Principles of Exercise Neuroscience 1527558134, 9781527558137

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Principles of Exercise Neuroscience

Principles of Exercise Neuroscience Edited by

Dawson J. Kidgell and Alan J. Pearce

Principles of Exercise Neuroscience Edited by Dawson J. Kidgell and Alan J. Pearce This book first published 2020 Cambridge Scholars Publishing Lady Stephenson Library, Newcastle upon Tyne, NE6 2PA, UK British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Copyright © 2020 by Dawson J. Kidgell and Alan J. Pearce and contributors All rights for this book reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the copyright owner. ISBN (10): 1-5275-5813-4 ISBN (13): 978-1-5275-5813-7

TABLE OF CONTENTS

Preface ...................................................................................................... xii Dawson J. Kidgell, PhD and Alan J. Pearce, PhD Acknowledgements ................................................................................. xiv Chapter 1 .................................................................................................... 1 The Nexus between Neuroscience and the Science of Exercise Alan J. Pearce, PhD 1. Background ....................................................................................... 1 1.1 The neuroscience of human movement........................................... 2 1.2 What is ‘motor control’? How does motor control relate within the larger discipline of exercise science? ........................................ 4 1.3 Motor learning and skill acquisition: similarities and contrasts to motor control ............................................................................... 6 1.4 The challenge of translating neuroscience to exercise science ....... 8 1.5 How to get the best from this book ................................................. 8 References............................................................................................. 9 Chapter 2 .................................................................................................. 10 Levels of Motor Control Dawson J. Kidgell, PhD and Alan J. Pearce, PhD 2. Background ..................................................................................... 10 2.1 Structural arrangement of the brain contributing to movement .... 11 2.1.1 Structure of the cerebral cortex ............................................ 11 2.1.2 The brain stem ...................................................................... 12 2.2 Transmission of cortical motor signals ......................................... 13 2.3 Motor functions of the cerebellum ................................................ 13 2.3.1 Anatomical and functional organisation of the cerebellum .... 14 2.3.2 Afferent and efferent pathways of the cerebellum ............... 15 2.4 Motor functions of the spinal cord ................................................ 16 2.4.1 Organisation of the spinal cord............................................. 17 2.5 Types of reflex pathways .............................................................. 18 2.5.1 Skeletal muscle reflexes ....................................................... 19 2.6 Hierarchical organisation of motor control ................................... 21

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2.7 Motor units, fibre types, and recruitment physiology ................... 23 2.7.1 The motor unit ...................................................................... 23 2.7.2 Physiological classification and recruitment ........................ 24 2.8 Principle of motor unit recruitment ............................................... 25 2.9 Motor unit synchrony .................................................................... 26 2.9.1 Quantifying the degree of motor unit synchrony .................. 27 2.9.2 Motor unit synchronization and human motor control ......... 27 2.9.3 Motor unit synchronization in upper and lower limb muscles .................................................................................... 28 2.10 Summary ..................................................................................... 29 References........................................................................................... 29 Chapter 3 .................................................................................................. 32 Techniques Contributing to the Understanding of Neuroscience in Exercise Alan J. Pearce, PhD and Dawson J. Kidgell, PhD 3. Background ..................................................................................... 32 3.1 Electroencephalography (EEG) .................................................... 33 3.2. Magnetoencephalography (MEG) ................................................ 34 3.3 Transcranial magnetic stimulation (TMS) .................................... 35 3.3.1 Single-pulse TMS ................................................................. 36 3.3.2 Paired-pulse TMS ................................................................. 38 3.4 Voluntary activation and neural drive ........................................... 40 3.4.1 Interpolated twitch and VATMS ............................................. 40 3.4.2 H-reflex ................................................................................ 42 3.4.3 V-wave ................................................................................. 44 3.5 Summary ....................................................................................... 45 References........................................................................................... 46 Chapter 4 .................................................................................................. 53 Principles of Neuroplasticity in Exercise Dawson J. Kidgell, PhD and Ashlyn K. Frazer, PhD 4. Neuroplasticity ................................................................................ 53 4.1 Mechanisms of neuroplasticity ..................................................... 53 4.2 Short and Long-term potentiation ................................................. 54 4.2.1 NMDA receptor activation and synaptic plasticity .............. 54 4.2.2 Brain-derived neurotrophic factor and neuroplasticity ......... 55 4.3 Experimentally-induced neuroplasticity ....................................... 56 4.3.1 Transcranial direct current stimulation and neuroplasticity .... 57 4.3.2 NIBS and functional connectivity ........................................ 58

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4.4 Is the induction of neuroplasticity via NIBS important for motor performance? ................................................................................. 60 4.4.1 Is the magnitude of neuroplasticity and motor performance improvement related to the BDNF polymorphism?................. 63 4.5 Is homeostatic plasticity important for motor performance? ........ 64 4.6 Use-dependent neuroplasticity ...................................................... 67 4.7 Summary ....................................................................................... 68 References........................................................................................... 69 Chapter 5 .................................................................................................. 76 Non-invasive Brain Stimulation and Exercise Performance Shapour Jaberzadeh, PhD and Maryam Zoghi, PhD 5. Introduction ..................................................................................... 76 5.1 Transcranial Direct Current Stimulation ....................................... 77 5.2 The mechanisms behind tDCS effects during stimulation (online effects) .............................................................................. 78 5.3 The mechanisms behind tDCS effects after the termination of stimulation ................................................................................ 79 5.4 Montages for application of tDCS: The conventional montage .... 80 5.4.1 HD-tDCS montage ............................................................... 80 5.4.2 Other tDCS montages........................................................... 81 5.5 tDCS as a stand-alone technique ................................................... 82 5.6 tDCS as a priming technique ........................................................ 82 5.7 Halo sport tDCS device ................................................................ 82 5.8 The effects of tDCS on EP ............................................................ 83 5.9 Ethical considerations for the use of tDCS for enhancement of EP .............................................................................................. 85 5.10 Summary ..................................................................................... 86 References........................................................................................... 86 Chapter 6 .................................................................................................. 92 Neural Control of Lengthening and Shortening Contractions Jamie Tallent, PhD and Glyn Howatson, PhD 6. Background ..................................................................................... 92 6.1 Shortening and Lengthening Contractions .................................... 92 6.1.1 Benefits of lengthening contractions .................................... 93 6.2 Motor control of lengthening and shortening muscle contractions . 93 6.2.1 Muscle .................................................................................. 94 6.2.2 Spinal.................................................................................... 94 6.2.3 Cortico-Spinal ...................................................................... 95 6.3 Adaptations to shortening and lengthening resistance training ..... 97

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6.4 Summary ..................................................................................... 100 References......................................................................................... 101 Chapter 7 ................................................................................................ 107 Neural Adaptations to Strength Training Dawson J. Kidgell, PhD 7. Background ................................................................................... 107 7.1 Acute neural responses to strength training ................................ 107 7.2 Using TMS to assess the neural responses to strength training ..... 108 7.3 MEPs are acutely facilitated following a strength training session ......................................................................................... 109 7.4 Intracortical facilitation is acutely enhanced following a strength training session ............................................................................ 110 7.5 Why does strength training increase corticospinal excitability and intracortical facilitation of the motor cortex? ....................... 111 7.6 Long-term neuroplastic adaptations to strength training ............. 114 7.7 Changes in strength following 2-8 weeks of strength training .... 116 7.8 Long-term strength training does not affect motor threshold or MEP amplitude............................................................................ 117 7.9 Long-term strength training reduces motor cortex mediated inhibition ..................................................................................... 118 7.10 Changes in spinal cord plasticity with strength training ........... 120 7.11 Changes in H-reflex and V-wave amplitude following strength training ........................................................................................ 120 7.12 Changes in motor unit activity following strength training ...... 122 7.12.1 Single motor unit behaviour following strength training.. 123 7.12.2 Motor unit synchronization following strength training ... 124 7.13 Summary ................................................................................... 125 References......................................................................................... 125 Chapter 8 ................................................................................................ 133 Neuromuscular Responses to Fatiguing Locomotor Exercise Callum Brownstein, PhD and Kevin Thomas, PhD 8. Background ................................................................................... 133 8.1 The role of exercise intensity and duration on neuromuscular responses to fatiguing exercise .................................................... 135 8.2 Neuromuscular responses to “all-out” exercise .......................... 136 8.3 Neuromuscular responses to severe intensity, short-duration exercise........................................................................................ 139 8.4 Neuromuscular responses to sustained exercise below critical power ........................................................................................... 141

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8.5 Neuromuscular responses to high-intensity intermittent exercise........................................................................................ 144 8.6 The effect of exercise modality on neuromuscular responses to locomotor exercise .................................................................. 146 8.7 Effect of exercise duration and intensity on recovery ................. 148 8.8 Origin of prolonged impairments in contractile function ............ 151 8.9 Origin of prolonged impairments in voluntary activation ........... 152 8.10 Summary ................................................................................... 153 References......................................................................................... 153 Chapter 9 ................................................................................................ 160 Sex Differences in Neuromuscular Function and Fatigability Paul Ansdell, PhD and Stuart Goodall, PhD 9. Introduction ................................................................................... 160 9.1. A brief history of the scientific study of sex and performance .. 160 9.2. Sex differences throughout the motor pathway.......................... 161 9.2.1. Pre-motor processes .......................................................... 162 9.2.2. Intracortical and corticospinal neurons.............................. 163 9.2.3. Motor unit properties ......................................................... 164 9.2.4. Contractile apparatus ......................................................... 165 9.3. Functional neuromuscular sex differences ................................. 165 9.3.1. Maximal force production ................................................. 165 9.3.2. Force steadiness and accuracy ........................................... 166 9.3.3. Fatigability ........................................................................ 166 9.4. Female-specific neuromuscular function ................................... 169 9.4.1. The influence of hormones in vitro ................................... 169 9.4.2. In-vitro evidence ............................................................... 169 9.4.3. Functional changes across the menstrual cycle ................. 170 9.5. Summary .................................................................................... 173 9.5.1. What do we know already? ............................................... 173 9.5.2. Where do we go from here? .............................................. 174 References......................................................................................... 174 Chapter 10 .............................................................................................. 185 Motor Control Responses following Exercise-Induced Muscle Damage Carlos Hermano Pinheiro, PhD and Alan J. Pearce, PhD 10. Background ................................................................................. 185 10.1 How does DOMS occur? .......................................................... 186 10.2 Functional outcomes affected by DOMS .................................. 188 10.3 Neuromuscular and motor control changes following DOMS .. 189 10.4 Neurophysiological studies in DOMS ...................................... 191

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10.5 Summary ................................................................................... 192 References......................................................................................... 192 Chapter 11 .............................................................................................. 196 Neuromuscular Alterations and Motor Performance in Healthy Aging Jakob Škarabot, PhD 11. Background ................................................................................. 196 11.1 Alterations in the central nervous system with advancing age .... 197 11.1.1 Alterations at the level of motor unit ................................ 197 11.1.2 The influence of synaptic inputs from spinal and supraspinal centres ................................................................. 197 11.1.3 Reflex inputs to motoneurons........................................... 198 11.1.4 Cortical inputs to motoneurons ........................................ 199 11.2 Reduction in motor performance in healthy aging adults ......... 200 11.2.1 Motor performance during maximal contractions ............ 200 11.2.2 Control of muscle force output during submaximal tasks .. 204 11.2.3 Fatigability ....................................................................... 206 11.3 Summary ................................................................................... 208 References......................................................................................... 209 Chapter 12 .............................................................................................. 222 Cross-education Ashlyn K. Frazer, PhD and Dawson J. Kidgell, PhD 12. Background ................................................................................. 222 12.1 Evidence of cross-education ..................................................... 222 12.2 Exercise prescription parameters .............................................. 223 12.3 Mechanisms of cross-education ................................................ 225 12.4 Interventions to enhance the cross-education effect.................. 228 12.4.1 Transcranial direct current stimulation and crosseducation................................................................................ 228 12.4.2 Electromyostimulation during cross-education ................ 229 12.4.3 Whole-body vibration (WBV) training and crosseducation ................................................................................ 230 12.5 Cross-education and neuromuscular injury ............................... 231 12.6 Summary ................................................................................... 232 References......................................................................................... 232

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Chapter 13 .............................................................................................. 237 Using Electrophysiology to Understand Responses following Concussion and Mild Brain Injury Alan J. Pearce, PhD and Michael E. Buckland, MBBS PhD 13. Background ................................................................................. 237 13.1 The definition of concussion ..................................................... 238 13.2 Current standard to diagnose concussion .................................. 239 13.3 Objective measures to assess concussion .................................. 239 13.4 Electrophysiology to assess concussion .................................... 240 13.5 Electroencephalography (EEG) ERPs in concussion research .. 241 13.5.1 Visual and vestibular evoked potentials ........................... 242 13.6 Transcranial Magnetic Stimulation (TMS) EPs and concussion research ....................................................................................... 243 13.6.1 Studies in acute concussion .............................................. 244 13.6.2 Studies in retired athletes with history of concussion and head trauma ..................................................................... 245 13.6.3 Studies in persistent post concussion symptoms .............. 246 13.7 Combined electrophysiological modalities to assess neurophysiology .......................................................................... 246 13.9 Summary ................................................................................... 248 References......................................................................................... 249

PREFACE DAWSON J. KIDGELL, PHD AND ALAN J. PEARCE, PHD

Over the last 30 years, there has been a significant rise in research and teaching interest that has examined the neuromuscular responses to different types of exercise interventions. Two fields of research, neuroscience and exercise science, have merged to provide evidence for how the nervous system responds to exercise. An important component of exercise science, but one area that is less recognised, although used ubiquitously through other topics within exercise science, is the study of motor control. Motor control is primarily concerned with the neurophysiological mechanisms that contribute to human movement. Motor control is at the very heart of exercise and sport science. Motor control drives how we program appropriate exercise in health, injury and disease. Motor control is what underpins this book we call Principles of Exercise Neuroscience. This textbook introduces the key concepts that emphasise human motor control and its application to exercise science and rehabilitation. The topics covered integrate research, theory and the clinical applications of Exercise Neuroscience that will support students, researchers and clinicians to understand how the nervous system responds, or adapts to physical activity, training, rehabilitation and disease. Exercise Neuroscience uses a mix of neuromuscular physiology, electrophysiology and muscle physiology to provide a synthesis of current knowledge and research in the field of exercise neuroscience that specifically examines the effects of exercise training, injury and rehabilitation on the human nervous system. It is never easy writing a textbook like this from scratch. Indeed, this textbook came about from the initial teaching notes to assist tertiary students with some difficult learning concepts within the motor control subjects taught at our respective universities. As time progressed, the lecture notes developed into full chapters that eventually evolved into this textbook. Further, through our international collaborative network, we were able to draw upon international experts who were very happy to contribute and provide their extensive knowledge and experience to develop our initial

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chapters into state of the art reviews. Our guest authors are leaders in their respective areas of enquiry and provide the latest research findings in three key areas: principles of motor control; neuroscience of exercise performance; and clinical exercise neuroscience (injury and neurorehabilitation). We sincerely thank each of them for their time and efforts in helping us complete this book. This is the first text devoted solely to the emerging topic of Exercise Neuroscience. It aims to assist readers in identifying current research findings and provide new avenues to explore the benefits of exercise on the human nervous system. We thoroughly enjoyed writing this and we hope that you find this text useful for your learning and professional development. Dawson J. Kidgell, Monash University, Melbourne, Australia. Alan J. Pearce, La Trobe University, Melbourne, Australia June, 2020

ACKNOWLEDGEMENTS

A text such as Principles of Exercise Neuroscience is not the effort of just two authors. This text brings together the contributions of many research scientists who have examined the changes that occur in the human nervous system. We would like to especially thank Dr Eric Frazer for his assistance in providing suggestions for revisions to this book, in particular his attention to detail and endless editing. We would also like to acknowledge the contributions of all the authors, in particular Professor Glyn Howatson, who has established and leads a remarkable program of Applied Neuromuscular Research at Northumbria University; many former PhD students from this laboratory have contributed to this text.

CHAPTER 1 THE NEXUS BETWEEN NEUROSCIENCE AND THE SCIENCE OF EXERCISE ALAN J. PEARCE, PHD

1. Background The past thirty years have seen a significant rise in the disciplines of neuroscience and exercise science. However, within the last ten years we have seen these two disciplines become intersected. While neuroscience has been an area of research for over a century, it is only in the last 20-30 years that neuroscience has transformed from an interdisciplinary speciality under the umbrella of neurology, psychiatry and psychology, to a stand-alone discipline, and is now the fifth largest field of study in the sciences (Rosvall and Bergstrom, 2010). Particularly in the last decade, with advancements in technologies such as neuroimaging and electrophysiology, neuroscience has captured the imagination of the wider community. In particular, the concept of neuroplasticity, where evidence has demonstrated that the brain has the ability to reorganise and readapt over the lifespan, has ignited a huge interest across many unrelated fields. This has, of course, been both positive, with advances in our understanding of the brain and nervous system, but also negative where we are seeing the rise in what some call “neurobabble”, the phenomenon whereby neuroscientific explanations of cognitive and motor behaviour are more persuasive simply because they sound more technical and authoritative (Varazzani, 2017). Despite this, continued advances in neuroscience research will improve the lives of everyday people, as well as contributing to improved athletic performance. In parallel, exercise science has also emerged from the discipline of physical education. Since the early 1990s, when the first exercise science courses were beginning to be established in Australia, the growth in exercise science has been so rapid that physical education can now be regarded as one of the areas under the broader discipline of exercise science (along with exercise physiology, strength and conditioning, biomechanics, nutrition, functional

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anatomy, skill acquisition and psychosocial determinants of health). Indeed, many exercise science graduates will continue their learning, pursuing postgraduate studies in education and developing courses in sport and exercise science in secondary schools. It should be no surprise then that the maturing disciplines of exercise science and neuroscience have overlapped. Significant interest and growth in research and application have occurred in the role of exercise on brain health, neural function and neuroplasticity. Conversely, greater understanding of clinical neurological disease has impacted on advancing exercise programming, with studies testing the efficacy of various exercise interventions on brain and neural integrity. Taken together, the understanding of human motor control occurs through exploring and investigating neural and neuromuscular function.

1.1 The neuroscience of human movement. It is important to note that neuroscience has many different areas of research and study. Notwithstanding animal models of neuroscience, just focusing on the neuroscience of human nervous systems can traverse a wide continuum of study areas (Figure 1.1).

Figure 1.1. Continuum of general areas of study within neuroscience

It is not uncommon that neuroscientists from different research areas will not necessarily understand each other’s work. For example, there are those who investigate the neuroscience of a single cell under physiological or pharmacological interventions. Conversely, there are others who are interested in neuroimaging of the healthy or diseased brain. In between, we have various levels of neuroscientific inquiry that can overlap but are specific sub-disciplines in their own right. Further, like exercise neuroscience or neurophysiology, various other combined disciplines exist such as neuroanatomy, neurochemistry, neuropsychology and the like (see Table 1.1 for a list with definitions).

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Table 1.1. List of combined areas of study within neuroscience. Neuroanatomy Neurochemistry Neuroendocrinology Neurodermatology Neurogenetics Neuroimmunology Neurooncology Neuroopthamology Neurotology Neurophysiology Neuropsychology

Study of anatomy of neurons and neural structures Study of the compounds that generate physiological functioning in and between neurons Study of the interaction between the endocrine and nervous systems Study of the neural structures within the skin contributing to not only neural health but also sensory reception. Study of the role of genetics in the development and function of the nervous system Study of the interaction between the immune and nervous systems Study of cancers of the brain and nervous system Study of the interaction between the visual system and nervous system Study of the neural contribution to vestibular function Study of the functioning of the nervous system Study of the nervous systems influence in a person’s cognition and behaviour

Of course, as this book will be focusing on aspects of exercise science and human movement, the aim will be to synthesize and translate the growing research output specific to human motor control. The result is that this text narrows itself to the neural basis of human movement. Pathways, structures, neurophysiological principles and application of these principles are discussed but always in the context of human function and movement. The scientific study of the neuroscience of movement is termed motor behaviour. Motor behaviour is an umbrella term that encompasses a range of sub-disciplines that overlap: Motor control – the study of the underlying neurophysiological mechanisms that contribute to movement; Motor learning and skill acquisition – understanding the optimal environments to practice and refine specific, purposeful movements progressing the individual towards an accomplished level of expertise; and Motor development – the study of movement across the lifespan; how children develop and learn fundamental motor skills, through to

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understanding the mechanisms by which older adults decline in motor control.

1.2 What is ‘motor control’? How does motor control relate within the larger discipline of exercise science? As teachers in motor control for over 15 years, this is usually the first question that pops up from students. This is because motor control, as a term, is rather indistinct. Indeed, as the term ‘motor control’ is becoming used more widely, it has also been used and taken on slightly different meanings to different professionals within the exercise and sport science domain. For example, biomechanists discuss motor control in terms of physics being applied on, or from within, the body during proficient performance of a movement. Sport psychologists reference motor control in terms of actions having an emotional or cognitive basis, for example, the use of motor imagery to assist with rehabilitation of function or to improve on sports skills. Physiotherapists discuss motor control in the rehabilitation setting, with patients focusing on reducing “abnormal” movement and increasing movement patterns that are less likely to cause injury (or re-injury). Exercise physiologists may discuss the reduction in motor control performance when an individual experiences fatigue. Strength and conditioning and personal trainers need to consider motor control issues when prescribing correct technique and resistance, to optimise strength improvements and reduce likelihood of injury. However, the core of motor control centres on the neuroscience that underpins neuromuscular movement of the human body. It goes without saying that people’s lives are filled with movement (Latash, 2008). As undergraduates studying exercise science and related fields, human movement is the core focus: using movement to improve strength and muscle mass; using movement to improve skills; using movement to help people lose weight; and using movement to assist people back to functional daily activities. Undergraduate study in exercise science will have motor control embedded within course content across a number of subjects, or as a ‘stand-alone’ course. As exercise neuroscientists, we may have a bias here (who doesn’t!) but it is quite easy to illustrate how elements of motor control permeate throughout the curriculum of the major or core subjects areas taught in exercise science courses (Table 1.2).

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Table 1.2. Interrelationship between motor control and other areas within the discipline of exercise science. Study area Anatomy (neuroanatomy)

Biomechanics/movement analysis Exercise/sport physiology

Psychology of exercise and sport

Nutrition

Strength and conditioning

Relationship to motor control Study of the structure and function of the brain and nervous system. Study of efficient voluntary movement. Study of biochemistry and physiological systems adapting to exercise and sport. Study of psychological influences in determining exercise behaviour, as well as psychological traits required in high performance sport. Study of nutrients in food, how the body uses nutrients, and the relationship between diet, health, and disease. Study of methods to induce resistance for training adaptations.

Example Damage to primary motor cortex leading to motor dysfunction. Relationship of sequential limb movement for a specific motor task. The role of the brain in fatiguing exercise.

Mood disorder influencing loss in motivation to exercise. Motor imagery to improve movement efficiency in sport. Gut/brain interaction, how food intake can affect movement (interrelationship with exercise physiology). Neural adaptations with strength or resistance training modalities.

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So, where does this lead us in terms of establishing what motor control actually is? The common denominator in all these areas is the interaction of the brain and muscle to provide meaningful (Latash, 2008) goal-directed movement (Enoka and Stuart, 2005). Therefore, it can be argued that motor control has a basis in neuroscience. Motor control is concerned with the neurophysiological processes that allow movements to become more efficient (which may also include improvement in skills, movement efficiency, and strength). Similarly, motor control investigates how movements can be retrained after an injury. These scenarios now have a well-described term, neuroplasticity (Doidge, 2007), which will be discussed later and throughout the text.

1.3 Motor learning and skill acquisition: similarities and contrasts to motor control. Historically, physical education courses have focussed on the understanding of the learning and teaching of motor skills (or motor learning). For example, understanding the development of fundamental motor skills (FMS) such as running, jumping, and throwing are important when teaching children these lifelong skills. These fundamental skills are the foundation for more advanced motor skills, particularly specific skills for sporting excellence. Teaching motor skills effectively is ingrained within the cognitive/psychological domain, whereby the teacher is creating an optimal learning environment. However, with the increased interest in the study of motor development (see section 1.4), exploration of FMS now sits within the development and changes of skills and motor actions across the lifespan, providing motor learning and skill acquisition the opportunity to have its own definition, succinct from motor control and motor development. Figure 1.2 addresses not only similarities, but also differences, between the sub-disciplines of motor control and motor learning. Motor learning, it can be suggested, is aimed at how people learn and acquire expertise at motor skills. What are the environments that best optimise learning? For example, should athletes practice the same movement action in an unchanging setting repeatedly in a series of blocks to reinforce motor pathways? Or, should athletes randomise the environment with the intention of learning a particular skill but retaining the motor memory? Is it better for individuals to learn implicitly through discovery, or have an external source (such as a teacher or instructor) providing feedback on how their attempt was executed? In reality, motor learning and skill acquisition are part of memory formation and, therefore, it can be argued that this sits within the domain of cognitive

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psychology. Motor learning is creating motor memory, similar to other forms of learning (e.g., content knowledge) that leads to memory retention.

Figure 1.2. Differences and overlap between motor control and motor learning/skill acquisition.

Motor development focuses on the stages and underlying processes that allow children to gain motor skills. Traditionally, within the domain of developmental psychology, the study of motor development has taken a ‘back seat’ compared to other developmental science areas such as cognitive, social, language, personality, perceptual and emotional development (Adolf and Robinson, 2015). However, there has been a resurgence of interest in studying motor development due to its advantage in direct observation and translation to the psychological sciences. Moreover, with increased understanding of neuroplasticity, the study of motor development allows for the understanding of neurophysiological processes, providing a foundation for how adults learn new motor skills and retain motor memory. Whilst motor development is primarily focussed in children and adolescents, it is also important to understand changes as people age, and the consequences of these. Our society is ageing, with significant increases in the number of people who will be aged 65 and older in the coming decades. Understanding the neuromuscular and motor control changes with ageing is important for current students, particularly in thinking of career opportunities

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upon graduating. As part of the Allied Health sector, exercise science students have a unique set of skills allowing application of their neuroscience knowledge to, for example, exercise programming for older people, or biomechanics to understand how to prevent falls. These will be key areas in the near future to which exercise science can make a unique contribution (if not already).

1.4 The challenge of translating neuroscience to exercise science So, why does neuroscience cause such difficulty to the majority of exercise science students? The simple reason is because the neuromuscular system is complex. It has a number of levels contributing to meaningful movement that work at the conscious and subconscious levels, spinal and supraspinal levels, and between sensory interactions with the motor response. Further, it also may not necessarily have anything to do with exercise. Motor control is as concerned with how an individual can create a pincer grip to control a pen during writing, as it is concerned with how a person can increase strength without muscle hypertrophy, or how the central nervous system controls movement as it also responds to fatiguing exercise. It is also concerned about how control can be lost, through degeneration, or how control is lost after a brain injury and, in some cases, how it can be regained. With the links between neuroscience and exercise becoming closer, it is important that exercise scientists have a competent understanding of the brain, and how it controls the neuromuscular pathways. Moreover, it is important that exercise scientists who may practice in related fields are able to constructively critique new developments in the area, and to confidently determine good neuroscience from neurobabble. With unprecedented access to news and information on a daily basis and the rise of pop-science, it is important that, as practitioners in the field, you will be able to advise your clients/patients what has been demonstrated using the scientific method, and what has not been supported (despite comprehensive spin). The contents in this book are based upon evidence-based research; however, each chapter aims to translate the findings allowing students to learn new concepts confidently.

1.5 How to get the best from this book The topics in this book have come from our teaching of motor control to students over the last 15 years. While the topics can and do overlap, each

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chapter can be read independently and in any order. For some, all chapters will be of interest. For others, specific chapters will be helpful for personal and professional development. As the chapters are research based, our aim is to guide students through each chapter with the most robust concepts, but also some insights into future areas of enquiry. For some students, this text may serve as a pathway to a career as an exercise science research student, as much as it may inform students moving towards a practitioner career. Either way, we hope that students find this book engaging, but also challenging, in attaining future knowledge through scientific enquiry.

References 1. 2. 3. 4. 5. 6.

Adolph KE, Robinson SR. Motor Development, 2015. Doidge N. The Brain That Changes Itself: Stories of Personal Triumph from the Frontiers of Brain Science. 2007. Enoka RM, Stuart DG. The contribution of neuroscience to exercise studies. 1985. Latash ML. Neurophysiological Basis of Movement. 2008. Rosvall M, Bergstrom CT. Mapping change in large networks. 2010. Varazzani C. The risks of ignoring the brain. 2017.

CHAPTER 2 LEVELS OF MOTOR CONTROL DAWSON J. KIDGELL, PHD AND ALAN J. PEARCE, PHD

2. Background Understanding motor control is through learning the macro and the micro arrangement of the nervous system. In this chapter, we will focus on the macro: structural, functional and hierarchical organisation of movement. In chapters four and five, we will discuss how motor control occurs through neuromodulation and neuroplastic changes. The study of neuroscience dictates that we need to learn the structural, functional and hierarchical anatomical arrangement of the nervous system in order for us to appreciate how individuals improve their movement, whether it be for sport, music, jobs requiring fine skills or, alternatively, how patients relearn and regain movement after an injury. The chapter will start with the basics: the major structures identified in the central nervous system. Where appropriate, and in keeping with the objective of this textbook, we will focus examples and discussion on how the region contributes to movement and motor control. Bear in mind which cognitive functions, such as attention and decision-making, can and do influence motor output. From there, we will outline the functional arrangement of motor control. Here, we will focus on topographic arrangement of motor functions and, in later chapters, how their arrangement allows for adaptation and re-organisation. Finally, we will discuss the hierarchical levels of motor control where we will incorporate structural and functional understanding on how movement is planned and executed.

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2.1 Structural arrangement of the brain contributing to movement In simplistic terms, the brain can be divided into three parts: the cerebrum, brain stem, and the cerebellum. Each of these structures makes important contributions to the regulation of movement. The cerebrum is the large ‘dome’ of the brain that is divided into right and left cerebral hemispheres. The most superficial layer of the cerebrum is the cerebral cortex, which is composed of tightly arranged neurons. The cortex performs three important motor functions: (1) the organization of complex movements; (2) the storage of learned experiences; and (3) the reception of sensory information. For the purpose of this chapter, we will limit our discussion to the role of the cortex in the organization of movement. However, to appreciate this, some information about the structure of the cerebral cortex is required.

2.1.1 Structure of the cerebral cortex The cerebral cortex is well endowed with neurons, neuroglia, and blood vessels. The structural organisation of the three types of cells that populate the cortex, being pyramidal cells, stellate neurons and fusiform neurons, enables the classification of the cortex into three types: allocortex, mesocortex and the neocortex. The allocortex is the oldest region and is composed of only three layers and is located in the limbic system. The mesocortex is younger and is composed of three to six layers and is predominantly located in the insula and cingulate gyrus. The neocortex is the youngest region of the cortex and is composed of six layers that comprise the bulk of the cerebral cortex (Rothwell 1994). Whilst the cerebral cortex is structurally organised into layers, it also has organisation through functional connections. Most corticospinal output is mediated through pyramidal neurons and stellate (or granule) cells. The cellular organisation of pyramidal and stellate cells within the cortex gives it a characteristic layered or laminar appearance that can be identified as six distinct layers: I. Molecular layer. A layer lying immediately inferior to the pia matter and containing very few cell bodies. II. External granular layer. A layer of densely packed small cells including small pyramidal and stellate cells.

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III. External pyramidal layer. A layer consisting of medium to large-sized pyramidal cells. IV. Internal granular layer. This layer is predominantly composed of densely packed stellate and pyramidal cells. V. Ganglionic layer. This layer contains large pyramidal cells (Betz cells). VI. Multiform layer. This layer is relatively thin and mostly composed of densely packed, spindle-shaped cells, many with axons leaving the cortex. Layer 1 contains mainly long horizontal dendrites and axons in deeper layers as well as thalamic afferents. The medium to large pyramidal cells in layers III and V contain long cortico-cortical connections. In addition, pyramidal cells in layer V also project to sub-cortical areas such as the basal ganglia, brain stem and to the spinal cord. Layers II and IV receive afferent inputs, with inputs from the thalamus generally terminating in layer IV. Layer VI contains small pyramidal cells which exhibit greater morphologic variability than the pyramidal cells located in other layers and have corticocortical and cortico-thalamic projections. Layers II through VI all contain stellate cells, some of which form excitatory connections onto pyramidal cells and some of which form inhibitory synapses.

2.1.2 The brain stem The brainstem is located inside the base of the skull just superior to the spinal cord and is essentially an extension of the spinal cord. It is composed of a series of complicated neural tracts and nuclei (groups of neurons). The major structures of the brain stem include the medulla, pons and midbrain. In addition to these structures, a complex neuronal network known as the reticular formation resides in the brainstem. The brainstem performs motor and sensory functions for the face and head (i.e., cranial nerves) along with providing support to the body against gravity. For example, muscles of the spinal column and the extensor muscles of the legs maintain the body against gravity. These muscles are under the control of specific brainstem nuclei, in particular the pontine reticular nuclei, which excite activity in the antigravity muscles, and the medullary reticular nuclei, which inhibit the activity of antigravity muscles. Importantly, the medullary reticular nuclei receive collateral input from the corticospinal pathway, rubrospinal pathway, and other motor pathways. These systems can activate the inhibitory action of the medullary reticular nuclei and counterbalance the signals from the pons.

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2.2 Transmission of cortical motor signals The spinal cord is under the control of a number of neurons that descend from the primary motor cortex (M1). The largest of these are the corticospinal neurons that have their origins in layer V of the cerebral cortex and extend to form the bulk of the corticospinal or pyramidal tract (Porter 1985). Although corticospinal neurons are located within six cortical regions, the M1 has the largest concentration (Porter, 1985). Within the M1, these corticospinal neurons are functionally organised to project to motoneurons that control specific muscle groups (He et al. 1993), thus they are somatotopically organised. Corticospinal neurons that arise within the M1 descend through the internal capsule, brainstem, and medulla oblongata and continue to descend in the dorsolateral funiculi of the spinal cord (Alawieh et al. 2017). As the corticospinal neurons leave the M1 and descend to the medulla, they are organised somatotopically. At the medullary spinal junction, approximately 85-90% of the corticospinal neurons cross the midline to form the motor pyramidal decussation (Alawieh et al. 2017), where they continue as the dorsolateral funiculi of the spinal cord and converge onto motoneurons within the ventral horn of the spinal cord that innervate limb muscles (Alawieh et al. 2017). Anatomical mapping studies reveal that the connectivity of the corticospinal tract suggests that the remaining uncrossed ipsilateral corticospinal tract fibres descend primarily in the dorsolateral lateral or ventral funiculi of the spinal cord (Alawieh et al. 2017). A small proportion of corticospinal tract fibres do not crossover at the pyramidal decussation at the medulla; rather, they project to ipsilateral spinal motoneurons, where they could alter the excitability of ipsilateral pathways (Porter and Lemon 1993, Carson 2005). In the clinical neurophysiology literature, it has been suggested that increased utilisation of the ipsilateral pathway may provide a viable method for re-establishing motor control of upper-limb muscles following lesions to the M1 (Alawieh et al. 2017, see Chapter 11 for more detail). There is now good evidence for a functional role of the corticospinal system in the control of the upper limb and changes in its functional organisation following different forms of exercise training (Frazer et al. 2018).

2.3 Motor functions of the cerebellum For a long time, the cerebellum has been referred to as the silent area of the brain mainly because electrical stimulation of the cerebellum does not

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evoke any conscious sensation or movement. However, intriguingly, removal of the cerebellum causes highly abnormal human movement. Although it is difficult to study the function of the cerebellum in a conscious person, it is generally accepted that the cerebellum is vital for rapid motor actions which include cycling, running, playing the piano and talking. Because the cerebellum does not cause muscle contraction per se, its main function is to assist in the sequencing of motor actions and to monitor and correct movements whilst they are being executed. The ability to adjust motor sequences during movements enables the M1 and other parts of the brain to ensure that the intended movements are performed correctly. The cerebellum continuously receives updated information about the intended/desired sequence of muscle activity from specific motor control centres of the cortex. It receives sensory information from peripheral receptors such as muscle spindles and Golgi tendon organs, thus allowing corrective adjustments to be sent to the motor system to increase or decrease the level of muscle activation of specific muscle groups. A critical function of the cerebellum is to assist the cerebral cortex to plan the next series of sequential movements in advance whilst the initial movement is still being performed. This essentially enables movements to transition smoothly from one movement to the next. The cerebellum also learns to correct movement, via special cerebellar nuclei (the deep cerebellar nuclei) that can adjust motor output. The cerebellum is able to perform these functions because of its anatomical and functional organisation.

2.3.1 Anatomical and functional organisation of the cerebellum The cerebellum is divided into three lobes: 1) anterior lobe, 2) posterior lobe and 3) flocculonodular lobe and two deep cerebellar fissures. The cerebellar hemispheres are separated along the longitudinal axis by a narrow band called the vermis. The vermis plays an important role in controlling muscle activity of the axial body, shoulders and head. To either side of the vermis, are cerebellar hemispheres, whereby each hemisphere divides into an intermediate and lateral zone. The intermediate zone controls motion of distal portions of upper and lower limbs especially the hands and feet, whilst the lateral zone controls sequencing movements of muscles including timing and coordination of muscle activity. The vermis and intermediate zones contain a topographical representation of the body, similar to that observed in the sensory cortex, motor cortex, basal ganglia, red nucleus and the reticular formation. The topographical regions of the vermis and lateral zones receive afferent projections from all respective parts of the body, as well as from corresponding topographical motor areas from the cerebral

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cortex. The lateral zone of the cerebellar hemisphere is not topographically organized, rather, it receives input signals from the cerebral cortex that enable the cerebellum to assist the motor pathways to organise and plan movement.

2.3.2 Afferent and efferent pathways of the cerebellum Several neural tracts from the cerebral cortex to the cerebellum regulate human movement. For example, the corticopontocerebellar pathway originates from motor and premotor areas of the cerebral cortex and somatosensory cortex and send their axons to mainly the lateral zone of the cerebellar hemisphere. In addition, neural tracts from the brainstem, such as the olivocerebellar tract, vestibulocerebellar tract (vestibular apparatus), and reticulocerebellar tract, terminate predominately in the vermis. The dorsal spinocerebellar tract transmits information mostly from muscle spindles but also from Golgi tendon organs, tactile, and joint receptors in the periphery to the cerebellum. Thus, it appraises the brain of the momentary status of muscle contraction, muscle tension and limb position and forces acting on the body surface. The other afferent pathway is the ventral spinocerebellar tract that is mainly excited by motor signals that arrive at the ventral horn of the spinal cord from the brain via the corticospinal tract and from the internal motor pattern generators of the spinal cord itself. The main efferent pathways that exit the cerebellum include the fastigioreticular tract and the cerebellothalamocortical tract, along with the cerebellar nuclei. The fastigioreticular tract works in close association with the equilibrium apparatus and brainstem nuclei to control balance and works with the reticular formation of the brainstem to control postural stability. The cerebellothalamocortical tract is extremely complex and passes through many neuronal structures to finally terminate at the level of the motor cortex, whereby it coordinates the reciprocal contractions of agonist and antagonist muscles in the limbs; thus, it is involved in the turning ‘on’ and ‘off’ of muscle activity. In addition to the efferent neural pathways located deep in the cerebellum, on each side are three deep cerebellar nuclei, the dentate, interposed and fastigial nuclei. The deep cerebellar nuclei all receive input signals from the cerebellar cortex and the deep sensory afferent tracts to the cerebellum. The primary source of motor output from the cerebellum is via the deep nuclear cells. Every time an input signal is sent to the cerebellum, the afferent signals go directly to the deep cerebellar nuclei and to the corresponding

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area of the cerebellar cortex overlying the deep nucleus. Once the input signal has been received by the deep nuclei, an inhibitory output signal from the deep nuclei is generated. Simplistically, all afferent signals to the cerebellum end in the deep nuclei as excitatory signals followed a few milliseconds later by inhibitory signals. These inhibitory signals from the deep nuclei exit the cerebellum via efferent pathways described above.

2.4 Motor functions of the spinal cord The spinal cord is about 45 cm long and approximately 14 mm in width. It has a laminar structure (i.e., flows down from the brain without any abrupt changes). Transection of the spinal cord reveals a characteristic butterfly picture, consisting of gray matter (cell bodies of spinal neurons) and the remaining white matter (neural tracts that transmit information to and from the brain) constitutes the rest of the spinal cord (Figure 2.1). The spinal cord is protected by the spinal vertebra and each vertebra has two pairs of horns. The dorsal horns Figure 2.1. Gross anatomy (closer to the back) serve as an input of the human spinal cord. pathway for sensory information from peripheral receptors. As you can see in Figure 2.2, the cell bodies of the receptors are located in spinal ganglia just outside of the spinal cord (i.e., the dorsal root ganglion). These cell bodies have T-shaped axons where their distal branches travel to sensory endings located in the periphery and their proximal branches enter the spinal cord via the dorsal horn. The axons of many different peripheral receptors (e.g., muscle spindles, golgi tendon organs) form a dorsal root and enter the spinal cord through the same dorsal horn. In contrast, the ventral horns are the major output pathway of neural signals to peripheral structures, in particular muscles (the axons of Į-motor neurons) and the muscle spindles (the axons of gamma-motor neurons). The axons of these neurons form the ventral roots. Most of the neurons within the spinal cord are not motor neurons, rather they are interneurons. These neurons receive information from both afferent and efferent fibres and

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axons of neurons within the central nervous system (CNS), to generate action potentials that are transmitted to other interneurons or motoneurons.

Figure 2.2. Cross-sectional anatomy of the spinal cord.

2.4.1 Organisation of the spinal cord The nervous system enables sensory information to be integrated and, once integrated, an appropriate motor response is generated that generally begins in the spinal cord via reflexes. More complex processing will eventually extend to the brainstem and cerebral cortex, whereby complex and intricate motor skills are performed. There are many neural circuits in the spinal cord that are vital for the control of movement and the spinal cord is organised in a specific manner, as briefly discussed above; however, here we will go into some specific detail. Anterior motor neurons are located at each spinal segment level of the anterior horn of the spinal gray matter (Figure 2.2). Each anterior horn contains several thousand specialised neurons that are about 50-100% larger in size than other neurons found in the nervous system; these are called anterior motoneurons. Anterior motoneurons give rise to the nerve fibres that exit the spinal cord via the anterior roots to directly innervate skeletal muscle. There are two types of neurons, Į- motoneurons and gamma-motoneurons.

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Alpha motoneurons are large type A Į- motor neurons with a large diameter that have many fibre branches as they enter and stimulate skeletal muscle. A single Į- motoneuron and the muscle fibres it innervates are known as a motor unit (see below). Transmission of nerve impulses into skeletal muscles occurs by activating motor units, which in turn stimulate muscle cells, which produce muscle force. In contrast, gamma motor-neurons transmit nerve impulses through much smaller type A gamma motor nerve fibres, which target the intrafusal fibres of skeletal muscle. These fibres comprise the middle of the muscle spindle, which helps control muscle tone via the stretch reflex. In order to facilitate the ongoing control of movement and all of the information that flows into the spinal cord, interneurons are present in all areas of the spinal cord. Interneurons are small, highly excitable neurons that have many interconnections with one another and many of them synapse directly onto anterior motoneurons, enabling the integrative function of the spinal cord to occur. This integrative process occurs via spinal reflex activity. All reflexes commence with a stimulus that is of sufficient intensity to activate a sensory receptor. Once activated, the sensory receptor will discharge action potentials through sensory afferent neurons to the CNS. The CNS, which includes the brain and spinal cord, is the major integrative centre that processes and evaluates all incoming (afferent) information and then coordinates an appropriate response. The appropriate response results in a series of action potentials in efferent neurons that then synapse onto an effector organ, such as a muscle to cause a response. This whole process is referred as the reflex arc.

2.5 Types of reflex pathways The common reflex pathways in the CNS consist of a network of neurons that link sensory receptors to muscles and glands. Because of this, reflexes can be classified in different ways, e.g., somatic reflex, spinal reflex, autonomic, etc. The most relevant classification for human movement is somatic and spinal reflexes. A somatic reflex involves the activation of somatic motor neurons and skeletal muscle, whilst spinal reflexes are reflexes that are integrated at the spinal level, but modulated by higher input from the brain. Reflexes can also be classified by the number of neurons within that reflex pathway. For example a monosynaptic reflex has a single synapse between an afferent neuron and an efferent neuron, whilst a polysynaptic reflex involves two or more synapses.

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2.5.1 Skeletal muscle reflexes Nearly every movement that we perform involves skeletal muscle reflexes. We have specialised receptors throughout the musculoskeletal system that sense changes in joint movement (position), muscle tension, and muscle length. These receptors provide the CNS with this information, which then responds in one of two ways. First, if the appropriate response is muscle contraction, the CNS will stimulate somatic motor neurons in the ventral horn to activate skeletal muscle or, if a muscle needs to relax, the CNS will activate inhibitory interneurons and inhibit the activity of somatic motor neurons. The specific reflex pathways for these responses are derived from Golgi tendon organs and muscle spindles. 2.5.1.1 Muscle spindles Muscle spindles are stretch sensitive receptors that propagate action potentials to the spinal cord and muscle, which inform the CNS about muscle length and the changes in muscle length that occur during movement. Each muscle spindle is enclosed in a connective tissue capsule that is infolded by a small group of muscle fibres known as intrafusal fibres.

Figure 2.3. Monosynaptic reflex arc.

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The intrafusual fibres are organised in such a manner that the ends are the contractile component, whilst the central region is enclosed with sensory nerve endings that respond to muscle stretch. The contractile components of the muscle spindle are innervated by gamma motoneurons. Even at rest, the central region of the muscle spindle is stretched enough to activate the sensory nerve endings, which in turn discharge action potentials that arrive at the spinal cord and synapse directly on Į- motoneurons innervating the muscle in which the muscle spindle lies, producing a classic monosynaptic reflex (Figure 2.3). This neural process occurs in order to maintain muscle tone (i.e., a resting muscle will still a have particular level of tension). During movement, muscle stretch activates muscles spindles, which produce the synonymous stretch reflex via gamma motoneuron activation. When Į-motoneurons discharge (due to muscle spindle input), the muscle will contract, which releases the stiffness on the capsule of the muscle spindle. In order to ensure normal spindle activity, gamma motoneurons innervate the contractile ending of the spindle by discharging action potentials at the same time as the central region discharges. The gamma motoneuron causes the muscle to contract and, thus, shorten via stimulating the intrafusal fibres. In simple terms, the discharge of the central regions leads to Į- motoneuron activation (via the monosynaptic stretch reflex pathway) and gamma motoneuron activation (which keeps the muscle spindle active) simultaneously via a process called alpha-gamma coactivation. 2.5.1.2 Golgi tendon organs (GTO) GTOs are specialised sensory receptors located at the junction of tendons and muscle fibres. GTOs respond to muscle tension during the isometric phase of a muscle contraction, thus they detect the amount of motor unit activity within a muscle. The discharge rate of GTOs increases as a function of increasing force production. Excitation of GTOs leads to an inhibitory reflex known as the results inverse myotatic reflex (Figure 2.4).

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Figure 2.4. Inverse myotatic reflex loop.

When a skeletal muscle contracts, it results in muscle tension that stimulates the GTOs in the tendon attached to the skeletal muscle. Activation of GTOs leads to the propagation of action potentials to the spinal cord by afferent neurons. Within the spinal cord, the afferent neuron synapses with an inhibitory interneuron and an excitatory Į- motoneuron. Because there is an inhibitory synapse (via an interneuron) with an Į- motoneuron that specifically innervates the muscle attached to the tendon, the inhibitory synapses lead to the relaxation of the contracted muscle, whilst the antagonist muscle is activated (e.g., autogenic inhibition). Thus, the GTO reflex pathway is a safety mechanism to ensure that the agonist is not producing too much force that could lead to tearing the muscle/tendon.

2.6 Hierarchical organisation of motor control Further to structural and functional divisions of the central nervous system, which provide an anatomical organisation of the brain and spinal cord, understanding of how movement is integrated and controlled can also be explained using a hierarchical approach.

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Whilst it is logical to assume structural levels of arrangement being cortical, subcortical and spinal cord, the hierarchical approach arranges motor control in terms of interactions between the structures and their contributions to the final motor output. Each level of the hierarchy has a specific function in motor control (Figure 2.5).

Figure 2.5. Hierarchical organisation of human motor control.

In order to commence a movement, such as elbow flexion with a dumbbell, “an intention” to commence the movement is generated at the highest level of the hierarchy (e.g., cerebral cortex, premotor areas, etc.). The exact cortical regions involved in these “intentions” are not completely known, but likely involve the pre-motor cortex, motor cortex and sensory cortex. As Figure 2.5 shows, “motor commands” will then descend from the higher motor-output centre “command neurons” to specific regions of the brain that constitute the middle level of the motor control hierarchy. Such regions include the brainstem, cerebellum, thalamus, sensoricortex and basal ganglia. All these structures form strong functional connections to the motor cortex. The main role of the middle level is to specify what postures and movement are required in order to carry out the intended motor action. The neurons located in the structures of the middle level receive their input from neurons in the higher level centres simultaneously as they receive afferent input from receptors in muscles, tendons, joints, skin, the vestibular system and visual system. The afferent input relays this information to the middlelevel neurons, which provide important information about the starting

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position of the body, posture and muscles to be recruited or activated to enable the movement to occur. These neurons also integrate all afferent information with the “command neurons” to develop a motor program (i.e., the pattern of neural activity required to perform a movement correctly). The information determined by the motor program (i.e., starting position, muscles to recruit, etc.) is then transmitted to the major descending motor pathways (corticospinal tract) to the lowest level of the motor control hierarchy, the brainstem and spinal cord. The lowest level (or local level) includes motor neurons and interneurons within the spinal cord. It is the final common pathway and it determines which motoneurons will be activated in order to achieve the desired movement. In addition, this level will also determine how many motor units will be recruited and what degree of muscle activation is required.

2.7 Motor units, fibre types, and recruitment physiology 2.7.1 The motor unit A motor unit is the functional unit of neural control for muscular activity. Motor units consist of a cell body, a Į-motoneuron, and all of the muscle fibres innervated by the Įmotoneuron (Figure 2.6; Enoka and Fuglevand, 2001). Motor units convert synaptic input received by Į-motoneurons into mechanical output by the muscle (Fang et al. 1997). The number of muscle fibres per motor unit varies and, to a certain extent, determines the motor performance of the muscle. The number of muscle fibres per motor unit can vary from as little as four for ocular muscles, 100 for small muscles involved in fine Figure 2.6. Functional organisation of motor performance, to as many human motor units. as a 1000 or more for larger muscles involved in gross motor patterns (Duchateau et al. 2006).

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2.7.2 Physiological classification and recruitment A group of Į-motoneurons located in the ventral horn of the spinal cord and the muscle they innervate are known as a motor unit pool. The motor units that form a motor unit pool are diverse in regard to the intrinsic properties of the Į-motoneurons and the muscle fibres that they innervate (Duchateau et al. 2006). A Į-motoneuron is typically characterised by its structure, excitability and distribution of synaptic input, whilst muscles fibres are classified based upon their contractile speed, force generating capacity, and resistance to fatigue (Burke et al. 1973). Most muscles in the human body contain muscle fibres that have differing contractile speeds. Given that all muscle fibres comprising a single motor unit have identical metabolic properties, muscles that have different contractile speeds belong to a different type of motor unit subtype. Experimental data confirm that motor units have specific physiological and biochemical properties, and therefore can be categorised based upon these properties (Kernell et al. 1999). The most generally accepted physiological classification for motor unit types are: slow contracting, fatigue resistant (S); fast contracting, fatigue resistant (FR); and fast contracting, fast to fatigue (FF) (Figure 2.7).

Figure 2.7: Twitch contraction profile of human motor units (fatigue resistant (S); fast contracting, fatigue resistant (FR); and fast contracting, fast to fatigue (FF).

Based upon this classification, it has been identified that S units have slow contraction times (i.e., time to peak force), produce a relatively small amount of tension, are recruited earlier, have slow conducting motor axons and have a greater resistance to fatigue, whilst the FR units have intermediate properties, for example, have a fast twitch, produce moderate tension, are recruited later, have faster conducting motor axons and are

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resistant to fatigue. In contrast to the S and FR unit properties, FF units have a fast twitch, develop large tension, and are vulnerable to fatigue (BiglandRitchie et al. 1998). In addition to this classification, motor unit subtypes have also been classified based upon their histochemical, biochemical, and molecular properties of the muscle fibres that they innervate (Enoka and Fuglevand 2001). For example, histochemical analysis has identified three types of muscle fibres: type I, type IIa and type IIx (Kernell et al. 1999). Type I fibres are often referred to as slow-twitch fibres, whilst type II are fast-twitch muscle fibres; however, they can be further categorised as Type IIa and IIx (Brooke and Kaiser 1974).

2.8 Principle of motor unit recruitment The recruitment of motor units is governed by the “size principle” which was first proposed by Henneman and colleagues in 1974. In the simplest elements, the size principle of motor unit recruitment states that the smallest Į-motoneurons (and motor units) are recruited first and orderly recruitment, relative to size, occurs thereafter. This appears to be a good strategy, because the smaller motor units which are recruited first are also the ones that are the most difficult to fatigue and thus can withstand long contraction durations. When gradually increasing force production, most muscles completely recruit all available motor units between 50 and 95% of maximum force production. During fast or ballistic contractions, the level of force required to recruit a specific motor unit is lowered. In other words, larger (the so-called high-threshold) motor units are recruited at a lower force level (i.e., more readily recruited) when the contraction is performed as fast as possible rather than during a slow contraction. Force production is also regulated by the rate at which a motor unit is activated. This simply means how often an electrical discharge passes along the Į-motoneuron (i.e., motor unit action potential). This is known as rate of motor unit firing (also known as discharge rate, firing frequency or rate coding). It has been demonstrated during gradually increasing force contractions (the so-called ramp contractions), already recruited motor units increase their firing rate as the force level increases. Firing rate patterns appear to be reversed during fast contractions, showing an initial burst of high firing rate followed by lowered rates once a certain level of force is attained, but recruitment still follows the size principle. Differences in the interaction between motor unit recruitment and firing rate may be dependent upon the type of muscle, with small muscles seemingly more reliant on firing rate to modulate force

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production while large force-producing muscles rely more on recruitment. Nevertheless, ultimately, neural control of force production is reliant upon motor unit recruitment and firing rate whose combination determines the final signal presented to the muscle.

2.9 Motor unit synchrony In some instances, depending on the motor task being performed, motor units may be recruited in a synchronous manner. Motor unit synchronisation is a time domain measure of the correlated activity of pairs of active motor units (Sears and Stagg 1976). Motor unit synchronisation provides information on the strength of branched common input to Į-motoneurons that is modulated by the corticospinal pathway (Figure 2.8) (Nordstrom et al. 1992).

Figure 2.8: Mechanism of motor unit synchronisation (Adapted from Semmler 2002).

The most direct method used to determine motor unit synchronisation in humans is cross-correlation analysis of individual discharge times from pairs of concurrently active motor units (Griffin et al. 2009). This procedure requires identifying the discharge time of one motor unit, which is used as a reference, and a histogram is constructed of the peri-event discharge times of a second motor unit (Farmer et al. 1997). If a tendency towards synchronisation exists, there will be a peak in the cross-correlation histogram around the time of firing of the reference motor unit (Moore et al.

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1966). The presence of a peak in the histogram is due to common input from branched corticospinal axons of single last-order neurons (Farmer et al. 1997) and represents the common input strength (CIS) (Wiegner and Wierzbicka 1987). This common input is thought to increase the probability of simultaneous discharge in the motor units sharing these inputs (Datta and Stephens 1990). The presence of correlated activity (i.e., synchronisation) appears as a peak in the centre of the histogram. The size of the peak in the histogram reflects the amount of common input that is shared between the neurons (Farmer et al. 1997) whilst the width of the peak is used to distinguish between direct and indirect common input onto Į-motoneurons (Farmer et al. 1997). Direct common input produces a narrow peak in the cross-correlation histogram and is known as short-term synchronisation of motor units (Moritz et al. 2005). In contrast, indirect common input produces a broader peak in the histogram and is known as broad-peak synchronisation (Lowery et al. 2007). As a result, the width of the peak can be used to discriminate between direct cortical connections to Į-motoneurons and those with an interposed neuron. Experimental recordings in humans suggest that the histogram peaks usually encompass a mixture of direct and indirect common inputs (Farmer et al. 1997).

2.9.1 Quantifying the degree of motor unit synchrony Motor unit synchronisation is quantified by applying the cumulative-sum technique to identify statistically significant peaks, as well as significant peak widths within the cross-correlation histogram (Wiegner and Wierzbicka 1987). Once peaks have been identified in the crosscorrelogram, synchronisation indices can be calculated for all peaks within the histogram (Nordstrom et al. 1992). For example, the K index is defined as the ratio of total counts in the peak region to chance in the crosscorrelogram (for review see Nordstrom et al. 1992). It has been shown that this synchronisation index tends to be smaller with higher motor unit discharge rates, therefore making it a sensitive measure for quantifying motor unit synchronisation at high force levels. The common input strength (CIS) is another index often used to measure the magnitude of motor unit synchronisation. CIS is estimated from the cross-correlogram as the number of extra counts in the synchronous peak above that expected by chance. Therefore, the CIS is a representation of the frequency of synchronised motor unit discharges. Another index used includes the E index. The E index quantifies the magnitude of motor unit synchronisation by counting the

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number of synchronous events normalised to the number of discharges of the reference motor unit (Datta and Stephens 1990).

2.9.2 Motor unit synchronization and human motor control Using cross-correlation analysis, several studies have demonstrated that motor unit synchronisation is modulated by the motor task performed (Semmler and Nordstrom 1998; Fling et al. 2009). For example, motor unit synchronisation is less during index finger flexion, but greater during index finger abduction (Bremner et al. 1991). Further, synchronisation is 35% greater during eccentric contractions compared to isometric and concentric (Semmler 2002). There is also evidence to suggest that the strength of motor unit synchronisation is modulated by regular physical activity. However, how motor unit synchronisation is altered following motor training is not clear. In light of this, Semmler and Nordstrom (1998) demonstrated that the strength of motor unit synchronisation was largest for the dominant and non-dominant hands in weightlifters, and was the lowest in both hands of a group of highly-skilled musicians. Fling et al. (2009) demonstrated that the strength of motor unit synchronisation was greatest in the first dorsal interosseous (FDI) and biceps brachii (BB) in weightlifters compared to a control group. It must be noted that, as no training intervention was performed in these studies, it is not known if the differences in synchronisation are related to some aspect of muscle strength, or more closely associated with skilled motor performance. Whilst there is limited data that has examined the effect of strength training on motor unit synchronisation, it is a common impression that increases in motor unit synchronisation lead to an increase in strength.

2.9.3 Motor unit synchronization in upper and lower limb muscles Given that the general line of evidence for motor unit synchronisation stems from last-order common pre-synaptic input from descending corticospinal neurons to Į-motoneurons (Farmer et al. 1997), the degree of synchronisation between muscles varies (Fling et al. 2009). Motor unit synchronisation is greater in the FDI, compared to the more proximal BB and the vastus medialis muscles, suggesting that the strength of motor unit synchronisation is influenced by the type of muscle (i.e., gross vs. fine). Recently, Fling et al. (2009) assessed the magnitude of motor synchronisation of the BB and FDI in a group of strength-trained participants. These results are in accordance with previous research (Semmler and Nordstrom 1998), demonstrating that the magnitude of motor unit synchronisation was greater

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in the FDI when compared to the BB. A possible mechanism that may account for this difference in motor unit synchronisation is that it may simply be a result of differences in the physiological strength of the corticospinal pathways monosynaptic connections onto the spinal motoneuron pool that controls the FDI being stronger compared to the BB (Kim et al. 2001, Fling et al. 2009). Furthermore, McKiernan et al. (1998) demonstrated that individual cortico-motoneuronal cells have more recurrent and more effective terminal connections onto the Į-motoneuron pools of distal muscles compared to proximal muscles. Support for a corticospinal origin for motor unit synchronisation has stemmed from research in patients with corticospinal lesions (Semmler 2002). In patients with amyotrophic lateral sclerosis, which is a progressive degenerative disease affecting large diameter corticospinal cells, motor unit synchronisation is almost absent (Schmied et al. 1999).

2.10 Summary The control of voluntary movement is complex and requires the cooperation of many areas within the CNS, including supraspinal and spinal structures. In order to perform a voluntary movement, both cortical and subcortical structures propagate descending volleys to the spinal cord via the cerebellum and basal ganglia, which in turn recruit the appropriate type and number of motor units to perform the given movement. Feedback to the CNS is derived from muscle receptors, which allows for the ongoing modification of the descending motor command, if required.

References 1.

2. 3. 4.

Alawieh, A., S. Tomlinson, D. Adkins, S. Kautz and W. Feng (2017). Preclinical and Clinical Evidence on Ipsilateral Corticospinal Projections: Implication for Motor Recovery. Translational stroke research 8(6): 529-540. Bigland-Ritchie, B., A. J. Fuglevand and C. K. Thomas (1998). Contractile properties of human motor units: Is man a cat? Nueroscientist 4: 240-249. Bremner, F. D., J. R. Baker and J. A. Stephens (1991). Effect of task on the degree of synchronization of intrinsic hand muscle motor units in man. J Neurophysiol 66: 2072-2083. Brooke, M. H. and K. K. Kaiser (1974). "The use and abuse of muscle histochemistry." Annals of the New York Academy of Sciences 228: 121-144.

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12. 13. 14. 15. 16.

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Burke, R. E., D. N. Levine, P. Tsairis and F. E. Zajac (1973). Physiological types and histochemical profiles in motor units of the cat gastrocnemius. J Physiol 234: 723-748. Carson, R. G. (2005). Neural pathways mediating bilateral interactions between the upper limbs. Brain Res Brain Res Rev 49: 641-662. Datta, A. K. and J. A. Stephens (1990). Synchronization of motor unit activity during voluntary contraction in man. J Physiol 422: 397419. Duchateau, J., J. G. Semmler and R. M. Enoka (2006). Training adaptations in the behaviour of human motor units. J Appl Physiol 101: 1766-1775. Enoka, R. M. and A. J. Fuglevand (2001). Motor unit physiology: some unresolved issues. Muscle Nerve 24: 4-17. Fang, J., B. T. Shahani and D. Graupe (1997). Motor unit number estimation by spatial-temporal summation of single motor unit potentials. Muscle Nerve 20: 461-468. Farmer, S. F., D. M. Halliday, B. A. Conway, J. A. Stephens and J. R. Rosenberg (1997). A review of recent applications of crosscorrelation methodologies to human motor unit recording. J Neurosci Methods 74: 175-187. Fling, B. W., C. Anita, A. and G. Kamen (2009). Motor unit synchronization in FDI and biceps brachii muscles of strengthtrained males." J electromyography kinesiol 19: 800-809. Griffin, L., P. Painter, A. Wadhwa and W. Spirduso (2009). Motor unit firing variability and synchronization during short-term lightload training in older adults. Exp Brain Res 197: 337-345. He, S. Q., Dum, R. P., and Strick, P. L. (1993). Topographic organization of corticospinal projections from the frontal lobe: motor areas on the lateral surface of the hemisphere. J Neurosci 13: 952. Kernell, D., Bakels, R., and Copray, J. C. (1999). Discharge properties of motoneurones: How are they matched to the properties and use of their muscle units? J Physiol 93: 87-96. Kim, M.S., Masakado, Y., Tomita, Y., Chino, N., Pae, Y.S., and Lee, K. (2001). Synchronization of single motor units during voluntary contractions in the upper and lower extremities. Clin Neurophysiol 112: 1243-1249. Lowery, M. M., Myersm L. J., and Erim, Z. (2007). Coherence between motor unit discharges in response to shared neural inputs. J Neurosci Meth 163: 384-391.

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18. 19. 20. 21. 22. 23. 24.

25. 26. 27. 28.

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Moore, G. P., Perkel, D. H., and Segundo, J. P. (1966). Statistical analysis and functional interpretation of neuronal spike data. Annu Rev Physiol 28: 493-522. Moritz, C. T., Christou, E. A., Meyer, F. G., and. Enoka, R. M (2005). Coherence at 16-32 Hz can be caused by short-term synchrony of motor units. J Neurophysiol 94: 105-118. Nordstrom, M. A., Fuglevand, A. J., and Enoka, R. M. (1992). Estimating the strength of common input to human motor neurons from the cross-correlogram. J Physiol 453: 547-574. Porter, R. (1985). The corticomotoneuronal component of the pyramidal tract: corticomotoneuronal connections and functions in primates. Brain Res 357: 1-26. Porter, R. and Lemon, R. N. (1993). Corticospinal Function and Voluntary Movement. New York, USA, Oxford Science Publications. Rothwell, J. C. (1994). Control of Voluntary Human Movement. London, Chapman & Hall. Schmied, A., Pouget, J., and Vedel, J. P. (1999). Electromechanical coupling and synchronous firing of single wrist extensor motor units in sporadic amyotrophic lateral sclerosis. Clin Neurophysiol 110: 960-974. Sears, T. A. and Stagg, D. (1976). Short-term synchronization of intercostal motoneurone activity. J Physiol 263: 357-381. Semmler, J. G. (2002). Motor unit synchronization and neuromuscular performance. Exerc Sport Sci Rev 30: 8-14. Semmler, J. G. and Nordstrom, M. A. (1998). Motor unit discharge and force tremor in skill- and strength-trained individuals. Exp Brain Res 119: 27-38. Wiegner, A. W. and Wierzbicka, M. M. (1987). A method for assessing significance of peaks in cross-correlation histograms. J Neurosci Meth 22: 125-131.

CHAPTER 3 TECHNIQUES CONTRIBUTING TO THE UNDERSTANDING OF NEUROSCIENCE IN EXERCISE ALAN J. PEARCE, PHD AND DAWSON J. KIDGELL, PHD

3. Background The ability to examine the human central nervous system (CNS) has developed remarkably over the last 30 years. Imaging techniques, such as functional Magnetic Resonance Imaging (fMRI) and positron emission topography (PET), indirectly measure the changes in blood flow associated with neural activity while participants perform a particular motor task (Jenkins et al. 1994). A number of investigations have demonstrated modifications in cortical activity during various movements. For example, there is a strong relationship between isometric force production, premovement activity and actual movement execution that results in increased cortical activity in the M1, supplementary motor area (SMA) and the dorsal portion of the anterior cingulate cortex (Dettmers et al. 1995; Thickbroom et al. 1999b; Farthing et al. 2007). Although these studies demonstrate changes in blood flow during movement preparation and execution, they do not provide any objective data concerning the excitatory and inhibitory synaptic events specific to the M1 during movement. Alternatively, transcranial stimulation techniques are able to produce excitatory and inhibitory interactions within the M1, and provide an objective measure of the strength and function of corticospinal cell projections (Hallett 2000). Transcranial electrical stimulation (TES) was initially utilised to assess neurophysiological function, however its painful and invasive nature prompted the development of transcranial magnetic stimulation (TMS), which is now widely used to quantify various corticospinal measures that are subject to both use-dependent and experimentally-induced neuroplasticity

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(Chapter 4) (Barker et al. 1985; Hallett 2000). The aim of this chapter is to provide an overview of the non-invasive techniques that are used to assess neurophysiological function in humans during exercise.

3.1 Electroencephalography (EEG) The most well-established electrophysiological technique, EEG provides valuable insight into cortical electrical behaviour that is based as a function of time (Wallace et al. 2001). The waveforms resulting from EEG can provide neuroscientists with data regarding normal and abnormal brain activity, allowing for diagnostic capacity for various conditions including epilepsy, sleep disorders, or tumours. Compared to neuroimaging technologies such as fMRI and PET, EEG is relatively inexpensive and can be administered conveniently, recording over multiple regions of the brain simultaneously. With multiple regions to analyse from, montages were created to measure various patterns of the electrical activity between two or more channels. Over time, the 10-20 system was developed and became the International Standard of electrode placement, allowing for relative comparison of electrical activity across different head regions (Wallace et al. 2001). Traditional EEG waveforms are separated into specific bands qualitatively based on shape and range of frequency for clinical applications. These generally occur within the limits of 0.1 – 35 Hz and include alpha, beta, delta, and theta waves (Wallace et al. 2001). Waveforms at frequencies of 8 – 13 Hz, known as Alpha waves, are thought to originate in the posterior region of the brain. Alpha waves are generally observed in the parietal, occipital, and posterior temporal areas. These waves are best detected when an individual is mentally sedentary, and are observed while awake but relaxed in an environment stimulus free. Beta waves include all frequencies greater than 13 Hz but have low amplitudes limited to less than 20 ȝ9 Whilst beta waves can exist simultaneously throughout the cortex at various low amplitude frequencies, they are most commonly seen in the frontal and central head regions in nearly all healthy adults. Delta rhythms consist of low frequency (0.5 – 4 Hz), high-amplitude waveforms (20-200 ȝ9  Delta waves can be seen in the posterior regions of the head, and/or they can occur on either side of the temporal region. However, they are most often recorded over the left cerebral cortex. These rhythms are produced by thalamocortical neurons and are virtually absent in the EEGs of normal alert individuals. Delta waves are associated with periods of unconsciousness, typically appearing in cerebral monitoring during sleep, coma, or after

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pathophysiological conditions such as traumatic brain injury (TBI) (Rumpl et al. 1979). Theta waves measure from 4 – 8 Hz and have low amplitudes (~10 ȝ9 (Shenal et al. 2001). They are presumed to originate in the thalamus and are associated with the hippocampus and limbic system. Theta rhythms can be recorded in the frontal, temporal, central, and posterior head regions and are rarely the predominant waveform, being frequently mixed with alpha and beta waves. Quantitative EEG (qEEG) is an extension of EEG that provides a greater depth of analysis using the EEG technique. Specifically, EEG waveforms reflect an electrophysiological signature indicative of “normal” individuals (Shenal et al. 2001). Consequently, qEEG measures that determine waveform activation patterns inconsistent with these established norms may provide a useful indicant of cortical dysfunction. For example, an individual demonstrating increased delta wave amplitudes localized at the temporal lobe following head trauma may indicate dysfunction associated with a lesion within this region (Rumple et al. 1979). The development of qEEG technology has, as suggested by some (Shenal et al. 2001; Kanda et al. 2009), created some controversy and debate, specifically around its clinical utility. As argued initially by Nuwer (1997), issues surround reliability and validity of the technique via operator expertise and clinical experience, as well as using qEEG as an adjunct to traditional EEG. The arguments around this are due to disparities in techniques between laboratories as well as post-processing of data producing different interpretations based upon the algorithms employed (Nuwer 1997). Despite progress in a number of areas of research, including qEEG for concussion and post-concussion syndrome (Duff 2004), there is continued debate about the diagnostic validity of qEEG, and it is suggested that further studies are required to corroborate and refine data collection and post-processing methods (Haneef et al. 2013).

3.2. Magnetoencephalography (MEG) In 1972, the first magnetoencephalogram was measured using a superconducting quantum interference device (SQUID) (Cohen 1972) by quantifying the magnetic fields emitted by the brain. Similar to EEG, MEG signals are derived from the net effect of ionic currents generated from neuronal dendrites during transmission that produce a quantifiable magnetic field. Generation of detectable spatial signals requires a neuronal bundle of approximately 50,000 active neurons (Okada 1983). Therefore, conceptually, the two are similar directly sampling the electromagnetic fields instantaneously

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generated by neuronal activity, giving both techniques excellent temporal resolution compared to neuroimaging. The sub-millisecond temporal resolution of MEG and EEG is noticeably superior to the temporal resolution of functional neuroimaging techniques, such as fMRI (hundreds of ms) or PET (min) (Lee and Huang 2014). Commercial whole-head MEG units were developed in the mid-1990s. An advantage of MEG lies in the difference in methods of measurement. Magnetic fields can pass undisturbed through human tissue, unlike electric fields which can be distorted by various tissues such as skull bone and scalp skin, and the underlying dura, vascular tissue and blood, as they pass from the brain to the sensors (Lee and Huang 2014). Further advantages of MEG over EEG are seen in better spatial localization of neuronal sources using MEG (mm) compared to EEG (cm). Comparative studies using a skull phantom between MEG and EEG showed that the signal-to-noise ratio of EEG was better than that of MEG, but the localisation error of MEG was significantly less than that of EEG (3 mm versus 8 mm, respectively) (Leahy et al. 1998). Disadvantages of MEG versus EEG are that the brain’s magnetic fields are extremely weak compared to other magnetic signals such as the earth’s magnetic field, as well as magnetic fields generated by other organs, such as the heart. In addition, it is often difficult or impossible to uniquely determine the sources of neuronal current which generates the MEG signals measured at the surface of the head (Lee and Huang 2014). Finally, the typical cost of an MEG unit, including its shielded room, is roughly the same as a high-field magnetic resonance imaging (MRI) scanner. Therefore, MEG units are not commonly found (for example, there are four MEG units in Australia).

3.3 Transcranial magnetic stimulation (TMS) TMS is a non-invasive brain stimulation technique that utilises the principles of electromagnetic induction to probe, assess and modulate corticospinal activity (Barker et al. 1985). Operating via a coil placed over the scalp, TMS generates magnetic pulses that painlessly penetrate the skull to reach the cortex with very little mitigation (Kobayashi and Pascual-Leone 2003). When placed over the motor cortex, the resulting depolarisation of underlying pyramidal cells produces a series of action potentials which synapse with lower motoneurones and consequently activate target muscles (Weber and Eisen 2002). The subsequent muscle twitch directly corresponds to the site of stimulation on the motor cortex (Koboyashi and Pascual-Leone 2003). TMS can generate single pulses, paired pulses or

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repetitive pulses to assess the excitability of the intracortical circuitry of the motor cortex.

3.3.1 Single-pulse TMS There are a number of basic TMS techniques that allow the assessment of the motor cortex and corticospinal tract (CST). The muscle activity generated by TMS can be captured by electromyographic (EMG) techniques in the form of a motor evoked potential (MEP, see Figure 3.1).

Figure 3.1: Single-pulse TMS; The depolarisation of underlying cortical nerurons causes a MEP; which is followed by the silent period (SP) duration, which reflects the absence of muscle activity following an active MEP.

The MEP is comprised of descending volleys generated by direct (D-waves) and indirect, synaptic (I-waves) activation of pyramidal cells (Di Lazzaro et al. 2002). Importantly, the muscle activity generated by TMS is dependent on neuronal excitability in both the M1 and spinal cord, and is thus typically considered a measure of corticospinal excitability (Kobayashi and Pascual-Leone 2003). Consequently, the MEP is frequently used to assess the neural adaptations to exercise (Kidgell et al. 2017, Mason et al. 2019). As MEP amplitude increases with increasing stimulus intensity (Siebner and Rothwell 2003), a common technique to assess corticospinal excitability is to construct a stimulus-response curve/recruitment curve. This technique is considered a comprehensive assessment of corticospinal excitability for a given muscle (Siebner and Rothwell 2003). A change in MEP amplitude at a given stimulus intensity following an intervention reflects alterations in excitability of the CST, which may be an important mechanism

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underpinning improvements in motor performance following exercise (Rogasch et al. 2009). In other words, an increase in the size of the MEP indicates that motoneurons are providing more output for a given synaptic input. High stimulation intensities recruit predominantly high-threshold motor units, whereas low stimulation intensities recruit mainly lowthreshold units (Classen and Benecke 1995). Immediately following the MEP, there is a period of non-activity on the EMG trace which is termed the corticospinal silent period, more recently referred to as the silent period (Škarabot et al. 2019, see Figure 3.1B). The silent period, only occurring in an active muscle, is mediated by the neurotransmitter gamma-aminobutyric acid-B (GABAB) (McDonnell et al. 2006), and indicates an interruption in volitional drive from the M1 and the removal of descending input to the spinal motoneuron pool (Škarabot, Mesquita et al. 2019). Increases in the duration of the silent period reflect greater levels of inhibition in the CST, with contributions arising from cortical and spinal levels (Rogasch et al. 2014). Although TMS is regarded as the gold standard for assessing corticospinal excitability, there are a number of factors that need to be controlled in order to ensure that changes in corticospinal excitability following single- pulse TMS are robust. For example, considerable inter- and intra-trial MEP variability exists, likely due to factors such as muscle activation and coil position and orientation (Weber and Eisen 2002). These limitations are circumvented by a range of strategies, including averaging MEP values across a number of trials, and using co-ordinates to replicate motor cortex hotspots between testing sessions. The MEP amplitude is particularly variable in relaxed muscles (Burke et al. 1995); however, increasing the probability of the motoneuron pool discharging via a low-level contraction of the target muscle can substantially reduce this variability (Kiers et al. 1993). Recent developments have also indicated the appropriate stimulus intensities and volume of stimuli for various measures to produce consistent measurements (Temesi et al. 2017), leading to an increase in reliability. A further single-pulse technique designed to assess the level of voluntary drive to the motoneuron pool of the target muscle is TMS-voluntary activation (VA TMS), which reflects how the motor cortex drives the recruitment of motor units that are used in muscle contraction and force generation (Lee et al. 2008). Given the proximity of motor units to the peripheral site, and their direct influence over muscle contraction, VATMS is capable of providing important insight as to how the motor cortex may adapt following different modes of exercise (e.g., aerobic versus anaerobic).

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Changes in single-pulse TMS assessments do not necessarily reflect changes in motor cortex as MEPs and the duration of the silent period are influenced by several factors along the entire neuroaxis (Di Lazzaro and Rothwell 2014). For example, MEP amplitude is influenced by the excitability of the intrinsic circuitry of the spinal cord, the efficacy of the corticospinal motoneuronal synapses, and changes in MEP amplitude may be produced at a spinal level without motor cortical input (Di Lazzaro and Ziemann 2013). Consequently, single-pulse TMS is unable to reveal the site of neural adaptation following exercise (Carroll et al. 2011). Two main techniques have been used to overcome the limitation of single-pulse TMS: 1) a range of paired-pulse TMS techniques; and 2) cervicomedullary motor evoked potentials (CMEPs). The analysis of CMEPs is an emerging single-pulse technique designed to establish the spinal output to the motoneuron pool (Taylor and Gandevia 2004). It is generated at the cervicomedullary junction and instigates a single descending volley (Berardelli et al. 1991) which, like the MEP, is measured with EMG. Due to the subcortical delivery of the stimulus, the amplitude of the CMEP removes the influence of the M1 and assesses excitability at a spinal level. Through the combined analysis of CMEPs and MEPs, it is possible to more precisely determine the site driving changes in excitable input to the motorneuron pool (Nuzzo et al. 2016, Nuzzo et al. 2017).

3.3.2 Paired-pulse TMS Paired-pulse TMS uses a sub-threshold conditioning stimulus (70-80% motor threshold) delivered 2-4 ms prior to a supra-threshold test stimulus and this results in a suppressed paired-pulse MEP compared to a baseline single-pulse MEP (Rothwell et al. 2009). This protocol allows the estimation of the excitability of GABAA-ergic circuits within the motor cortex by calculating the ratio between the conditioned and unconditioned MEPs, which is known as short-interval intracortical inhibition (SICI). SICI is synaptic in origin and mediated by the activation of low-threshold inhibitory circuits that have a presence of GABAA receptors from the subthreshold conditioning stimulus (Kujirai et al. 1993). Paired-pulse TMS enables the measurement of synaptic efficacy of inhibitory neural networks detectable at the level of the M1 following different exercise interventions. The presence of low-level voluntary contraction causes a significant reduction in SICI, indicating that investigation of cortical inhibition via paired-pulse TMS is ideally performed either in resting conditions, or with close monitoring of background muscle activity during testing (Ridding et

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al. 1995). Most studies examining SICI have focused on the intrinsic muscles of the hand, where inhibition seems to be greatest, possibly due to the fine nature of motor performance (Dettmers et al. 1995). However, SICI is also measurable in proximal arm muscles such as the biceps brachii (BB), flexor carpi radialis (FCR) and extensor carpi radialis (ECR) (Abbruzzese et al. 1999, Rantalainen et al. 2013). Intracortical facilitation (ICF) occurs when a conditioning stimulus is delivered 6-20 ms prior to a test stimulus, causing a net increase in cortical output (Kujirai et al. 1993, Ziemann et al. 1996). A detailed investigation into ICF and SICI found that they are likely to be mediated by different mechanisms, due to the higher intensity of conditioning stimulus required to obtain ICF (80% of predetermined motor threshold or greater), and the observation that ICF is dependent on current direction, while SICI is not (Figure 3.2, Ziemann et al. 1996).

Figure 3.2: Paired-pulse (TMS) paradigms: (Top left) Short-interval intracortical inhibition, (Top right) Intracortical facilitation and (Bottom left) long-interval cortical inhibition.

It was concluded that separate populations of inhibitory and excitatory interneurons, most likely within the superficial layers of the motor cortex, influence the net cortical output by acting either before or directly on to pyramidal cells (Ziemann et al. 1996).

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In a similar manner, when a supra-threshold TMS pulse is applied at ISIs of 50-200 ms, MEPs are significantly reduced and are referred to as longinterval intracortical inhibition (LICI) which is representative of a slowphase inhibitory circuit (Valls-Solé et al. 1992). Similar to the corticospinal silent period, LICI is thought to reflect GABAB-mediated cortical inhibition.

3.4 Voluntary activation and neural drive To generate maximal muscle force, the central nervous system must drive all motor neurons that innervate contributing motor units at a rate sufficient to produce tetanus. If this does not occur, the maximal voluntary contraction (MVC) is less than the maximal force-generating capacity, and voluntary activation (VA) is considered to be incomplete. In most muscle groups, VA is typically high, but incomplete, and can be comprised during exercise as result of central and or peripheral fatigue. The conventional method for assessing VA involves the application of a single supramaximal electrical stimulus to the motor nerve (peripheral nerve stimulation, PNS) during an MVC. VA is calculated by determining the extra force evoked by the PNS during contraction, which is referred to as the superimposed twitch. The extra forced evoked is expressed as a percentage of the force produced by the same, supramaximal stimulus at rest, which is known as the resting twitch. There are several approaches to measuring VA, which include the interpolated twitch technique and VATMS. The magnitude of VA is thought to represent the level of ‘neural drive’ to the motoneuron pool controlling the motor units of the target muscle. Neural drive can also be measured by applying an electrical stimulus during an MVC and recording the corresponding surface electromyographic response, a procedure known as the V-wave.

3.4.1 Interpolated twitch and VATMS The interpolated twitch technique (ITT) is commonly used to assess the completeness of muscle activation during voluntary contractions. The ability to quantify the level of VA is important when considering the effects of exercise, as it can be used to assess the magnitude of neural drive following exercise. When a supramaximal electrical stimulus is applied to a peripheral nerve of an active muscle during voluntary contraction, any motor units that have not been recruited will respond by generating a twitch response. As expected, as the level of muscle activation increases, there will be fewer motor units available for recruitment, the twitch response begins

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to diminish and the superimposed twitch becomes smaller, and eventually is eliminated during a MVC (Figure 3.3).

Figure 3.3: Superimposed twitch-like responses obtained during an MVC.

Early experiments revealed the progressive occlusion of the ITT by voluntary contraction, suggesting that muscle groups could be activated completely by voluntary command (Denny-Brown and Sherrington 1928). Following this, several authors indicated that, in most healthy individuals, complete activation of most muscles to which ITT was applied could be achieved. Based upon these studies and the negative linear relationship between evoked and voluntary force production, the extent of inactivation can be quantified by expressing the interpolated twitch force as a percentage of the twitch force evoked in a relaxed muscle, thus determining voluntary activation (VA) of the stimulated muscle: Voluntary activation (%) = [1-(superimposed twitch/control twitch)] ×100). The superimposed twitch is the force increment obtained during a MVC at the time of stimulation and the control twitch is the force evoked in a relaxed muscle. Although interpolated twitch data obtained via PNS quantifies the completeness of voluntary drive, this procedure provides no indication of the site of failure within the CNS when neural drive is incomplete. Consequently, a method of twitch interpolation using TMS was developed to estimate “cortical” voluntary drive. This measure provides information regarding the net motor output from the motor cortex (i.e., TMS voluntary activation) and identifies sites of neural drive impairment (Todd et al. 2003, Todd et al. 2004). To quantify submaximal motor cortex output, the level of neural drive to the muscle is determined by the presence of a superimposed twitch force that is produced by single-pulse TMS during a MVC (Lee et al. 2008). The superimposed twitch represents the single-pulse

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TMS eliciting extra force from the muscle during a MVC due to submaximal motor output from the motor cortex, while the absence of a superimposed twitch suggests maximal output from the motor cortex (i.e., maximal neural drive) (Todd et al. 2004, Todd et al. 2007, Lee et al. 2008, Goodall et al. 2009) (Figure 3.4).

Figure 3.4: (A) Raw force traces for three levels of wrist flexor voluntary contraction force taken from a representative subject in a typical testing trial. TMS was delivered over the contralateral motor cortex during 100%, 75% and 50% MVIC. (B) Raw traces of the superimposed twitches produced by cortical stimulation during 100%, 75% and 50% MVIC. (C) Raw EMG responses (MEPs) produced by cortical stimulation during 100%, 75% and 50% MVIC.

TMS has been shown to be a reliable and valid measurement of VA in the human wrist extensor, knee extensor and elbow flexor muscle (Todd et al. 2004, Todd et al. 2007, Lee et al. 2008, Goodall et al. 2009, Sidhu et al. 2009).

3.4.2 H-reflex There has been some attempt to investigate changes in reflex physiology following exercise, providing evidence for changes in spinal cord excitability/inhibition. The Hoffman’s or H-reflex can be used to evaluate motoneuron excitability and the efficacy of 1a afferent synapses. The neural circuitry underlying the H-reflex is characterized by monosynaptic projections from group 1a afferents onto the corresponding motoneuron pool. When a peripheral nerve is stimulated percutaneously by very brief

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low-intensity electric currents, action potentials elicited within sensory 1a afferents propagate to the spinal cord where they produce an excitatory postsynaptic potential and synapse with alpha motor neurons. Activation of the alpha motor neurons elicits action potentials which travel towards the muscle, where they are recorded at the muscle as the H-reflex response (Figure 3.5).

Figure 3.5: Neural circuitry of the H-reflex.

An inherent limitation to the H-reflex is that the magnitude of the H-reflex response is influenced by the level of presynaptic inhibition, which limits the interpretation of this technique as a quantifiable measure of motor neuron excitability. Irrespective of this, the H-reflex is commonly used to examine the effect of exercise, particularly strength training, on motor neuron excitability. H-reflex amplitudes recorded during background muscle activity are inconsistent, with some studies reporting increased Hreflex amplitudes (Aagaard et al. 2002, Lagerquist et al. 2006, Holtermann et al. 2007, Duclay et al. 2008), and other studies reporting no changes (Del Balso and Cafarelli 2007, Fimland et al. 2009) following resistance training. A recent systematic review demonstrated that resistance training has no effect on the excitability of the motoneuron pool (Figure 3.6, Siddique et al. 2020).

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Figure 3.6: Pooled effect of resistance training on the excitability of the motoneuron pool.

3.4.3 V-wave The volitional or V-wave is an electrical variant of the H-reflex which is recorded during a MVC. In contrast to the H-reflex, which uses submaximal electrical stimulation, the V-wave is evoked by supramaximal electrical stimulation of a peripheral nerve during a MVC. The supramaximal stimulus produces a series of action potentials (antidromic) in all the motor axons and in all group 1a afferents. In the motor axons that are involved in the voluntary contraction, there is a collision between the voluntary descending action potentials (i.e., orthodromic) and the electrically-evoked action potentials (i.e., antidromic), thus leaving the axons to produce a reflex response in the muscle (Mcneil et al. 2013). As with most reflexes, the amplitude of the V-wave is influenced by a range of factors, such as the level of muscle activation in which the reflex is recorded. When the V-wave is obtained during a MVC, the amplitude is thought to reflect or indicate the level of descending drive to the motoneuron pool; thus, an increase in the V-wave indicates an increase in the discharge rate and recruitment of motoneurons. This would suggest that there is an increase in supraspinal input to the motoneuron; however, this should be

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interpreted with caution, because the discharge rate of motoneurons reflects all inputs which arrive at the motor neuron, not just supraspinal inputs. Thus, the cause of any increase in the V-wave is undefined. There have been several resistance training studies that have reported increases in V-wave amplitude, suggesting exercise increases efferent drive and enhances activation of the motoneuron pool (Figure 3.7).

Figure 3.7: Pooled effect of resistance training on the neural drive.

There has also been an inclination to attribute increases in motoneuron activation as an adaptation that occurs at a supraspinal level, particularly when V-wave changes are observed in parallel with H-reflexes (Aagaard et al. 2002). However, a caveat to this interpretation is that the V-wave is an indirect measure of the potential role of cortical mechanisms contributing to efferent neural drive. In addition, the amplitude of the V-wave is influenced by several factors, including the number and firing rate of motoneurons that are involved in the voluntary contraction, the responsiveness of the motoneurons, and the efficacy of synaptic transmission between 1a afferents and the motoneurons. Because the V-wave, like the H-reflex, is largely a monosynaptic reflex circuit from the 1a afferents to motoneurons, any change in V-wave amplitude could simply be due to a change in synaptic transmission (either 1a excitation or disynaptic inhibition) (Aagaard et al. 2002).

3.5 Summary There are several electrophysiological assessment techniques that are now available to measure the neural responses and adaptations to exercise. Of particular importance, these techniques enable the assessment of the entire

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neuroaxis (i.e., cortex to motor neuron pool), allowing the locus of adaptation within the CNS to be identified.

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CHAPTER 4 PRINCIPLES OF NEUROPLASTICITY IN EXERCISE DAWSON J. KIDGELL, PHD AND ASHLYN K. FRAZER, PHD

4. Neuroplasticity Neuroplasticity represents the ability of the central nervous system (CNS) to change in response to experience, use, or environmental demands, and is understood to be a neural substrate for skill acquisition and recovery from brain injury (Classen et al. 1998). Neuroplasticity can be induced via experimental non-invasive brain stimulation techniques (NIBS) or by usedependent mechanism, such as exercise. The purpose of this chapter is to describe the mechanisms of neuroplasticity and the ways in which plasticity of the human motor system can be induced. In this regard, we will discuss experimentally induced plasticity and use-dependent plasticity as models to understand the neural adaptations to exercise.

4.1 Mechanisms of neuroplasticity There are several physiological mechanisms that contribute to neuroplasticity, however, the most attractive cellular mechanisms include synaptic plasticity, which involves long-term potentiation (LTP), Short-term potentiation (STP) and Long-term depression (LTD), unmasking of latent neural connections due to changes in Gamma-aminobutyric acid (GABA) mediated inhibition and N-methyl-D-aspartate (NMDA)-mediated activation. The removal of local inhibition within neural circuits that control voluntary movement underpins neuroplasticity. Modulation of inhibition is usually achieved as a result of synaptic plasticity (i.e., each neuron has the capacity to adapt dynamically), which results in up and down regulation of synaptic efficacy in response to external stimuli (including exercise). Although synaptic plasticity exists in many forms, LTP and LTD (e.g., lasting modifications

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in synaptic strength) are most relevant to exercise-induced changes in synaptic plasticity.

4.2 Short and Long-term potentiation Enhancement of synaptic plasticity can involve STP, which lasts 5-20 minutes, and LTP, which can last from 30 minutes, hours or days (Bliss & Collingridge 1993). LTP is an activity-dependent process that results in the long-lasting enhancement of synaptic transmission (i.e., efficacy) that provides the basis for information storage within the cerebral cortex (Bear & Malenka 1994, Hess et al. 1996). LTP is characterised by three distinct properties including cooperativity, associativity and input-sensitivity (Bliss & Collingridge 1993). Cooperativity refers to the range of threshold intensities required for the induction of activity-dependent potentiation. The threshold necessary for the induction of LTP is dependent on the interaction between intensity and pattern of tetanic stimulation. Unless stimulation is ‘strong’, LTP will not be triggered, resulting in STP and post-tetanic potentiation (PTP) being induced (McNaughton et al. 1978, Malenka 1991). Importantly, LTP is associative, meaning a weak input can only be potentiated if it is active at the same time as a strong input (McNaughton et al. 1978, Collingridge & Bliss 1987). These three properties are mediated by the NMDA receptor which is located on the post-synaptic dendrites of excitatory synapses (Collingridge & Bliss 1987).

4.2.1 NMDA receptor activation and synaptic plasticity NMDA is an essential molecule for regulating neuroplasticity in humans and operates as the channel responsible for LTP (Castro-Alamancos et al. 1995). To trigger the induction of LTP, two processes must occur involving the NMDA receptor channel complex (Bliss & Collingridge 1993). First, post-synaptic depolarization releases glutamate, resulting in the activation of post-synaptic NMDA receptors. This event reduces the voltagedependent block of the NMDA receptor channel by magnesium (Mg2+), allowing the influx of calcium (Ca2+) into the post-synaptic dendritic spine. The level of depolarization will consequently determine whether the cooperativity threshold will be enough to induce LTP. Failure to induce LTP is a result of inadequate reduction of the Mg2+ block, rather than the insufficient release of glutamate to activate the NMDA receptors (Bliss & Collingridge 1993).

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Bliss and Collingridge (1993) demonstrated the necessity of NMDA receptor activation for the induction of LTP. This finding prompted investigation into the potential relationship between NMDA receptor activation and the induction of experimental and use-dependent neuroplasticity (Liebetanz et al. 2002, Nitsche et al. 2003a). Using pharmacological agents, Butefisch (2000) demonstrated that use-dependent plasticity of the hand area of the primary motor cortex (M1) following motor training was significantly reduced when the NMDA receptor was blocked. Similarly, the necessity of NMDA receptor activation for the induction of experimentallyinduced plasticity was confirmed when the administration of the NMDA receptor antagonist, dextromethorphan was found to inhibit the long-lasting effects of transcranial direct current stimulation (tDCS) (Liebetanz et al. 2002). Collectively, these findings show that the NMDA receptor is an important operating mechanism in the formation of use-dependent and experimentally-induced neuroplasticity via activating LTP processes. In addition, other cellular mechanisms involving neurotrophic factors (i.e., neurotrophins, glial cell-line derived neurotrophic factor family ligands, and neuropoietic cytokines) that interact with the NMDA receptor and the induction of LTP have also been recognized in shaping neuroplasticity.

4.2.2 Brain-derived neurotrophic factor (BDNF) and neuroplasticity Although the mechanisms that underpin neuroplasticity have been described within the literature, the extent of neuroplasticity appears to be influenced by genetic factors. BDNF is a neurotrophin which is involved in a variety of CNS functions including but not limited to cell survival, proliferation and synaptic growth (Antal et al. 2010). In humans, a naturally occurring single nucleotide polymorphism results in the substitution of valine to methionine at codon 66 (val66met), which has been associated with reduced episodic memory and increased risk of neuropsychiatric disorders (Bath & Lee 2006). The distribution of the polymorphism varies widely between regions and ethnicities, with approximately 30-50% of people worldwide identified as either heterozygous (Val/Met) or homozygous (Met/Met) for the Met substitution. Expression of the Met allele is more commonly found among Asian (51% in Japan) compared to Caucasian populations (30% in America) (Shimizu et al. 2004) with evidence suggesting those of Caucasian descent have larger associated cognitive and behavioural consequences (Bath & Lee 2006). Abnormal cortical morphology is a shared characteristic among carriers of the Met variant form of BDNF (Bath & Lee 2006). Smaller hippocampal volumes and poorer performance

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on memory tasks have been revealed, determining the anatomical and functional consequences of the BDNF polymorphism (Pezawas et al. 2004). More recently, neurotrophic factors, particularly BDNF, have been identified as critical molecules involved in the regulation of neuroplasticity in the human brain (Bath & Lee 2006). Evidence from hippocampal in vitro studies has demonstrated the modulatory role of BDNF on NMDA receptordependent LTP and LTD (Woo et al. 2005). The facilitation of LTP because of BDNF secretion suggests the importance of BDNF in regulating experimentally-induced and use-dependent neuroplasticity (Gottmann et al. 2009). However, the interaction between BDNF and LTP processes has yet to be investigated beyond a theoretical model. In addition, it is unclear what modulatory effect the BDNF polymorphism may have on experimentally induced and use-dependent neuroplasticity.

4.3 Experimentally-induced neuroplasticity Several NIBS methods have been used to assess the potential underlying mechanisms and regulators of neuroplasticity, including tDCS, theta burst stimulation (TBS), paired associative stimulation (PAS), I-wave periodicity TMS (iTMS) and repetitive transcranial magnetic stimulation (rTMS) (Nitsche et al. 2003b, Siebner & Rothwell 2003, Sale et al. 2007). Such NIBS techniques have been shown to modify levels of neuroplasticity that have been attributed to LTP and LTD. More recently, tDCS has emerged as a common NIBS technique used to modulate neuroplasticity with the aim of modifying motor behaviour in both healthy and clinical populations (Ridding & Ziemann 2010). More specifically, tDCS has been used in combination with motor training, which has evolved into a popular paradigm known as ‘motor priming’. Motor priming is thought to facilitate motor learning and involves the application of tDCS either prior or during motor learning (Stoykov & Madhavan 2015). Two established priming theories have been proposed which include gating and homeostatic plasticity (Siebner 2010). Gating occurs concurrently with motor training (i.e., tDCS while training), while homeostatic plasticity involves modulating the resting state of neurons prior to training (i.e., tDCS applied before motor training). Despite extensive research examining the indices of neuroplasticity of the stimulated M1 following anodal tDCS (Bastani & Jaberzadeh 2012), little is understood about the bilateral effects (i.e., non-stimulated M1) of unihemisphere stimulation. Given that other NIBS techniques have been shown to modulate not only the intended stimulated tissue but also distal areas of

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the brain including the contralateral hemisphere (Gilio et al. 2003), it would appear evident that the bilateral effects of anodal tDCS must be explored to ensure the feasibility of tDCS as a priming method for inducing homeostatic plasticity prior to motor training. Furthermore, individual corticospinal responses to anodal tDCS are highly variable and the expression of the BDNF polymorphism has been identified as a potential contributing factor (Antal et al. 2010). Differential modulation of neuroplasticity between different BDNF genotype carriers is of interest when examining the induction of LTP, which is an essential physiological process involved in neuroplasticity and motor learning (Cirillo et al. 2012). Therefore, the following discussion will examine the induction of homeostatic plasticity following tDCS, the use of tDCS in the absence of motor training and prior to motor training (motor priming) to enhance motor performance, and the potential regulatory role of the BDNF polymorphism.

4.3.1 Transcranial direct current stimulation and neuroplasticity In contrast to other NIBS techniques, tDCS does not rely on rapid depolarisation resulting in the induction of action potentials to stimulate neuroplasticity (Nitsche et al. 2008). Rather, this method is considered to be a ‘neuromodulator’, whereby a weak electrical current is passed through electrodes placed on the scalp resulting in polarity specific changes of the M1 (Nitsche & Paulus 2000). A number of parameters have been shown to influence the efficacy of tDCS including current strength, electrode size, stimulation duration and orientation of the electrode field (Nitsche et al. 2008). Orientation includes the position and polarity of electrodes, which determines the direction of modulation (increase/decrease corticospinal excitability, Figure 4.1). Anodal stimulation (positively charged electrode) results in neuronal depolarisation and an increase in corticospinal excitability (inducing LTP). Cathodal stimulation has the opposite effect whereby hyperpolarization of neurons occurs leading to decreased corticospinal excitability (LTD) (Nitsche & Paulus 2000). Two common electrode arrangements used to modulate neuroplasticity in healthy and clinical populations are uni-hemisphere and dual-hemisphere tDCS. Uni-hemisphere tDCS, where the anode is placed over the M1 of interest, has been shown to increase corticospinal excitability for up to 90 min post stimulation (Nitsche & Paulus 2001). In contrast, dual-hemisphere tDCS involves simultaneously applying anodal tDCS to one hemisphere and cathodal tDCS to the other. This arrangement leads to inhibitory effects in one hemisphere and increased excitability in the opposite (Di Lazzaro et al. 2012a). Interestingly, the immediate and time-course effects of tDCS appear

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to be mediated by different mechanisms. Initially, tDCS is thought to modify corticospinal excitability primarily through altering the resting membrane potential (Nitsche & Paulus 2000, Nitsche et al. 2008). However, the longer-lasting effects of tDCS appear to be dependent upon NMDA receptor function, indicating that changes in corticospinal excitability are likely due to LTP-like mechanisms (Liebetanz et al. 2002, Nitsche et al. 2004a, Nitsche et al. 2004b, Ridding & Ziemann 2010). At present, the consensus is that anodal tDCS induces focal changes in corticospinal excitability and inhibition of the M1 (Nitsche & Paulus 2000, Nitsche et al. 2008). However, it has recently been shown that NIBS techniques, including tDCS, not only exert a neuro-modulatory effect over the stimulated region, but also distal areas connected to the region of stimulation (Gilio et al. 2003, Lang et al. 2004).

Figure 4.1: Uni-hemipshere anodal-tDCS, with the anode placed over the left primary motor cortex and the cathode placed over the contralateral orbital region.

4.3.2 NIBS and functional connectivity Emerging evidence from transcranial magnetic stimulation (TMS) studies has revealed that NIBS techniques modulate not only the intended stimulated tissue but also distal connecting tissue and structures, as well as

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the opposite non-stimulated hemisphere (Gilio et al. 2003, Lang et al. 2004). This concept is termed “functional connectivity” and is based upon the working hypothesis that changes in localised brain activity can influence distant, but functionally related, areas which is an essential function of the healthy brain (Sale et al. 2015). Functional connectivity has evolved from the parallel use of neuroimaging techniques (i.e., fMRI) and NIBS methods (i.e., TMS, tDCS etc.) with the aim of understanding the interaction between distant neural structures caused by activity of interconnected brain zones (Sale et al. 2015). Previously, tDCS of the motor association cortex was shown to induce inhibitory effects in the M1 (Kirimoto et al. 2011), and stimulation of the premotor cortex facilitated the M1 by reducing SICI (Boros et al. 2008). Critically, the limited number of TMS studies examining the bilateral effect of uni-hemisphere stimulation have shown highly diverse findings regarding the direction of excitability of the nonstimulated hemisphere following various NIBS techniques (Gilio et al. 2003, Lang et al. 2004, Di Lazzaro et al. 2011). For example, various protocols using iTBS have shown increases in corticospinal excitability of the stimulated hemisphere and a decrease in corticospinal excitability of the non-stimulated hemisphere (Di Lazzaro et al. 2008, Di Lazzaro et al. 2011). rTMS and PAS have been shown to increase excitability of both the stimulated and non-stimulated M1 (Shin & Sohn 2011) and decrease interhemispheric inhibition (IHI) between the left and right M1 (Gilio et al. 2003). Likewise, Lang et al. (2004) found that 10 min of anodal and cathodal tDCS at 1 mA modulated transcallosal inhibition. Interestingly, this finding was not accompanied by a bilateral increase in M1 excitability, with only an increase in MEP amplitude seen in the stimulated M1. Importantly, it should be highlighted that a key methodological component of the studies investigating NIBS techniques and the concept of functional connectivity is that many used a dominant M1 arrangement whereby the stimulated hemisphere was the dominant M1 (left) and non-stimulated hemisphere was the non-dominant M1 (right). Notably, it has previously been shown that a hemispheric imbalance exists (dominant vs non-dominant) as demonstrated by the non-dominant hemisphere having a lower motor threshold, higher MEPs (De Gennaro et al. 2004) and shorter cortical silent period durations (Priori et al. 1999). A potential difference in hemispheric baseline characteristics poses an interesting question as to whether the magnitude of bilateral neuroplasticity is affected by the direction of stimulation (dominant vs non-dominant M1 stimulated), and if there is a greater scope for the induction of corticospinal plasticity of the nondominant hemisphere.

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4.4 Is the induction of neuroplasticity via NIBS important for motor performance? There is promising evidence that the induction of homeostatic plasticity following a single session of anodal tDCS (i.e., increase in corticospinal excitability and inhibition) in the absence of training can also facilitate fine motor performance and increase muscle strength (Boggio et al. 2006, Vines et al. 2006, Kidgell et al. 2013, Frazer et al. 2016). For example, following a single session of tDCS (in the absence of motor training), improved motor performance in tasks such as the Jebson Taylor Test (JTT), maximal strength of the elbow flexors and knee extensors, the Purdue pegboard test, maximal pinch force, reaction time, and tests of motor sequencing tasks have all been reported (Boggio et al. 2006, Vines et al. 2006, Kidgell et al. 2013). In healthy adults, accumulated bouts of anodal tDCS have been shown to improve motor performance with retention lasting up to three months following stimulation (Boggio et al. 2007, Reis et al. 2009). Although the underlying physiological changes were not examined, the induction of LTP has been suggested to underlie the improvement in motor performance (Reis et al. 2009). Previously, changes in corticospinal excitability have been examined over a five-day period whereby participants were exposed to daily anodal tDCS stimulation (Alonzo et al. 2012). Corticospinal excitability was shown to significantly increase but, unfortunately, no motor performance outcome was used to assess any functional effects of the tDCS intervention. A recent study investigating the effect of repeated sessions of anodal tDCS demonstrated an increase in corticospinal excitability accompanied by an increase in muscle strength (Frazer et al. 2016). Interestingly, there was no change in shorti-interval intracortical inhibition (SICI), however a reduction in cortical silent period was reported suggesting that accumulated bouts of anodal tDCS appear to modulate GABAB rather than GABAA neurons (Frazer et al. 2016). Because the cortical silent period that follows the excitatory MEP is caused by activation of long-lasting GABAB mediated inhibition and reflects the temporary suppression in motor cortical output (Werhahn et al. 2007), it appears that cumulative bouts of anodal tDCS specifically target neural circuits that use GABAB as their neurotransmitter (Frazer et al. 2016, Figure 4.2), resulting in the release of pyramidal tract neurons from inhibition (Floeter & Rothwell 1999). Therefore, a reduction in the temporary suppression of motor cortical output may be a putative neural mechanism underlying the changes in strength.

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Figure 4.2: Mean (± SE) changes in MEP amplitude following four consecutive sessions of (A) sham tDCS and (B) anodal tDCS. (C) Changes in MEP amplitude before and after four consecutive sessions of anodal tDCS in healthy subjects with different BDNF genotypes. *significant to sham tDCS; † significant to baseline.

To date, the TMS literature has primarily focused on the acute effects of tDCS modulating corticospinal excitability and the subsequent change in motor performance. Although these studies have provided valuable insight into possible acute physiological mechanisms, motor output from the M1 can also be quantified via TMS voluntary activation which provides further insight into the mechanisms regulating corticospinal plasticity and the expression of strength. The level of neural drive to a muscle during exercise is commonly termed ‘voluntary activation’ (Gandevia et al. 1995) and can be estimated by interpolation of a single supramaximal electrical stimulus to the motor nerve during an isometric voluntary contraction (Merton 1954). Although twitch interpolation assesses neural drive to a muscle during

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exercise, it cannot provide insight into the precise location of any neural drive impairment (cortical or sub-cortical) (Lee et al. 2009, Carroll et al. 2011). In light of this, TMS has been employed to measure net motor output from the M1 (i.e., TMS voluntary activation) identifying potential sites of neural drive impairment (Todd et al. 2003, Todd et al. 2004). This technique can provide additional information regarding corticospinal efficiency following anodal tDCS by demonstrating changes in motor cortical output via the recruitment of motor units used in force generation. At present, studies have concentrated on the reliability and validity of TMS to measure TMS voluntary activation in various muscle groups (Todd et al. 2003, Todd et al. 2004, Lee et al. 2008, Sidhu et al. 2009). However, translation of this technique into applied research settings, such as assessing changes in corticospinal plasticity following accumulated bouts of anodal tDCS, has been only examined once (Frazer et al. 2016). Interestingly, an increase in TMS voluntary activation and strength was observed, suggesting that accumulated bouts of anodal tDCS modulate synaptic efficacy, which improves the net descending drive (i.e., increased motor cortical drive) to the motor neuron pool, representing as an increase in muscle strength (Frazer et al. 2016, Figure 4.3).

Figure 4.3: Mean (± SE) changes in MVIC strength of the right wrist flexors following four consecutive sessions of sham and anodal tDCS. * significant to sham tDCS; † significant to baseline. Anodal tDCS stimulation resulted in an 8% increase in isometric wrist flexor strength compared to 3% following sham tDCS. Figure on the right displays the mean (± SE) changes in TMS voluntary activation following four consecutive sessions of sham and anodal tDCS. * significant to sham tDCS; † significant to baseline.

At a minimum, these data show that just the induction alone of neuroplasticity can have a positive effect on motor performance. This finding shows that the use of tDCS to induce homeostatic plasticity (i.e., modify corticospinal excitability) and improve motor performance is well established (Nitsche et al. 2008, Vines et al. 2008, Kidgell et al. 2013, Frazer et al. 2016), but the efficacy of tDCS may also be influenced by individual

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genetic variations such as the BDNF polymorphism (Antal et al. 2010, Puri et al. 2015, Frazer et al. 2016). Therefore, it appears vital to identify individual variants that may impact on the effectiveness of tDCS protocols to experimental-induce neuroplasticity.

4.4.1 Is the magnitude of neuroplasticity and motor performance improvement related to the BDNF polymorphism? Corticospinal responses to various NIBS techniques have been shown to differ significantly between individuals (Chang et al. 2014, Hwang et al. 2015). Genetic factors, including the role of BDNF, have been reported as potential contributors to the variability of results observed within the literature. Evidence using TMS to evaluate the efficacy of NIBS techniques generally suggests that the presence of the BDNF polymorphism significantly impacts corticospinal plasticity and motor performance. This is highlighted by the response of Met allele carriers being different to Val66Val individuals following several NIBS protocols (Cheeran et al. 2008, Cirillo et al. 2012, Chang et al. 2014, Hwang et al. 2015). For example, Cheeran et al. (2008) found individuals that expressed the BDNF polymorphism demonstrated altered corticospinal responses to continuous and intermittent TBS, PAS and cathodal tDCS followed by rTMS compared to those without the BDNF polymorphism. Using a larger sample size and classification of three genotypes (Val/Val, Val/Met, Met/Met), Cirillo et al. (2012) confirmed the important role that BDNF plays in PAS-induced plasticity. Similarly, the influence of the BDNF polymorphism on the induction of homeostatic plasticity following rTMS has been further demonstrated in both healthy and clinical populations (Chang et al. 2014, Hwang et al. 2015). However, the influence of BDNF on NIBS protocols is not always consistent with some studies showing no difference in corticospinal plasticity between Val66Val and Val66Met carriers following rTMS and iTBS (Li Voti et al. 2011, Nakamura et al. 2011). Importantly, it should be noted that the protocol duration used in these studies may not have been sufficient to activate cellular processes of activity-dependent BDNF secretion (Li Voti et al. 2011, Nakamura et al. 2011). Interestingly, only a limited number of studies have investigated the impact of the BDNF polymorphism on corticospinal plasticity induced by anodal tDCS in young and older adults (Antal et al. 2010, Puri et al. 2015, Frazer et al. 2016). One study found that carriers of the BDNF Met allele (Val/Met) displayed enhanced corticospinal responses to a single session of anodal tDCS compared to the Val/Val genotype (Antal et al. 2010). Antal and

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colleagues (2010) concluded that this finding was due to tDCS modifying the transmembrane neuronal potential compared to the other NIBS techniques which act upon LTP mechanisms. However, given that longlasting changes in motor behaviour associated with repeated tDCS stimulation are likely to occur as a result of LTP-like mechanisms (Liebetanz et al. 2002), it was not unexpected that a recent study found that carriers of the BDNF Met allele displayed reduced corticospinal responses to accumulated bouts of anodal tDCS (Frazer et al. 2016). Given the evidence that the corticospinal responses to NIBS techniques are largely due to LTP mechanisms and the interaction between BDNF secretion and LTP/LTD processes, it is highly likely that BDNF is involved in the regulation of corticospinal plasticity and, potentially, subsequent changes in motor performance. However, further study is required to establish the impact that the BDNF polymorphism may have in mediating different forms of experimentally-induced plasticity and specifically what mechanisms are involved. Furthermore, given the BDNF polymorphism has been shown to shape an individual’s responsiveness to both experimentally-induced (i.e., tDCS) and use-dependent (i.e., motor skill training) plasticity protocols (Kleim et al. 2006, Cheeran et al. 2008, Antal et al. 2010), it would be critical to identify whether this genetic factor may also influence the effectiveness of using tDCS as a priming protocol prior to motor training to augment the corticospinal responses to a single bout of strength training.

4.5 Is homeostatic plasticity important for motor performance? Historically, tDCS has been used as a NIBS technique to modulate neuroplasticity and modify motor behaviour (Ridding & Ziemann 2010). However, in an effort to further explore the efficacy of tDCS to enhance motor performance, the technique has evolved into a popular paradigm of motor priming which is believed to facilitate motor learning (Stoykov & Madhavan 2015). Motor priming involves the application of tDCS before or during motor training, with the working hypothesis that enhanced neural activity within the M1 will facilitate the mechanisms associated with LTP or LTD (Ziemann & Siebner 2008). Two theories have been proposed to underlie the response of corticospinal output neurons following priming protocols including gating and homeostatic plasticity (Siebner 2010). The theory of gating occurs instantaneously and describes the influx of calcium ions to the targeted corticospinal neurons resulting in the disinhibition of intracortical inhibitory circuits (Ziemann & Siebner 2008, Siebner 2010). Gating is attained concurrently with motor training and has been shown to

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facilitate motor performance tasks such as hand function using the JTT, maximal strength, movement speed, reaction time and speed-accuracy trade-off (Nitsche et al. 2003d, Boggio et al. 2006, Galea & Celnik 2009, Hunter et al. 2009, Reis et al. 2009, Stagg et al. 2011, Hendy & Kidgell 2014). For example, Christova et al. (2015) showed a significant reduction in SICI following the application of anodal tDCS during grooved pegboard training. However, it appears that the efficacy of priming during training may be limited to fine motor skill training tasks (i.e., pegboard). In support of this notion, Hendy et al. (2013) investigated the use of anodal tDCS applied to the active M1 during training to enhance maximal voluntary strength (Figure 4.4). Interestingly, there was no difference in strength gain between conditions, suggesting that strength training appeared to have a powerful effect on modulating mechanisms associated with LTP, therefore, potentially limiting further corticospinal responses induced by anodal tDCS.

Figure 4.4: The effect of using anodal-tDCS during strength training (exploiting the plasticity principle of gating) on the percentage change in 1RM wrist extension strength (mean ± SD). The ST + sham group wrist extension force increased by 11% following the intervention, while the ST + a-tDCS group increased by 14%. The increase in strength for both the ST + sham and ST + a-tDCS group was significantly greater than the control group, which did not change. * represents a significant time effect (P < 0.05). † represents a significant difference from the control group (group by time interaction, P < 0.05).

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The principle of homeostatic plasticity whereby the resting state of corticospinal neurons is altered prior to training (increased/decreased level of excitability following a low/high level of synaptic activity) due to changes in postsynaptic glutamate receptor activity (Ziemann & Siebner 2008, Siebner 2010) is also an emerging NIBS technique that can be used to facilitate motor learning. Importantly, the lack of interactions observed between conditions by Hendy et al. (2013, 2014) suggests that a critical consideration to maximise the effectiveness of anodal tDCS as a M1 priming technique is the timing of application (i.e., during or prior the training). Given that anodal tDCS has been shown to modulate NMDA receptors, and subsequently produce a shift in the resting membrane potential (Nitsche & Paulus 2000), it would be conceivable that anodal tDCS is a promising priming tool to increase synaptic activity prior to a single bout of strength training to further augment the acute neuroplastic responses to strength training. Recently, it has been reported the M1 responses to strength training increase when anodal-tDCS is applied during training due physiological mechanisms associated with gating. An additional approach to improve the M1 responses to strength training, which has not been explored, is to use anodal-tDCS to prime the M1 before a bout of strength training. Frazer et al. (2019), using a randomized double-blinded cross-over design, measured the changes in isometric strength, corticospinal excitability and inhibition using TMS, when participants were exposed to 20-min of anodal and sham-tDCS prior to a single bout of strength training. The experimental design exploited the mechanism of homeostatic plasticity, with the hypothesis that priming the M1 and subsequently inducing neuroplasticity would enhance the neuroplastic effects of the following strength training session. TMS revealed a 24% increase in corticospinal excitability following anodal-tDCS and strength training, but this increase was not different between conditions, which was similar to the previous findings by Hendy and Kidgell (2014). However, there was a 14% reduction in corticospinal inhibition when anodal-tDCS was applied prior to strength training when compared to sham-tDCS and strength training (all P < 0.05, Figure 4.5).

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Figure 4.5: The effect of homeostatic plasticity on the corticospinal responses to strength training. The AURC obtained prior to anodal-tDCS + ST intervention is shaded in white. The additional area enclosed by the recruitment curve obtained following anodal-tDCS + ST is shaded in grey. The AURC was calculated from corticospinal inhibition curves for 13 participants in the anodal-tDCS + ST condition whereby the silent period was plotted against stimulus intensity. * indicates significant within-condition-effect. # Indicates significant difference to sham + ST (between-condition-effect).

These findings suggest that priming anodal-tDCS had a limited effect in facilitating corticospinal excitability following an acute bout of strength training. Interestingly, the interaction of anodal-tDCS and strength training appears to affect the excitability of intracortical inhibitory circuits of the M1 via non-homeostatic mechanisms.

4.6 Use-dependent neuroplasticity Use-dependent plasticity underlying improvements in motor performance occurs as a series of overlapping and complementary events rather than a single, measurable process. However, the acquisition of a novel motor skill is characterised by distinct early and late stages that are driven by separate mechanisms of neuroplasticity (Floyer-Lea and Matthews 2005; Kleim et al. 2004; Kleim et al. 2006; Rosenkranz et al. 2007; Karni et al. 1998; Dayan & Cohen 2011). The duration of these stages is highly specific to the demands of the training task involved, such as the complexity and sensory feedback involved (Karni et al. 1995; Doyon & Benali 2005). Further, adaptations to motor skill training can occur online during training or offline in the period between training sessions (Dayan & Cohen 2011).The earliest mechanisms of neuroplasticity are restricted to existing structures, and

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occur through a selective release of inhibition that unmasks latent synaptic connections, which improves synaptic efficacy (Hess et al. 1996; Hess & Donoghue 1994; Rioult-Pedotti et al. 1998; Rioult-Pedotti et al. 2000). This rapid improvement in synaptic efficacy likely occurs at first exposure to a novel task and is therefore likely present online during motor training because of mechanisms associated with STP (Muellbacher et al. 2002; Coxon et al. 2014). For example, evidence of use-dependent plasticity has been observed following associated ballistic motor skill training (Muellbacher et al. 2001, Selvanayagam et al. 2011). Ballistic motor skill tasks share similar characteristics to strength training and other forms of motor training, as both require the repeated generation of high force production and movement repetition (Carroll et al. 2008, Hinder et al. 2013). In regards to a strength task, the requirements (muscle recruitment, timing of muscle activation between agonists and antagonists, joint positioning) indicate that a level of skill and learning is necessary for the successful completion of the movement (Carroll et al. 2001b). Due to the similarity between training paradigms, the notion that motor performance gains are mediated by mechanism associated with use-dependent neuroplasticity likely explains the rapid improvement in strength and motor skill function that is commonly observed in the literature (Mason et al. 2019). In the temporal space between training sessions, substantial offline adaptations may occur which reflect consolidation of a skill so it can be retained for future performance upon recall (Muellbacher et al. 2002; Roberston, Pascual-Leone & Miall 2004; Fischer et al. 2005; Reis et al. 2009; Romano et al.2010). Structural plasticity, such as synaptogenesis, and persistent reorganisation in M1 movement representations are likely to occur offline in the late stage of motor skill acquisition (Mednick et al. 2011; Kleim et al. 2004). Ultimately, an interaction between online and offline mechanisms of neuroplasticity contributes to long-lasting improvements in motor performance (Romano et al. 2010). Importantly, the rapid increases in synaptic efficacy that occur as a result of motor training are considered a mediating step in determining LTP and long-term structural adaptations such as synaptogenesis and motor map expansion (Jacobs & Donoghue 1991, Ziemann, Hallett & Cohen 1998; Kleim et al. 2004). Thus, there is now good evidence for this progression of neuroplasticity throughout the acquisition of motor skills as a result of use-dependent neuropalsticity (Kleim et al. 2004; Rosenkranz et al. 2007; Kleim et al. 2006; Adkins et al. 2006). Use-dependent neuroplasticity typically involves both “online” and “offline” mechanisms of plasticity. Online adaptations refer to those corticospinal

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responses during and immediately following a session of motor training or strength training, in reference to their elevation being an immediate result of the preceding activity. Further, ‘offline’ usually refers to adaptations occurring following the cessation of one session and the commencement of the next; however, ‘offline’ also represent changes that fall outside the training period and occur in the temporal space following the transient effects of the prior training session, thus occurring between training sessions (Mason et al. 2019). Although the concepts of online and offline adaptations and, in particular, early and late phases of neuroplasticity, are well established in context of skill acquisition (Floyer-Lea and Matthews 2005; Kleim et al. 2004; Kleim et al. 2006; Rosenkranz, Kacar & Rothwell 2007; Karni et al. 1998; Dayan & Cohen 2011), similar frameworks describing the neuroplastic processes underlying the development of muscular strength (because of usedependent mechanisms) are relatively absent. However, in Chapter 5 and 7, we present experimental evidence for both on-line and off-line usedependent neuroplasticity following skill and strength training.

4.7 Summary Neuroplasticity can be induced by experimental techniques involving NIBS and following different types of exercise. The degree of plasticity is dependent upon specific genetic polymorphisms, with BDNF being the most studied neurotrophic factor that shapes neuroplasticity. Both motor skill training and strength training are two common techniques of usedependent plasticity. Combining NIBS with use-dependent plasticity results in differing levels of neuroplasticity and motor function, depending on the timing of NIBS application relative to the timing of exercise.

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CHAPTER 5 NON-INVASIVE BRAIN STIMULATION AND EXERCISE PERFORMANCE SHAPOUR JABERZADEH, PHD AND MARYAM ZOGHI, PHD

5. Introduction Exercise performance (EP) is affected by a number of factors including physical, physiological, and psychological factors (Figure 5.1) (McCormick et al. 2015; Neumayr et al. 2003). Physical factors refer to physical attributes of the body such as height, weight, body mass index, level of body fat and muscle mass. Psychological factors include lifestyle, personality characteristics, arousal, motivation and stress level. Physiological factors involve muscle strength, skill, energy production (anaerobic and aerobic), muscle fatigue and fibre type. Boosting EP was the focus of huge research during the last century to affect one or a combination of the aforementioned factors (Schubert and Astorino 2013). During the last two decades, the focus has shifted to the brain and how it could boost EP. Literature indicates that the brain is crucially taking part in the foundation of fatigue and, therefore, EP (Noakes 2011, 2012, Gandevia 2001, Van Cutsem et al 2017). In addition, literature also indicates the boosting effects of some centrally-acting performance modifiers on EP (Noakes 2012; Van Cutsem et al 2017). Non-invasive brain stimulation (NIBS) techniques, including transcranial direct current stimulation (tDCS), were also used as a priming technique for enhancement of EP. In this chapter, we offer an overview of tDCS for the enhancement of EP. tDCS is a NIBS technique with an excellent safety record which is well tolerated, relatively inexpensive and readily available.

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Figure 5.1. Factors affecting exercise performance.

5.1 Transcranial Direct Current Stimulation tDCS is a neuromodulatory technique in which a subthreshold direct current, with an intensity of up to 2mA, is applied to the scalp through surface electrodes for tens of minutes to induce cortical excitability changes (Nitsche and Paulus 2001; Nitsche et al. 2003) and changes in spike rates (Bogaard et al. 2019). Traditionally, it was assumed that the effect of tDCS on neuronal excitability was polarity dependent. Accordingly, placement of the anode electrode over the target brain area (a-tDCS) increases while application of the cathode over this area (c-tDCS) decreases neuronal excitability (Nitsche and Paulus 2001; Nitsche et al. 2003). Due to a nonlinear dose-response relationship, this notion is considered very simplistic and is not supported by new literature. New literature indicates that, under certain circumstances, a-tDCS can decrease and c-tDCS can increase neural excitability (Esmaeilpour et al. 2018; Jamil et al. 2017; Batsikadze et al. 2013). Having said that, we adopt the conventional anodal and cathodal terminology for the purpose of this chapter. Commonly, research indicates that the effects of tDCS remain beyond the completion of the stimulation session for up to two hours (Nitsche and Paulus 2001; Nitsche et al. 2003).

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5.2 The mechanisms behind tDCS effects during stimulation (online effects) Early animal studies showed that tDCS of the cerebral cortex induces changes in resting membrane potentials (Purpura and McMurtry 1965). AtDCS of the cortex increases while c-tDCS reduces spontaneous neuronal activity (Bindman et al. 1964; Creutzfeld et al. 1962; Purpura and McMurtry 1965). This effect is not uniform throughout the cortex and may depend on the types of neurons which may have different modulation thresholds, location of the neurons within the cortex and also their orientation relative to the electrical fields (Purpura and McMurtry 1965).

Figure 5.2. The physiological mechanisms underlying the effects of tDCS during stimulation.

Results from human studies using pharmacological approaches showed that the effects of tDCS during stimulation are mainly dependent on changes in the resting membrane potential. These findings are also supported by the findings in animal studies (Purpura and McMurtry 1965; Jackson et al. 2016). Research also suggested redistribution of polarization across the

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cellular axis (one dendritic branch versus another) as the mechanism underlying tDCS effects during stimulation (Fritsch et al. 2010; Rahman et al. 2013; Arlotti et al.; 2012; Kabakov et al. 2012).

5.3 The mechanisms behind tDCS effects after the termination of stimulation Pharmacological studies indicate that the after-effects of tDCS are mainly mediated by modification of NMDA-receptor sensitivity (Liebantz et al. 2002; Nitsche et al. 2006). It is also proposed that the after-effects of tDCS may be mediated by changes in the concentration of intracortical neurotransmitters such as GABA or glutamate (Stagg et al. 2009). In line with this effect, literature also indicates the regulation of a broad variety of other neurotransmitters, including dopamine, acetylcholine and serotonin as the mechanisms behind neuroplastic changes following application of tDCS (Nitsche et al. 2006; Kuo et al. 2007; Monte Silva et al. 2009). tDCS after-effects may also rely on non-synaptic mechanisms (Ardolino 2005) such as changes in transmembrane proteins or electrolysis-related changes in [H+] induced by exposure to a constant electric field (Rae et al. 2013).

Figure 5.3 The mechanisms behind tDCS effects after termination of stimulation.

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5.4 Montages for application of tDCS: The conventional montage This montage involves the application of a subthreshold direct current via two surface electrodes placed over the scalp of a participant. In this montage, one electrode is known as the active electrode and is placed over the target cortical area. The other is known as the return electrode and is usually placed over the contralateral supraorbital area. During stimulation, the applied current flows from the anode which passes through the cortical tissue toward the cathode to complete the circuit.

Figure 5.4. Conventional montage. Anodal tDCS of left primary motor cortex (C3). This montage includes an active electrode (i.e., anode) which is connected to a return electrode (i.e., cathode). The current density under the return electrode is equal to the density under the active electrode. Electrical field intensities throughout the brain are modelled using a finite-element-method approach.

5.4.1 HD-tDCS montage HD-tDCS is a novel montage for application of tDCS. In this montage, smaller electrodes are used to minimise spread of the applied current outside of the target area (Datta et al. 2009). For a-tDCS, using a 1 x 4 ring electrode configuration, the positive central electrode (anode) could be located on the area of interest (i.e., left dorsolateral prefrontal cortex [DLPFC], F3 based on the international 10–20 EEG system), while it is surrounded by four

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negative electrodes (cathode) on adjacent electrode sites such as F5, AF3, F1, FC3 (based on the international 10–20 EEG system). In this montage, the electrodes are usually kept in place by using plastic holders mounted into an EEG cap. Modelling studies have shown that HD-tDCS stimulates more focally and therefore induces neuromodulatory changes within the area of interest.

Figure 5.5 HD-tDCS montage includes a central active electrode (i.e., anode) over the brain target area which is surrounded by a number of return electrodes (i.e., cathodes). The current density under each return electrode is 25% of the density under the active electrode. Electrical field intensities throughout the brain are modelled using a finite-element-method approach.

5.4.2 Other tDCS montages To stimulate two parallel cortices (i.e., left and right primary motor cortex), the electrodes may be applied “bihemispherically” (e.g., the parietal cortices) (Benwell et al. 2015). In this montage, the aim is to purposefully upregulate one brain region using a positive electrode (anode) while downregulating another region using a negative electrode (cathode) (Lindenberg et al. 2010).

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5.5 tDCS as a stand-alone technique tDCS can be used as a stand-alone technique in the absence of a concurrent intervention. This technique has a promising potential particularly for the management of pain and reduction of symptoms in multiple neurological and psychiatric conditions. Due to the fact that the effect of tDCS on cortical excitability is cumulative, these interventions typically include the application of multiple tDCS sessions over succeeding days ranging from one to a number of weeks (Alonzo et al. 2012; Galvez et al. 2013). Methodological considerations for the use of tDCS as a stand-alone technique require careful consideration of: 1. Number and intervals between the treatment sessions; 2. Stimulation duration, current intensity/density and montage; 3. Stimulation site (Brain target) for induction of the desired effects; 4. Standardised patient’s activity during the stimulation sessions; and 5. Control for potential brain state effects during application of stimulation.

5.6 tDCS as a priming technique In this technique, tDCS is an adjunct technique which is combined with other therapeutic interventions such as pharmacotherapy, other NIBS techniques, or behavioural interventions such as exercise or other forms of training or rehabilitative strategies. In all of these combined applications, tDCS is used to prime, or precondition prior to application of the second intervention (i.e., homeostatic plasticity, see Chapter 4, Stagg and Nitsche 2011). Animal studies indicate that the presence of ongoing background activity is essential for the induction of long-term neuroplastic changes in the brain (Fritsch et al. 2010). A critical consideration in the use of this technique is the timing of tDCS application relative to the execution of the behavioural intervention. Literature indicates the superiority of the online (concurrent) application of the combined techniques compared to their offline applications, i.e., application of tDCS prior to or after completion of the behavioural task (Stagg et al. 2011; Martin et al. 2014).

5.7 Halo sport tDCS device Halo Sport is a brain stimulator device which was introduced for the first time in 2017 by Halo Neuroscience (San Francisco, CA, United States). It is indeed a tDCS device which is incorporated into a self-contained headset similar in appearance to an audio headphone (Figure 5.6). The electrical contact with the scalp is provided through three wet studded foam electrodes.

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These electrodes are connected to a direct current battery-operated generator. The current intensity of maximum 2.2 mA is controlled by an application using a mobile phone or iPad. The device works by applying a small electric current (1-2 mA) to the area of the brain that controls movement (Figure 5.6) to boost athletic performance. The headset emits direct current to the brain via rubber pads. The current modulates the motor cortex and induces neuroplasticity (Chapter 3). Depending on the site of stimulation, it can improve both mental and physical performance.

Figure 5.6. Halo Sport headset could be easily used by individuals to modulate primary motor cortex for both upper and lower limbs in left and right sides of the body.

This technology mainly works by priming the training effects for enhancement of EP. The protocol includes a 20-min Halo Sport session at rest which is followed by a normal 60 minutes training session. A recent study by Huang et al. (2019) indicated that application of tDCS with the Halo Sport device improved repeated sprint cycling power output and Stroop performance.

5.8 The effects of tDCS on EP Numerous brain mechanisms are involved in the regulation of EP which indicates the contribution of different areas of the brain in this process. Excellence in EP requires not only physical and motor capabilities, but also sensory-cognitive skills (Moscatelli et al. 2016). Considering the neuromodulatory effects of tDCS on the motor, sensory and cognitive regions of the brain, tDCS was extensively used for enhancement of EP.

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Due to its driving role in the control of muscle activity during exercise, targeting the primary motor area with tDCS has been used to improve EP. tDCS can be used to compensate for the reduction in corticospinal excitability (CSE) following central fatigue exercise (Cogiamanian et al. 2007). Exercise-induced pain also plays a significant role in the regulation of EP (Mauger 2013). Therefore, due to the connections between the M1 to insula and thalamus, modulation of pain perception is another reason for stimulation of this area to improve EP (Vaseghi et al. 2014). Vitor-Costa (2015) showed that bilateral a-tDCS (2 mA) of M1 for lower limb muscles increased exercise tolerance in a cycling-based, constant-load exercise test, performed at 80% of peak power. In another study, Abdelmoula et al. (2016) investigated the effects of a-tDCS on neuromuscular fatigability and concluded that 10 minutes a-tDCS of M1 significantly reduced fatigue. Similarly, Cogiamanian et al. (2007) applied a-tDCS over the M1 of the elbow flexors and showed improved muscle endurance and reduced muscle fatigue in both healthy participants and patients with pathological conditions. Because of its role in top-down control of exercise-related internal and external cues (Robertson and Robino 2016), a-tDCS of the DLPFC can also be used to improve EP. A systematic review of the literature indicated that reduced endurance performance was associated with increased cognitive load (Van Cutsem et al. 2017). Therefore, the application of tDCS over the DLPFC could improve the function of this area in the maintenance of the volitional drive to the motor cortex and therefore improve EP. The temporal and insular cortex (TC, IC), regions of the brain involved in autonomic control of the cardiorespiratory system, are other targets for the use of tDCS to improve EP. Literature indicates the role of the right and left IC in sympathetic and parasympathetic activity (Oppenheimer and Cechetto 1990; Oppenheimer et al. 1992; Napadowe al 2008). Okano et al. (2015) concluded that the application of a-tDCS over the TC modulates the activity of the autonomic nervous system. This modulation delayed muscular fatigue by increasing the time exercising with a lower cardiovascular load. In addition, Okano et al. (2013), in a tDCS study on trained cyclists, showed that 20 minutes of a-tDCS (2 mA) over the left TC significantly reduced both heart rate and rating of perceived exertion during submaximal exercise levels. These authors also reported a significant increase in peak power output during a stepwise maximal exertion test. Due to the large size of the

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electrodes used, the stimulation may also have an affect on the IC, which is involved in the awareness of different parts of the body related to an athlete’s perception of physical exertion during exercise, and parts of the frontal lobe (Craig 2003; Williamson et al. 1999). In line with these findings, Cogiamanian et al. (2007) demonstrated increased time-to-exhaustion following a-tDCS (1.5 mA, 10 minutes) of the motor cortex during an isometric arm endurance task. Similarly, Tanaka et al. (2009) also reported that a-tDCS (2 mA, 10 min) of the lower leg motor cortex significantly increased maximal pinch force in the lower leg. In another study, Angius et al. (2015) investigated the effects of tDCS (2 mA, 10 min) on exercise-induced pain and reported improved pain tolerance during a cold pressor test. Unlike the studies with enhancing effects of tDCS on EP, there are also a number of others studies which have failed to show any positive effects on EP (Barwood et al. 2016; Kan et al. 2013; Muthalib et al. 2013; Angius et al. 2015). The mixed findings could be caused by the methodological differences in these studies. Therefore, future double-blinded randomized controlled studies, using larger sample size and protocols that are more robust, are required to shed light in this area of research.

5.9 Ethical considerations for the use of tDCS for enhancement of EP Fairness and the ethics of tDCS use for enhancement of EP were also subject of a number of opinion articles in the literature (Davis 2013; Banissy and Muggleton 2013; Colzato et al. 2017; Park 2017). In these articles, tDCS is considered as a technique which potentially can become a “neuro-doping” agent for enhancement of EP. The authors in these articles argued that tDCS is associated with physical, behavioral and ethical risks for its users and should be banned because it is a new doping technique. It should be noted that the use of tDCS is not a banned substance/modality by the World Anti-Doping Agency (WADA) (Stewart et al. 2013). These articles suggest there is a need to develop anti-doping regulations by sports governing bodies, to consider the prohibition of tDCS application for the enhancement of EP.

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5.10 Summary The focus of research for enhancement of EP during the last two decades has shifted from physical, physiological, and psychological factors towards the brain. The main reason behind this shift is the development of centrallyacting performance modifiers and also the development of NIBS techniques such as tDCS. This is a technique with an excellent safety record which is well tolerated, relatively inexpensive and readily available. Research indicates that, while a large number of studies are in support of using tDCS for enhancement of EP, there are also a number of other studies which have failed to show any positive effects. The mixed findings could be caused by the methodological differences in these studies. Therefore, future doubleblinded randomized controlled studies, using larger sample size and protocols that are more robust, are required to shed light in this area of research.

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CHAPTER 6 NEURAL CONTROL OF LENGTHENING AND SHORTENING CONTRACTIONS JAMIE TALLENT, PHD AND GLYN HOWATSON, PHD

6. Background It is well accepted that the performance capacity of skeletal muscle is different between shortening and lengthening contractions, which suggests that the neural control strategy must also differ. The purpose of this chapter is to detail and compare the activation strategies used by the nervous system to control muscle force during shortening and lengthening contractions. The chapter will also compare the neural adaptations to lengthening and shortening contractions performed during resistance training.

6.1 Shortening and Lengthening Contractions Shortening (concentric) muscle contractions occur when the muscle’s contractile apparatus shortens, whilst lengthening (eccentric) muscle contractions occur as the muscle’s contractile apparatus lengthens, usually under tension (Asmussen, 1953). Lengthening muscle contractions consist of resisting load, such as lowering a weight and walking downhill, whilst shortening muscle contractions overcome external resistance, such as lifting a weight or climbing stairs; however, both are critical for everyday human movement (Isner-Horobeti et al. 2013). It has long been established that lengthening muscle contractions can produce and withstand greater forces compared to shortening contractions (Crenshaw et al. 1995; Doss and Karpovich, 1965; Westing et al. 1991; Westing and Seger, 1989). Early reports have demonstrated up to an 80% higher force generating capacity of lengthening when compared to shortening muscle contractions (Rodgers and Berger, 1974), although smaller differences have also been reported (Aagaard et al. 2000). As a result, the increased stimulus provided by

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lengthening compared to shortening contractions during resistance training has the potential for greater gains in muscle mass and strength (Roig et al. 2009). Another unique and advantageous characteristic of lengthening contractions is the lower metabolic cost when compared to shortening actions at the same absolute load (Okamoto et al. 2006; Vallejo et al. 2006). Collectively, these characteristics make lengthening actions a potentially powerful exercise stimulus for numerous groups ranging from clinical through to elite athlete populations; therefore, an understanding of the neural control and acute and chronic responses to resistance training is essential in maximising rehabilitation and performance programmes.

6.1.1 Benefits of lengthening contractions Muscle contractions provide a key part of rehabilitation and resistance training programmes for both clinical and athletic populations. For example, high intensity lengthening muscle contractions have been shown to increase strength in Parkinson’s patients (Hirsch et al. 2003) and consequently improve physical capacity, such as walking (Scandalis et al. 2001). Resistance training that incorporates lengthening contractions has also been shown to have a greater influence on strength changes in stroke patients when compared to shortening contractions (Engardt et al. 1995). Furthermore, the addition of lengthening resistance training in an exercise programme augmented an increase in lean tissue in type II diabetes patients (Marcus et al. 2008) and has been shown to be an effective tool to increase strength in cardiovascular disease patients (Meyer et al. 2003). In an athletic population, overloading lengthening contractions has resulted in a reduction in hamstring injuries (Askling et al. 2003) and an enhancement in explosive power (Clarka et al. 2005). Furthermore, lengthening contractions have been shown to reduce the risk of falls in the elderly (LaStayo et al. 2003). Whilst this list is not exhaustive, it provides multiple examples that illustrate the benefits of lengthening contractions. The apparent superiority of lengthening muscle contractions to optimise rehabilitation and the responses to resistance training are likely, at least in part, to be due to their unique neurological control strategies which are subsequently presented.

6.2 Motor control of lengthening and shortening muscle contractions During voluntary contractions, there is a large body of evidence showing lower surface electromyography (EMG) amplitude in lengthening compared to shortening muscle contractions (Aagaard et al. 2000; Amiridis

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et al. 1996; Duclay et al. 2011; Komi et al. 2000; Benjamin et al. 2000; Teschet al. 1990) (Figure 6.1) even though a higher force producing capacity for lengthening muscle contractions is often observed (Crenshaw et al. 1995; Westing et al. 1991; Westing and Seger, 1989). For any given force, there is also a reduction in oxygen cost (Bigland-Ritchie and Woods, 1976) indicating a more metabolic efficient contraction. Furthermore, the greater force twitch during maximal voluntary contracts (MVCs) further demonstrates unique neurological qualities during lengthening contractions (Löscher and Nordlund, 2002). This section will describe the differences in the motor control of lengthening and shortening contractions at the muscle, spine and motor cortex.

6.2.1 Muscle Early evidence suggested that the recruitment order of motor units is altered during lengthening contractions (Howell et al. 1995; Nardone et al. 1989). Type II higher threshold motor units are selectively recruited in preference to lower threshold type I motor units (Howell et al. 1995; Nardone et al. 1989). Because the EMG activity is lower, it could be assumed that fewer motor units are required for the same level of force; however, this is not a widely supported idea (Bawa and Jones, 1999; Pasquet et al. 2006; Stotz and Bawa, 2001). The lower EMG values recorded during lengthening compared to maximal shortening contractions might be due to increased inhibition during a lengthening action (Aagaard et al. 2000). Specifically, during a maximal lengthening contraction, Golgi organs excite the Ib afferents that activate inhibitory interneurons, thus resulting in less muscle activity (Aagaard et al. 2000). It has been reported that the Golgi organs inhibit muscle activity to act as a protective mechanism during lengthening contractions (Tomberlin et al. 1991), although the evidence for this proposed protective mechanism from damage is unclear (Chalmers 2002). Finally, synchronization could also influence the EMG signal; a greater synchronization of motor units has been reported during lengthening muscle contractions when compared to shortening contractions (Semmler et al. 2002).

6.2.2 Spinal Differences between lengthening and shortening muscle contractions at a spinal level are often assessed through the Hoffman Reflex (H-reflex). The

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H-reflex is a reflex that excludes gamma motor neurons spindle discharge (Zehr, 2002) and requires low intensity, short duration electrical stimulation of the peripheral nerve to be evoked. Volitional drive (V-wave) is an EMG variant response of the H-reflex recorded during maximal voluntary contractions (Aagaard et al. 2002). To elicit a V-wave, supramaximal stimulation is applied to the peripheral nerve during a maximal contraction and consequently represents modulations in efferent output during maximal contractions (Aaggard et al. 2002). These techniques will be referred to in this section to discuss differences in motor control strategies between shortening and lengthening contractions. In a rested state, a reduction in lengthening compared to shortening H-reflex amplitude has been demonstrated in numerous studies (Duclay and Martin, 2005; Nordlund et al. 2002; Pinniger et al. 2001). Specifically, an increased presynaptic inhibition and increased post-activation depression have been reported as the specific mechanisms for a reduction in the H-reflex during passive lengthening contractions (Duclay and Martin, 2005; Hultborn et al. 1987; Rudomin and Schmidt, 1999). There is also evidence of a reduction in H-reflex amplitude during active lengthening compared to shortening contraction (Duclay and Martin, 2005; Duclay et al. 2011), with no change in volitional drive (Duclay et al. 2008; Hahn et al. 2012). The information presented here strongly suggests that supraspinal drive is inhibited at a spinal level during lengthening contractions and may be a contributor for the reduced EMG activity at the muscle. As discussed earlier, lengthening contractions generate a greater force compared to shortening contractions. It is also plausible that afferent feedback from the Golgi organs might contribute to the reduction of muscle activity recorded at the muscle during a lengthening contraction (Aagaard et al. 2000). Due to the H-reflex being a Ia afferent reflex, presynaptic inhibition of the Ia afferents appears an equally likely mechanism in reducing EMG activity recorded at the muscle during lengthening contractions (Duclay and Martin, 2005). More recently, recurrent inhibition has been shown for the first time to increase during lengthening compared to shortening maximal contractions (Barrue-Belou et al. 2018) and it may also be a key regulating or protective mechanism at the level of the spinal cord circuitry. In summary, it appears that no single mechanism is responsible for the suppression of EMG at the muscle and, hence, there are likely to be a multitude of inhibitory factors.

6.2.3 Cortico-Spinal Transcranial Magnetic Stimulation (TMS) (Duclay et al. 2011; Löscher and Nordlund 2002) and electroencephalogram (EEG) (Fang et al. 2001; 2004)

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studies have investigated differences in motor control between lengthening and shortening muscle contractions at a cortical level. Fang et al. (2001) investigated movement related cortical potentials (MRCP) from EEG recordings and showed them to be higher during lengthening contractions compared to shortening contractions. The authors concluded that lengthening contractions are processed and planned differently to shortening contractions due to the complexity of the movement. Therefore, the greater MRCP might be a result of a greater cortical activity to process the amount of sensory feedback immediately prior and during lengthening muscle contractions (Fang et al. 2001; Fang et al. 2004). There appears to be greater supraspinal activity during lengthening muscle contraction and reduced activity at a spinal level which results in a lower EMG response. Interestingly, during maximal contractions, the majority of previous work has reported no difference in corticospinal excitability of lengthening and shortening muscle contractions (Duclay et al. 2011; Löscher and Nordlund, 2002); however, there is evidence to suggest motor evoked potentials (MEP) are reduced during lengthening contractions when recorded during MVCs (Duclay et al. 2011). The reduction in corticospinal excitability during muscle lengthening appears to be more evident at submaximal contraction intensities (Abbruzzese et al. 1994; Sekiguchi et al. 2001), however the exact mechanisms why this might happen remain unclear. The MEPs reflect the balance between excitability and inhibition along the brain to muscle pathway (Hallett, 2000). Consequently, an increase in cortical excitability might occur, but heightened spinal inhibition (as discussed in the previous section) could reduce the EMG response seen at the muscle. No change in cervicomedullary motor evoked potential (CMEP) has previously been reported between lengthening when compared to shortening contractions (Hahn et al. 2012), though this is not consistant in the literature. Gruber et al. (2009) investigated the ratio of MEPs evoked at the primary motor cortex and the CMEP to determine the control mechanisms of lengthening and isometric contractions at a cortical level. Lengthening contractions showed lower CMEP and MEP amplitude than isometric contractions, however the MEP-to-CMEP ratios increased. This information further suggests that there is heightened cortical activity and spinal inhibition during lengthening compared to isometric contractions. More specifically, this section suggests that spinal inhibition occurs further up the spinal tract in additional to at a reflex level. The amount of neural drive to the muscle during a maximal muscle contraction is defined as voluntary activation (Gandevia et al. 1995) and has been used to investigate changes in the neurological control strategies of

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shortening and lengthening contractions. More specifically, lengthening contractions showed a voluntary activation of only 79% compared to 92% during shortening muscle contractions. Figure 6.1 shows an example individual trace showing the large interpolated twitch response from a maximal lengthening contraction compared to isometric and shortening contractions. The data indicate that, during maximal contractions, excitation of the motoneuron pool is lower. This, in addition to the unique control strategies presented in previous sections, may suggest that lengthening contractions have the potential for greater adaptation to resistance training. This will be explored in the next section.

6.3 Adaptations to shortening and lengthening resistance training As described in the previous section, motor control strategies of lengthening and shortening muscle contractions differ from the motor cortex to the muscle (Duchateau and Enoka, 2008). Given the pool of literature investigating the neurological differences between shortening and lengthening contractions, it is surprising that relatively few studies have investigated nervous system adaptations from lengthening and shortening contractions, particularly at multiple levels of the central nervous system (CNS).

Figure 6.1. Representative torque traces, isometric (A), shortening (B), and lengthening (C) maximal voluntary contractions for knee flexion with superimposed twitch. Timing of superimposed twitch is indicated through the arrow. Adapted from Beltman, Sargeant, van Mechelen, & de Haan (2004).

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Hortobagyi et al. (1996) showed a 3.5-fold increase in strength that was accompanied by a 7-fold increase in EMG. The large increase in strength was attributed to a greater recruitment (from the CNS) of type II muscle fibres. Further evidence has also reported a preferential recruitment of type II muscle fibres during lengthening contractions (Nardone et al. 1989). Whilst Hortobagyi et al. (1996) showed an increase in neural drive from lengthening resistance training, Seger and Thorstensson (2005) did not detect changes in EMG between shortening and lengthening resistance training, which is likely attributable to the very small sample size (n = 5 per group) that were moderately trained. Trained individuals have been shown to have a lower superimposed twitch force compared to untrained (Amiridis et al. 1996), suggesting a reduction in the neural inhibitory mechanism discussed in the previous section (Aagaard et al. 2000; Webber and Kriellaars, 1997). Even though Higbe et al. (1996) found a significant increase in EMG from pre to post training in both the shortening and the lengthening muscle contraction groups, there was no difference in post training EMG between groups. Variability of EMG can play a significant role in the lack of findings in some studies. For example, Blazevich et al. (2008) did not detect any changes in EMG pre to post and post to detraining in shortening or lengthening training groups despite changes in the rate of force development (RFD) and MVC. Whilst experimenters make every effort to reduce variability, the nature of EMG can make detecting changes difficult. Factors such as phase out cancellation and high within-subject variability are two significant limitations of this technique (Farina et al. 2014). Assessing adaptation during a dynamic muscle contraction further adds to the potential variability of EMG testing. EMG signals recorded at the muscle belly are highly susceptible to small muscle movements (Rainoldi et al. 2000) over skin-mounted electrodes. The emergence of high-density surface EMG has the potential to reduce some of these limitations and is able to detect the specific behaviour associated with exercise-induced alterations in individual motor units (Sleutjes et al. 2018). Furthermore, the segmental assessment of changes in the motor cortex to muscle has allowed for a greater insight into neurological changes than a volitional EMG measure alone. Lengthening contractions have been coupled with shortening contractions in a dynamic task to investigate the effects on volitional drive and spinal excitability (Ekblom, 2010). Participants that performed calf raises to raise (shortening) and lower (lengthening), respectively showed no differences in H-reflex during rest or high intensity muscle contractions (HSUP), although an increase in V-wave was evident during shortening and lengthening

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contractions (Ekblom, 2010). In this study (Ekblom, 2010), it was difficult to separate the influence that lengthening resistance training had on adaptations to the nervous system because the lengthening training was coupled with shortening contractions. Therefore, the remainder of this section will focus on isolated muscle actions performed during resistance training. Only a couple of studies have appropriately isolated lengthening and shortening resistance training. Duclay et al. (2008) used peripheral nerve stimulation to investigate the effect of solely lengthening resistance training on H-reflex and V-wave during lengthening and shortening muscle contractions. H-reflex and V-wave were assessed in the soleus and medial gastrocnemius with differing results. No changes were found in the resting H-reflex regardless of muscle contraction; however, in contrast, an increase in HSUP was found during lengthening, shortening and isometric muscle contractions in the medial gastrocnemius, but only during lengthening in the soleus. As HSUP is recorded during maximal contractions, it can be suggested that adaptations in spinal excitability/inhibition appear to be intensity specific from lengthening resistance training. Furthermore, differences between the two studies (Duclay et al. 2008; Ekblom, 2010) might be due to the lack of kinematic specificity to the conditions HSUP was assessed under and the resistance training procedure. Ekblom (2010) performed dynamic resistance training whilst the assessment was conducted on an isokinetic dynamometer, so arguably, this would have had less transfer compared to Duclay et al. (2008) who conducted training and assessment on the isokinetic dynamometer. Furthermore, a faster angular velocity (20°/s) was used by Duclay et al. (2008) compared to 5°/s by Ekblom (2010), which most likely resulted in a higher level of presynaptic inhibition during fast compared to slow lengthening contractions. Further, Ekblom (2010) suggested that the faster lengthening contraction used by Duclay et al. (2008) allowed for a greater potential to reduce presynaptic inhibition. Presynaptic inhibition was highlighted earlier in this chapter as being higher in lengthening compared to shortening contractions and thus a greater potential for modification. An increase in volitional drive was also found post resistance training in all muscle contractions and in both muscles, apart from during maximal shortening contractions (Duclay et al. 2008). The data suggested that increased maximal force during lengthening muscle contractions could be due to an enhanced supraspinal volitional drive and increased excitability coupled with reduced presynaptic and post-synaptic spinal inhibition. Differences in findings between muscles within the same study (Duclay et al. 2008) might be due to the specific neural mechanisms

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that influence spinal excitability of each muscle (Meunier and PierrotDeseilligny, 1998). More recently, work from our laboratory (Tallent et al. 2017) has segmentally assessed neurological adaptations following a 4-week shortening or lengthening resistance-training intervention. Shortening and lengthening resistance training improved MVCs in a task-specific manner (i.e., lengthening training improved eccentric strength more than shortening). Interestingly, V-wave showed an increase of 57% following lengthening resistance training during maximal lengthening contractions compared to an increase of 23% in maximal shortening contractions following shortening resistance training. Changes were independent of any alterations in muscle mass. These data suggest that the superiority of lengthening contractions in enhancing strength may be, in part, due to the enhancement of volitions drive. However, it must be noted that this study did not find a superiority of lengthening contractions in enhancing strength. For practitioners, there appears to be an advantage of using lengthening resistance training in enhancing strength through neurological adaptations. The exact mechanisms are probably due to improved volitional drive and a reduction in inhibition at a spinal level, though further research is required.

6.4 Summary Lengthening muscle contractions consist of resisting load, and key locomotion activities such as walking downhill or stairs. In an athletic population, lengthening contractions decelerate the body which is a critical physical attribute in many sports. Lengthening contractions are a fundamental part of rehabilitation programmes and performance programmes. Consequently, researching optimal strategies for modifying the neurological system and enhancing force generating capacity of the muscle is a vital area for exercise-science research. The neural control of shortening and lengthening contractions appears to differ at a cortical, spinal and muscle level. At a cortical level, it appears that there is an increase in cortical output that is likely reflective of the complexity of the movement, through a potential decrease in corticospinal inhibition. Spinal inhibition through actions of the Golgi organs exciting Ib afferents could cause a reduction in muscle activity. This may be a contributing factor in the dampening of the neurological response at the muscle. Force generating capacity of the muscle appears to increase to a greater extent following lengthening compared to shortening training. Adaptions appear to be associated with a

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greater increase in volitional drive and decrease in inhibition at a spinal level.

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CHAPTER 7 NEURAL ADAPTATIONS TO STRENGTH TRAINING DAWSON J. KIDGELL, PHD

7. Background The purpose of this chapter is to introduce the acute and long-term effects of strength training on the function of the neuromuscular system. It is important to consider that the neuromuscular system (i.e., the interaction between nerves and muscles) functions as an interactive unit. The terminalpoint of the nervous system is the neuromuscular junction (the point where motoneurons and muscle fibres are inter-digitated). The commencement of the nervous system is difficult to define, however, for simplicity; it can be regarded as the primary motor cortex (M1) and the corticospinal pathway. Consequently, it is preferable to consider the interaction of the nervous and muscular system as a continuum rather than in isolation; thus, structures between the M1 and the muscles are likely to undergo subtle changes following strength training. Keeping this in mind, the following sections will discuss the effect of strength training on M1 plasticity and changes in corticospinal activity, including spinal cord plasticity and changes in motor unit behaviour.

7.1 Acute neural responses to strength training The nervous system is able to modify its function in response to activity or experience (Sanes 2003). This response has been termed ‘plasticity’ and involves reorganisation of neural circuits that control movement. In particular, the M1 can experience plasticity following various types of physical activity. Although plasticity can be stimulated in a variety of ways, recently, it has been reported following various types of strength training (Kidgell et al. 2017).

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Muscle strength is the maximal force or torque that can be developed by the muscles while performing a specific movement (Enoka 1988), and changes in the nervous system play an important role in force production and strength development (Kidgell et al. 2017). Muscles increase in strength by being forced to contract at relatively high tensions. Strength training uses specific equipment (free weights and machines) to increase specific tension in the trained muscles. Experimental data now shows that specific neurological factors contribute to the development of muscle strength (Siddique et al. 2020). These studies have used a variety of neuromuscular assessment techniques which include peripheral measures such as surface electromyography (sEMG), evoked spinal-reflex recordings and single motor-unit recordings (Aagaard 2003; Kidgell et al. 2006) to identify the neural mechanisms that contribute to force production. However, other lines of enquiry include using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) to examine the acute and longterm neural responses to strength training (Frazer et al. 2018; Mason et al. 2019).

7.2 Using TMS to assess the neural responses to strength training TMS has emerged as the leading candidate to provide insight into the synaptic activity of the cortico-cortical circuitry of the M1. When singlepulse TMS is applied over the M1, a number of physiological variables can be determined that represent M1 excitability, such as motor threshold (MT) and motor-evoked potential amplitude (MEP). When a MEP is recorded during voluntary muscle activation, there is a pause in the ongoing sEMG signal, which is termed the corticospinal silent period; this is a measure of intracortical inhibition. The corticospinal silent period is mediated by the neurotransmitter gamma-aminobutyric acid-B (GABAB) and indicates an interruption in volitional drive from the M1 (i.e., neural drive) and withdrawal of descending input to the spinal motoneuron pool (Yacyshyn et al. 2016). The corticospinal silent period can be used as a proxy measure for M1 excitability, particularly when reductions are observed following an intervention (Kidgell and Pearce 2010). Although single-pulse TMS is used to determine synaptic efficacy of the M1 and corticospinal tract, it does have limitations as it is unable to examine the excitability of the intracortical micro-circuits of the M1 (Burke and Pierrot-Deseilligny 2010).

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Paired-pulse TMS allows for an assessment of the physiology of the intrinsic intra-cortical connections within the M1 following strength training (Kidgell et al. 2017). Depending on the inter-stimulus interval between the conditioning and test pulse, the excitability of the intracortical inhibitory (e.g., 2-5ms) and long intracortical inhibition (e.g., 100-150ms) and intracortical facilitatory circuits (e.g., 8-15ms) of the M1 can be examined, providing important information about the effects of strength training on the GABA-ergic system (Hanajima et al. 2003). The acute neural responses from strength training likely arise from changes in synaptic efficacy along the corticospinal pathway and in the intrinsic circuitry of the M1. However, because TMS is limited in that it cannot identify the precise location of synaptic modification following strength training, stimulating the axons of corticospinal fibres assists in identifying the level of synaptic modification. For example, cervicomedullary motor evoked potentials (CMEPs) can be generated subcortically via electrical stimulation at the cervicomedullary junction. When an electrical current passes through electrodes positioned on the mastoid processes, it evokes a descending volley which, like TMS, is captured using sEMG (Nuzzo et al. 2018). Importantly, because cervicomedullary stimulation is delivered inferior to the level of the M1, it is regarded as a measure of spinal excitability (Taylor and Gandevia 2004; Taylor 2006). By comparing changes in CMEP and MEP amplitudes following strength training, researchers are able to infer whether increases in excitability occur at a cortical or spinal level, or both.

7.3 MEPs are acutely facilitated following a strength training session There are several TMS studies that have examined changes in the excitability of the M1 following a single session of strength training (Selvanayagam et al. 2011; Hendy and Kidgell 2014; Brandner et al. 2015; Leung et al. 2015; Latella et al. 2017; Latella et al. 2018; Latella et al. 2018; Mason et al. 2019). Leung et al. (2015) showed that a single session of heavy-load elbow flexion strength exercise increased MEPs evoked by single-pulse TMS (Figure 7.1).

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Figure 7.1. Change in corticospinal excitability following a single bout of heavyloaded elbow flexions. MEP amplitude increased by 95% following the strength training. Adapted from Leung et al. (2015).

More recently, Latella et al. in 2017and 2018 reported increased MEP amplitude following a single session of both heavy-loaded and hypertrophybased strength training. However, in contrast, Latella et al. (2016) and Selvanayagam et al. (2011) reported reduced MEP amplitude following a single session of strength training. Because of these inconsistent findings, Mason et al. (2019) systematically examined the pooled effect of a single session of strength training on M1 excitability and confirmed that strength training increases M1 excitability (effect size, 1.26). Although the pooled effect was strong, there was a high degree of heterogeneity across studies. Nonetheless, the available data suggest that an acute bout of strength training increases M1 excitability.

7.4 Intracortical facilitation is acutely enhanced following a strength training session Beyond measuring the excitability of corticospinal excitability with singlepulse TMS, paired-pulse TMS is also capable of assessing intracortical facilitation (ICF), which estimates cortical excitability evoked by a conditioning stimulus followed by a test stimulus. There is now preliminary evidence to suggest that a single bout of strength training affects the excitability of the intracortical micro-circuitry of the M1 towards facilitation (Figure 7.2; Latella et al. 2016; Latella et al. 2017; Latella et al. 2018; Latella et al. 2018).

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Figure 7.2. Acute increase in the excitability of the intracortical facilitatory microcircuitry of the M1 following hypertrophy strength training. Hypertrophy training increased the size of the conditioned TMS induced MEP by 142%. Adapted from Latella et al. (2018).

This finding is now confirmed by systematic evidence that shows strength training specifically targets the facilitatory intracortical neurons of the M1 that use glutamate. Specifically, the acute responses to strength training revealed a large pooled effect for increased intracortical facilitation (effect size, 1.6).

7.5 Why does strength training increase corticospinal excitability and intracortical facilitation of the motor cortex? Recent data, from the systematic review by Mason et al. (2019), show that the overall pooled estimate obtained from the nine eligible studies reveals a large effect size (SMD, 1.26, 95% CI 0.88, 1.63, P < 0.0001) for increased MEP amplitude in the period immediately following a single session of strength training. This enhancement of MEP amplitude is reasonably consistent between studies and extends across a range of muscle groups that have been trained, including biceps brachii (Leung et al. 2015) and wrist flexors (Nuzzo et al. 2018). Although this finding is interesting, it remains unclear whether similar acute neural responses occur in the lower limb. In fact, to date, only one study has examined the acute neural responses

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following lower-limb strength training (Latella et al. 2017). Despite this, increased MEP amplitude is a consistent finding across different types of muscle actions, with both isometric (Nuzzo et al. 2016; Nuzzo et al. 2018), isotonic (Brandner et al. 2015; Leung et al. 2015; Latella et al. 2017), concentric and eccentric (Latella et al. 2018) strength training eliciting an increase. At a fundamental level, these findings show that there is a rapid increase in MEP amplitude following a single strength training session, which may simply be a transient response closely resembling those associated with motor learning (Butefisch et al. 2000). Interestingly, following a single bout of motor learning, MEP amplitude is also rapidly and transiently elevated (Cirillo et al. 2011) suggesting that neural processes associated with ‘early consolidation of learning a motor skill’ begin within M1 from the first exposure to a new task (Muellbacher et al. 2002). In novice strength trainers, first exposure to a loaded strengthtraining stimulus may also be akin to motor learning processes, with MEP amplitude increasing as an early ‘neural’ response in order to acquire and consolidate the strength-training task. In addition, the acute increase in MEP amplitude following a single session of strength training could also be a mechanism to attenuate muscle fatigue that is generated through the strength-training task (Latella et al. 2017; Latella et al. 2018). Strength training-induced fatigue is accompanied by a number of physiological responses which are known to modify the acute chemical environment, subsequently modulating changes in MEP amplitude and the intrinsic micro-circuitry of the M1 (Goodall et al. 2018). Strength training is also sufficient to induce increases in lactate which is associated with increased MEP amplitude (Coco et al. 2010). In addition to changes in MEP amplitude, strength training increases ICF showing that strength training is a clinically useful tool to modulate intracortical micro-circuits involved in motor control. Overall, strength training targets glutamatergic neuronal populations located specifically in the M1, revealing that the intracortical circuits of the M1 become facilitated, and this could be an important neural response that contributes to longerterm strength development (Di Lazzaro and Ziemann 2013). Change in the duration of the corticospinal silent period is a ‘primary’ mechanism that underlies motor performance (Kidgell and Pearce 2010). Increased corticospinal silent period duration reflects greater levels of inhibition in the corticospinal pathway, thus reducing neural drive to the trained muscles, with contributions arising from both cortical and spinal levels (Yacyshyn et al. 2016). Reductions in the duration of the

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corticospinal silent period have been observed to accompany changes in muscle strength following strength training (Latella et al. 2012; Christie and Kamen 2013; Hendy and Kidgell 2013; Mason et al. 2017; Mason et al. 2018). However, the evidence regarding changes in the duration of the corticospinal silent period following a single session of strength training is limited. Recent experimental findings show that the duration of the corticospinal silent period is reduced immediately following both heavyload and hypertrophy-based strength training (Figure 7.3, Latella et al. 2016; Latella et al. 2017; Latella et al. 2018); this is in conflict with earlier findings which suggest increases in corticospinal silent period duration occur throughout and immediately following a single session of strength training (Ruotsalainen et al. 2014).

Figure 7.3. The effect of hypertrophy strength training on the duration of the silent period, a measure of inhibition within the corticospinal pathway, mediated by GABA-B.

Similar to the corticospinal silent period, some studies have assessed the effect of strength training on short-interval cortical inhibition (SICI, Figure 7.4). There is now good evidence to show that SICI is reduced following a single session of strength training (Latella et al. 2012; Hendy and Kidgell 2014; Leung et al. 2015; Latella et al. 2016; Latella et al. 2017; Latella et al. 2018). These experiments reveal that, at a minimum, strength training increases the MEP amplitudes from the test response of a SICI-inducing protocol, confirming that strength training affects the excitability of the intracortical inhibitory micro-circuits of the M1. This clearly shows that strength training targets/recruits corticospinal cells that use both GABAA and GABAB which collectively act to reduce the withdrawal of descending inputs to the spinal

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motoneuron pool, which in turn increases motor unit activation (Griffin and Cafarelli 2007) via increased neural drive.

Figure 7.4. Changes in SICI following heavy-load strength training of the elbow flexors. There was a 34% reduction in inhibition, showing that strength training increases the size of the conditioned MEP, which shows that strength training reduces the excitability of the intracortical inhibitory neurons. Adapted from Leung et al. (2017).

Long-interval intracortical inhibition (LICI) can be assessed using longer inter-stimulus intervals of 50 to 200 ms, and is used to measure GABABmediated cortical inhibition within M1 (Opie et al. 2017). SICI and LICI are modulated by independent cell populations (Sanger et al. 2001) and could exhibit unique responses to strength training. Unlike measures of corticospinal excitability and inhibition, there is limited experimental data that have examined LICI following strength training. There are only three studies that have examined LICI following strength training (Latella et al. 2016; Latella et al. 2017; Manca et al. 2018). All three studies showed that the long-latency inhibitory networks remain unchanged following strength training. At this point, it is unclear how strength training may affect the long-latency inhibitory micro-circuits of the M1.

7.6 Long-term neuroplastic adaptations to strength training Longer-term strength training results in changes in muscle strength that are accompanied by adaptive alterations in the neuromuscular system, particularly during the early phases (i.e., 2-8 weeks of training) (Carroll et al. 2002; Duchateau and Enoka 2002; Jensen et al. 2005; Lee et al. 2009). Most of the available evidence for changes in the nervous system following

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strength training has been provided by studies that have used sEMG, evoked spinal reflex recordings, and via single motor unit recordings (Duchateau et al. 2006; Del Balso and Cafarelli 2007). Changes in the amplitude of the sEMG signal have, by default, been interpreted as increases in neural drive, therefore contributing to the increase in force production (Sale 1988; Griffin and Cafarelli 2007; Coombs et al. 2016). Measurement of evoked spinal reflexes, such as the Hoffman reflex and volitional drive (V-wave), have also been shown to increase following a period of strength training, possibly contributing to training-related increases in strength (Aagaard et al. 2002; Fimland et al. 2010). There are now many training studies that have used TMS to investigate the neural adaptations to strength training. In healthy untrained participants, Carroll et al. (2002) observed that strength training of a hand muscle increased muscle strength; however, corticospinal excitability remained unchanged at rest, but decreased significantly at higher force levels (50% of maximal voluntary contraction [MVC]). Similarly, Jensen et al. (2005) also reported a significant reduction in the size of the maximal MEP and slope of the stimulus-response curve at rest following four weeks strength training of the biceps brachii muscle in healthy untrained participants. Lee et al. (2009) observed that four weeks strength training of the wrist abductors did not modify the size of the TMS-evoked MEP. More recently, Coombs et al. (2016) showed that three weeks of wrist extensor strength training had no effect on corticospinal excitability, despite significant increases in muscle strength. However, in contrast, Griffin and Cafarelli (2007) observed a 32% increase in MEP amplitude following isometric strength training of the tibialis anterior. Other relatively recent contributions show that strength training of both the upper and lower limbs, paced to an audible metronome, increases MEP amplitude following isotonic strength training in untrained participants (Kidgell et al. 2010; Goodwill et al. 2012; Weier et al. 2012; Leung et al. 2017; Mason et al. 2017; Mason et al. 2019; Mason et al. 2019). Whilst the MEP amplitude has provided equivocal findings, changes in corticospinal inhibition appear to be more consistent. This suggests that an important neural adaptation that might underpin the rapid increase in strength following strength training could be a reduction in corticospinal inhibition. Several studies in both healthy untrained younger and older adults have reported that the duration of the cortical silent period is reduced following isometric and isotonic strength training (Kidgell and Pearce 2010; Hendy and Kidgell 2013; Latella et al. 2017; Mason et al. 2017; Latella et al. 2018; Mason et al. 2019). At the very least, these results are suggestive

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that strength training targets specific populations of intracortical neurons that are GABAB sensitive, which manifests as an increase in the activation of the spinal motoneuron pool of the trained muscle. In a similar manner, a reduction in SICI may also represent an important neural adaptation to strength training (Weier et al. 2012; Manca et al. 2018; Mason et al. 2019). For example, several strength-training studies have now reported reduced SICI, which shows that the amplitude of MEPs from the test response of the SICI inducing protocol is facilitated (Weier et al. 2012; Leung et al. 2017; Mason et al. 2019). Therefore, it is feasible that strength training affects the low-threshold inhibitory circuits that use the neurotransmitter GABAA, which reduces the efficacy of inhibitory circuits within the M1 (Kidgell et al. 2017).

7.7 Changes in strength following 2-8 weeks of strength training The early muscle-strength gains that occur after strength training predominantly involve neural adaptations; however, the exact site of adaptation within the nervous system has eluded scientists. Despite this, several experimental studies have reported changes in motor unit behavior, such as increased sEMG amplitude, increased discharge rate, doublet firing of motor units, reduced co-activation of antagonists and increased neural drive (i.e., changes in V-wave amplitude), representing neural changes at multiple points along the neuroaxis (Sale 1988). However, keeping in focus of the current chapter, the specific role of the M1 underpinning strength training remains more elusive (Kidgell et al. 2017). In an attempt to explain why strength increases in a rapid manner, there have been recent suggestions that the concept of use-dependent cortical plasticity of neural networks (see Chapter 4 for more details) may be similar between skill training and strength training (Leung et al. 2017; Mason et al. 2019) and that this may be the mechanism underpinning strength gain. As an example, neural adaptations have been shown to be the preliminary step that improves the acquisition of motor skills (Pascual-Leone et al. 1995). Particularly in humans, long-term potentiation (LTP) is considered to occur at existing synapses during the early stages of skill acquisition (Rosenkranz et al. 2007). In a similar manner, strength training also leads to LTP, and these changes in synaptic activity within the M1 underpin rapid strength gain (Kidgell et al. 2017; Siddique et al. 2020). However, to date, it is unclear whether the neural adaptations that occur following strength training involve a similar mechanism to skill training (Leung et al. 2017;

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Mason et al. 2019), which predominantly involves increased M1 excitability.

7.8 Long-term strength training does not affect motor threshold or MEP amplitude One potential site of neural adaptation that could underpin the rapid gain in muscle strength is the M1. Certainly, motor skills that require greater dexterity have consistently demonstrated cortical plasticity within the M1, with increased MEP amplitudes and a reduction in intracortical inhibition (Classen et al. 1998; Garry et al. 2004). Conversely, relatively few studies have examined the training-related M1 responses to strength training (Kidgell and Pearce 2010). Recent experimental evidence now confirms that long-term strength training only has a borderline affect for increasing M1 excitability (Kidgell et al. 2017). Unlike motor skill training, strength training does not alter motor threshold (Pascual-Leone et al. 1995), therefore, strength training does not affect membrane excitability of corticospinal neurons and interneurons within the M1. Given that motor threshold is a representative measure of synaptic efficacy of presynaptic neurons within the M1, the evidence suggests that strength training does not alter membrane excitability. Interestingly, Kidgell et al. (2017) systematically appraised the available evidence to show that strength training has no effect on overall M1 excitability. This finding is in contrast to the acute neural responses to strength training which strongly show there are specific neurons targeted during acute strength training. This systematic finding is interesting because there are several TMS strength-training studies that have reported increased M1 excitability (Griffin and Cafarelli 2007; Kidgell et al. 2010; Goodwill et al. 2012; Weier et al. 2012; Hendy and Kidgell 2013; Leung et al. 2017; Mason et al. 2017; Latella et al. 2018; Mason et al. 2019; Mason et al. 2019). However, when these data are pooled together, the overall effect on corticospinal excitability is inconsistent because of high levels of heterogeneity (Kidgell et al. 2017). For example, to date, four studies have reported increased M1 excitability, one study reported a decrease and the remaining 14 studies showed no change in corticospinal excitability, with the overall pooled effect being borderline (P = 0.054). This pooled effect, in essence, supports the previous inconsistencies in the literature. It seems that these inconsistent findings are mostly related to the different strengthtraining tasks performed during training (static vs. dynamic, tonic vs. ballistic, etc.), the duration of the training intervention, and/or different

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methodological techniques used to determine M1 excitability. The biggest issue seems to be related to TMS-evoked MEPs being assessed at rest, which are likely to be insensitive to detecting changes as the state of the M1 (increased excitability with increased force) is not being assessed under the same conditions in which the strength training is being performed. Based upon the current literature, the overall conclusion is that long-term strength training does not alter M1 excitability or the efficacy of neural transmission along the peripheral motor pathway.

7.9 Long-term strength training reduces motor cortex mediated inhibition Experimental evidence indicates that long-term strength training reduces the corticospinal silent period duration and SICI. Therefore, strength training reduces the synaptic efficacy of inhibitory networks within the M1 and corticospinal pathway, representing an important, reproducible neural adaptation to strength training (Kidgell et al. 2017). Because the duration of the silent period is modulated by GABAB-mediated inhibition (Yacyshyn et al. 2016) confined within the M1, strength training specifically targets intracortical inhibitory neurons that collectively act to increase neural drive to the spinal motoneurone pool, which increases muscle strength.

Figure 7.5. The area under the recruitment curve obtained prior to the strengthtraining intervention is shaded in grey. The additional area enclosed by the recruitment curve obtained following 3 weeks of strength training is the right biceps brachii patterned. Inhibition reduced by 17.5%, showing that strength training targets neurons in the M1 that use GABA-B. AMT, active motort hreshold. Adapted from Frazer et al. (2019).

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In addition to reductions in silent period duration, a reduction in SICI, a measure of GABA-ergic inhibition within intracortical micro-circuits of the M1, is also important for strength gain. It has been known for some time that cortico-cortical cells increase their discharge rate in a linear manner with increasing force production (Ashe 1997). It has been shown that the M1 is a critical determinant of muscle strength (Kidgell and Pearce 2010; Goodwill et al. 2012; Weier et al. 2012) because several experiments that have used a model of immobilization have reported an increase in corticocortical inhibition and a reduction in muscle strength (Clark et al. 2010; Clark et al. 2014); however, strength training seems to attenuate the prolongation of the cortical silent period (Pearce et al. 2013). At a minimum, strength training reduces cortical inhibition (cortical silent period and shortinterval intracortical inhibition) which releases the cortical representation of the trained muscles from inhibition and increases the subsequent excitatory drive to produce the intended movement (Floeter and Rothwell 1999). Overall, strength training causes a reduction in the responsiveness of intracortical inhibitory neurons because of training. It is likely strength training targets neurons that use GABAA and GABAB, which leads to reduced synaptic efficacy of their synapses onto corticospinal neurons, thus increasing the recruitment of cortico-motoneuron cells.

Figure 7.6. Significant decrease in strength (45%) and a significant increase in cortical inhibition (13.5%) following limb immobilization (Adapted from Pearce et al. 2013).

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7.10 Changes in spinal cord plasticity with strength training Reflex responses in the spinal cord can be produced by electrical stimulation of peripheral nerves. Such a technique has the potential to contribute important information about the sites and mechanisms of neural adaptation to strength training. There are two common reflexes that are evoked to determine the contribution of the spinal cord to increases in strength following a training period. Both the Hoffman reflex, or H-reflex, and volitional wave, or V-wave, are produced by electrical stimulation of a mixed nerve. Both reflexes are modulated by the monosynaptic circuit from 1a afferent fibres to motoneurons. When an electrical stimulus is applied to a mixed nerve, it produces an afferent volley that causes a synchronous discharge of the spinal motoneurons. With the H-reflex, this response gradually increases with stimulation intensity as the afferent volley increases in size, but, it progressively falls to zero as the intensity of electrical stimulation approaches levels that are supramaximal for activating motor axons. The V-wave is predominantly produced by supramaximal electrical stimulation that is applied during a voluntary contraction, usually a MVC. The cancellation of action potentials that diminish the H-reflex at high stimulus intensities is inhibited in some motoneurons by action potentials that are produced by volution during the MVC. Thus, in some motoneurons, a voluntary action potential will collide with the antidromic action potential that is produced by the electrical stimulus. In general, the overall size of the reflex will depend on the responsiveness of the motoneurons and the efficacy of synaptic transmission between the afferents and the motoneurons themselves. Although these reflexes do provide important information about the efficacy of the 1a afferent pathway and the output from the motoneuron pool during voluntary contraction, they are not without their limitations; however, there is evidence to suggest that strength training may alter this pathway.

7.11 Changes in H-reflex and V-wave amplitude following strength training Over the last 40 years, there have been several investigations into the underlying changes in reflex physiology following strength training. Over this time, there have been no reports of an increase in H-reflex amplitude at rest following strength training (Scaglioni et al. 2002; Holtermann et al. 2007; Duclay et al. 2008; Fimland et al. 2009; Fimland et al. 2009; Fimland et al. 2010). However, a handful of studies have reported increased V-wave

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amplitude in lower-limb muscles following strength training (Sale et al. 1983; Aagaard et al. 2002; Gondin et al. 2006; Ekblom 2010). H-reflex amplitudes recorded during background muscle activity are inconsistent following strength training. Some studies have reported increased H-reflex amplitudes (Lagerquist et al. 2006; Duclay et al. 2008), whilst other studies report that, it remains unaffected following strength training (Beck et al. 2007; Del Balso and Cafarelli 2007; Figure 7.7).

Figure 7.7. Effect of strength training on H-reflexes (Adapted from Siddique et al. 2020).

Increases in V-wave amplitude following strength training have been frequently cited as evidence of increased motoneuron activation. There has also been an inclination to attribute increases in motoneuron activation as an adaptation that occurs at a supraspinal level, particularly when V-wave changes are observed in parallel with H-reflexes (Aagaard et al. 2002). While an increase in neural drive to motoneurons is a plausible explanation for increased V-wave amplitudes, there are numerous alternative possibilities that should not be discounted (i.e., changes in the excitability of intrinsic neuronal circuits of the M1). Irrespective of this, there is evidence to suggest that strength training increases motoneuron activation by multiple mechanisms associated with increased descending neural drive (Figure 7.8; Siddique et al. 2020).

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Figure 7.8. Effect of strength training on V-wave amplitude (Adapted from Siddique et al. 2020).

7.12 Changes in motor unit activity following strength training The basic function of a motor unit is to transform synaptic input received by the motoneurons into mechanical output activity by skeletal muscle. It is not uncommon to measure the strength of a muscle as the peak force achieved during a maximal voluntary contraction. Any changes in MVC following a strength training intervention could be as a result of adaptations in the activation of the involved motor units. In studying the neural effects of strength training on motor unit activity, a common approach is to assess the maximum strength of a contraction using sEMG. The amplitude of the sEMG signal is inferred as a measure of motor-unit recruitment and firing rate (rate coding), with increases in sEMG amplitude and rate coding being an indication of neural adaptation to strength training. For several decades, many studies have reported increased sEMG amplitudes during maximum force production in untrained individuals (Hakkinen and Komi 1983; Häkkinen et al. 1993; Aagaard et al. 2002; Duchateau et al. 2006). Several lines of evidence also show that strength training increases the rate of rise of EMG amplitude (or average EMG) over the initial period of force production during ballistic contractions (Van Cutsem et al. 1998; Patten and Kamen 2000; Patten et al. 2001; Kamen and Knight 2004), suggesting increased neural drive and rate of force development (Aagaard et al. 2002). However, because there are significant limitations with the sEMG technique, it is likely that other central mechanisms account for the increases in EMG amplitude following strength training (Farina et al. 2014; Del Vecchio et al. 2019). One approach to overcome this limitation is to record single motor unit behaviour with indwelling EMG.

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7.12.1 Single motor unit behaviour following strength training Single motor unit behaviour can be recorded by using a fine-wire electrode that is inserted into the muscle of interest. The fine-wire electrode can record the discharge of identifiable single motor units because the fine wire is inserted in only a few muscle fibres. Therefore, it is able to provide information on the discharge characteristics of motoneurons in the spinal cord due to transmission of each action potential to the muscle being innervated. However, similar to sEMG, there are technical difficulties associated with single motor unit recordings but, despite this, there is good evidence to show that strength training alters the behaviour of single motor units. For example, Kamen and Knight (2004) reported a 33% increase in force production following six weeks of strength training the knee extensor. They reported increased motor unit discharge rates in both young (by 15%) and older adults (by 49%). This finding was also supported by Van Cutsem et al. (1988) following 12 weeks of ballistic strength training of the tibialis anterior. In this study, rate of force development and sEMG increased, which were associated with increases in the instantaneous discharge rate for the first four action potentials in single motor units. Although the recruitment order of motor units remained unchanged following training, the average instantaneous discharge rate increased from 69 to 96 pulses per second with training. Furthermore, strength training caused a significant increase in the number of motor units (from 5 to 33%) that discharged with brief interspike intervals (