Use of Transcranial Magnetic Stimulation to Measure Muscle Activation and Response to Exercise

Transcription

1 i Use of Transcranial Magnetic Stimulation to Measure Muscle Activation and Response to Exercise BY MIRIAM R. RAFFERTY B.A., Saint Mary s College of Maryland, 2003 D.P.T., Washington University in St. Louis, 2006 Board Certified Neurologic Physical Therapist Specialist, 2010 DISSERTATION Submitted as partial fulfillment of the requirements for the degree of Doctor of Philosophy in Neuroscience in the Graduate College of the University of Illinois at Chicago, 2015 Chicago, Illinois Defense Committee: Daniel Corcos, Advisor Sangeetha Madhavan, Chair Simon Alford T. George Hornby John Rothwell, University College London

2 This thesis is dedicated to my parents, Louise Pasternack Rafferty and F. Thomas Rafferty, whose overwhelming support of my early and higher education has made this work possible. ii

3 iii ACKNOWLEDGEMENTS I would like to thank my thesis committee, Drs. Daniel Corcos, Sangeetha Madhavan, John Rothwell, George Hornby, and Simon Alford, for their time and commitment during the completion of this thesis. Dr. Corcos, I thank you for all your support and guidance during my PhD. Your mentorship has provided me with valuable education and experiences that have helped me to succeed as a doctoral student, and which will serve me well in my future academic endeavors. Dr. Madhavan, thank you for sharing your equipment, providing your expertise in TMS, and helping me feel connected to Department of Physical Therapy at UIC. I would like to thank Dr. Rothwell for his time, expertise, and willingness to advise from a distance. I appreciate the opportunity to learn about TMS from one of the world s experts. I would like to thank Dr. Hornby for being my first research mentor and for inspiring me to become an independent researcher. Finally, I would like to thank Dr. Alford for being an excellent instructor who helped me to understand neuroscience from the molecular level up. Next, I would like to thank the lab members who have shared their office space, their wisdom, and experience. This includes, but is not limited, to Dr. Janey Prodoehl, Dr. Fabian David, Dr. Julie Robichaud, Dr. Cynthia Poon, Dr. Lisa Chin, Dr. Hyosub Kim, and Dr. Guillaume Lamotte. All of you have made excellent mentors and collaborators. I would also like to extend thanks to the many research interns who contributed to this work through their time spent in the labs of my mentors. Finally, this research would not have been possible without the willingness of my lab mates, class mates, and other students and friends who have willingly volunteered their time as research participants. I would also like to acknowledge the funding sources which have contributed to my success as a graduate student. I would like to thank the University of Illinois at Chicago (UIC)

4 iv ACKNOWLEDGEMENTS (continued) Graduate College for the University Fellowships which supported two years of my Ph.D. training ( , ). I would like to thank the National Institute of Mental Health, Training in the Neurosciences of Mental Health training grant ( ; PI Mark Rasenick). I would also like to thank the UIC Center for Clinical and Translational Science which funded two years of my training through the National Institutes of Health (NIH), National Center for Advancing Translational Sciences, center grant UL1TR ( ). I would also like to thank the Foundation for Physical Therapy for their additional support through the Promotion of Doctoral Studies (PODS) scholarships (PODS Level I , PODS Level II , PODS Level II ). Finally I would like to thank the past and current leaders and administrators of the UIC Graduate Program in Neuroscience for their support, including Dr. John Larson, Dr. James Unnerstall, Benn Williams, Perry Clark, and Deja Spikes. The content of this dissertation is solely the responsibility of the author and does not represent the official views of UIC, NIH, or the Foundation for Physical Therapy. Finally, I would like to thank my family and friends for providing me with love and support during the PhD process. My interest and confidence in pursuing higher... and higher... educational goals has been supported by generations of respect for graduate education and a love of learning. Last but not least, I would like to thank my fiancé, Jeffrey Snell, who has been unbelievably supportive of my personal and professional goals. MRR

5 v TABLE OF CONTENTS CHAPTER PAGE 1. INTRODUCTION Organization of the Dissertation Introduction to Transcranial Magnetic Stimulation Introduction to the First Dorsal Interosseous Muscle Use of TMS as a Measurement Tool TMS as a Force-Based Measure of Muscle Activation TMS as a Measure of Corticomotor Excitability TMS as a Measure of Intracortical Inhibition TMS as a Measure of Intracortical Facilitation Inter-Individual and Intra-Individual Variability of TMS Measures Reliability of TMS Measurement Sensitivity of TMS Measures to Quantify Change Following Exercise Summary of Chapter One REPRODUCIBILITY, SENSITIVITY, AND VALIDITY OF MEASURING MUSCLE ACTIVATION IN THE FIRST DORSAL INTEROSSEOUS MUSCLE USING PERIPHERAL NERVE AND TRANSCRANIAL MAGNETIC STIMULATION Introduction Materials and Methods for Experiment One Participants Experimental Protocol Peripheral Nerve Stimulation Transcranial Magnetic Stimulation Force Recording and Processing Electromyography Recording and Processing Statistical Analysis Results of Experiment One Reproducibility of Force-Based Muscle Activation Measures during Maximal and Submaximal Contractions Summary of Force-Based Muscle Activation Estimates during Maximal Contractions Sensitivity of Twitch Amplitudes and Muscle Activation Estimates during Submaximal Contractions EMG-Based TMS Measures during Twitch Interpolation Summary of Experiment One Introduction to Experiment Two Materials and Methods of Experiment Two Participants Experimental Protocol Force and EMG Recording and Processing Statistical Analysis Results of Experiment Two Sensitivity of Twitch Amplitudes and Muscle Activation Estimates with TMS Analysis of Evoked Potentials using EMG... 40

6 vi TABLE OF CONTENTS (continued) CHAPTER PAGE 2.8. Summary of Experiment Two Chapter Two Discussion Anatomical, physiological, and technical limitations of the twitch interpolation technique in the FDI with PNS and TMS Implications for future applications of twitch interpolation in the FDI DIFFERENTIAL EFFECTS OF MODERATE AND HIGH INTENSITY EXERCISE ON CORTICOMOTOR EXCITABILITY, INTRACORTICAL INHIBITION AND INTRACORTICAL FACILITATION Introduction Materials and Methods Participants Electromyography Transcranial Magnetic Stimulation Exercise Sessions Self-Reported Exercise Behavior Statistical Analysis Results Participants and Exercise Sessions Effect of moderate and high intensity treadmill walking on TMS measures Covariates that mediate the changes in TMS measures Correlations between changes in physiologically related measures Discussion Exercise-induced changes in corticomotor excitability and neurotransmitter activity Exercise-induced changes in intracortical inhibition and neurotransmitter activity Exercise-induced changes in intracortical facilitation and neurotransmitter activity Possible contributors to the differential effects of moderate and high intensity treadmill walking Mediators of change in intracortical inhibition and intracortical facilitation Proposed impact of acute exercise on exercise training and neural plasticity Conclusion of Chapter Three CONCLUSIONS AND SUGGESTIONS FOR FUTURE RESEARCH Summary and Conclusions of Chapter Two Suggestions for Future Research using TMS to Measure Specific Changes in Corticomotor Physiology Following Exercise Summary and Conclusions of Chapter Three Suggestions for Future Studies using TMS to Measure Global Changes in Corticomotor Physiology Following Exercise... 95

7 vii TABLE OF CONTENTS (continued) CHAPTER PAGE LITERATURE CITED APPENDICES APPENDIX A: Institutional Review Board University of Illinois at Chicago APPENDIX B: Institutional Review Board Northwestern University VITA

8 viii LIST OF TABLES TABLE PAGE 2.1. Methods to estimate muscle activation using the twitch interpolation technique Summary of maximal muscle activation measure results Unadjusted mean TMS measures before and after treadmill walking Summary of covariance effects tested in full model Comparison of ANCOVA models for LICI, first reduced to exclude the nonsignificant interaction terms, reduced further to exclude the non-significant past participation in high intensity exercise Correlations between changes in physiologically-related transcranial magnetic stimulation measures Correlations between changes in paired pulse transcranial magnetic stimulation measures and corresponding change in test stimulus intensity 74

9 ix LIST OF FIGURES FIGURE PAGE 2.1. The experimental setup including electrode placement and hand stabilization technique. Abbreviations: ADM, abductor digiti minimi; ECRB, extensor carpi radialis brevis; EMG, electromyography; FDI, first dorsal interosseous Experiment one twitch amplitude and muscle activation estimates across contraction strengths. (A) Superimposed twitch amplitude is presented a percent of control MVC with PNS (solid line) and TMS (dashed line). (B) CAR, an estimate of voluntary activation (%) is presented across voluntary force levels for PNS and TMS. VA was estimated with PNS (solid square) and TMS (open square) only during maximal voluntary contractions (part b, right side). Data represent the average of test session 1 and 2 for all participants and are presented as ± 1 standard error Single subject twitch amplitude data. Single subject data from two participants in experiment one demonstrates inter-individual variability in the patterns of twitch amplitudes evoked by PNS (A & B) and TMS (C & D). Data from test session 1 (solid diamonds) and test session 2 (open squares) are shown. During analysis with TMS, a linear regression is performed with data at 50%, 75%, and 100% MVC (shaded bars), as described by Todd and colleagues (2003). The y-intercept of the linear regressions for test sessions 1 (solid line) and 2 (dashed line) is the estimated resting twitch used in VATMS calculation Experiment one TMS evoked potentials measured during the twitch interpolation technique. (A) Amplified MEP traces in one participant in the FDI throughout the range of voluntary force levels. Abductor digiti minimi (ADM) MEPs are presented for comparison. (B) FDI MEP areas as a percent of FDI M-wave areas across voluntary force levels are presented for both test sessions. Data represents average data for all participants ± 1 standard error EMG-Based TMS Measures. (A) Amplified traces of motor evoked potentials (MEP) in the first dorsal interosseous (FDI) muscle in a representative participant through the range of voluntary force levels. Abductor digiti minimi (ADM) MEPs are presented for comparison. (B) FDI MEP areas as a percent of FDI M-wave areas across voluntary force levels are presented for both test sessions. Data represents average data for all participants ± 1 standard error

10 x LIST OF FIGURES (continued) FIGURE PAGE 2.6. Voluntary and evoked contributions of six upper limb muscles during maximal force generation with the FDI. During strong FDI contractions (solid line, solid square), there were moderate contractions of the FCR (dashed line, open square) and ECR (solid line, open triangle) with weak contractions of the ADM (dashed line, closed square), brachioradialis (dotted line, closed circle) and biceps (solid line, open circle) under both (A) TMS120 and (B) TMS150 stimulation conditions. Large MEPs occurred in the FCR with both (C) TMS120 and (D) TMS150 conditions. Moderately sized MEPs occurred in the ADM and ECF. Abbreviations: ADM, abductor digiti minimi; Biceps, Biceps Brachii, Br Rad, Brachioradialis; ECR, extensor carpi radialis; EMG, electromyography; FCR, flexor carpi radialis; FDI, first dorsal interosseous; Max, maximum; TMS, transcranial magnetic stimulation Least square means adjusted change in four measures of corticomotor excitability following moderate and high intensity treadmill walking: (A) stimulus intensity for resting motor threshold (RMT), (B) the stimulus intensity required to elicit a 1 mv motor evoked potential (MEP), (C) MEP amplitude tested at 120% RMT with the muscle at rest, and (D) MEP amplitude tested at 120% RMT with weak muscle activity. * Indicates a significant change from baseline at that intensity or a significant difference between moderate and high intensity treadmill walking at p Note: the Y-axis scale for A and B have been reversed so that increased excitability is up Least square means adjusted change in four measures of intracortical inhibition following moderate and high intensity treadmill walking: (A) Cortical silent period (CSP) duration, and the paired pulse measures in a resting muscle of (B) longlatency intracortical inhibition (LICI), (C) short-latency intracortical inhibition (SICI) with a 70% RMT conditioning stimulus (CS) intensity, and (D) SICI with an 80% RMT conditioning stimulus intensity. * Indicates a significant change from baseline at that intensity or a significant difference between moderate and high intensity treadmill walking at p Note: the Y-axis scale for B, C, and D have been reversed so that increased inhibition is up Least square means adjusted change in two measures of intracortical facilitation following moderate and high intensity aerobic treadmill walking: (A) intracortical facilitation (ICF) measured with an interstimulus interval of 12 ms and (B) and short-latency intracortical facilitation (SICF) measured with an interstimulus interval of 1.5 ms. * Indicates a significant change from baseline at that intensity or a significant difference between moderate and high intensity treadmill walking at p

11 xi LIST OF FIGURES (continued) FIGURE PAGE 3.4. Post Hoc Analysis Demonstrating Change Following Exercise at Different Baseline Values of RMT, SICI, and SICF. Least square means adjusted change in (A-C) resting motor threshold (RMT), (D-F) short-latency intracortical inhibition (SICI) with a conditioning stimulus intensity of 70% RMT, and (G-I) shortlatency intracortical facilitation (SICF). * Indicates significance at p Note: the Y-axis scale for A-F have been reversed so that increased excitability and inhibition are up

12 xii LIST OF ABBREVIATIONS ADM AMPA AMT ANCOVA Br Rad CAR CS CSP DA ECRB EMG FCR FDI GABA HR max ICF ISI LICI Mmax Max ms mv Abductor Digiti Minimi α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid Active Motor Threshold Analysis of Covariance Brachioradialis Central Activation Ratio Conditioning Stimulus Cortical Silent Period Dopamine/Dopaminergic Extensor Carpi Radialis Brevis Electromyography Flexor Carpi Radialis First Dorsal Interosseous γ-aminobutyric acid Maximal Heart Rate Intracortical Facilitation (the measure) Inter-stimulus Interval Long-latency Intracortical Inhibition Maximum M-Wave Maximum Millisecond Millivolt

13 xiii LIST OF ABBREVIATIONS (continued) MVC MEP MSO NA NMDA PNS RM RMS RMT SD SE SICF SICI TMS TMS120 TMS150 TS V VA VO2 max Maximal Voluntary Contraction Motor Evoked Potential Maximum Stimulator Output Noradrenaline/Norepinephrine/Noradrenergic N-methyl-D-aspartate Peripheral Nerve Stimulation Repeated Measures Root Mean Square Resting Motor Threshold Standard Deviation Standard Error Short-latency Intracortical Facilitation Short-latency Intracortical Inhibition Transcranial Magnetic Stimulation Transcranial Magnetic Stimulation at 120% Resting Motor Threshold Transcranial Magnetic Stimulation at 150% Resting Motor Threshold Test Stimulus Volts Voluntary Activation Maximal Volume of Oxygen Consumed µv Microvolt

14 xiv SUMMARY Exercise training is known to confer neurological benefits, although the underlying mechanisms of exercise and the ability of different types of exercise to maximize the benefit of exercise training are not completely understood (Matta Mello Portugal et al. 2013, McDonnell, Smith, and Mackintosh 2011). Mechanistic rehabilitation research requires the use of measurement tools that can provide insight into the therapeutic effects of exercise on central nervous system operations. The experiments reported in this dissertation use transcranial magnetic stimulation (TMS) to measure neurophysiological changes following exercise interventions. Experiments described in chapter two investigate the use of force-based TMS measures to estimate muscle activation with increasing contraction intensity. Force-based TMS measures can be used to measure neural adaptations to strength training in the specific muscle that was trained (Kidgell and Pearce 2011, Carroll, Riek, and Carson 2001a, Carroll et al. 2009). Chapter three examines the use of traditional TMS measures, which utilize surface electromyogram (EMG) recordings, to measure global changes in corticomotor physiology following a single bout of moderate or high intensity treadmill walking (Singh and Staines 2015). This experiment investigates changes occurring in a muscle distant from the type of exercise performed as a measure of global change in corticomotor physiology immediately following exercise. Taken together, these experiments support the use of EMG-based TMS measures to document neural changes following exercise, and chapter three further demonstrates that changes in corticomotor physiology immediately following exercise may depend on exercise intensity. Chapter two describes the use of the twitch interpolation technique in the first dorsal interosseous (FDI) muscle, comparing TMS with peripheral nerve stimulation (PNS). The twitch interpolation technique measures the extent to which central neural drive is translated into muscle

15 xv SUMMARY (continued) force. Force-based TMS measures included superimposed twitch amplitudes, central activation ratios (CAR), and voluntary activation (VA). Two experiments were performed to investigate whether these measurements were reproducible, sensitive to change, and valid methods of estimating muscle activation in the FDI. The measures were reproducible. However, CARPNS was greater than 96% for all contractions greater than 75% MVC, indicating that activation estimates with PNS would not be sensitive to change during strong contractions. Twitch amplitudes were larger with TMS than PNS, which led to low and variable VATMS estimates (68.4 ± 27.5%) compared to the other maximal activation estimates (>93% activation), indicating that VATMS was not validly capturing the same measurement as PNS. The second part of this experiment was designed to determine if use of a lower TMS stimulation intensity would improve validity of force-based TMS measures in the FDI, but we could not support the use of force-based TMS measures in the FDI with any stimulation intensity. In contrast, the EMG-based TMS measures were reproducible and increased as expected with increased contraction strengths. Taken together, we conclude that force-based TMS measures may lack validity due to excess force generated by synergistic muscles during strong contractions. These limitations did not extend to EMG-based TMS measures. Chapter two concludes with a discussion of the anatomical, physiological, and technical limitations of estimating muscle activation in the FDI with PNS or TMS. Chapter three focuses on the use of EMG-based TMS measures to test the effect of moderate and high intensity treadmill walking on global corticomotor physiology. Single bouts of moderate intensity aerobic cycling have been shown to have an immediate effect on intracortical inhibition and intracortical facilitation measured with TMS during the 30 minutes following exercise (Singh et al. 2014, Smith et al. 2014). The experiment described in chapter three

16 xvi SUMMARY (continued) examines whether a bout of treadmill walking led to immediate changes in TMS measures, and whether the response was sensitive to exercise intensity. Covariates, including prior experience with high intensity exercise and baseline inter-individual variability of TMS measures, were examined. Twenty-two participants exercised on a treadmill for 30 minutes. They walked on an incline at a brisk pace with the intensity targeted to 65% and 80% of age-predicted maximum heart rate on two, non-consecutive days. They were tested with single and paired pulse TMS before and after exercise. Data were analyzed with repeated measures analysis of covariance. Following moderate intensity treadmill walking, corticomotor excitability increased as measured by the motor evoked potential (MEP) amplitude, intracortical inhibition increased as demonstrated by a lengthened cortical silent period (CSP) duration, and short-latency intracortical facilitation (SICF) increased. Following high intensity walking, corticomotor excitability decreased as demonstrated by increased stimulus intensity required to elicit a 1 mv MEP, longlatency intracortical inhibition (LICI) decreased, and SICF decreased. There were no changes in short-latency intracortical inhibition following either walking intensity. These differences were not mediated by past participation in high intensity exercise, but baseline variability in TMS values was a significant covariate for active MEP amplitude, all measures of intracortical inhibition, and all measures of intracortical facilitation. The response following moderate intensity treadmill walking was a net gain in corticomotor excitability and facilitation, which could put the brain into a more plastic state. The differential effect following high intensity treadmill walking could indicate an immediate decrease in neural plasticity. The apparent contrast in cortical excitability induced by moderate and high intensity treadmill walking could be due to U-shaped relationships between exercise intensity and specific neurotransmitter activation patterns, cortisol, or cerebral blood flow.

17 1 1. INTRODUCTION 1.1. Organization of the Dissertation Exercise is known to confer neurological benefits, although the underlying mechanisms of the effect of exercise, the types of exercise, and dose of exercise to maximize the benefits of exercise are not completely understood (Matta Mello Portugal et al. 2013, McDonnell, Smith, and Mackintosh 2011). Thus, rehabilitation research requires the use of measurement tools that can provide insight into the therapeutic effects of exercise on the central nervous system. Transcranial magnetic stimulation (TMS) is one such neurophysiological measurement tool. The purpose of this dissertation is to investigate the use of TMS as a means to probe corticomotor physiology during the production of muscle force and also following a bout of aerobic exercise. TMS can be used to measure the ability to voluntarily activate a muscle producing the desired force through central neural drive, as well as aspects of corticomotor excitability, intracortical facilitation, and intracortical inhibition within the motor system. Chapter one provides the background of what is known about the use of TMS to probe corticomotor physiology. Chapter two describes two experiments that were performed using TMS to estimate voluntary activation. Estimations of voluntary activation rely on measurement of the force produced in response to TMS using the twitch interpolation technique. These experiments describe the reproducibility, sensitivity, and validity of different methods of estimating voluntary muscle activation in the FDI. Chapter three describes an experiment performed to measure changes in corticomotor physiology in response to different doses of aerobic exercise using TMS. This study compares changes in single and paired pulse TMS measures immediately following bouts of moderate and high intensity aerobic exercise. Finally,

18 2 chapter four summarizes the findings of this dissertation and provides recommendations for future exercise research studies using TMS Introduction to Transcranial Magnetic Stimulation Transcranial magnetic stimulation (TMS) is a neurophysiological research technique that relies on the principles of electromagnetic induction. TMS equipment stores an electrical charge on a capacitor, and when the charge is released, the current travels around an electrical coil that is held over the scalp. The electrical current creates a magnetic field that is perpendicular to the coil. The magnetic field passes painlessly through the skull, causing current to flow in the brain tissue. When the stimulation reaches a threshold intensity, it leads to action potentials in the underlying neurons (Rossi et al. 2009). In the experiments of this dissertation, the coil was positioned over the primary motor cortex; therefore, the action potentials traveled from the motor cortex, down the corticospinal pathway to the contralateral spinal motor neurons, and then to the muscle of interest. Surface electromyography (EMG) is used record the response of the target muscle, as well as other nearby muscles. Visual inspection of the EMG signal from the target muscle and control muscle(s) is used to guide TMS to the area of the primary motor cortex controlling the muscle of interest (Chipchase et al. 2012). This hotspot is defined as the coil position that produces the largest response in the muscle of interest, with minimal activation of other muscles. The EMG responses are known as the motor evoked potential (MEP). During offline processing, the size of the MEPs are measured and averaged across trials. The size of the MEP reflects the excitability of the entire corticomotor pathway: motor cortex, corticospinal pathway, spinal motor neuron, and muscle. The size of the MEPs and the conditions under which they are elicited also provide information regarding cortical processes thought to be mediated by intracortical interneurons, including intracortical inhibition and intracortical facilitation.

19 3 Different stimulation conditions include the level of muscle contraction, as well as whether the stimulus is preceded by a conditioning stimulus. In addition to the MEP measured by EMG, TMS also causes force to be produced by the targeted muscle. Chapter two investigates the force produced by TMS, while chapter three focuses on measurement of corticomotor excitability, intracortical inhibition, and intracortical facilitation using the EMG signal. Both experiments use of the first dorsal interosseous muscle (FDI) as the target muscle Introduction to the First Dorsal Interosseous Muscle The first dorsal interosseous (FDI) muscle was chosen as the target muscle for the experiments in this dissertation because the FDI has three characteristics that make it a good model muscle for testing. First, the FDI has strong corticomotor connections to the muscles of the hand due to the requirements of fine motor control in the hand muscles. The strong corticomotor connections result in a large cortical representation of FDI, which simplifies the process of locating the hotspot for the FDI and results in a lower threshold for stimulation. The large hotspot and lower threshold for stimulation enable the setup of TMS to be quicker and more comfortable for most participants. A second reason that the FDI was chosen was due to the ease of recording evoked responses (Zijdewind and Kernell 1994). The position of the FDI makes it easy to localize, and the electrodes can be placed with minimal risk of recording crosstalk from nearby muscles. Third, the FDI is the primary index finger abductor, thus cortical drive to voluntarily abduct the index finger will be directed primarily to the FDI muscle. The ease of isolating FDI contractions, as well as eliciting and recording responses in the FDI makes it an ideal target for future clinical trials. These characteristics reduce the burden of time and reduce the risk of error during experimental set up.

20 Use of TMS as a Measurement Tool Transcranial magnetic stimulation is increasingly being used in rehabilitation research as a measurement of corticomotor physiology (Abbruzzese and Trompetto 2002, Ziemann et al. 2014), cortical connectivity (Taylor, Walsh, and Eimer 2008, Chrysikou and Hamilton 2011), or as a treatment tool (Rossi et al. 2009, Machado et al. 2008). This dissertation will focus on the use of TMS as a measurement tool of corticomotor physiology, or measurement of the communication between the brain and the muscles via the corticospinal tract, and the inhibitory and facilitatory interneuron networks which act upon the corticospinal neurons. Mapping the motor cortex and measuring interhemispheric connectivity, sensorimotor connectivity, and cognitive-motor connectivity are outside the scope of this dissertation. The four main categories of corticomotor physiology that will be discussed in this dissertation include: (1) force-based measures of muscle activation, and EMG-based measures of (2) corticomotor excitability, (3) intracortical inhibition, and (4) intracortical facilitation. The first of these four measures, force-based measures of muscle activation relies on measuring the force generated by the stimulus. Section provides a basic definition and description of the force-based measures of muscle activation. Measures of corticomotor excitability, intracortical inhibition, and intracortical facilitation are all EMG-based measures, which are performed with single and paired pulse TMS. Sections through define and introduce these measures and their mechanisms as currently understood. EMG-based TMS measures are more common. The foundational building block of the EMG-based measures is the MEP, a stereotypical response to TMS. The MEP provides information about the combined output of the motor cortex, spinal motor neuron, and muscle (Kobayashi and Pascual-Leone 2003). The size of the MEP can be modulated by inhibitory and facilitatory intracortical interneuron networks. Information about the MEP, including its size and the methods by which it

21 5 can be inhibited or facilitated have been used to provide information regarding disease processes (Lefaucheur 2005, Madhavan et al. 2011, Stinear et al. 2007, MacKinnon et al. 2005). Measuring MEPs can also provide information about neurotransmitter activity based on pharmacological studies (Ziemann et al. 2014) TMS as a Force-Based Measure of Muscle Activation The force-based measure of muscle activation is measured using a technique known as twitch interpolation. The twitch interpolation technique measures how neural drive to a muscle is translated into force, which can then be translated into a percent of full muscle activation (Merton 1954, Kamen 2004, Taylor 2009). Twitch interpolation studies refer to their findings as muscle activation, although many researchers may prefer to reserve the term muscle activation for EMG-based measures. For the purpose of this dissertation, the term muscle activation will refer to force-based measures of muscle activation obtained using the twitch interpolation technique. The original study of twitch interpolation evaluated maximal force generating capacity using peripheral nerve stimulation (PNS) in the adductor pollicis muscle (Merton 1954). Twitch interpolation has been adapted to estimate the extent of muscle activation during maximal and submaximal contractions (Kalmar and Cafarelli 2004, Kent-Braun 1997, Lee, Gandevia, and Carroll 2009). In larger muscle groups, or when nerve stimulation is very uncomfortable, such as when stimulating the femoral nerve to activate the quadriceps muscle, twitch interpolation has been adapted to be performed with direct stimulation of the muscle (Stevens-Lapsley, Kluger, and Schenkman 2012). Most pertinent to this dissertation, the twitch interpolation technique has also been modified to be performed with TMS (Todd, Taylor, and Gandevia 2003), which can produce less subjective discomfort than both PNS and muscle stimulation. However, performing

22 6 twitch interpolation with TMS, remains a relatively new technique, which is less common than using the EMG-based measures discussed below. The twitch interpolation technique, performed with any stimulation technique, quantifies how voluntary contractions are translated into force. It compares voluntary force with the force generated by the addition of superimposed stimuli (Merton 1954, Kamen 2004, Taylor 2009). Traditionally, twitch interpolation tests whether a supramaximal stimulus, delivered during a maximal voluntary contraction (MVC), generates force above what the individual generates volitionally. If an individual is able to achieve full maximal voluntary muscle activation, the superimposed stimulus will fail to generate additional force during a MVC. If additional force, or a twitch, is produced by the superimposed stimulus, it suggests that the individual s motor units were not fully activated due to incomplete motor unit recruitment and/or submaximal firing frequency (Belanger and McComas 1981). Well-motivated, healthy individuals achieve near maximal activation of their upper and lower limb muscles during isometric MVCs when measured with both PNS and TMS (Kalmar and Cafarelli 2004, Thomas, Woods, and Bigland- Ritchie 1989, Sidhu, Bentley, and Carroll 2009). In contrast, individuals with neuromuscular disorders demonstrate deficits in force-based measures of muscle activation when measured with the twitch interpolation technique (Stevens-Lapsley, Kluger, and Schenkman 2012, Moreno Catalá, Woitalla, and Arampatzis 2013, Madhavan et al. 2011, Steens et al. 2012, Hornby et al. 2009, Thompson et al. 2011, Machner, Pap, and Awiszus 2002). Performing the twitch interpolation technique with both a peripheral form of stimulation and with TMS can provide information regarding the locus of the force generation deficit (Todd, Taylor, and Gandevia 2003). For example, stimulating with TMS during an MVC did not elicit superimposed twitches in people with incomplete spinal cord injury, but PNS elicited sizable twitches (Thomas et al. 1997). These data were interpreted as evidence that people with

23 7 incomplete spinal cord injury maximally drive their higher motor centers, while motor neuron and muscular contributions to volitional force generation were impaired (Thomas et al. 1997). Conversely, individuals with supraspinal impairments in corticomotor output, such Parkinson s disease or Stroke, would demonstrate incomplete muscle activation when measured with both TMS and peripheral stimulation (Stevens-Lapsley, Kluger, and Schenkman 2012, Madhavan et al. 2011). Data suggesting impairments in neurologic patient populations opens the question of whether interventions targeted to improve corticomotor output to a specific muscle group, such as strength training, could improve measures of muscle activation in that muscle. Since the original study in the adductor pollicis (Merton 1954), PNS-induced twitch interpolation has been used in many other muscle groups, including the first dorsal interosseous (FDI) (Thomas, Woods, and Bigland-Ritchie 1989, Kalmar and Cafarelli 2004), the triceps (Thomas et al. 1997), the soleus (Kalmar, Del Balso, and Cafarelli 2006), and the dorsiflexors (Kent-Braun and Le Blanc 1996). Twitch interpolation has been validated using TMS in the adductor pollicis (Herbert and Gandevia 1996), biceps brachii (Todd, Taylor, and Gandevia 2003, 2004), triceps (Thomas et al. 1997), wrist extensors (Lee, Gandevia, and Carroll 2008), and knee extensors (Sidhu, Bentley, and Carroll 2009). Of note, twitch interpolation has been used experimentally in the FDI with PNS, but not with TMS (Thomas, Woods, and Bigland- Ritchie 1989, Kalmar and Cafarelli 2004). Therefore the purpose of chapter two will be to determine whether TMS-induced twitch interpolation is a reproducible, sensitive, and valid technique to measure muscle activation in the FDI, a muscle that is important for hand function, including grip and dexterity. Chapter two will explore several methods that have been developed to quantify muscle activation, or force generating capacity, using the twitch interpolation technique. The first measure is to quantify muscle activation through the superimposed twitch amplitude itself

24 8 (Merton 1954, Lee, Gandevia, and Carroll 2009, Herbert and Gandevia 1996, Carroll et al. 2009). Twitch amplitude can also be converted to an estimate of muscle activation. Two such estimates are the Central Activation Ratio (CAR) (Kent-Braun and Le Blanc 1996) and Voluntary Activation (VA) (Thomas, Woods, and Bigland-Ritchie 1989). CAR is a formula that relies on the ratio of the background contraction force to the total force with the superimposed stimulation (Kent-Braun 1997). VA is a formula that relies on the ratio of the superimposed twitch amplitude to the size of a potentiated resting twitch (Thomas, Woods, and Bigland-Ritchie 1989). Although the CAR formula does not depend on the type of stimulation used, the VA formula differs depending on whether it is derived from PNS or TMS. Todd et. al. (2003) developed the formula used to estimate VA with TMS (VATMS) that takes into account the reduced twitch size at rest, due to low corticospinal excitability at rest when tested with TMS compared to PNS. The VATMS formula estimates the resting twitch based on the linear relationship of twitch amplitude to background force at 50%, 75%, and 100% MVC (Todd, Taylor, and Gandevia 2003). This formula has been tested and validated in the elbow flexors (Todd, Taylor, and Gandevia 2003, 2004), knee extensors (Sidhu, Bentley, and Carroll 2009) and wrist abductors (Lee, Gandevia, and Carroll 2009). Chapter two will provide examples of these force-based measures in the FDI TMS as a Measure of Corticomotor Excitability The two primary measures of corticomotor excitability are the motor threshold and the MEP size. These measures are thought to represent the composite excitability of the motor cortex, the spinal cord, the alpha motor neurons, and the muscle itself. Each of these measures can be quantified at rest or with muscle activity.

25 9 The resting motor threshold (RMT) is the most basic unit of corticomotor excitability. It is typically defined as the lowest stimulation intensity that is required to elicit a peak-to-peak MEP amplitude of 50 µv in the target muscle, when the target muscle is at rest (Ziemann et al. 2014). Active motor threshold (AMT), is the lowest stimulation intensity required to elicit a very small MEP, approximately 200 µv, when the target muscle is contracting weakly, generally 5-10% of the MVC. The AMT is found at a lower stimulus intensity than the RMT due to the increased excitability in an active corticomotor pathway, compared to low corticomotor excitability at rest. For example, one study determined the average RMT to be 47.2% of the maximum stimulator output (MSO) in their study of 13 individuals, while the average AMT was 32.5% MSO (Smith et al. 2014). Thus, the AMT was approximately 31% lower than the RMT. The difference between RMT and AMT highlights the phenomenon of contraction-induced facilitation. The difference between resting and weak muscle contractions is large, and the degree of facilitation decreases as the muscle contractions become stronger and then levels off with strong contraction strengths (Valls-Solé et al. 1994). When measured with TMS, increased corticomotor excitability due to contraction-induced facilitation occurs due to changes within both the motor cortex and the spinal cord (Hess, Mills, and Murray 1987). However, studies of cervico-medullary stimulation suggest that the majority of this facilitation occurs within the spinal cord (Oya, Hoffman, and Cresswell 2008, Giesebrecht et al. 2011). The mechanisms of the motor threshold relate to the underlying excitability of portions of the corticomotor pathway, which propagate the action potentials elicited by TMS. At threshold, TMS is thought to stimulate the pyramidal corticospinal neurons trans-synaptically, meaning that the stimulus first generates action potentials in the small diameter axons of intracortical interneurons, which then synapse onto the corticospinal neurons. The action potential is then propagated down the corticospinal tract, to the alpha motor neuron, and out to the muscle. The

26 10 underlying excitability of this system is governed by voltage gated sodium channels. Drugs that block voltage gated sodium channels increase motor threshold, indicating reduced corticomotor (Boroojerdi et al. 2001, Ziemann et al. 2014). The excitability of the motor threshold also relies on fast excitatory synaptic neurotransmission within the corticomotor pathway. This synaptic transmission is primarily governed by the ionotropic glutamatergic α-amino-3-hydroxy-5- methyl-4-isoxazolepropionic acid (AMPA) receptors. Facilitating AMPA receptors decreases motor threshold, indicating increased corticomotor excitability at threshold (Di Lazzaro et al. 2003, Ziemann et al. 2014). Thorough reviews on the topic have determined that other neurotransmitters and neuromodulators have inconsistent or no effects on motor threshold (Ziemann et al. 2014, Paulus et al. 2008). In studies that compare MEP sizes, MEPs are elicited using a suprathreshold stimulation intensity that is relative to the motor threshold, for example 120% of the resting motor threshold. The size of the MEP is most commonly presented as peak-to-peak MEP amplitude, but can also be presented as MEP area. MEP areas are most useful in the presence of compound MEPs that have multiple peaks. MEP area is the method of quantifying MEP size in the twitch interpolation studies since these studies are performed with muscle activation and with high stimulation intensities, which frequently lead to compound MEPs (Todd, Taylor, and Gandevia 2003). The size of the MEP is either presented as a single value at one stimulation intensity, or as a recruitment curve that compares MEP amplitudes across multiple stimulation intensities. As stimulation intensity increases, MEP amplitudes of hand muscles increase in a sigmoid fashion (Hess, Mills, and Murray 1987, Boroojerdi et al. 2001). In addition to comparing MEP amplitudes at any given suprathreshold stimulus intensity, one can also quantify the slope of the recruitment curve or the maximum MEP amplitude elicited by TMS (Boroojerdi et al. 2001).

27 11 The mechanisms of suprathreshold corticomotor excitability, as measured by MEP amplitude, are influenced by mechanisms that are different from the mechanisms that influence corticomotor excitability near threshold. Mechanistic studies have demonstrated that when MEP amplitude is measured at, or just above TMS threshold with a current induced in the posterior to anterior direction, a direct wave and one single additional descending volley are recorded in the spinal column. The additional descending volley is known as the first indirect wave (I1-wave). With higher suprathreshold stimulation intensities, more descending volleys are measured in the spinal cord (I2 through I4 waves) (Ziemann et al. 2014). Surface EMG records the summative effects of the descending spinal volleys as a single MEP in the muscle of interest. Therefore, the increase in MEP amplitude with increasing stimulation intensities reflects the summation of the late I-waves. The late I-waves with suprathreshold stimulation are thought to be produced by excitatory interneuron circuits in the motor cortex (Ziemann et al. 2014, Di Lazzaro and Ziemann 2013). Many neurotransmitters and neuromodulators can impact corticomotor excitability when measured with suprathreshold stimuli. Specifically, suprathreshold corticomotor excitability, quantified as the MEP amplitude, has been shown to increase with increased glutamatergic, noradrenergic (NA), and serotonergic neurotransmission, while it has been shown to decrease with increased γ-aminobutyric acid (GABA) type A receptor or dopamine (DA) activity (Ziemann et al. 2014) TMS as a Measure of Intracortical Inhibition Three common measures of intracortical inhibition are the cortical silent period (CSP) duration, long-latency intracortical inhibition (LICI), and short-latency intracortical inhibition (SICI). CSP duration is measured with single pulse TMS, while LICI and SICI are measured with paired pulse TMS. When the motor cortex is stimulated while the target muscle is active,

28 12 there is a period of EMG silence immediately following the MEP. The duration of this EMG silence is known as the CSP, and is frequently measured from the time of the stimulation to the time when EMG activity returns, measured by visual inspection or with computer algorithms. The paired pulse measures of inhibition involve a preceding conditioning stimulus (CS), followed by a test stimulus (TS), with a given inter-stimulus interval (ISI). Paired pulse measures are traditionally measured in a resting muscle, but can also be measured with submaximal muscle activity (Wassermann et al. 1996, Hammond and Vallence 2007). When measuring LICI, a suprathreshold CS precedes another suprathreshold TS by approximately milliseconds (ms) (Benwell, Mastaglia, and Thickbroom 2007). When measuring SICI, a subthreshold CS precedes a suprathreshold TS by 1-5 ms (Kujirai et al. 1993, Ilić et al. 2002, Rothwell et al. 2009). Studies have been able to identify both distinct and overlapping neurophysiological mechanisms which impact these three measures of intracortical inhibition (Ziemann et al. 2014, Benwell, Mastaglia, and Thickbroom 2007, Sanger, Garg, and Chen 2001, Hammond and Vallence 2007). The overall duration of the CSP is thought to reflect intracortical inhibition (Ziemann et al. 2014). However, the first 50 ms of the silent period have been shown to occur due to spinal mechanisms (Abbruzzese and Trompetto 2002, Inghilleri et al. 1993, Chen, Lozano, and Ashby 1999, Fuhr, Agostino, and Hallett 1991, Cantello et al. 1992). Beyond the spinal portion of the silent period, the overall duration of the TMS-generated CSP is thought to be driven by long lasting, or slow-acting, inhibitory processes, which could be due to sensory feedback after the initial stimulus (Hammond and Vallence 2007). There is some evidence that the mechanisms of CSP duration relate to supraspinal regions upstream from the primary motor cortex, such as the basal ganglia (Benwell, Mastaglia, and Thickbroom 2007, Priori, Berardelli, Inghilleri, Accornero, et al. 1994). These processes are frequently attributed to GABA-B receptor mediated

29 13 inhibitory post synaptic potentials (Ziemann et al. 2014, Benwell, Mastaglia, and Thickbroom 2007, Werhahn et al. 1999). However, other neurotransmitters have been shown to impact the CSP duration (Ziemann et al. 2014). Specifically, increased DA is associated with lengthened CSP duration (Priori, Berardelli, Inghilleri, Accornero, et al. 1994). In addition, GABA-A receptor activation appears to have a differential impact on CSP duration. Increased GABA-A receptor activity lengthens the short CSP durations that are associated with weaker stimulation intensities, while increased GABA-A receptor activity also shortens the longer CSP durations associated with higher stimulation intensities (Kimiskidis et al. 2006). The paired pulse TMS measure of LICI is another measure that is attributed to longlasting, or slow-acting, intracortical inhibitory processes. LICI is measured using a suprathreshold CS, paired with a suprathreshold TS, with an ISI of ms (Benwell, Mastaglia, and Thickbroom 2007). The duration of LICI is similar to the duration of the CSP. Evidence supports that the mechanisms of both LICI and CSP could have similar functional relevance related to sensory feedback from the initial stimulus (Hammond and Vallence 2007). Pharmacological studies support that, like CSP duration, LICI may also be mediated primarily by GABA-B receptors (Ziemann et al. 2014). In addition, there is some evidence that blocking glutamatergic N-methyl-D-aspartate (NMDA) receptors could increase LICI (Reis et al. 2006). The mechanisms of LICI are less clear than CSP duration, in part because the drugs found to impact LICI, such as Amantadine and Pregabalin, have multiple mechanisms of action (Reis et al. 2006, Lang et al. 2006). Although LICI and CSP duration share many similarities, it has been suggested that the two measures may be brought about by distinct complementary mechanisms, rather than the same mechanism. Specifically, while CSP duration may be associated with changes in the basal ganglia, it appears as if LICI may be associated with changes within the

30 14 inhibitory interneuron circuits of the primary motor cortex, (Benwell, Mastaglia, and Thickbroom 2007, Priori, Berardelli, Inghilleri, Accornero, et al. 1994). In contrast to the long-lasting, slow-acting inhibitory processes measured as CSP or LICI, the paired pulse TMS measure of SICI represents short-lasting, or fast-acting, inhibitory postsynaptic potentials. SICI measures the extent to which a subthreshold stimulation, which should stimulate the low-threshold, small diameter intracortical interneurons and not the pyramidal neuron itself, inhibits the test stimulus (Ilić et al. 2002, Kujirai et al. 1993). The subthreshold CS is set to precede the TS by an ISI of 1 5 ms (Ziemann et al. 2014). The subthreshold CS intensity is frequently set to 80% of RMT or 80% of AMT. When SICI is measured along a continuum of CS intensities it creates U-shaped curve where inhibition is greatest at 70% - 80% RMT, and inhibition is less a 60% and 90% RMT. The intensity of the TS, or second pulse in the pair, is set to 120% RMT or to the stimulation intensity required to elicit a 1 mv MEP. SICI is thought to be mediated primarily by the fast-acting ionotropic GABA-A receptors (Ziemann et al. 2014). Although GABA-A receptor activity is the primary mechanism underlying SICI, SICI has also been found to be modulated by other neurotransmitters (Ziemann et al. 2014). For example, DA agonists have been found to increase SICI, while DA antagonists may decrease SICI (Ziemann et al. 1997). NA may also decrease SICI (Ilić, Korchounov, and Ziemann 2003) TMS as a Measure of Intracortical Facilitation There are two measures of intracortical facilitation: one is just referred to as intracortical facilitation (ICF), while the other is known as short-latency intracortical facilitation (SICF). For the purposes of this dissertation, the acronym ICF will refer to the measure, rather than the overall concept of intracortical facilitation. Although they are measured differently, both ICF and

31 15 SICF have mechanisms that are thought to reflect facilitation by excitatory intracortical interneurons. The measure of ICF utilizes a subthreshold CS intensity, followed by a suprathreshold TS, at an ISI of 8 to 20 ms. Frequently the same stimulus intensities are used for the measurement of ICF that are used for SICI: CS = 80% RMT and TS = 1 mv MEP (Ziemann 2004). As a measure of net excitation, ICF represents facilitation from excitatory intracortical circuits as well as being influenced by some of the mechanisms of SICI (Ziemann et al. 2014). As such, ICF is influenced by many neurotransmitters. For example, ICF has been shown to decrease under the influence of NMDA antagonists, as well as with positive modulation of GABA-A receptors. ICF has been shown to increase under the influence of NA agonists (Ziemann et al. 2014, Ilić, Korchounov, and Ziemann 2003). The TMS measure of SICF also reflects net facilitation due to excitatory intracortical interneurons. In contrast to ICF, SICF is measured by two stimuli with an ISI of 1.5 ms. Both stimuli are delivered at or near threshold (Ilić et al. 2002). SICF is thought to be the result of facilitation of the early I-waves (Hanajima et al. 2002). SICF has been shown to decrease following pharmacologic manipulation with NMDA antagonists, positive modulators of GABA- A receptor activity, as well as DA agonists. SICF has also been shown to increase under the influence of NA agonists (Ziemann et al. 2014, Ilić, Korchounov, and Ziemann 2003) Inter-Individual and Intra-Individual Variability of TMS Measures Change in neurotransmitter activity is one known source of variability in TMS measures; however, many other sources of inter-individual and intra-individual variability influence TMS measures of corticomotor physiology. In some cases, inter-individual variability, or between subjects variability, is due to underlying neuropathology, such as Parkinson s disease, dystonia,

32 16 or stroke (Lefaucheur 2005, Rothwell 2007, Stinear et al. 2007). In addition to pathology, interindividual variability could be due to age, gender, and anatomical differences, such as skull thickness, the distance between the motor cortex and the scalp, and the location of the motor representations (Chipchase et al. 2012). Intra-individual variability of TMS measures manifests itself in two ways: (1) between stimuli in a block of stimuli, and (2) between averages when tested on two occasions. First when the same measurement is completed once every 4 seconds, the response varies between stimuli (Ellaway et al. 1998, Truccolo et al. 2002). Thus, increasing the number of stimuli averaged for each measure has been shown to decrease the coefficient of variation (Boroojerdi et al. 2000). Another method that has been shown to reduce variability within blocks of the same measure is to complete the measurement with weak muscle activity (Darling, Wolf, and Butler 2006, Wassermann et al. 1996). Although this method of controlling variability is common with single pulse measures, it is less common to perform paired pulse TMS studies completed with muscle activity (Hammond and Vallence 2007). The second way that intra-individual variability is manifested is through test-retest variability when testing occurs on two occasions. Some TMS measures are known to change based on the time of day (Lang et al. 2011, Sale, Ridding, and Nordstrom 2007). Other sources of variability between test sessions include blood glucose and caffeine (Chipchase et al. 2012, Specterman et al. 2005). When possible, it is important to document, and to attempt to control, these sources of variability through experimental design and instructions to participants (Chipchase et al. 2012) Reliability of TMS Measurement Intra-individual variability, or within subjects variability can also be tested through testretest reliability studies. Ensuring good reliability of measures is important for interpreting TMS

33 17 measures in longitudinal studies. Test-retest reliability can be different across muscles and within different populations. This brief review of test-retest reliability of the TMS measures will focus on studies completed in the FDI in healthy controls. The definition of acceptable reliability for the purpose of this dissertation are based on those described for use in clinical research, as our hope is that these TMS measures will be useful for future clinical trials (Portney and Watkins 2009). Portney and Watkins (2009) summarize that intraclass correlation coefficient (ICC) values of less than 0.50 can be interpreted as poor reliability, ICC values of 0.50 to 0.75 represent moderate reliability, ICC values of 0.75 to 0.90 represent good reliability, and ICC values greater than 0.90 represent excellent validity. Resting MEP amplitudes in the FDI with suprathreshold, but not maximal, stimulation intensity have been shown to have ICCs of 0.70 to 0.87 (Kamen 2004, Ngomo et al. 2012), while active MEP amplitudes in the FDI have been shown to have ICCs of 0.53 to 0.66 (Ngomo et al. 2012). Other aspects of the FDI MEP recruitment curve in one session, such as the maximal MEP amplitude and RMT have been shown to have ICCs of 0.60 and 0.89, respectively (Carroll, Riek, and Carson 2001b, Ngomo et al. 2012), while active motor thresholds demonstrated ICCs of 0.88 (Ngomo et al. 2012). Carroll and colleagues (2001) report that recruitment curve variables become more reliable when data is combined across multiple days, but this technique is not practical for most clinical trial experimental designs. Reliability of paired pulse measures has also been studied. SICI at rest has been shown to have an ICC of 0.83, while SICI in an active muscle had an ICC of 0.55 (Ngomo et al. 2012). From this limited data, we conclude that there is moderate to good reliability of several TMS measures in the FDI (Portney and Watkins 2009).

34 Sensitivity of TMS Measures to Quantify Change Following Exercise Like reliability, sensitivity to change is another characteristic of TMS measures that is necessary for valid interpretation of TMS measures in longitudinal exercise intervention studies. Despite the variability present in TMS measures, TMS measures have been used to study acute responses to a single exercise session, as well as to study the effects of exercise training (Goodall et al. 2014). These changes have been measured in muscles that have undergone specific training, as well as in muscles distant to the training effect. For the purpose of this dissertation, I will refer to changes in TMS measures as being either specific or global depending on whether the training was associated with the same muscle that was tested. Specific changes have been measured using TMS following strength training, aerobic training, and skill training interventions of the target muscle. One example of specific changes measured using TMS is that sets of fatiguing muscle exercises lead to immediate post exercise facilitation of corticomotor excitability, followed by post exercise depression of corticomotor excitability, in the exercised muscle (Lou et al. 2003, Teo et al. 2012, Lentz and Nielsen 2002). In contrast to these single sessions, other studies have measured the effects of four weeks of strength training. Change following strength training interventions have been measured with TMS using the force-based twitch interpolation technique (Carroll et al. 2009, Lee, Gandevia, and Carroll 2009) as well as EMG-based measures of corticomotor excitability and intracortical inhibition (Kidgell and Pearce 2010). Specific changes in corticomotor physiology have also been measured following aerobic cycling, as well as task-specific skilled motor training (Yamaguchi et al. 2012, Beck et al. 2007) in a trained muscle. Global neurophysiological changes have also been measured using TMS following single bouts of exercise, as well as exercise training. For example, several studies have investigated changes in TMS measures in the FDI following moderate intensity aerobic cycling

35 19 with the lower limbs (Smith et al. 2014, Singh et al. 2014). In addition, at least one study investigated change in intracortical inhibition in the FDI, measured with CSP duration, following 8 weeks of high intensity treadmill training in people with Parkinson s disease (Fisher et al. 2008). Changes observed in the FDI in these studies represent global changes in corticomotor physiology in response to exercise that is not specific to the muscle tested Summary of Chapter One Chapter one summarizes existing research on the use of single and paired pulse TMS of the FDI as measurements of corticomotor physiology. After initial development in 1980s (Barker, Jalinous, and Freeston 1985), there has been much progress in our understanding of the safe use of TMS measures, as well as the mechanisms of force-based TMS measures and EMGbased TMS measures (Rossi et al. 2009, Ziemann et al. 2014). Although this dissertation focuses on the use of TMS in an intrinsic hand muscle, the FDI, TMS measures have been developed in other distal and proximal muscles. Different muscles have different reliability profiles. For example, the FDI has been shown to have a more reliable recruitment curve slope than the flexor carpi radialis (ICC = 0.82 vs 0.60, respectively) (Malcolm et al. 2006). In general, the reliability of TMS measures in the FDI has been shown to be moderate to good, which make this a promising measure for future research. Chapters two and three of this dissertation describe experiments using the TMS measures described above, which will provide further information regarding the use of TMS measures in the FDI to measure changes related to force in a specific muscle, as well as global changes in an unexercised muscle.

36 20 2. REPRODUCIBILITY, SENSITIVITY, AND VALIDITY OF MEASURING MUSCLE ACTIVATION IN THE FIRST DORSAL INTEROSSEOUS MUSCLE USING PERIPHERAL NERVE AND TRANSCRANIAL MAGNETIC STIMULATION 2.1. Introduction The twitch interpolation technique, which measures how neural drive to a muscle is translated into force, was originally performed using peripheral nerve stimulation (PNS) in the adductor pollicis muscle (Merton 1954). PNS-induced twitch interpolation has since been used in many other muscle groups, including the first dorsal interosseous (FDI) (Thomas, Woods, and Bigland-Ritchie 1989, Kalmar and Cafarelli 2004), the triceps (Thomas et al. 1997), the soleus (Kalmar, Del Balso, and Cafarelli 2006), and the dorsiflexors (Kent-Braun and Le Blanc 1996). The twitch interpolation technique has also been performed with transcranial magnetic stimulation (TMS) in many muscles, including the adductor pollicis (Herbert and Gandevia 1996), biceps brachii (Todd, Taylor, and Gandevia 2003, 2004), triceps (Thomas et al. 1997), wrist extensors (Lee, Gandevia, and Carroll 2008), and knee extensors (Sidhu, Bentley, and Carroll 2009). The premise of twitch interpolation is that a superimposed stimulus will produce additional force, or a twitch, during a maximal voluntary contraction (MVC) when the individual s motor units are not fully activated due to incomplete motor unit recruitment and/or submaximal firing frequency (Belanger and McComas 1981, Taylor 2009). Performing twitch interpolation with both PNS and TMS can provide information regarding the location of a muscle activation deficit in people with neurological deficits (Todd, Taylor, and Gandevia 2003, Thomas et al. 1997). We chose to investigate the twitch interpolation technique in the FDI because the FDI is a common muscle of interest in upper limb studies due to the ease of eliciting and recording

37 21 responses with both TMS and PNS (Zijdewind and Kernell 1994). Twitch interpolation studies that estimated muscle activation in the FDI have used PNS, but not TMS (Kalmar and Cafarelli 2004, Thomas, Woods, and Bigland-Ritchie 1989). With TMS of the FDI, the motor evoked potentials (MEPs) measured with electromyography (EMG) have been shown to be reliable (Malcolm et al. 2006, Kamen 2004), but to our knowledge, the reproducibility, sensitivity, and validity of the force-based TMS measures are unknown. Our study is composed of two different experiments. In the first experiment, we compare twitch amplitude and the muscle activation estimates of voluntary activation (VA) and the central activation ratio (CAR). Testing was completed on two occasions to determine whether the twitch interpolation measurements were reproducible in the FDI. We examined the sensitivity of twitch amplitudes and CAR to changes in force production over a range of voluntary force levels. In addition, evoked EMG responses with PNS and TMS were measured to ensure that TMS activated a majority of motor neurons, as is required for the twitch interpolation technique (Todd, Taylor, and Gandevia 2003, 2004, Sidhu, Bentley, and Carroll 2009). Although TMS with 150% resting motor threshold (RMT) activated the majority of motor neurons, it also activated synergistic muscles, which limited the sensitivity and validity of the force-based TMS measurements in the FDI. Therefore, in the second experiment, we compared stimulation at 120% RMT and 150% RMT to determine if reducing the stimulation intensity reduced the contribution of synergistic muscles, which could improve the validity of the measures derived from twitch interpolation.

38 Materials and Methods for Experiment One Participants Fourteen healthy, right-handed participants (7 male, age years) were tested twice, at the same time of day at least 2 days apart (4.6 ± 3.5 days). Participants were excluded if they had any known neuromuscular disorders or had experienced an injury to their right arm or hand in the last 12 months. All participants provided written, informed consent. The protocol was approved by the institutional review boards of the University of Illinois at Chicago and conducted according to the Declaration of Helsinki Experimental Protocol Participants were familiarized with the hand position and isometric index finger abduction contractions prior to data collection (Figure 2.1, next page). The right hand was tested for all individuals. Participants were trained to reach target forces while receiving visual feedback of force and the root mean square (RMS) EMG from the FDI. Visual feedback of RMS EMG was also provided for the abductor digiti minimi (ADM) and extensor carpi radialis (ECR) muscles as control muscles. Cues were provided to maximize force production in the FDI, while minimizing synergistic activation in other muscles. Data collection began with 3 brief control MVCs, separated by 1-2 minutes rest. The peak force obtained during the control MVCs was used to set the visual targets for the experiment. Participants received 28 stimuli with PNS and 28 stimuli with TMS. Four stimuli were given at each of 7 target forces: rest, 10%, 25%, 50%, 75%, 90%, and 100% MVC. The contraction levels and stimulation conditions were randomized by participant, except that the rest condition was assigned to follow 100% MVCs to allow for measurement of potentiated resting twitches two seconds following the MVC with PNS. During each contraction, participants had

39 Figure 2.1. The experimental setup including electrode placement and hand stabilization technique. Abbreviations: ADM, abductor digiti minimi; ECR, extensor carpi radialis; EMG, electromyography; FDI, first dorsal interosseous. 23