensory cortex and biceps brachii (CMC-B) in the alpha frequency range; however, there was a Protocol by Time interaction (F(1,15) = 4.642, p = 0.048, ηP2 = 0.236). Post-hoc analyses showed that alpha CMC-B was significantly greater at the end than start of the matched-torque condition (174.6 ± 98.0% of the first minute; t(15) = -2.720, p = 0.016, d = -0.680), but did not change from the beginning to the end of the matched-EMG protocol (125.6 ± 94.3% of the first minute; t(15) = 0.253, p = 0.804, d = 0.063) (Figure 4.4). CMC-B in the beta frequency range did not have a Protocol by Time interaction (F(1,15) = 0.001, p = 0.977, ηP2 < 0.001) or main effects of either Protocol (F(1,15) = 1.751, p = 0.206, ηP2 = 0.105) or Time (F(1,15) = 0.868, p = 0.366, ηP2 = 0.055). 76 Relative to the first minute of the fatiguing contraction, beta coherence in the final minute was 102.0 ± 29.3% and 102.9 ± 29.7% for matched-torque and matched-EMG contractions, respectively (Figure 4.4). Coherence between the somatosensory cortex and triceps brachii (CMC-T) had neither an interaction nor main effects for both the alpha (Interaction: F(1,15) = 0.005, p = 0.946, ηP2 < 0.001; Protocol: F(1,15) = 0.673, p = 0.425, ηP2 = 0.043; Time: F(1,15) = 1.688, p = 0.214, ηP2 = 0.101) and beta (Interaction: F(1,15) = 0.004, p = 0.948, ηP2 < 0.001; Protocol: F(1,15) = 2.178, p = 0.161, ηP2 = 0.127; Time: F(1,15) = 0.084, p = 0.776, ηP2 = 0.006) frequency bandwidths. Relative to the first minute of the fatiguing contraction, beta CMC-T in the final minute was 103.5 ± 30.1% and 109.5 ± 52.1% for matched-torque and matched-EMG contractions, respectively, whereas alpha coherence was 123.2 ± 83.7% and 140.6 ± 86.0%, respectively. Lastly, IMC (coherence between surface EMG of biceps and triceps brachii) had a Protocol by Time interaction for the alpha (F(1,15) = 5.014, p = 0.041, ηP2 = 0.251) but not beta frequency range (F(1,15) = 2.858, p = 0.112, ηP2 = 0.160). Neither frequency range had a main effect of Protocol (Alpha: F(1,15) = 0.486, p = 0.496, ηP2 = 0.031; Beta: F(1,15) = 2.150, p = 0.163, ηP2 = 0.125) or Time (Alpha: F(1,15) = 3.008, p = 0.103, ηP2 = 0.167; Beta: F(1,15) = 0.059, p = 0.811, ηP2 = 0.004). Alpha IMC was larger by the end compared to beginning of the matched-torque protocol (197.4 ± 137.4% of the first minute; t(15) = -2.715, p = 0.016, d = -0.679), but did not change during the matched-EMG protocol (118.7 ± 113.4% of the first minute; t(15) = 0.584, p = 0.592, d = 0.137). Relative to the first minute of the fatiguing contraction, beta IMC in the final minute was 113.4 ± 42.9% and 92.0 ± 24.0% for matched-torque and matched-EMG contractions, respectively. 77 4.3.6 Power A Protocol by Sex interaction was found for alpha power at the Cz electrode location (F(1,14) = 9.117, p = 0.009, ηP2 = 0.394). When sexes were assessed independently, a main effect of Protocol was found for males (F(1,7) = 7.327, p = 0.030, ηP2 = 0.511). Post-hoc analyses showed that alpha power was not different between protocols at the beginning of the contractions (t(7) = 1.722, p = 0.129, d = 0.609), but was higher for the matched-torque than matched-EMG contraction during the last minute of the protocol (t(7) = 2.650, p = 0.033, d = 0.937). No Protocol by Time (F(1,7) = 2.576, p = 0.153, ηP2 = 0.269) or main effect of Time (F(1,7) = 5.189, p = 0.057, ηP2 = 0.426) was found for males. No main or interaction effects were found for females (Interaction: F(1,7) = 0.216, p = 0.656, ηP2 = 0.030; Time: F(1,7) = 1.627, p = 0.243, ηP2 Figure 4.4 Corticomuscular (CMC) and intermuscular (IMC) coherence during the first (solid line) and last (dotted line) minutes of the (A) matched-torque and (B) matched-EMG fatiguing contractions. Alpha coherence between the somatosensory cortex and biceps brachii (Cz-BB) and between biceps and triceps brachii (BB-TB) was significantly greater at the end than start of the matched-torque condition (*, p < 0.05). 0 20 4000.020.040.060.08CoherenceBB-Cz0 20 4000.020.040.060.08 TB-Cz0 20 4000.050.10.15 BB-TB BeginningEndAlphaBeta0 20 40Frequency (Hz)00.020.040.060.08CoherenceBB-Cz0 20 40Frequency (Hz)00.020.040.060.08 TB-Cz0 20 40Frequency (Hz)00.050.10.15 BB-TB BeginningEndAlphaBetaA) * * Cz-BB Cz-TB BB-TB Cz-BB Cz-TB -T 78 = 0.189; Protocol: F(1,7) = 1.791, p = 0.223, ηP2 = 0.204). A Protocol by Time by Sex interaction was also found for beta power at the Cz electrode location (F(1,14) = 11.235, p = 0.005, ηP2 = 0.445). A Protocol by Time interaction was found for males (F(1,7) = 17.368, p = 0.004, ηP2 = 0.713) but not females (F(1,7) = 0.473, p = 0.514, ηP2 = 0.063). Males also had a main effect of Time (F(1,7) = 6.242, p = 0.041, ηP2 = 0.471). No main effect of Protocol was found for males (F(1,70 = 4.313, p = 0.076, ηP2 = 0.381). T-tests of data from male participants showed an increase in power during the matched-torque (t(7) = -3.934, p = 0.006, d = -1.391) but not the matched-EMG (t(7) = -0.227, p = 0.827, d = -0.080) contraction. Females had neither a main effect of Protocol (F(1,7) = 0.013, p = 0.914, ηP2 = 0.002) nor Time (F(1,7) = 2.816, p = 0.137, ηP2 = 0.287). Relative to the first minute of the fatiguing contraction, alpha power during the final minute was 596.4 ± 971.0% and 41.4 ± 96.1% for the matched-torque contraction, and 62.3 ± 204.6% and 61.2 ± 163.4% for the matched-EMG contraction for males and females, respectively. Final-minute beta power was 365.0 ± 419.7% and 32.7 ± 52.3% of the first minute for the matched-torque contraction, and 30.5 ± 77.8% and 30.5 ± 62.0% for the matched-EMG contraction for males and females, respectively. A Time by Protocol interaction and main effects of Protocol and Time were found for biceps brachii power in both the alpha (Interaction: F(1,15) = 7.071, p = 0.018, ηP2 = 0.320; Protocol: F(1,15) = 6.895, p = 0.019, ηP2 = 0.315; Time: F(1,15) = 19.336, p < 0.001, ηP2 = 0.563;) and beta (Interaction: F(1,15) = 22.284, p < 0.001, ηP2 = 0.598; Protocol: F(1,15) = 9.605, p = 0.007, ηP2 = 0.390; Time: F(1,15) = 38.660, p < 0.001, ηP2 = 0.720) frequency range. Post-hoc analyses showed significantly higher power at the end compared to the beginning of the matched-torque protocol for both the alpha (399.2 ± 326.2% of the first minute; t(15) = -3.649, p = 0.002, d = -0.912) and beta (398.7 ± 264.7% of the first minute; t(15) = -6.341, p < 0.001, d = -79 1.58 ηP2 = 0.3905) frequencies. However, power in both the alpha (t(15) = -2.2028, p = 0.061, d = -0.507) and beta (t(15) = -0.421, p = 0.680, d = -0.105) range did not change throughout the matched-EMG protocol (129.6 ± 51.0% and 120.3 ± 69.2% of the first minute, respectively). Both alpha (t(15) = 0.756, p = 0.461, d = 0.189) and beta (t(15) = 0.574, p = 0.574, d = 0.144) power were not different between contraction types during the first minute of the fatiguing contraction; however, both alpha (t(15) = 3.947, p = 0.001, d = 0.987) and beta (t(15) = 5.405, p < 0.001, d = 1.351) power were higher for the matched-torque than matched-EMG contraction during the last minute of the task. For triceps brachii, the alpha power had no Protocol by Time Interaction (F(1,15) = 2.577, p = 0.129, ηP2 = 0.147), nor main effects of Protocol (F(1,15) = 2.316, p = 0.149, ηP2 = 0.134) or Time (F(1,15) = 0.015, p = 0.903, ηP2 = 0.001). Relative to the first minute of the fatiguing contraction, alpha power during the final minute was 279.0 ± 298.9% and 110.1 ± 125.2% for matched-torque and matched-EMG contractions, respectively. Beta power had a main effect of Protocol (F(1,15) = 5.785, p = 0.030, ηP2 = 0.278), but no interaction (F(1,15) = 2.584, p = 0.129, ηP2 = 0.147) or Time (F(1,15) = 0.251, p = 0.624, ηP2 = 0.016) effect was found. By the end of the protocols, beta power was 156.2 ± 315.8% and 89.3 ± 32.4% of its initial value for the matched-torque and matched-EMG contractions, respectively. Beta power was not different between protocols for the first minute of the contraction (t(15) = 0.085, p = 0.933, d = 0.021), but was higher for the matched-torque compared to matched-EMG contraction during the last minute of the protocol (t(15) = 2.857, p = 0.012, d = 0.714). 80 4.3.7 Motor unit discharge rate A total of 84 and 94 MUs were analyzed during the first minute and 112 and 91 during the last minute of the matched-torque (Figure 4.6) and matched-EMG (Figure 4.7) contraction, respectively. Of these units, 74 and 61 were sampled throughout the 10-minute contraction (at least 75% of the time) for the matched-torque and matched-EMG contractions, respectively. Across all MUs, a Time by Protocol interaction effect was present (F(1,15) = 41.203, p < 0.001, ηP2 = 0.733); however, no main effect of Time (F(1,15) = 1.657, p = 0.218, ηP2 = 0.099) or A) 0 20 400510Power (V2)Cz Power0 20 4000.0050.01Power (mV2)BB Power0 20 400246Power (mV2)10-5 TB Power BeginningEndAlphaBeta0 20 40Frequency (Hz)0510Power (V2)Cz Power0 20 40Frequency (Hz)00.0050.01Power (mV2)BB Power0 20 40Frequency (Hz)0246Power (mV2)10-5 TB PowerBeginningEndAlphaBeta* * * Figure 4.5 Somatosensory cortex (Cz), biceps brachii (BB) and triceps brachii (TB) alpha and beta power during the first (solid line) and last (dotted line) minutes of the (A) matched-torque and (B) matched-EMG fatiguing contractions. Beta power increased from the beginning to the end of the matched-torque contraction for males (*, p < 0.05). With data pooled for the sexes, both alpha and beta power increased throughout the matched-torque protocol at the BB location (*, p < 0.05). 81 Protocol (F(1,15) = 0.031, p = 0.864, ηP2 = 0.002) was found. MU discharge rates were greater during the first minute of the matched-EMG contraction (16.4 ± 3.0 Hz) compared to the first minute of the matched-torque contraction (14.8 ± 1.4 Hz) (t(15) = 2.213, p = 0.043, d = 0.553). However, during the last minute of the task, MU discharge rates were higher during the matched-torque (17.2 ± 1.4 Hz) compared to matched-EMG (15.0 ± 2.4 Hz) contraction (t(15) = -3.519, p = 0.003, d = -0.880). Finally, MU discharge rates were higher during the last minute of the contraction compared to the first minute for the matched-torque (t(15) = -6.029, p < 0.001, d = -1.507) contraction, but were lower during the last compared to first minute for the matched-EMG protocol (t(15) = 3.210, p = 0.006, d = 0.802). A Protocol by Sex interaction was found for discharge rates of continuously active MUs (i.e., MUs that could be successfully tracked across the 10-minute contraction) (F(1,12) = 5.715, p = 0.034, ηP2 = 0.323). When considering continuously active MUs for males only, the initial MU discharge rates were 15.8 ± 2.0 Hz and 16.3 ± 3.2 Hz for the matched-torque and matched-EMG contractions, respectively, and 18.3 ± 2.6 Hz and 14.9 ± 3.2 Hz for the last minute of the 0510152025Matched-TQ Matched-EMGMUDR (Hz) of all unitsBeginning EndAll participants * * Figure 4.6 Discharge rates of all motor units during the matched-torque (TQ) and matched-EMG fatiguing contractions for all participants. Discharge rates increased during the matched-torque contraction and decreased during the matched-EMG contraction (*, p < 0.05). 82 contraction for the matched-torque and matched-EMG contractions, respectively. For females, MUs that discharged throughout the contraction showed initial rates of 14.7 ± 1.7 Hz and 18.0 ± 3.8 Hz and final discharge rates of and 17.2 ± 1.9 Hz and 16.4 ± 2.7 Hz for the matched-torque and matched-EMG contractions, respectively. Males had a Protocol by Time interaction (F(1,6) = 54.549, p < 0.001, ηP2 = 0.901) and a main effect of Protocol (F(1,6) = 8.753, p = 0.025, ηP2 = 0.593), but no main effect of Time (F(1,6) = 2.494, p = 0.165, ηP2 = 0.294), whereas females had only a Protocol by Time interaction (Interaction: F(1,6) = 12.520, p = 0.012, ηP2 = 0.676; Protocol: F(1,7) = 1.131, p = 0.328, ηP2 = 0.159; Time: F(1,6) = 1.236, p = 0.309, ηP2 = 0.171). Post-hoc analyses showed that MU discharge rate increased during the matched-torque contraction only for females (t(7) = -4.649, p = 0.002, d = -1.644), but increased during the matched-torque (t(6) = -5.149, p = 0.002, d = -1.946) and decreased during the matched-EMG (t(7) = 4.174, p = 0.004, d = 1.476) contraction for males. Discharge rate was not different between contraction types at the Figure 4.7 Discharge rates of continuously active (CA) motor units during the matched-torque (TQ) and matched-EMG fatiguing contractions for males (left) and females (right). Discharge rates increased for males and females during the matched-torque contraction (*, p < 0.05). Discharge rates decreased during the matched-EMG contraction for males only (*, p < 0.05). 0510152025Matched-TQ Matched-EMGMUDR (Hz) of CO unitsBeginning End0510152025Matched-TQ Matched-EMGMUDR (Hz) of CO unitsBeginning EndMales Females * * * MUDR (Hz) of CA units MUDR (Hz) of CA units 83 beginning of the contraction for males (t(6) = -0.649, p = 0.540, d = -0.245). However, discharge rates were significantly higher at the end of the matched-torque compared to matched-EMG contraction for males (t(7) = -2.438, p = 0.045, d = -0.862). Figure 4.8 Motor unit discharge rate for male (M) and female (F) participants during the matched-torque fatiguing contraction. Each trace represents an individual MU for the given participant. Contraction length is expressed as a % of the total task duration (10 minutes). 0 50 10010203040Discharge rate (Hz) M10 50 10010203040 M20 50 10010203040 M30 50 10010203040 M40 50 10010203040Discharge rate (Hz) M50 50 10010203040 M60 50 10010203040 M70 50 10010203040 M80 50 10010203040Discharge rate (Hz) F10 50 10010203040 F20 50 10010203040 F30 50 10010203040 F40 50 100Task duration (% total)10203040Discharge rate (Hz) F50 50 100Task duration (% total)10203040 F60 50 100Task duration (% total)10203040 F70 50 100Task duration (% total)10203040 F884 4.4 Discussion The current study provides the first evidence of both performance (i.e., MVC torque, VA, and steadiness) and neural (i.e., CMC, IMC and MU discharge rates) differences between a matched-torque and a matched-EMG submaximal fatiguing contraction. Specifically, a greater fatigue-related reduction was found for MVC torque immediately following the matched-torque compared to the matched-EMG protocol, with only the matched-torque protocol leading to a reduction in VA. Further, CMC-B, MU discharge rates, as well as CV of elbow flexor torque Figure 4.9 Motor unit discharge rate for male (M) and female (F) participants during the matched-EMG fatiguing contraction. Each trace represents an individual MU for the given participant. Contraction length is expressed as a % of the total task duration (10 minutes). 0 50 10010203040Discharge rate (Hz) M10 50 10010203040 M20 50 10010203040 M30 50 10010203040 M40 50 10010203040Discharge rate (Hz) M50 50 10010203040 M60 50 10010203040 M70 50 10010203040 M80 50 10010203040Discharge rate (Hz) F10 50 10010203040 F20 50 10010203040 F30 50 10010203040 F40 50 100Task duration (% total)10203040Discharge rate (Hz) F50 50 100Task duration (% total)10203040 F60 50 100Task duration (% total)10203040 F70 50 100Task duration (% total)10203040 F885 increased by the end of the matched-torque contraction, whereas MU discharge rates dropped and CMC as well as iEMG steadiness were unaltered from the beginning to the end of the matched-EMG contraction. Due to the increased descending drive during the matched-torque contraction, the capacity to recruit new MUs or increase the discharge rate of the active MUs is less than it was at the beginning of the contraction. Because of this, the nervous system may rely on increased CMC as a compensatory mechanism. 4.4.1 Rating of perceived effort, maximal voluntary contraction torque, and voluntary activation The RPE score was greater than the initial value by one minute into the contraction for both the matched-torque and matched-EMG protocol. However, scores were higher for the matched-torque compared to matched-EMG protocol by 6 minutes. Therefore, despite maintained motoneuron pool output for the matched-EMG task, there is seemingly an increase in descending drive, albeit a lesser one compared to the matched-torque contraction. In line with these findings, participants’ VA scores were reduced immediately following task termination during the matched-torque protocol, but not for the matched-EMG protocol. The reduction of VA after a matched-torque fatiguing protocol aligns with previous work (e.g., Smith et al., 2007; Søgaard et al., 2006); however, the maintenance of VA for the matched-EMG protocol conflicts with earlier findings (Yacyshyn & McNeil., 2020; Magnuson et al., 2022). Task duration is the most likely explanation for the discrepancy between the current study (10-min contraction) and these other studies with a matched-EMG contraction (15- or 20-min contraction); i.e., the longer the task, the greater the contribution of central fatigue to the impairment of MVC torque (Smith et al., 2007; Søgaard et al., 2006). In addition to muscle performance measures, it is important to consider aspects of central mechanisms that could underlie this reduced VA following the 86 matched-torque but not the matched-EMG protocol, such as changes in CMC and discharge rates of individual MUs. 4.4.2 Corticomuscular and intermuscular coherence and power In the context of the matched-torque fatiguing contraction, the increased alpha coherence between the somatosensory cortex (Cz) and the biceps brachii may act as a compensatory mechanism, such that enhanced alpha-band oscillatory coherence between the cortex and muscle aids in the performance of a sustained isometric contraction (Ushiyama et al., 2011). Specifically, the increased alpha-band CMC suggests an increased common synaptic input from cortical neurons and/or afferent feedback from the biceps brachii (CMC-B) spinal motoneuron pool (Wang et al., 2015). Once it became difficult for participants to maintain torque output and torque steadiness with the progressive development of fatigue during the matched-torque contraction, they likely needed to increase common input to the agonist muscles. This would align with previous work that shows reduced complexity of motor control with fatigue (Pethick et al., 2015). Coherence values were not altered during the matched-EMG protocol, which indicates that a submaximal matched-EMG contraction of the elbow flexors does not require the same compensatory rhythmic neural drive to maintain task performance as is seen during the matched-torque fatiguing contraction. The increased IMC between biceps and triceps brachii during the matched-torque fatiguing contraction could indicate a compensatory mechanism which functions to increase coherence between antagonist muscles in at attempt to maintain torque steadiness (Wang et al., 2015). Alternatively, the increased coherence could be a spillover effect in that as fatigue sets in, greater neural drive and common inputs are being sent to both muscle groups to allow for 87 increased activation from the muscles (i.e., a negative consequence of fatigue rather than a compensatory mechanism). On that note, it is important to consider the possibility that CMC is simply a consequence of increased cortical or EMG power at a given frequency, rather than being an important functional process in the control of movement (Riddle et al., 2004). Thus, we measured both CMC in the alpha and beta frequency range alongside alpha and beta power at the somatosensory cortex, as well as at biceps and triceps brachii locations. Importantly, no main or interaction effects were statistically significant for alpha power at the somatosensory cortex. As such, the increased alpha activity for CMC-B and IMC cannot be explained by a simple increase in alpha power during the matched-torque contraction. Alpha power increased at the biceps brachii location, but given the power data at the cortex, the increased alpha CMC-B and IMC could be driven by increased output from the motoneuron pool. Overall, the current study provides evidence that, during a submaximal matched-torque isometric contraction of the elbow flexors, there is enhanced supraspinal drive (increased beta power) alongside specific enhancement in alpha coherence between the somatosensory cortex and an agonist muscle. These mechanisms may function to compensate for reduced excitation of the motoneurons due to repetitive activation (Gandevia et al., 1999; McNeil et al., 2011; Rossi et al., 2012). However, the increased coherence between agonist and antagonist muscles may function to limit the impairment of torque steadiness, while simultaneously limiting the functional benefit of the potentially compensatory processes occurring between cortex and agonist muscles. 88 4.4.3 Motor unit discharge rate For the matched-torque contraction, intramuscular EMG recordings revealed increased MU discharge rates. Multiple studies have reported reduced discharge rates with the development of fatigue (Bigland-Ritchie et al., 1986; Carpentier et al., 2001; Dalton et al., 2010; Garland et al., 1994), whereas other studies have reported an increase of MU discharge rate (Adam & De Luca, 2005; Griffin et al., 2001; Kuchinad et al., 2004; Maton & Gamet, 1989). Interestingly, the studies that reported a fatigue-related reduction typically involved a higher intensity, shorter duration contraction compared to the studies that reported increased MU discharge rates. Further, with simulated data generated by a MU behaviour model for muscle fatigue, MU discharge rates increased during a sustained contraction at 20% MVC torque; however, with a simulated torque ≥50% of maximum, MU discharge rates began to increase and/or decline throughout the contraction (Potvin & Fuglevand, 2017). Thus, the disparity between the studies mentioned in the first sentence of this paragraph is likely owing to contraction duration and intensity. In the current study, the increase in MU discharge rates for the matched-torque contraction is likely reflective of greater descending drive to maintain torque output as fatigue develops, which is supported by the surface EMG data. This increased descending drive/MU discharge rate likely functions to compensate for increased inhibitory feedback, lowered spinal motoneuron pool excitability, and/or contractile failure occurring at the muscle (Gandevia, 2001; Taylor et al., 2016). In contrast to the increased MU discharge rates observed in the matched-torque contraction, MU discharge rates decreased from the beginning to the end of the matched-EMG protocol. These data indicate that, when maintaining a given motoneuron pool output during a fatiguing contraction, the nervous system may prefer strategies such as MU rotation over 89 increasing discharge rates of already active MUs as a way of compensating for fatigue-related processes occurring at the level of the muscle (evidenced by reduced MVC torque post-fatigue). In conclusion, VA is worse after the matched-torque contraction only, and CMC and IMC results show increased alpha-band coherence between biceps brachii and the somatosensory cortex and between biceps and triceps brachii by the end of the matched-torque contraction, but no change during the matched-EMG contraction. These findings, paired with the increased MU discharge rates during the matched-torque contraction but reduced rates during the matched-EMG contraction could indicate that once the nervous system reaches a given level of fatigue, it increases MU discharge rates, and enhances rhythmic drive to maintain torque production at the cost of torque steadiness as shown by the reduced steadiness from the beginning to the end of the matched-torque but not the matched-EMG protocol (Ushiyama et al., 2011). 90 Chapter 5: The orderly recruitment of motor units is modified when a muscle is acting as an antagonist Justine R. Magnuson, Briah H. Dalton, Chris J. McNeil Under review in: Medicine & Science in Sports & Exercise (MSSE-D-22-00916) Abstract: Despite the perceived importance of antagonist muscle activity, it is unknown if motor unit (MU) behaviour at recruitment differs when a muscle acts as an antagonist versus agonist. Fourteen healthy participants (six female) performed ramped, isometric elbow flexor and extensor contractions to 50 or 100% maximal voluntary contraction (MVC) torque. Surface and fine-wire intramuscular electromyographic (EMG) recordings were sampled from biceps and triceps brachii. During agonist contractions, low-threshold MUs (recruitment threshold <10% MVC torque) were sampled in all participants, with a total of 107 and 90 for biceps and triceps, respectively. At the peak of the ramped MVCs, surface EMG co-activation was 8.3 ± 6.6% for biceps and 15.2 ± 7.3% for triceps. However, antagonist single MU activity was recorded from only four and one participants for elbow extensor and flexor contractions, respectively. All antagonist MUs were successfully detected during agonist contractions, but many (~43%) were recruited >10% MVC torque. For MUs recorded during both agonist and antagonist contractions, RMS EMG at recruitment was seemingly similar and greater for biceps and triceps MUs, respectively. In all participants, initial discharge rates were lower for antagonist than agonist contractions. Co-existence of typical levels of surface EMG-derived co-activation with scant antagonist MU recordings, including none from 10 (biceps) or 13 (triceps) participants, leads to the conclusion that MUs recruited early during an agonist contraction seldom appear to be 91 among those first recruited during an antagonist contraction. These findings highlight a modification of orderly recruitment when a motoneuron pool is acting as an antagonist. 4.1 Introduction The motor unit (MU) is the smallest functional unit of the neuromuscular system and, therefore, MUs are central to our understanding of motor control. With more than 100 years of research devoted to the MU, there have been critical discoveries about its physiology, such as the fundamental observation that MUs of a contracting muscle are recruited in an orderly fashion according to motoneuron size (Denny-Brown & Pennybacker, 1938; Henneman, 1957). However, we know virtually nothing about MU behaviour when a muscle is acting as an antagonist, including whether the fixed orderly recruitment of MUs still applies. Although overlooked to date, behaviour of antagonist MUs is important because torque production about a joint is dictated by the net activation of agonist and antagonist muscle groups. For example, based on surface electromyography (EMG), triceps brachii reaches ~10-25% of its peak activation during an elbow flexor isometric maximal voluntary contraction (MVC), whereas biceps brachii attains ~5-10% co-activation during an elbow extensor MVC (Bazzucchi et al., 2006; Klein et al., 2001; Maeo et al., 2013). Elucidating the behaviour of antagonist MUs will advance our understanding of, and ability to model, the complex interaction of opposing muscle groups during human movement. Only two studies have assessed individual MU activity in the antagonist muscle during a voluntary contraction (De Luca & Mambrito, 1987; Reece & Herda, 2021). De Luca and Mambrito (1987) used intramuscular electromyography (a quadrifilar needle electrode) to record MU activity of the flexor and extensor pollicis longus muscles in three participants during various isometric contractions up to 60% MVC torque. They reported predominantly reciprocal 92 activation (one muscle was silent when the other was contracting), with antagonist MU activity detected only when participants were uncertain of the task (i.e., the torque trajectory was continuously updated in real time) or when quick compensatory torque was required (De Luca and Mambrito 1987). In contrast, Reece and Herda (2021) reported recruitment of triceps brachii MUs at only 10% MVC torque during isometric, ramped trapezoidal elbow flexion contractions. Moreover, the authors stated that the recruitment and discharge rate pattern of antagonist MUs were similar to those seen for agonist MUs. However, the findings from Reece and Herda were based on decomposition of surface EMG with a five-pin array, which is less precise than intramuscular EMG for MU decomposition. Importantly, with the techniques used, neither study could indicate whether MUs recorded during an antagonist contraction matched those recorded during an agonist contraction; and, if so, whether recruitment threshold (based on surface EMG) and initial discharge rate differed for antagonist vs. agonist contractions. The aim of this study was to use intramuscular EMG to examine MU behaviour of the biceps and triceps brachii during ramped elbow flexor and extensor contractions. Based on the findings of Reece and Herda (2021), we expected to record antagonist MU activity during the early stages of the contraction (starting at ~10% MVC), and that the activity would increase throughout the contraction alongside measures of antagonist co-activation (surface EMG). Further, according to the size principle (Henneman, 1957) and orderly recruitment, we predicted that the first MUs to discharge during an agonist contraction would also be the first MUs recruited when the muscle was acting as an antagonist. Due to increased inhibitory input, we hypothesized that MUs would require more neural drive (as measured by surface EMG) to be recruited when acting as an antagonist versus agonist. Lastly, we predicted that the discharge rates at recruitment would not be different between agonist and antagonist contractions. This 93 study will expand our understanding of the neural control of movement, ultimately informing how we consider antagonist MU behaviour. 5.2 Methods 5.2.1 Participants Fifteen participants completed the experiment; however, only data from fourteen participants (29.3 ± 6.7 years old; six females) were included in the analysis due to a technical issue during data collection for one female participant. Exclusion criteria for the study were: a myopathic condition or motor neuropathy; a major, recent (< 6 months) injury or surgery to the limb being tested (e.g., fracture, joint replacement); medications with side effects of dizziness, lack of motor control, or slowed reaction time. Participants gave written informed consent prior to testing, and all procedures were approved by the local institutional ethics committee (H19-03310). 5.2.2 Experimental setup Participants were seated with their dominant arm secured (by an adjustable strap at the wrist) to a custom-built isometric myograph used to record elbow flexor and extensor muscle torque via a linear strain gauge (SBO300; Transducer Techniques, Temecula, CA). Surface EMG of biceps brachii and triceps brachii were measured using self-adhesive Ag-AgCl electrodes (10mm diameter) arranged over the muscle belly with a centre-to-centre spacing of 2cm. Individual MU activity was recorded using three-to-four pairs of fine-wire electrodes inserted into biceps and triceps brachii. Indwelling EMG electrodes were made manually with 44-gauge fine wire (0.0508mm diameter) (California Fine Wire Company, Grover Beach, CA). Two wires were twisted around each other, threaded through a 25-gauge needle, then bent to a ~45° angle at the insertion end. The tips of the wires (~2mm) at the insertion end were then burned prior to 94 autoclave sterilization. Fine-wire electrode pairs were inserted into the muscle belly using one hypodermic needle (Becton Dickinson or Exel 25-30 G 1.5”), which was removed following insertion. Thus, the custom-made electrodes were inserted into the muscle in a bipolar arrangement. Torque and EMG signals were stored to a computer using a 16-bit A/D converter (Power 1401-3) and Spike2 software (version 8; Cambridge Electronic Design Ltd., Cambridge, UK). The torque data were amplified (×100) and low-pass filtered (500Hz) using a 1902 amplifier (Cambridge Electronic Design Ltd), and sampled at 1010Hz. Surface EMG signals were amplified (×300) and band-pass filtered (10-1000Hz) with 1902 amplifiers, and sampled at 1894Hz. Intramuscular EMG signals were amplified (×100) and high-pass filtered (10Hz) with a NeuroLog pre-amplifier and isolator (NL844 and NL820, respectively; Digitimer, Welwyn Garden City, UK), low-pass filtered (5000Hz) with the NL136 module, and sampled at 15152Hz. 5.2.3 Experimental procedures Participants first performed two brief MVCs of both the elbow flexors and extensors (Figure 5.1). A third contraction was performed if peak torque varied by >5% for the first two contractions. Strong verbal encouragement and visual feedback of the torque were provided during all MVCs, and at least 90s of rest separated each contraction. Next, participants performed six ramped elbow flexion, followed by six ramped elbow extension contractions. Contractions alternated between the peak torque targets of 50 and 100% MVC, and were separated by at least 2min of rest. To ensure the rate at which torque increased and decreased was consistent within and between individuals, participants were asked to follow a trace on a computer monitor. For the 50 and 100% MVC contractions, participants ramped up and down at a rate of 5 and 10% MVC/s, and maintained the peak torque for 5s and 1s during the plateau 95 phase, respectively. Although participants did not receive considerable training with the ramp contractions, they were familiar with isometric elbow flexions and extensions in the device used. 5.2.4 Data analysis and statistics The following analyses were performed for both the elbow flexion and extension contractions. To quantify co-activation, surface EMG root-mean-square (RMS) amplitude during an antagonist MVC was expressed as a percentage of the value during an agonist MVC. RMS data were centered on a 1-s window at peak torque during the hold phase for these MVCs. MU action potentials were initially discriminated based on the Spike2 software’s template-matching algorithm. Following this initial decomposition, individual MUs were confirmed manually. Using the Spike2 software, recruitment thresholds and discharge rates at recruitment were determined for all MUs that were recorded before 10% MVC torque during an agonist contraction and for all recruited antagonist MUs. To compare how a given MU acts as an agonist vs. antagonist, the same MU that turned on during an antagonist contraction was identified during an agonist contraction by manual matching of size and shape of the individual action Figure 5.1 Experimental procedures timeline for the elbow flexion (EF) and elbow extension (EE) contractions. As peak torque during a slow, ramped MVC fails to match that during a brief MVC, the amplitude of each ramped MVC was set to 90% of peak torque from the brief MVCs. 96 potentials. For many of these MUs, it was necessary to search beyond 10% MVC torque during the agonist contractions. Initial discharge rates at recruitment were based on the first three MU spikes once the unit began firing consistently (inter-spike interval less than ~200 ms), with doublets (inter-spike interval less than ~20 ms) excluded (Malley, 2006; Tracy et al., 2005). As a measure of global neural activity, surface EMG RMS amplitudes at recruitment for the antagonist MUs were also recorded over a window of 10ms (Hu et al., 2014). Paired samples t-tests were used to assess differences in recruitment thresholds between the ramped 50 and 100% MVC contractions for both biceps and triceps brachii. The level of statistical significance was set at p ≤ 0.05 and adjusted for violations in sphericity. Data are presented as mean ± standard deviation, with values first averaged for each participant to prevent individuals with a larger number of MUs from having an undue influence on the results. 5.3 Results 5.3.1 Agonist activity: During agonist contractions, low-threshold MUs (recruitment threshold <10% MVC torque) were successfully sampled in all participants, with at least two per person and totals of 107 and 90 MUs for biceps and triceps brachii, respectively. Recruitment thresholds of the low-threshold MUs were 4.4 ± 1.8% MVC for biceps brachii (Table 5.1) and 3.8 ± 0.9% MVC for triceps brachii (Table 5.2). These recruitment thresholds were not different between the 50 and 100% MVC contractions for biceps brachii (p = 0.93) nor for triceps brachii (p = 0.26) across units recruited during both 50 and 100% MVC contractions. MU discharge rate was 9.8 ± 1.6% for biceps brachii low threshold units and 10.6 ± 1.3% for triceps brachii units. Lastly, co-activation was 8.3 ± 6.6% for biceps brachii during ramped elbow extensor MVCs and 15.2 ± 7.3% for triceps brachii during ramped elbow flexor MVCs. Despite appreciable surface EMG-detected 97 co-activation, antagonist single MU activity was recorded in 4 of 14 participants during elbow extension contractions and in only 1 participant during elbow flexion contractions (Figure 5.2). Figure 5.2 Representative traces of single MUs that were recruited during both agonist (left) and antagonist (right) contractions from biceps (A) and triceps brachii (B). Each tracing includes an overlay of five action potentials. 98 Table 5.1 Single MU data recorded from biceps brachii of each participant. Number of MUs recruited RT (% MVC torque) Initial MUDR (Hz) RT (% MVC torque) RMS (% MVC RMS) Initial MUDR (Hz) Ag Antag Ag Ag Ag Antag Ag Antag Ag Antag 9 0 3.4 10.6 - - - - - - 8 16 4.4 10.8 10.5 47.9 9.4 7.8 9.7 7.1 6 0 7.5 8.9 - - - - - - 8 0 2.1 8.9 - - - - - - 3 0 8.3 10.4 - - - - - - 7 5 1.6 13.0 8.8 63.7 6.7 6.0 12.5 11.2 6 0 4.9 8.4 - - - - - - 12 0 3.9 7.8 - - - - - - 8 0 4.5 8.7 - - - - - - 6 0 4.9 7.5 - - - - - - 5 0 5.6 10.4 - - - - - - 9 0 4.0 10.1 - - - - - - 12 16 3.3 9.8 11.4 52.9 7.7 6.0 13.6 10.5 8 5 3.5 12.4 7.2 63.7 4.7 5.9 10.9 8.7 107 42 4.4 ± 1.8 9.8 ± 1.6 9.5 ± 1.9 57.1 ± 7.9 7.1 ± 2.0 6.4 ± 0.9 11.7 ± 1.7 9.4 ± 1.8 Each row represents one of the 14 participants, with bolded text indicating group totals (left section) or mean values (middle and right sections). Left section: number of low-threshold (<10% MVC torque) MUs recruited during agonist (Ag) and antagonist (Antag) contractions. Middle section: mean recruitment threshold (RT) and initial MU discharge rate (MUDR) of all low-threshold agonist MUs. Right section: mean RT, surface EMG activity at recruitment, and initial MUDR for all MUs that were recorded from both agonist and antagonist contractions. 99 Table 5.2 Single MU data recorded from triceps brachii of each participant. Number of MUs recruited RT (% MVC torque) Initial MUDR (Hz) RT (% MVC torque) RMS (% MVC RMS) Initial MUDR (Hz) Ag Antag Ag Ag Ag Antag Ag Antag Ag Antag 9 0 2.8 11.5 - - - - - - 6 0 4.3 8.0 - - - - - - 3 0 5.3 9.4 - - - - - - 8 0 3.9 11.2 - - - - - - 5 0 2.3 10.9 - - - - - - 4 5 3.7 9.4 19.2 40.1 20.5 29.7 14.9 11.2 10 0 2.0 9.0 - - - - - - 10 0 3.5 10.1 - - - - - - 4 0 3.7 11.3 - - - - - - 6 0 3.9 12.6 - - - - - - 4 0 4.8 11.4 - - - - - - 6 0 3.9 11.9 - - - - - - 7 0 4.6 9.8 - - - - - - 8 0 4.3 11.6 - - - - - - 90 5 3.8 ± 0.9 10.6 ± 1.3 19.2 40.1 20.5 29.7 14.9 11.2 Each row represents one of the 14 participants, with bolded text indicating group totals (left section) or mean values (middle and right sections). Left section: number of low-threshold (<10% MVC torque) MUs recruited during agonist (Ag) and antagonist (Antag) contractions. Middle section: mean recruitment threshold (RT) and initial MU discharge rate (MUDR) of all low-threshold agonist MUs. Right section: mean RT, surface EMG activity at recruitment, and initial MUDR for all MUs that were recorded from both agonist and antagonist contractions. 5.3.2 Antagonist activity during elbow extensions: In the four individuals with antagonist MU activity during elbow extension contractions, 16 antagonist MUs were decomposed in the first individual, 5 in the second, 16 in the third (during maximal ramped contractions only), and 5 in the fourth individual. Additional antagonist MUs were likely recruited in the third individual; however, the signal became too noisy for reliable decomposition. In these same individuals, 72 agonist MUs were recruited up to 30% 100 MVC torque and 35 MUs were recruited up to 10% MVC torque. Of these 35 low-threshold units, 10 failed to be recruited during antagonist contractions. All except for three antagonist MUs matched a MU recorded below 30% MVC torque during agonist contractions. Of the 42 biceps brachii MUs (pooled across all participants) that were recruited during antagonist contractions, only 25 were recruited below 10% MVC torque during an agonist contraction, with the highest recruitment threshold being 39.3% MVC torque. When recruitment threshold is considered in terms of global neural activity, biceps brachii MUs recorded during antagonistic contractions were seemingly recruited at an RMS EMG value similar to that of an agonist contraction (mean values of 6.4 ± 0.9 and 7.1 ± 2.0% MVC RMS, respectively). However, these MUs had an initial discharge rate of 9.4 ± 1.8 when acting as an antagonist compared to 11.7 ± 1.7Hz when acting as an agonist. 5.3.3 Antagonist activity during elbow flexions: Of the 5 triceps brachii MUs that were recruited during both agonist and antagonist contractions in the one individual, only 2 were recruited below 10% MVC torque during an agonist contraction. These 5 triceps brachii MUs were recruited at 29.8 ± 6.0% RMS EMG when acting as an antagonist, compared to 20.5 ± 12.0% during agonist contractions, and had an initial discharge rate of 11.2 ± 1.5 and 14.9 ± 2.6Hz when acting as an antagonist vs. agonist, respectively. Of note, a few individuals had a difficult time completely relaxing their muscles, and thus, recruited ‘antagonist’ MU(s) at rest. These units either turned off during the antagonist contraction or their firing rate slowed significantly. 5.4 Discussion Although 197 low-threshold MUs were sampled from participants during agonist contractions, minimal, if any, activity was detected from these MUs during antagonist contractions. 101 Specifically, for contractions up to 100% MVC torque, we did not record any antagonist MUs in 10 of 14 participants. However, based on surface EMG, these 10 individuals showed ~10% co-activation of biceps and triceps brachii during maximal elbow flexion and extension. Taken together, these results provide strong evidence that MUs recruited early during an agonist contraction do not appear to be among those first recruited during an antagonist contraction. For the three individuals who showed antagonist MU activity during an elbow extension contraction and the one individual who showed antagonist activity during both elbow flexion and extension contractions, only ~57% of the antagonist MUs matched those recruited at less than 10% MVC torque during an agonist contraction, with most of the remainder recruited before 30% MVC torque. Hence, even for these few individuals, orderly recruitment does not appear to hold when MUs act as an antagonist. It is possible that specific antagonist MUs (possibly low-threshold units when acting as an agonist) are more susceptible to higher net inhibition, causing some higher threshold MUs to be recruited first. However, at least for the biceps brachii, the global neural activity at recruitment was seemingly similar between agonist and antagonist contractions, which may indicate that low-threshold MUs are not necessarily more susceptible to inhibition when acting as an antagonist. Although not statistically tested due to the limited number of MUs recorded during antagonist contractions, the mean discharge rate at recruitment for both biceps and triceps brachii MUs was lower when acting as an antagonist versus an agonist for each participant, which provides further evidence of the unique activation characteristics for antagonist contractions. Despite these intriguing speculations based on data from four individuals, the most striking and important result of the present study is that no antagonist MU activity was detected across three or four intramuscular recording sites for 10 of the 14 participants. Given this 102 surprising finding, it would be reasonable to question whether the lack of antagonist MUs is a result of a sampling error such that the fine-wire electrodes were not recording antagonist units that were active. However, this explanation is highly unlikely given that we could record several low-threshold MUs, including the same ones, when a very low-intensity (less than 4% MVC torque) agonist contraction was performed before and after a ramped antagonist MVC (see Figure 5.3). If it is true that other antagonist MUs were active and we were simply not recording from those sites in 10 and 13 out of 14 participants, at minimum, our results still show that the same units to be recruited initially during an agonist contraction are not among those first recruited during an antagonist contraction. Thus, it is clear from the dataset that MU behaviour is different than the typical orderly recruitment strategy observed during ramp contraction for an agonist muscle group. The absence of antagonist MU activity in nearly all participants aligns with De Luca & Mambrito’s proposal of a centrally-mediated control scheme of reciprocal flexion and extension commands that allows MUs in the extensor muscles to be inhibited when a predictable flexion contraction is occurring and vice versa (1987). This reasoning then raises the question of how do we explain the marked co-activation during strong contractions? This co-activation in the absence of observable individual MU recruitment of the antagonist could be attributed to other factors such as recruitment of MUs in different parts of the muscle, cross-talk from other muscles, or potentially a combination of these factors (De Luca & Merletti, 1988). The co-activation values reported in this study corroborate published data (Bazzucchi et al., 2006; Klein et al., 2001; Maeo et al., 2013), which suggests that our sample was not made up of individuals with abnormally low levels of coactivation. Further, when acting as an agonist, the initial 103 discharge rates of low-threshold biceps and triceps brachii MUs (~10 and ~15Hz, respectively) in the current study align with other reports (Christie et al., 2009; Griffin et al., Figure 5.3 Single participant data showing four triceps brachii (TB) intramuscular recordings before, during, and after a ramped MVC of the elbow flexors. To exclude the possibility that the absence of antagonist MU activity from TB was caused by a recording issue, the participant performed a very weak contraction (~4% MVC torque) of the elbow extensors before and after the MVC. During these minimal-effort contractions, there were at least two low-threshold MUs recorded on each channel (see individual traces), yet none of these MUs, nor any others, were detected when TB acted as an antagonist. The dashed horizontal line indicates the torque baseline when both muscle groups are relaxed. 104 2001; Kukulka & Clamann, 1981; Riley et al., 2008), indicating that we recorded expected MU behaviour. Our findings of minimal antagonist MU activity alongside ~10% co-activation during ramped isometric contractions could have important practical implications. However, due to the scarcity of data, future studies should include an even larger number of intramuscular recording sites or implement assessments based on surface arrays with a high electrode count. Overall, it is important that we gain a better understanding of the neural control of antagonistic control of MUs because co-activation is important for joint and limb stability, as well as accuracy of goal-directed movements (e.g., Gribble et al., 2003). Further, elevated co-activation has been suggested to contribute to muscle weakness or poorer functional outcomes in older adults (Klein et al., 2001) and patient groups (e.g., Lodha et al., 2019). Our current findings question the source of co-activation as assessed by surface EMG, which limits its utility in both research and clinical settings. For example, biomedical interfacing devices are currently being used to modulate EMG co-activation in certain populations (Mugler et al., 2019); however, without knowing the source of this co-activation, it could be difficult to improve treatment protocols. In conclusion, despite detection of many low-threshold MUs during agonist contractions, and the presence of ~10% co-activation according to surface EMG, antagonist MUs were only recorded from 4 of 14 participants during ramped contractions to maximal torque. Of the limited number of antagonist MUs sampled, only ~57% were among those with a low recruitment threshold when acting as an agonist (<10% MVC torque), which, along with the co-activation data, indicates that MU recruitment order differs when a muscle acts as an agonist vs. antagonist. Although scarcity of data prohibited statistical analysis, 38 of 47 antagonist MUs had an initial discharge rate as an antagonist that was at least 1Hz lower than when acting as an agonist (mean 105 of 2.6Hz). This disparity for discharge rate at recruitment provides additional evidence of a markedly different activation pattern for MUs during antagonist than agonist contractions. Given the importance of co-activation to overall function, and the apparent deviation from orderly recruitment of antagonist MUs, additional investigation of MU behaviour during antagonist contractions is imperative. 106 Chapter 6: Conclusion The overall aim of my dissertation was to examine the effects of: a) mental fatigue, induced by either an acute cognitively demanding task or sleep deprivation, and b) physical fatigue on cortical and neuromuscular function. As a result of my third study assessing the effect of physical fatigue on neuromuscular outcomes, I discovered an interesting finding relating to antagonist MU behaviour. Thus, for my fourth study, I assessed antagonist MU behaviour during ramped, isometric contractions. The conclusions, strengths, and limitations of each study are presenting in the following section, followed by an overview of potential future directions, and, finally, a summary statement. 6.1 Effects of mental fatigue on cognitive and motor function - 6.1.1 Conclusions The main aim of my first study was to assess the effects of a 1-hour N-back task on cognitive and motor function. Specifically, I set out to determine whether acutely-induced mental fatigue would affect ongoing neural frequency activity, ERP amplitudes, inhibitory control task performance, gross and fine motor function, and perceived ratings of fatigue. Given the limited research on the recovery of mental fatigue, I measured these outcomes up to 60-mintues post-mental fatigue. The results of this study showed slowed ongoing neural frequency activity and increased ratings of perceived fatigue throughout the mentally-fatiguing task that lasted until at least 60 minutes post-task. However, motor skills were impaired initially then recovered by ~45 minutes post-task. These findings brought new insight to the field by showing for the first time that neural outcomes were impacted up to 60-minutes post-task, whereas motor task performance was not, suggesting different recovery time between cortical processing and actual performance. 107 These findings also show the importance of using objective measures of fatigue, given that cortical alterations (i.e., slowing of neural activity) are evident for at least 60-minutes post-mental fatigue, possibly suggesting that individuals need longer than originally expected to recover from a mentally-fatiguing task. 6.1.2 Strengths The main strength of this study was the use of behavioural, perceived, and objective measures of mental fatigue, and the use of these measures across a 1-hour long recovery period. Specifically, the finding of increased alpha and theta activity for at least 60 minutes post-fatigue provides information that was not otherwise identifiable by simple behavioural measures (task performance scores on an inhibitory control task, and gross and fine motor function tests). Another strength of this study was that EEG was used to identify cortical changes with mental fatigue, making this work easily transferrable to clinical/workplace practices due to the relatively quick, affordable, non-invasive nature of EEG recordings. 6.1.3 Limitations One limitation to this study was that there may have been a learning effect or participants may have changed their task strategies throughout the hour-long N-back task, which could explain why no differences in task performance were observed from the beginning to the end of the task. Further, although all participants were tested between 8:00 and 11:00am, it is possible that motor performance and alpha power were affected by time-of-day. Specifically, Higuchi et al. (2001) showed increased alpha power alongside reduced sleepiness and improved reaction times during a vigilance task at 11:00am compared to 8:00am. These results do not align with our study findings (i.e., we showed worse performance scores as the morning hours progressed); however, future studies could more tightly control for any possible time of day effects. 108 6.2 Effects of sleep deprivation on cognitive and motor function 6.2.1 Conclusions Sleep deprivation is highly prevalent and results in many negative social and behavioural outcomes, as well as workplace accidents. Despite this, we have a limited understanding of neural and motor implications of sleep deprivation. Thus, my second dissertation study aimed to assess the effects of 24 hours of sleep deprivation on cortical outcomes and task performance. Participants completed a complex gross motor function task as well as two inhibitory control GNG tasks, one of which involved emotion processing. EEG recordings were also obtained during a 3-minute resting period. Resting EEG power in the theta band was higher after sleep deprivation compared to a rested state. Participants were also slower in their response times and had worse accuracy on the emotional inhibitory control task vs. the inhibitory control task alone, suggesting some interference of emotion processing on task performance. ERPs were also impaired after sleep deprivation compared to control for both tasks. Lastly, during the complex gross motor function task, participants hit more distractor shapes. These results show, for the first time, that sleep deprivation leads to impairments in inhibitory control task performance, especially in the context of emotion processing, slowed neural processing, and impaired gross motor function. 6.2.2 Strengths This is the first study to employ a mixed emotional/inhibitory control task in the context of sleep deprivation. In addition to providing information on basic inhibitory control abilities as is measured during a shape GNG task, the emotional GNG task used in the current study provides 109 information on one’s ability to process emotional stimuli, and any interference these stimuli have on response inhibition. By using this task, my study provides insight into a person’s ability to process emotional stimuli and inhibit a response to a negative emotional stimulus while sleep deprived. This could have important implications for how individuals respond to certain social situations when deprived of sleep. Additionally, only one other study has assessed the effect of sleep deprivation on performance of a speed-accuracy movement task (Skurvydas et al., 2020). However, the study by Skurvydas and colleagues could not identify key aspects that were involved in the object hit and avoid task used in our study, such as bilateral arm coordination and inhibitory control- processes that are important for performing effective and accurate movements and avoiding distracting information. The all-female sample is another strength of the current study, given the underrepresentation of females in neurophysiological research alongside the high prevalence of females in nightshift occupations such as nursing, as well as life outside of one’s career, including post-partum. 6.2.3 Limitations The first limitation of this study was the use of only 12 participants. This is a relatively low sample size, especially for measures such as alpha and theta power, which have high variability within and across participants. Sensitivity analyses indicated that I was likely underpowered to detect small effects, and more likely to have inflated effect sizes for significant effects. Sleep quality was only measured via self-reports, which is not as accurate as objective measures such as actigraphy. Another limitation to the study is that the shape and emotional GNG tasks were not randomized. Despite the short duration of the shape GNG task (8.5 minutes), it is possible that it could have resulted in additional cognitive fatigue, thus, artificially impairing task performance/cortical processing to the emotional GNG task, which was performed second. 110 6.3 Effects of physical fatigue on task performance and neuromuscular outcomes 6.3.1 Conclusions It is becoming increasingly common to assess fatigability during a matched-EMG isometric protocol because of the ability to maintain motoneuron pool output during such a task (vs. increased neural drive required during a matched-torque fatiguing contraction). Despite the usefulness of a matched-EMG task, very little is known about the cortical activity and single MU discharge rates during this type of task compared to a matched-torque fatiguing task. Thus, for my third dissertation study, I had participants complete, on two separate days, a matched-torque or a matched-EMG 10-minute submaximal, isometric elbow flexion contraction. By measuring EEG and surface and intramuscular EMG, I found that the matched-torque protocol led to increased CMC, IMC and MU discharge rates, alongside reduced torque steadiness and VA. In contrast to these findings, there were no changes to CMC, IMC, torque steadiness or VA, and a reduction of MU discharge rates for the matched-EMG contraction. The multitude of measures assessed in the current study provide some insight into the different neural strategies of a matched-torque vs. a matched-EMG fatiguing protocol. Specifically, when greater demands are placed on the nervous system in the context of fatigue, it may rely more on increased rhythmic drive and increased firing rates of individual MUs, whereas when motoneuron pool output is maintained, the nervous system may rely more on MU rotation, among other compensatory mechanisms. 111 6.3.2 Strengths One important strength of the current study is that I assessed, for the first time in the context of a matched-EMG contraction, MU discharge rates using fine-wire intramuscular EMG recordings, as well as CMC and IMC. Each of these outcome variables was also contrasted to data collected from the more traditional submaximal fatiguing contraction (i.e., matched-torque). Using global measures of neural activity (i.e., surface EMG and EEG) alongside individual MU recordings, this study provided the most comprehensive overview yet of the neuromuscular alterations occurring during these two submaximal fatiguing tasks. The use of fine-wire needle EMG electrodes also allowed us to maintain a fixed position for the EMG electrode throughout the duration of the contraction, allowing for more accurate MU tracking compared to other methods such as surface EMG decomposition, which can be influenced by slight position changes and sweating, for example, during a fatiguing contraction. 6.3.3 Limitations One limitation to the current study is that there were different numbers of MUs tracked across participants. For example, at the low end, one participant only had 5 MUs that were decomposed during one of the fatigue protocols, whereas 20 MUs were isolated for another participant. Although the statistics were run across participants (vs. individual MUs), each MU from the individual with only 5 units would hold more weight than a MU from the individual with 20 units. Another limitation to this study is that we did not control for menstrual cycle in females. In her review, Hunter (2009) notes that there is little evidence to suggest that performance during a fatiguing contraction differs across the menstrual cycle (e.g., Hunter et al., 2004, 2006; Janse De Jonge et al., 2001); however, a more recent study showed evidence of potential differences across the menstrual cycle for some neuromuscular measures (VA and short-interval intracortical 112 inhibition) in the context of an intermittent isometric knee extensors fatiguing task (Ansdell et al., 2019). Thus, the failure to control for menstrual cycle across female participants should be noted as a potential limitation of the current study. 6.4 Antagonist motor unit behaviour 6.4.1 Conclusions My fourth dissertation study assessed, for the first time, whether MUs behave similarly when acting as an antagonist vs. an agonist. To do this, I examined individual MU activity in triceps and biceps brachii during ramped elbow flexion and extension contractions. Intramuscular recordings showed that the MUs first recruited during an agonist contraction were seldom among those first recruited during an antagonist contraction. Importantly, most participants did not show any antagonist MU activity according to the intramuscular EMG recordings during isometric ramped contractions of either the elbow flexors or extensors, despite appreciable co-activation at the surface EMG level. This study highlights an important new finding, which is that MU recruitment is modified when acting as an antagonist. 6.4.2 Strengths The main strength of this study is that it was the first to assess individual MU activity during both agonist and antagonist contractions. Another important aspect of the study was that we used 3-4 intramuscular EMG electrodes in both biceps and triceps brachii during ramped contractions to identify agonist/antagonist MU behaviour. Using 3-4 intramuscular recordings is a strength given that: a) intramuscular EMG is the gold standard method of tracking individual MUs compared to other methods such as decomposition of surface EMG, and b) the high number of 113 recordings allowed for a relatively large number of MUs to be sampled. Lastly, we had participants perform multiple ramped contractions up to 100% MVC torque for both biceps and triceps brachii, providing several opportunities to successfully isolate individual MUs. 6.4.3 Limitations One limitation to the study was the possible human error involved in decomposition/matching of MUs for the few individuals who had antagonist MU recruitment. MU action potentials were matched between agonist and antagonist contractions according to size and shape as determined by human eye. However, 10 or 13 of 14 participants had no antagonist MUs, thus, this possible error would have minimal influence on the key study findings. 6.5 Future directions For my first dissertation study, I found that alpha power and perceived fatigue were still elevated at 60 minutes into the recovery period. Thus, a logical next step is to run an additional study that assesses neural activity for longer than 60-minutes post-mental fatigue task. It is possible that neural processing is slowed for hours after a prolonged long cognitively-demanding task. Additionally, although we had participants complete a task aversion rating scale, it would have been interesting to directly assess participant motivation, given the strong link between motivation and task performance in the context of mental fatigue (Herlambang et al., 2021). My second dissertation study involved an all-female sample; thus, future work should examine the possibility that sleep deprivation affects cortical and motor outcomes differently for females and males. Further, our study employed an acute 24-hour sleep deprivation model. It would be worthwhile to assess the effects of chronic, incomplete sleep deprivation on neural and motor 114 function. Studies done on mice have found that chronic sleep deprivation may lead to neurodegenerative diseases including Alzheimer’s because of build-up of waste products that accumulate in the brain and contribute to the destruction of neurons (Bobic et al., 2016). It has also been shown that the negative effects of acute sleep deprivation may be alleviated with caffeine, nicotine and physical activity; however little is known about the neural mechanisms underlying these changes, and it is unknown how these interventions could affect cortical and motor outcomes of chronic sleep deprivation (Bobić et al., 2016). One future direction for my third dissertation study could be to assess contractile properties of the muscle alongside MU discharge rate, given their possible relationship according to the muscle wisdom hypothesis. I also only investigated individual MU recordings from the biceps brachii, one of three primary elbow flexor muscles. As such, a future study should assess MU discharge rates and CMC/IMC from brachioradialis to assess possible compensatory mechanisms at play during a matched-EMG vs. matched-torque fatiguing contraction. Further, there were multiple instances of a Sex-based interaction, however, I was underpowered to adequately assess sex differences in the current study. Thus, future research should address possible sex-based differences in CMC, IMC, and MU discharge rates during a matched-EMG contraction, when motoneuron pool output is maintained. Lastly, given that my dissertation involved studies assessing both mental and physical fatigue, and the common neural networks underlying both types of fatigue that have been reported, it would be interesting to employ a study which directly assesses the relationship between mental and physical fatigue. In addition to neural, cognitive, and motor performance outcomes, it would seem important to assess participant’s levels of motivation during such a study given the direct role it plays in both mental and physical fatigue. 115 Finally, for my fourth dissertation study, I chose to measure MU activity using intramuscular bipolar EMG electrodes. Using intramuscular electrodes designed for less focal recordings could have provided additional information on whether more ‘distant’ MUs were firing in the antagonist muscle. Additionally, one could assess whether my findings apply to other agonist-antagonist muscle groups, namely lower-limb muscles with different functional purposes. Lastly, another future direction could be to run a position-matching task, rather than a fixed, isometric task as was used in the current study. Such an approach would provide information on antagonist MU recruitment strategies for contractions that require maintenance of joint position (De Luca & Mambrito, 1987). 6.6 Summary Both mental and physical fatigue are complex processes that affect every individual on the planet, and some on a chronic basis. Despite the well-known impairments that mental and physical fatigue have on behaviour and task performance, less is known about their effect on cortical and neuromuscular function. My dissertation sheds light on the neural underpinnings of fatigue and fatigue-related performance outcomes. Specifically, I found less efficient neural processing and slower neural activity in response to acute mental fatigue and sleep deprivation. In the context of physical fatigue, my third dissertation study showed that under certain conditions (i.e., when motoneuron pool output is maintained) the nervous system may employ strategies such as MU rotation over increasing MU discharge rate and rhythmic firing between cortex and muscle, which will impair force steadiness. This new information about the mechanisms underlying fatigue could be used to better inform practices related to workplace safety. For example, by developing less subjective measures of mental fatigue, companies could more objectively assess worker fatigue levels. There are also implications for the physical 116 fatigue research completed during my dissertation, which could include better practices for physical training and/or clinical populations experiencing muscle fatigue. Specifically, a matched-EMG fatiguing contraction appears to rely on different compensatory mechanisms to maintain task production, and thus, depending on the source of motor impairment, could either be a less or a more optimal method of investigating fatigue. For example, individuals with incomplete spinal cord injury appear to have lower CMC values compared to healthy controls, (Cremoux et al., 2017; Norton & Gorassini, 2006), thus, it could be important to employ fatiguing tasks which increase CMC as a method of maintaining task performance for therapeutic purposes. Alternatively, based on our study findings, CMC and MU discharge rate could be used as methods to objectively assess fatigue progression in clinical populations. Lastly, by studying antagonist MU activity I advanced the overall understanding of MU discharge strategies in antagonist muscles by showing, for the first time, that the first MUs to discharge during an agonist contraction do not appear to be among those first recruited during an antagonist contraction. This improved understanding of antagonist MU recruitment could be important for therapies aimed at assisting with motor function/control through brain/neuromuscular machine interfacing devices, given the promising use of individual MUs as a source of control for brain machine interfacing devices (Formento et al., 2021). 117 Bibliography Adam, A., & De Luca, C. J. (2005). Firing rates of motor units in human vastus lateralis muscle during fatiguing isometric contractions. Journal of Applied Physiology, 99(1). https://doi.org/10.1152/japplphysiol.01344.2004 Anderson, C., & Platten, C. R. (2011). Sleep deprivation lowers inhibition and enhances impulsivity to negative stimuli. Behavioural Brain Research, 217(2), 463–466. https://doi.org/10.1016/j.bbr.2010.09.020 Ansdell, P., Brownstein, C. G., Skarabot, J., Hicks, K. M., Simoes, D. C. M., Thomas, K., Howatson, G., Hunter, S. K., & Goodall, S. (2019). Menstrual cycle-associated modulations in neuromuscular function and fatigability of the knee extensors in eumenorrheic women. Journal of Applied Physiology, 126(6). https://doi.org/10.1152/japplphysiol.01041.2018 Baker, S. N. (2007). Oscillatory interactions between sensorimotor cortex and the periphery. In Current Opinion in Neurobiology (Vol. 17, Issue 6, pp. 649–655). https://doi.org/10.1016/j.conb.2008.01.007 Baratta, R., Solomonow, M., Zhou, B. H., Letson, D., Chuinard, R., & D’Ambrosia, R. (1988). Muscular coactivation. The role of the antagonist musculature in maintaining knee stability. The American Journal of Sports Medicine, 16(2). https://doi.org/10.1177/036354658801600205 Barry, R. J., Steiner, G. Z., De Blasio, F. M., Fogarty, J. S., Karamacoska, D., & MacDonald, B. (2020). Components in the P300: Don’t forget the Novelty P3! Psychophysiology, 57(7). https://doi.org/10.1111/psyp.13371 Barwick, F., Arnett, P., & Slobounov, S. (2012). EEG correlates of fatigue during administration of a neuropsychological test battery. Clinical Neurophysiology, 123(2), 278–284. https://doi.org/10.1016/j.clinph.2011.06.027 Bauer, C. C. C., Rozenkrantz, L., Caballero, C., Nieto-Castanon, A., Scherer, E., West, M. R., Mrazek, M., Phillips, D. T., Gabrieli, J. D. E., & Whitfield-Gabrieli, S. (2020). Mindfulness training preserves sustained attention and resting state anticorrelation between default-mode network and dorsolateral prefrontal cortex: A randomized controlled trial. Human Brain Mapping, 41(18). https://doi.org/10.1002/hbm.25197 Bazzucchi, I., Sbriccoli, P., Marzattinocci, G., & Felici, F. (2006). Coactivation of the elbow antagonist muscles is not affected by the speed of movement in isokinetic exercise. Muscle and Nerve, 33(2). https://doi.org/10.1002/mus.20462 Belanger, A. Y., & McComas, A. J. (1981). Extent of motor unit activation during effort. Journal of Applied Physiology Respiratory Environmental and Exercise Physiology, 51(5). https://doi.org/10.1152/jappl.1981.51.5.1131 Bernardi, G., Siclari, F., Yu, I., Zennig, C., Bellesi, M., Ricciardi, E., Cirelli, C., Ghilardi, M. F., Pietrini, P., & Tononi, G. (2015). Neural and behavioral correlates of extended training during sleep deprivation in humans: Evidence for local, task-specific effects. Journal of Neuroscience, 35(11), 4487–4500. https://doi.org/10.1523/JNEUROSCI.4567-14.2015 Bigland-Ritchie, B., Jones, D. A., & Woods, J. J. (1979). Excitation frequency and muscle fatigue: Electrical responses during human voluntary and stimulated contractions. Experimental Neurology, 64(2). https://doi.org/10.1016/0014-4886(79)90280-2 Bigland-Ritchie, B. R., Dawson, N. J., Johansson, R. S., & Lippold, O. C. (1986). Reflex origin for the slowing of motoneurone firing rates in fatigue of human voluntary contractions. The Journal of Physiology, 379(1). https://doi.org/10.1113/jphysiol.1986.sp016263 118 Bigland-Ritchie, B., Rice, C. L., Garland, S. J., & Walsh, M. L. (1995). Task-dependent factors in fatigue of human voluntary contractions. In Advances in Experimental Medicine and Biology (Vol. 384). https://doi.org/10.1007/978-1-4899-1016-5_29 Bobić, T. T., Šečić, A., Zavoreo, I., Matijević, V., Filipović, B., Kolak, Ž., Kes, V. B., Ciliga, D., & Sajković, D. (2016). The impact of sleep deprivation on the brain. Acta Clinica Croatica, 55(3), 469–473. https://doi.org/10.20471/acc.2016.55.03.17 Boksem, M. A. S., Meijman, T. F., & Lorist, M. M. (2005). Effects of mental fatigue on attention: An ERP study. Cognitive Brain Research, 25(1), 107–116. https://doi.org/10.1016/j.cogbrainres.2005.04.011 Bokura, H., Yamaguchi, S., & Kobayashi, S. (2001). Electrophysiological correlates for response inhibition in a Go/NoGo task. Clinical Neurophysiology, 112(12), 2224–2232. https://doi.org/10.1016/S1388-2457(01)00691-5 Boonstra, T. W., Daffertshofer, A., & Beek, P. J. (2005). Effects of sleep deprivation on event-related fields and alpha activity during rhythmic force production. Neuroscience Letters, 388(1). https://doi.org/10.1016/j.neulet.2005.06.045 Boonstra, T. W., Stins, J. F., Daffertshofer, A., & Beek, P. J. (2007). Effects of sleep deprivation on neural functioning: An integrative review. In Cellular and Molecular Life Sciences (Vol. 64, Issues 7–8, pp. 934–946). https://doi.org/10.1007/s00018-007-6457-8 Boonstra, Tjeerd W., & Breakspear, M. (2012). Neural mechanisms of intermuscular coherence: Implications for the rectification of surface electromyography. Journal of Neurophysiology, 107(3). https://doi.org/10.1152/jn.00066.2011 Bourke, T. C., Lowrey, C. R., Dukelow, S. P., Bagg, S. D., Norman, K. E., & Scott, S. H. (2016). A robot-based behavioural task to quantify impairments in rapid motor decisions and actions after stroke. Journal of NeuroEngineering and Rehabilitation, 13(1), 1–13. https://doi.org/10.1186/s12984-016-0201-2 Bruin, K. J., & Wijers, A. A. (2002). Inhibition, response mode, and stimulus probability: A comparative event-related potential study. Clinical Neurophysiology, 113(7), 1172–1182. https://doi.org/10.1016/S1388-2457(02)00141-4 Burle, B., Vidal, F., & Bonnet, M. (2004). Electroencephalographic nogo potentials in a no-movement context: The case of motor imagery in humans. Neuroscience Letters, 360(1–2), 77–80. https://doi.org/10.1016/j.neulet.2004.02.034 Cain, S. W., Silva, E. J., Chang, A. M., Ronda, J. M., & Duffy, J. F. (2011). One night of sleep deprivation affects reaction time, but not interference or facilitation in a Stroop task. Brain and Cognition, 76(1). https://doi.org/10.1016/j.bandc.2011.03.005 Cajochen, C., Brunner, D. P., Krauchi, K., Graw, P., & Wirz-Justice, A. (1995). Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness. Sleep, 18(10), 890–894. https://doi.org/10.1093/sleep/18.10.890 Carpentier, A., Duchateau, J., & Hainaut, K. (2001). Motor unit behaviour and contractile changes during fatigue in the human first dorsal interosseus. Journal of Physiology, 534(3). https://doi.org/10.1111/j.1469-7793.2001.00903.x Centers for Disease Control and Prevention (CDC). (2011). Effect of short sleep duration on daily activities--United States, 2005-2008. MMWR. Morbidity and Mortality Weekly Report, 60(8). Christie, A., Kamen, G., Greig Inglis, J., & Gabriel, D. A. (2009). Relationships between surface EMG variables and motor unit firing rates. European Journal of Applied Physiology, 107(2). https://doi.org/10.1007/s00421-009-1113-7 119 Chuah, L. Y. M., Dolcos, F., Chen, A. K., Zheng, H., Parimal, S., & Chee, M. W. L. (2010). Sleep deprivation and interference by emotional distracters. Sleep, 33(10), 1305–1313. https://doi.org/10.1093/sleep/33.10.1305 Cohen, M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice. In MIT Press. Connell, A., Danzo, S., Magee, K., & Dawson, G. (2020). Rumination in Early Adolescent Girls: An EEG Study of Cognitive Control and Emotional Responding in an Emotional Go/NoGo Task. Cognitive, Affective and Behavioral Neuroscience, 20(1), 181–194. https://doi.org/10.3758/s13415-019-00761-9 Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3(3), 201–215. https://doi.org/10.1038/nrn755 Cote, K. A., Mondloch, C. J., Sergeeva, V., Taylor, M., & Semplonius, T. (2014). Impact of total sleep deprivation on behavioural neural processing of emotionally expressive faces. Experimental Brain Research, 232(5), 1429–1442. https://doi.org/10.1007/s00221-013-3780-1 Cote, Kimberly A., Lustig, K. A., & MacDonald, K. J. (2019). The Role of Sleep in Processing Emotional Information. In Handbook of Behavioral Neuroscience (Vol. 30). https://doi.org/10.1016/B978-0-12-813743-7.00033-5 Cremoux, S., Tallet, J., Dal Maso, F., Berton, E., & Amarantini, D. (2017). Impaired corticomuscular coherence during isometric elbow flexion contractions in humans with cervical spinal cord injury. European Journal of Neuroscience, 46(4). https://doi.org/10.1111/ejn.13641 Dalton, B. H., Harwood, B., Davidson, A. W., & Rice, C. L. (2010). Recovery of motoneuron output is delayed in old men following high-intensity fatigue. Journal of Neurophysiology, 103(2). https://doi.org/10.1152/jn.00908.2009 De Luca, C. J., & Mambrito, B. (1987). Voluntary control of motor units in human antagonist muscles: Coactivation and reciprocal activation. Journal of Neurophysiology, 58(3). https://doi.org/10.1152/jn.1987.58.3.525 De Luca, Carlo J., & Merletti, R. (1988). Surface myoelectric signal cross-talk among muscles of the leg. Electroencephalography and Clinical Neurophysiology, 69(6). https://doi.org/10.1016/0013-4694(88)90169-1 Denny-brown, D., & Pennybacker, J. B. (1938). Fibrillation and fasciculation in voluntary muscle. Brain, 61(3). https://doi.org/10.1093/brain/61.3.311 Donkers, F. C. L., & Van Boxtel, G. J. M. (2004). The N2 in go/no-go tasks reflects conflict monitoring not response inhibition. Brain and Cognition, 56(2 SPEC. ISS.), 165–176. https://doi.org/10.1016/j.bandc.2004.04.005 Dorrian, J., Lamond, N., & Dawson, D. (2000). The ability to self-monitor performance when fatigued. Journal of Sleep Research, 9(2), 137–144. https://doi.org/10.1046/j.1365-2869.2000.00195.x Drummond, S. P. A., Meloy, M. J., Yanagi, M. A., Orff, H. J., & Brown, G. G. (2005). Compensatory recruitment after sleep deprivation and the relationship with performance. Psychiatry Research - Neuroimaging, 140(3), 211–223. https://doi.org/10.1016/j.pscychresns.2005.06.007 Eimer, M. (2012). The Face-Sensitive N170 Component of the Event-Related Brain Potential. In Oxford Handbook of Face Perception. https://doi.org/10.1093/oxfordhb/9780199559053.013.0017 Enoka, R. M., & Stuart, D. G. (1992). Neurobiology of muscle fatigue. In Journal of Applied 120 Physiology (Vol. 72, Issue 5, pp. 1631–1648). https://doi.org/10.1152/jappl.1992.72.5.1631 Enoka, Roger M., & Duchateau, J. (2016). Translating fatigue to human performance. Medicine and Science in Sports and Exercise, 48(11). https://doi.org/10.1249/MSS.0000000000000929 Farina, D., Negro, F., & Jiang, N. (2013). Identification of common synaptic inputs to motor neurons from the rectified electromyogram. Journal of Physiology, 591(10). https://doi.org/10.1113/jphysiol.2012.246082 Finelli, L. A., Baumann, H., Borbély, A. A., & Achermann, P. (2000). Dual electroencephalogram markers of human sleep homeostasis: Correlation between theta activity in waking and slow-wave activity in sleep. Neuroscience, 101(3), 523–529. https://doi.org/10.1016/S0306-4522(00)00409-7 Finsterer, J., & Mahjoub, S. Z. (2014). Fatigue in Healthy and Diseased Individuals. American Journal of Hospice and Palliative Medicine, 31(5), 562–575. https://doi.org/10.1177/1049909113494748 Folstein, J. R., & Van Petten, C. (2008). Influence of cognitive control and mismatch on the N2 component of the ERP: A review. In Psychophysiology (Vol. 45, Issue 1, pp. 152–170). https://doi.org/10.1111/j.1469-8986.2007.00602.x Formento, E., Botros, P., & Carmena, J. M. (2021). Skilled independent control of individual motor units via a non-invasive neuromuscular machine interface. Journal of Neural Engineering, 18(6). https://doi.org/10.1088/1741-2552/ac35ac Fuglevand, A. J., & Keen, D. A. (2003). Re-evaluation of muscle wisdom in the human adductor pollicis using physiological rates of stimulation. Journal of Physiology, 549(3). https://doi.org/10.1113/jphysiol.2003.038836 Gandevia, S. C. (2001). Spinal and supraspinal factors in human muscle fatigue. In Physiological Reviews (Vol. 81, Issue 4, pp. 1725–1789). https://doi.org/10.1152/physrev.2001.81.4.1725 Gandevia, S. C., Petersen, N., Butler, J. E., & Taylor, J. L. (1999). Impaired response of human motoneurones to corticospinal stimulation after voluntary exercise. Journal of Physiology, 521(3), 749–759. https://doi.org/10.1111/j.1469-7793.1999.00749.x Garland, S. J., Enoka, R. M., Serrano, L. P., & Robinson, G. A. (1994). Behavior of motor units in human biceps brachii during a submaximal fatiguing contraction. Journal of Applied Physiology, 76(6). https://doi.org/10.1152/jappl.1994.76.6.2411 Goldman, R. I., Stern, J. M., Engel, J., & Cohen, M. S. (2002). Simultaneous EEG and fMRI of the alpha rhythm. NeuroReport, 13(18), 2487–2492. https://doi.org/10.1097/00001756-200212200-00022 Gosselin, A., De Koninck, J., & Campbell, K. B. (2019). Disentangling specific inhibitory versus general decision-making processes during sleep deprivation using a Go/NoGo ERP paradigm. International Journal of Psychophysiology, 141, 18–27. https://doi.org/10.1016/j.ijpsycho.2019.04.007 Griffin, L., Garland, S. J., Ivanova, T., & Gossen, E. R. (2001). Muscle vibration sustains motor unit firing rate during submaximal isometric fatigue in humans. Journal of Physiology, 535(3). https://doi.org/10.1111/j.1469-7793.2001.00929.x Gujar, N., Yoo, S. S., Hu, P., & Walker, M. P. (2011). Sleep deprivation amplifies reactivity of brain reward networks, biasing the appraisal of positive emotional experiences. Journal of Neuroscience, 31(12), 4466–4474. https://doi.org/10.1523/JNEUROSCI.3220-10.2011 Guo, Z., Chen, R., Zhang, K., Pan, Y., & Wu, J. (2016). The impairing effect of mental fatigue on visual sustained attention under monotonous multi-object visual attention task in long 121 durations: An event-related potential based study. PLoS ONE, 11(9). https://doi.org/10.1371/journal.pone.0163360 Haubert, A., Walsh, M., Boyd, R., Morris, M., Wiedbusch, M., Krusmark, M., & Gunzelmann, G. (2018). Relationship of event-related potentials to the vigilance decrement. Frontiers in Psychology, 9(MAR). https://doi.org/10.3389/fpsyg.2018.00237 Haynes, E. M. K., & Kim, C. (2021). Antagonist surface electromyogram decomposition and the case of the missing motor units. Journal of Neurophysiology, 126(6). https://doi.org/10.1152/jn.00435.2021 Henneman, E. (1957). Relation between size of neurons and their susceptibility to discharge. Science, 126(3287). https://doi.org/10.1126/science.126.3287.1345 Herlambang, M. B., Cnossen, F., & Taatgen, N. A. (2021). The effects of intrinsic motivation on mental fatigue. PLoS ONE, 16(1 January). https://doi.org/10.1371/journal.pone.0243754 Héroux, M. E., & Gandevia, S. C. (2013). Human muscle fatigue, eccentric damage and coherence in the EMG. Acta Physiologica, 208(4), 294–295. https://doi.org/10.1111/apha.12133 Herrmann, C. S., & Knight, R. T. (2001). Mechanisms of human attention: Event-related potentials and oscillations. Neuroscience and Biobehavioral Reviews, 25(6), 465–476. https://doi.org/10.1016/S0149-7634(01)00027-6 Higuchi, S., Liu, Y., Yuasa, T., Maeda, A., & Motohashi, Y. (2001). Diurnal variations in alpha power density and subjective sleepiness while performing repeated vigilance tasks. Clinical Neurophysiology, 112(6), 997–1000. https://doi.org/10.1016/S1388-2457(01)00527-2 Hillyard, S. A., Hink, R. F., Schwent, V. L., & Picton, T. W. (1973). Electrical signs of selective attention in the human brain. Science, 182(4108), 177–180. https://doi.org/10.1126/science.182.4108.177 Hinojosa, J. A., Mercado, F., & Carretié, L. (2015). N170 sensitivity to facial expression: A meta-analysis. In Neuroscience and Biobehavioral Reviews (Vol. 55, pp. 498–509). https://doi.org/10.1016/j.neubiorev.2015.06.002 Hopstaken, J. F., van der Linden, D., Bakker, A. B., & Kompier, M. A. J. (2015). A multifaceted investigation of the link between mental fatigue and task disengagement. Psychophysiology, 52(3), 305–315. https://doi.org/10.1111/psyp.12339 Hu, X., Rymer, W. Z., & Suresh, N. L. (2014). Accuracy assessment of a surface electromyogram decomposition system in human first dorsal interosseus muscle. Journal of Neural Engineering, 11(2). https://doi.org/10.1088/1741-2560/11/2/026007 Hunter, S.K. (2009). Sex differences and mechanisms of task specific muscle fatigue. Exercise and sport sciences reviews, 37(3), 113. Hunter, S. K., Butler, J. E., Todd, G., Gandevia, S. C., & Taylor, J. L. (2006). Supraspinal fatigue does not explain the sex difference in muscle fatigue of maximal contractions. Journal of Applied Physiology, 101(4). https://doi.org/10.1152/japplphysiol.00103.2006 Hunter, S. K., Critchlow, A., Shin, I. S., & Enoka, R. M. (2004). Men are more fatigable than strength-matched women when performing intermittent submaximal contractions. Journal of Applied Physiology, 96(6). https://doi.org/10.1152/japplphysiol.01342.2003 Hureau, T. J., Romer, L. M., & Amann, M. (2018). The ‘sensory tolerance limit’: A hypothetical construct determining exercise performance? European Journal of Sport Science, 18(1), 13–24. https://doi.org/10.1080/17461391.2016.1252428 Huster, R. J., Enriquez-Geppert, S., Lavallee, C. F., Falkenstein, M., & Herrmann, C. S. (2013). Electroencephalography of response inhibition tasks: Functional networks and cognitive 122 contributions. In International Journal of Psychophysiology (Vol. 87, Issue 3, pp. 217–233). https://doi.org/10.1016/j.ijpsycho.2012.08.001 Ishii, A., Tanaka, M., & Watanabe, Y. (2014). Neural mechanisms of mental fatigue. Reviews in the Neurosciences, 25(4), 469-479. Ishii, A., Tanaka, M., Shigihara, Y., Kanai, E., Funakura, M., & Watanabe, Y. (2013). Neural effects of prolonged mental fatigue: A magnetoencephalography study. Brain Research, 1529, 105–112. https://doi.org/10.1016/j.brainres.2013.07.022 Janse De Jonge, X. A. K., Boot, C. R. L., Thom, J. M., Ruell, P. A., & Thompson, M. W. (2001). The influence of menstrual cycle phase on skeletal muscle contractile characteristics in humans. Journal of Physiology, 530(1). https://doi.org/10.1111/j.1469-7793.2001.0161m.x Jansen, M., White, T. P., Mullinger, K. J., Liddle, E. B., Gowland, P. A., Francis, S. T., Bowtell, R., & Liddle, P. F. (2012). Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data. NeuroImage, 59(1), 261–270. https://doi.org/10.1016/j.neuroimage.2011.06.094 Jin, X., Ye, E., Qi, J., Wang, L., Lei, Y., Chen, P., Mi, G., Zou, F., Shao, Y., & Yang, Z. (2015). Recovery sleep reverses impaired response inhibition due to sleep restriction: Evidence from a visual event related potentials study. PLoS ONE, 10(12). https://doi.org/10.1371/journal.pone.0142361 Jones, D. A., Bigland-Ritchie, B., & Edwards, R. H. T. (1979). Excitation frequency and muscle fatigue: Mechanical responses during voluntary and stimulated contractions. Experimental Neurology, 64(2). https://doi.org/10.1016/0014-4886(79)90279-6 Jones, S. R., Pinto, D. J., Kaper, T. J., & Kopell, N. (2000). Alpha-frequency rhythms desynchronize over long cortical distances: A modeling study. Journal of Computational Neuroscience, 9(3), 271–291. https://doi.org/10.1023/A:1026539805445 Kato, Y., Endo, H., & Kizuka, T. (2009). Mental fatigue and impaired response processes: Event-related brain potentials in a Go/NoGo task. International Journal of Psychophysiology, 72(2), 204–211. https://doi.org/10.1016/j.ijpsycho.2008.12.008 Kennedy, D. S., McNeil, C. J., Gandevia, S. C., & Taylor, J. L. (2014). Fatigue-related firing of distal muscle nociceptors reduces voluntary activation of proximal muscles of the same limb. Journal of Applied Physiology, 116(4). https://doi.org/10.1152/japplphysiol.01166.2013 Kenville, R., Maudrich, T., Vidaurre, C., Maudrich, D., Villringer, A., Nikulin, V. V., & Ragert, P. (2020). Corticomuscular interactions during different movement periods in a multi-joint compound movement. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-61909-z Klein, C. S., Rice, C. L., & Marsh, G. D. (2001). Normalized force, activation, and coactivation in the arm muscles of young and old men. Journal of Applied Physiology, 91(3). https://doi.org/10.1152/jappl.2001.91.3.1341 Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. In Brain Research Reviews (Vol. 29, Issues 2–3, pp. 169–195). https://doi.org/10.1016/S0165-0173(98)00056-3 Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition-timing hypothesis. In Brain Research Reviews (Vol. 53, Issue 1, pp. 63–88). https://doi.org/10.1016/j.brainresrev.2006.06.003 Knutsson, E., & Richards, C. (1979). Different types of disturbed motor control in gait of hemiparetic patients. Brain, 102(2). https://doi.org/10.1093/brain/102.2.405 123 Kuchinad, R. A., Ivanova, T. D., & Garland, S. J. (2004). Modulation of motor unit discharge rate and H-reflex amplitude during submaximal fatigue of the human soleus muscle. Experimental Brain Research, 158(3). https://doi.org/10.1007/s00221-004-1907-0 Kukulka, C. G., & Clamann, H. P. (1981). Comparison of the recruitment and discharge properties of motor units in human brachial biceps and adductor pollicis during isometric contractions. Brain Research, 219(1). https://doi.org/10.1016/0006-8993(81)90266-3 Laine, C. M., Yavuz, U., D’Amico, J. M., Gorassini, M. A., Türker, K. S., & Farina, D. (2015). Jaw tremor as a physiological biomarker of bruxism. Clinical Neurophysiology, 126(9). https://doi.org/10.1016/j.clinph.2014.11.022 Laine, Christopher M., & Valero-Cuevas, F. J. (2017). Intermuscular coherence reflects functional coordination. Journal of Neurophysiology, 118(3). https://doi.org/10.1152/jn.00204.2017 Lal, S. K. L., & Craig, A. (2000). Driver Fatigue: Psychophysiological Effects. 4th International Conference on Fatigue and Transportation. http://trid.trb.org/view.aspx?id=708523 Lamontagne, A., Richards, C. L., & Malouin, F. (2000). Coactivation during gait as an adaptive behavior after stroke. Journal of Electromyography and Kinesiology, 10(6). https://doi.org/10.1016/S1050-6411(00)00028-6 Langner, R., Steinborn, M. B., Chatterjee, A., Sturm, W., & Willmes, K. (2010). Mental fatigue and temporal preparation in simple reaction-time performance. Acta Psychologica, 133(1), 64–72. https://doi.org/10.1016/j.actpsy.2009.10.001 Levy, W. J. (1987). Effect of epoch length on power spectrum analysis of the EEG. Anesthesiology, 66(4), 489–495. https://doi.org/10.1097/00000542-198704000-00007 Lim, J., & Dinges, D. F. (2010). A Meta-Analysis of the Impact of Short-Term Sleep Deprivation on Cognitive Variables. Psychological Bulletin, 136(3). https://doi.org/10.1037/a0018883 Lim, J., Quevenco, F. C., & Kwok, K. (2013). EEG alpha activity is associated with individual differences in post-break improvement. NeuroImage, 76, 81–89. https://doi.org/10.1016/j.neuroimage.2013.03.018 Lim, J., Wu, W. chau, Wang, J., Detre, J. A., Dinges, D. F., & Rao, H. (2010). Imaging brain fatigue from sustained mental workload: An ASL perfusion study of the time-on-task effect. NeuroImage, 49(4), 3426–3435. https://doi.org/10.1016/j.neuroimage.2009.11.020 Liu, J., Sheng, Y., & Liu, H. (2019). Corticomuscular coherence and its applications: A review. Frontiers in Human Neuroscience, 13. https://doi.org/10.3389/fnhum.2019.00100 Liu, Q., Zhou, R., Liu, L., & Zhao, X. (2015). Effects of 72 hours total sleep deprivation on male astronauts’ executive functions and emotion. Comprehensive Psychiatry, 61, 28–35. https://doi.org/10.1016/j.comppsych.2015.05.015 Lorenzo, I., Ramos, J., Arce, C., Guevara, M. A., & Corsi-Cabrera, M. (1995). Effect of total sleep deprivation on reaction time and waking eeg activity in man. Sleep, 18(5). https://doi.org/10.1093/sleep/18.5.346 Lu, T., Li, Y., Xia, P., Zhang, G., & Wu, D. (2014). Analysis on reliability and validity of the Pittsburgh sleep quality index. Chongqing Medical Journal, 43(3). Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique, second edition. In The MIT Press. Luck, S. J. (2012). Acknowledgments. In Medical Physics (Vol. 39, Issue 6Part1). https://doi.org/10.1118/1.4736938 Machado, M. O., Kang, N. Y. C., Tai, F., Sambhi, R. D. S., Berk, M., Carvalho, A. F., Chada, L. 124 P., Merola, J. F., Piguet, V., & Alavi, A. (2021). Measuring fatigue: a meta-review. In International Journal of Dermatology (Vol. 60, Issue 9). https://doi.org/10.1111/ijd.15341 MacNamara, A., Rabinak, C. A., Kennedy, A. E., & Phan, K. L. (2018). Convergence of fMRI and ERP measures of emotional face processing in combat-exposed U. S. military veterans. Psychophysiology, 55(2). https://doi.org/10.1111/psyp.12988 Maeo, S., Takahashi, T., Takai, Y., & Kanehisa, H. (2013). Trainability of muscular activity level during maximal voluntary co-contraction: Comparison between bodybuilders and nonathletes. PLoS ONE, 8(11). https://doi.org/10.1371/journal.pone.0079486 Magnuson, J. R., Doesburg, S. M., & McNeil, C. J. (2021). Development and recovery time of mental fatigue and its impact on motor function. Biological Psychology, 161. https://doi.org/10.1016/j.biopsycho.2021.108076 Malley, D. (2006). DeLuca, J. (Ed.). (2005). Fatigue as a window to the brain. London: The MIT Press. Neuropsychological Rehabilitation, 16(5). https://doi.org/10.1080/09602010600685210 Mang, C. S., Whitten, T. A., Cosh, M. S., Scott, S. H., Wiley, J. P., Debert, C. T., Dukelow, S. P., & Benson, B. W. (2018). Test-retest reliability of the KINARM end-point robot for assessment of sensory, motor and neurocognitive function in young adult athletes. PLoS ONE, 13(4), 1–21. https://doi.org/10.1371/journal.pone.0196205 Marsden, C. D., Meadows, J. C., & Merton, P. A. (1983). “Muscular wisdom” that minimizes fatigue during prolonged effort in man: peak rates of motoneuron discharge and slowing of discharge during fatigue. Advances in Neurology, 39. Maton, B., & Gamet, D. (1989). The fatigability of two agonistic muscles in human isometric voluntary submaximal contraction: an EMG study - II. Motor unit firing rate and recruitment. European Journal of Applied Physiology and Occupational Physiology, 58(4). https://doi.org/10.1007/BF00643511 McKenzie, D. K., Bigland-Ritchie, B., Gorman, R. B., & Gandevia, S. C. (1992). Central and peripheral fatigue of human diaphragm and limb muscles assessed by twitch interpolation. The Journal of Physiology, 454(1). https://doi.org/10.1113/jphysiol.1992.sp019284 McManus, L., Hu, X., Rymer, W. Z., Suresh, N. L., & Lowery, M. M. (2016). Muscle fatigue increases beta-band coherence between the firing times of simultaneously active motor units in the first dorsal interosseous muscle. Journal of Neurophysiology, 115(6), 2830–2839. https://doi.org/10.1152/jn.00097.2016 McNeil, C. J., Giesebrecht, S., Gandevia, S. C., & Taylor, J. L. (2011). Behaviour of the motoneurone pool in a fatiguing submaximal contraction. Journal of Physiology, 589(14), 3533–3544. https://doi.org/10.1113/jphysiol.2011.207191 McNeil, C. J., Giesebrecht, S., Khan, S. I., Gandevia, S. C., & Taylor, J. L. (2011). The reduction in human motoneurone responsiveness during muscle fatigue is not prevented by increased muscle spindle discharge. Journal of Physiology, 589(15), 3731–3738. https://doi.org/10.1113/jphysiol.2011.210252 McNeil, C. J., Martin, P. G., Gandevia, S. C., & Taylor, J. L. (2009). The response to paired motor cortical stimuli is abolished at a spinal level during human muscle fatigue. Journal of Physiology, 587(23), 5601–5612. https://doi.org/10.1113/jphysiol.2009.180968 McNeil, C. J., Martin, P. G., Gandevia, S. C., & Taylor, J. L. (2011). Long-interval intracortical inhibition in a human hand muscle. Experimental Brain Research, 209(2), 287–297. https://doi.org/10.1007/s00221-011-2552-z Mehrkanoon, S., Breakspear, M., & Boonstra, T. W. (2014). The reorganization of 125 corticomuscular coherence during a transition between sensorimotor states. NeuroImage, 100, 692–702. https://doi.org/10.1016/j.neuroimage.2014.06.050 Merton, P. A. (1954). Voluntary strength and fatigue. The Journal of Physiology, 123(3). https://doi.org/10.1113/jphysiol.1954.sp005070 Möckel, T., Beste, C., & Wascher, E. (2015). The Effects of Time on Task in Response Selection - An ERP Study of Mental Fatigue. Scientific Reports, 5. https://doi.org/10.1038/srep10113 Mugler, E. M., Tomic, G., Singh, A., Hameed, S., Lindberg, E. W., Gaide, J., Alqadi, M., Robinson, E., Dalzotto, K., Limoli, C., Jacobson, T., Lee, J., & Slutzky, M. W. (2019). Myoelectric Computer Interface Training for Reducing Co-Activation and Enhancing Arm Movement in Chronic Stroke Survivors: A Randomized Trial. Neurorehabilitation and Neural Repair, 33(4). https://doi.org/10.1177/1545968319834903 Murata, A., Uetake, A., & Takasawa, Y. (2005). Evaluation of mental fatigue using feature parameter extracted from event-related potential. International Journal of Industrial Ergonomics, 35(8), 761–770. https://doi.org/10.1016/j.ergon.2004.12.003 Nayak, C. S., & Anilkumar, A. C. (2020). EEG Normal Waveforms. StatPearls. Norton, J. A., & Gorassini, M. A. (2006). Changes in cortically related intermuscular coherence accompanying improvements in locomotor skills in incomplete spinal cord injury. Journal of Neurophysiology, 95(4), 2580–2589. https://doi.org/10.1152/jn.01289.2005 Pattyn, N., Neyt, X., Henderickx, D., & Soetens, E. (2008). Psychophysiological investigation of vigilance decrement: Boredom or cognitive fatigue? Physiology and Behavior, 93(1–2), 369–378. https://doi.org/10.1016/j.physbeh.2007.09.016 Pethick, J., Winter, S. L., & Burnley, M. (2015). Fatigue reduces the complexity of knee extensor torque fluctuations during maximal and submaximal intermittent isometric contractions in man. Journal of Physiology, 593(8). https://doi.org/10.1113/jphysiol.2015.284380 Piper, B. F., Dibble, S. L., Dodd, M. J., Weiss, M. C., Slaughter, R. E., & Paul, S. M. (1998). The revised Piper Fatigue Scale: psychometric evaluation in women with breast cancer. Oncology Nursing Forum, 25(4). Potvin, J. R., & Fuglevand, A. J. (2017). A motor unit-based model of muscle fatigue. PLoS Computational Biology, 13(6). https://doi.org/10.1371/journal.pcbi.1005581 Qi, J.-L., Shao, Y.-C., Miao, D., Fan, M., Bi, G.-H., & Yang, Z. (2010). The Effects of 43 Hours of Sleep Deprivation on Executive Control Functions: Event-Related Potentials in a Visual Go/No Go Task. Social Behavior and Personality: An International Journal, 38(1), 29–42. https://doi.org/10.2224/sbp.2010.38.1.29 Reece, T. M., & Herda, T. J. (2021). An examination of a potential organized motor unit firing rate and recruitment scheme of an antagonist muscle during isometric contractions. Journal of Neurophysiology, 126(6). https://doi.org/10.1152/jn.00034.2021 Renn, R. P., & Cote, K. A. (2013). Performance monitoring following total sleep deprivation: Effects of task type and error rate. International Journal of Psychophysiology, 88(1), 64–73. https://doi.org/10.1016/j.ijpsycho.2013.01.013 Riddle, C. N., Baker, M. R., & Baker, S. N. (2004). The effect of carbamazepine on human corticomuscular coherence. NeuroImage, 22(1). https://doi.org/10.1016/j.neuroimage.2003.12.040 Riley, Z. A., Maerz, A. H., Litsey, J. C., & Enoka, R. M. (2008). Motor unit recruitment in human biceps brachii during sustained voluntary contractions. Journal of Physiology, 586(8). https://doi.org/10.1113/jphysiol.2008.150698 126 Rossi, A., Rossi, S., & Ginanneschi, F. (2012). Activity-dependent changes in intrinsic excitability of human spinal motoneurones produced by natural activity. Journal of Neurophysiology, 108(9), 2473–2480. https://doi.org/10.1152/jn.00477.2012 Rozand, V., Lebon, F., Papaxanthis, C., & Lepers, R. (2015). Effect of mental fatigue on speed-accuracy trade-off. Neuroscience, 297, 219–230. https://doi.org/10.1016/j.neuroscience.2015.03.066 Scheffers, M. K., Humphrey, D. G., Stanny, R. R., Kramer, A. F., & Coles, M. G. H. (1999). Error-related processing during a period of extended wakefulness. Psychophysiology, 36(2), 149–157. https://doi.org/10.1017/S0048577299980307 Schellekens, J. M. H., Sijtsma, G. J., Vegter, E., & Meijman, T. F. (2000). Immediate and delayed after-effects of long lasting mentally demanding work. Biological Psychology, 53(1), 37–56. https://doi.org/10.1016/S0301-0511(00)00039-9 Schoffelen, J. M., Poort, J., Oostenveld, R., & Fries, P. (2011). Selective movement preparation is subserved by selective increases in corticomuscular gamma-band coherence. Journal of Neuroscience, 31(18). https://doi.org/10.1523/JNEUROSCI.4882-10.2011 Schulz, H., Übelacker, T., Keil, J., Müller, N., & Weisz, N. (2014). Now i am ready - Now i am not: The influence of pre-TMS oscillations and corticomuscular coherence on motor-evoked potentials. Cerebral Cortex, 24(7), 1708–1719. https://doi.org/10.1093/cercor/bht024 Schulz, K. P., Fan, J., Magidina, O., Marks, D. J., Hahn, B., & Halperin, J. M. (2007). Does the emotional go/no-go task really measure behavioral inhibition?. Convergence with measures on a non-emotional analog. Archives of Clinical Neuropsychology, 22(2), 151–160. https://doi.org/10.1016/j.acn.2006.12.001 Shigihara, Y., Tanaka, M., Ishii, A., Tajima, S., Kanai, E., Funakura, M., & Watanabe, Y. (2013). Two different types of mental fatigue produce different styles of task performance. Neurology Psychiatry and Brain Research, 19(1), 5–11. https://doi.org/10.1016/j.npbr.2012.07.002 Sidhu, S. K., Weavil, J. C., Venturelli, M., Garten, R. S., Rossman, M. J., Richardson, R. S., Gmelch, B. S., Morgan, D. E., & Amann, M. (2014). Spinal μ-opioid receptor-sensitive lower limb muscle afferents determine corticospinal responsiveness and promote central fatigue in upper limb muscle. Journal of Physiology, 592(22). https://doi.org/10.1113/jphysiol.2014.275438 Skurvydas, A., Zlibinaite, L., Solianik, R., Brazaitis, M., Valanciene, D., Baranauskiene, N., Majauskiene, D., Mickeviciene, D., Venckunas, T., & Kamandulis, S. (2020). One night of sleep deprivation impairs executive function but does not affect psychomotor or motor performance. Biology of Sport, 37(1). https://doi.org/10.5114/biolsport.2020.89936 Smallwood, J. (2013). Penetrating the fog of the decoupled mind: The effects of visual salience in the sustained attention to response task. Canadian Journal of Experimental Psychology, 67(1). https://doi.org/10.1037/a0030760 Smith, J. L., Johnstone, S. J., & Barry, R. J. (2008). Movement-related potentials in the Go/NoGo task: The P3 reflects both cognitive and motor inhibition. Clinical Neurophysiology, 119(3), 704–714. https://doi.org/10.1016/j.clinph.2007.11.042 Smith, J. L., Martin, P. G., Gandevia, S. C., & Taylor, J. L. (2007). Sustained contraction at very low forces produces prominent supraspinal fatigue in human elbow flexor muscles. Journal of Applied Physiology, 103(2). https://doi.org/10.1152/japplphysiol.00220.2007 Smith, M. E., McEvoy, L. K., & Gevins, A. (2002). The impact of moderate sleep loss on neurophysiologic signals during working-memory task performance. Sleep, 25(7), 784–794. 127 https://doi.org/10.1093/sleep/25.7.56 Søgaard, K., Gandevia, S. C., Todd, G., Petersen, N. T., & Taylor, J. L. (2006). The effect of sustained low-intensity contractions on supraspinal fatigue in human elbow flexor muscles. Journal of Physiology, 573(2). https://doi.org/10.1113/jphysiol.2005.103598 Sur, S., & Sinha, V. (2009). Event-related potential: An overview. Industrial Psychiatry Journal, 18(1), 70. https://doi.org/10.4103/0972-6748.57865 Tanaka, M., Mizuno, K., Tajima, S., Sasabe, T., & Watanabe, Y. (2009). Central nervous system fatigue alters autonomic nerve activity. Life Sciences, 84(7–8), 235–239. https://doi.org/10.1016/j.lfs.2008.12.004 Tanaka, M., Shigihara, Y., Funakura, M., Kanai, E., & Watanabe, Y. (2012). Fatigue-associated alterations of cognitive function and electroencephalographic power densities. PLoS ONE, 7(4). https://doi.org/10.1371/journal.pone.0034774 Tanaka, M., Shigihara, Y., Ishii, A., Funakura, M., Kanai, E., & Watanabe, Y. (2012). Effect of mental fatigue on the central nervous system: an electroencephalography study. Behavioral and Brain Functions, 8. https://doi.org/10.1186/1744-9081-8-48 Tanaka, M., & Watanabe, Y. (2012). Supraspinal regulation of physical fatigue. In Neuroscience and Biobehavioral Reviews (Vol. 36, Issue 1, pp. 727–734). https://doi.org/10.1016/j.neubiorev.2011.10.004 Taylor, J. L., Amann, M., Duchateau, J., Meeusen, R., & Rice, C. L. (2016). Neural contributions to muscle fatigue: From the brain to the muscle and back again. Medicine and Science in Sports and Exercise, 48(11), 2294–2306. https://doi.org/10.1249/MSS.0000000000000923 Taylor, J. L., Butler, J. E., Allen, G. M., & Gandevia, S. C. (1996). Changes in motor cortical excitability during human muscle fatigue. Journal of Physiology, 490(2), 519–528. https://doi.org/10.1113/jphysiol.1996.sp021163 Tecchio, F., Porcaro, C., Zappasodi, F., Pesenti, A., Ercolani, M., & Rossini, P. M. (2006). Cortical short-term fatigue effects assessed via rhythmic brain-muscle coherence. Experimental Brain Research, 174(1), 144–151. https://doi.org/10.1007/s00221-006-0432-8 Thomas, K., Goodall, S., Stone, M., Howatson, G., Gibson, A. S. C., & Ansley, L. (2014). Central and peripheral fatigue in male cyclists after 4-, 20-, and 40-km time trials. Medicine and Science in Sports and Exercise, 47(3). https://doi.org/10.1249/MSS.0000000000000448 Todd, G., Taylor, J. L., & Gandevia, S. C. (2003). Measurement of voluntary activation of fresh and fatigued human muscles using transcranial magnetic stimulation. Journal of Physiology, 551(2), 661–671. https://doi.org/10.1113/jphysiol.2003.044099 Tracy, B. L., Maluf, K. S., Stephenson, J. L., Hunter, S. K., & Enoka, R. M. (2005). Variability of motor unit discharge and force fluctuations across a range of muscle forces in older adults. Muscle and Nerve, 32(4). https://doi.org/10.1002/mus.20392 Tran, Y., Craig, A., Craig, R., Chai, R., & Nguyen, H. (2020). The influence of mental fatigue on brain activity: Evidence from a systematic review with meta-analyses. In Psychophysiology (Vol. 57, Issue 5). https://doi.org/10.1111/psyp.13554 Trejo, L. J., Kubitz, K., Rosipal, R., Kochavi, R. L., & Montgomery, L. D. (2015). EEG-Based Estimation and Classification of Mental Fatigue. Psychology, 06(05), 572–589. https://doi.org/10.4236/psych.2015.65055 Ujfalussy, B., & Kiss, T. (2006). How do glutamatergic and GABAergic cells contribute to synchronization in the medial septum? Journal of Computational Neuroscience, 21(3), 343–357. https://doi.org/10.1007/s10827-006-9082-x Ushiyama, J., Katsu, M., Masakado, Y., Kimura, A., Liu, M., & Ushiba, J. (2011). Muscle 128 fatigue-induced enhancement of corticomuscular coherence following sustained submaximal isometric contraction of the tibialis anterior muscle. Journal of Applied Physiology, 110(5), 1233–1240. https://doi.org/10.1152/japplphysiol.01194.2010 Van Den Berg, J., & Neely, G. (2006). Performance on a simple reaction time task while sleep deprived. Perceptual and Motor Skills, 102(2). https://doi.org/10.2466/PMS.102.2.589-599 Verweij, I. M., Romeijn, N., Smit, D. J. A., Piantoni, G., Van Someren, E. J. W., & van der Werf, Y. D. (2014). Sleep deprivation leads to a loss of functional connectivity in frontal brain regions. BMC Neuroscience, 15. https://doi.org/10.1186/1471-2202-15-88 Vuillier, L., Bryce, D., Szücs, D., & Whitebread, D. (2016). The maturation of interference suppression and response inhibition: ERP analysis of a cued Go/Nogo task. PLoS ONE, 11(11). https://doi.org/10.1371/journal.pone.0165697 Wang, L., Lu, A., Zhang, S., Niu, W., Zheng, F., & Gong, M. (2015). Fatigue-related electromyographic coherence and phase synchronization analysis between antagonistic elbow muscles. Experimental Brain Research, 233(3), 971–982. https://doi.org/10.1007/s00221-014-4172-x Wascher, E., & Getzmann, S. (2014). Rapid Mental Fatigue Amplifies Age-Related Attentional Deficits. Journal of Psychophysiology, 28(3), 215–224. https://doi.org/10.1027/0269-8803/a000127 Wascher, E., Rasch, B., Sänger, J., Hoffmann, S., Schneider, D., Rinkenauer, G., Heuer, H., & Gutberlet, I. (2014). Frontal theta activity reflects distinct aspects of mental fatigue. Biological Psychology, 96(1), 57–65. https://doi.org/10.1016/j.biopsycho.2013.11.010 Wickens, C. D., Hutchins, S. D., Laux, L., & Sebok, A. (2015). The Impact of Sleep Disruption on Complex Cognitive Tasks: A Meta-Analysis. Human Factors: The Journal of the Human Factors and Ergonomics Society. Yacyshyn, A. F., & McNeil, C. J. (2020). The Sexes Do Not Differ for Neural Responses to Submaximal Elbow Extensor Fatigue. Medicine and Science in Sports and Exercise, 52(9). https://doi.org/10.1249/MSS.0000000000002342 Yancosek, K. E., & Howell, D. (2009). A Narrative Review of Dexterity Assessments. Journal of Hand Therapy, 22(3), 258–270. https://doi.org/10.1016/j.jht.2008.11.004 Yang, Q., Fang, Y., Sun, C. K., Siemionow, V., Ranganathan, V. K., Khoshknabi, D., Davis, M. P., Walsh, D., Sahgal, V., & Yue, G. H. (2009). Weakening of functional corticomuscular coupling during muscle fatigue. Brain Research, 1250, 101–112. https://doi.org/10.1016/j.brainres.2008.10.074 Yang, Q., Siemionow, V., Yao, W., Sahgal, V., & Yue, G. H. (2010). Single-trial EEG-EMG coherence analysis reveals muscle fatigue-related progressive alterations in corticomuscular coupling. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(2), 97–106. https://doi.org/10.1109/TNSRE.2010.2047173 Yang, S., Luo, W., Zhu, X., Broster, L. S., Chen, T., Li, J., & Luo, Y. (2014). Emotional content modulates response inhibition and perceptual processing. Psychophysiology, 51(11), 1139–1146. https://doi.org/10.1111/psyp.12255 Yazdi, Z., & Sadeghniiat-Haghighi, K. (2015). Fatigue management in the workplace. Industrial Psychiatry Journal, 24(1), 12. https://doi.org/10.4103/0972-6748.160915 Yoo, S. S., Gujar, N., Hu, P., Jolesz, F. A., & Walker, M. P. (2007). The human emotional brain without sleep - a prefrontal amygdala disconnect. In Current Biology (Vol. 17, Issue 20). https://doi.org/10.1016/j.cub.2007.08.007 Yuan, H., Liu, T., Szarkowski, R., Rios, C., Ashe, J., & He, B. (2010). Negative covariation 129 between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements. NeuroImage, 49(3), 2596–2606. https://doi.org/10.1016/j.neuroimage.2009.10.028 Zhang, J., Lau, E. Y. Y., & Hsiao, J. H. (2019a). Sleep deprivation compromises resting-state emotional regulatory processes: An EEG study. Journal of Sleep Research, 28(3). https://doi.org/10.1111/jsr.12671 Zhang, J., Lau, E. Y. Y., & Hsiao, J. H. wen. (2019b). Using emotion regulation strategies after sleep deprivation: ERP and behavioral findings. Cognitive, Affective and Behavioral Neuroscience, 19(2), 283–295. https://doi.org/10.3758/s13415-018-00667-y Zhang, W., Ding, Q., Chen, N., Wei, Q., Zhao, C., Zhang, P., Li, X., Liu, Q., & Li, H. (2016). The development of automatic emotion regulation in an implicit emotional Go/NoGo paradigm and the association with depressive symptoms and anhedonia during adolescence. NeuroImage: Clinical, 11, 116–123. https://doi.org/10.1016/j.nicl.2016.01.018 Zhao, C., Zhao, M., Liu, J., & Zheng, C. (2012). Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator. Accident Analysis and Prevention, 45, 83–90. https://doi.org/10.1016/j.aap.2011.11.019
Thesis/Dissertation
eng
Kinesiology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
McNeil, Chris
Neural and motor responses to mental and physical fatigue
Text