The Behavioral and Neurophysiological Effects of Music Training on Cognitive and Motor

The behavioral and neurophysiological effects of music training on cognitive and motor

inhibition in aging adults

Patricia Izbicki

A dissertation proposal submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Program of Study Committee: Elizabeth Stegemöller, Major Professor Mack Shelley Ann Smiley Christina Svec Auriel Willette

The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is conferred.

Iowa State University

Ames, Iowa

2020

TABLE OF CONTENTS

Page

LIST OF FIGURES iv

LIST OF TABLES vi

ACKNOWLEDGEMENTS viii

ABSTRACT x

CHAPTER 1: BACKGROUND 1 1.1 General Introduction 1 1.2 Inhibitory Control – A Brief History 3 1.3 Cognitive Inhibition 5 1.3.1 Behavior and Brain 6 1.4 Motor Inhibition 11 1.4.1 Behavior and Brain 11 1.5 Relevance of Studying Cognitive and Motor Inhibition 18 1.6 Aging 19 1.6.1 Aging and Cognition 20 1.6.2 Aging and Motor Control 21 1.6.3 Aging and Neurophysiology 22 1.6.4 Aging and Cognitive Inhibition 25 1.6.5 Aging and Motor Inhibition 28 1.7 Musicians vs. Non-Musicians Throughout the Lifespan 31 1.7.1 Behavioral & Neurophysiological in Young Adult Musicians 31 1.7.2 Behavioral & Neurophysiological Differences in Cognitive Inhibition in Young Adult Musicians 34 1.7.3 Behavioral & Neurophysiological Differences in Motor Inhibition in Young Adult Musicians 35 1.7.4 Behavior & Neurophysiology in Older Adult Musicians 36 1.7.5 Behavioral & Neurophysiological Differences in Cognitive Inhibition in Older Adult Musicians 39 1.7.6 Behavioral & Neurophysiological Differences in Motor Inhibition in Older Adult Musicians 40 1.8 Why Music Training as an Alternative Strategy to Combat Aging 41 1.9 Why Does Music Training Work? Potential Mechanisms of Music Training 42

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CHAPTER 2: EXPERIMENT 1 45 2.1 Background 45 2.2 Methods 48 2.2.1 Participants 48 2.2.2 Stimuli 50 2.2.3 Procedure 50 2.3 Results 52 2.4 Discussion 69

CHAPTER 3: EXPERIMENT 2 76 3.1 Background 76 3.2 Methods 78 3.2.1 Participants 78 3.2.2 Procedure 80 3.3 Results 83 3.4 Discussion 88

CHAPTER 4: EXPERIMENT 3 93 4.1 Background 93 4.2 Methods 96 4.2.1 Participants 96 4.2.2 Stimuli 98 4.2.3 Procedure 98 4.3 Results 102 4.4 Discussion 114

CHAPTER 5: EXPERIMENT 4 122 5.1 Background 122 5.2 Methods 125 5.2.1 Participants 125 5.2.2 Stimuli 127 5.2.3 Procedure 127 5.3 Results 128 5.4 Discussion 134

CHAPTER 6: GENERAL DISUCSSION 138 6.1 Summary 138 6.2 Aim 1 139 6.3 Aim 2 139 6.4 Implications for Music Training in Healthy Aging 140 6.5 Conclusions 141

REFERENCES 143

APPENDIX: INFORMED CONSENT/IRB APPROVAL 174

LIST OF FIGURES

Figure 1: Inhibition in Aging Behavior and Brain 1

Figure 2: Separation of Inhibition into Cognitive and Behavioral Inhibition 5

Figure 3: Example of Incongruent Stimuli in the Stroop Task 7

Figure 4: Meta-Analysis Results for Cognitive Inhibition 8

Figure 5: Example of P300 Waveform 10

Figure 6: Meta-Analysis Results for Motor Inhibition 13

Figure 7: Functional Connectivity During Motor Inhibition 14

Figure 8: Motor Inhibition Schema Involving Fronto-Striatal Connections 15

Figure 9: Transcranial Magnetic Stimulation 17

Figure 10: Stroop Results for Accuracy 54

Figure 11: Stroop Results for Response Time 55

Figure 12: Stroop Interference Results for Response Time 56

Figure 13: Stroop Interference Results for Response Time 57

Figure 14: P300 Latency Results for Fz 58

Figure 15: P300 Latency Results for Cz 59

Figure 16: P300 Latency Results for Pz 60

Figure 17: P300 Latency Results for F3 61

Figure 18: P300 Latency Results for F4 62

Figure 19: P300 Amplitude Results for Fz 63

Figure 20: P300 Amplitude Results for Cz 64

Figure 21: P300 Amplitude Results for Pz 65

Figure 22: P300 Amplitude Results for F3 66

Figure 23: P300 Amplitude Results for F4 67

Figure 24: P300 Latency and Amplitude Result Summary 67

Figure 25: Pre-Background Electromyography Activity Results 85

Figure 26: Post-Background Electromyography Activity Results 85

Figure 27: Motor Evoked Potential Peak-to-Peak Electromyography Results 86

Figure 28: Inhibition Percent Results 87

Figure 29: Finger Tapping Accuracy Results 104

Figure 30: Finger Tapping Accuracy Difference Results 104

Figure 31: Spread of Finger Tapping Accuracy Difference Results 105

Figure 32: Pre-Background Electromyography Single-Pulse Activity Results 106

Figure 33: Pre-Background Electromyography Inhibition Activity Results 107

Figure 34: Post-Background Electromyography Single-Pulse Activity Results 108

Figure 35: Post-Background Electromyography Inhibition Activity Results 109

Figure 36: Motor Evoked Potential Peak-to-Peak Single-Pulse Activity Results 110

Figure 37: Motor Evoked Potential Peak-to-Peak Inhibition Activity Results 111

Figure 38: Inhibition Percent for Synchronization Results 112

Figure 39: Inhibition Percent for Syncopation Results 112

LIST OF TABLES

Table 1: Healthy Young Adult Demographic Information (Experiment 1) 49

Table 2: Healthy Older Adult Demographic Information (Experiment 1) 49

Table 3: Stepwise Multiple Linear Regression for Incongruent Accuracy 68

Table 4: Stepwise Multiple Linear Regression for Incongruent Response Time 69

Table 5: Healthy Young Adult Demographic Information (Experiment 2) 80

Table 6: Healthy Older Adult Demographic Information (Experiment 2) 80

Table 7: Multiple Regression for Single-Pulse MEP 88

Table 8: Multiple Regression for SICI MEP 88

Table 9: Multiple Regression for Inhibition Percent 88

Table 10: Healthy Young Adult Demographic Information (Experiment 3) 97

Table 11: Healthy Older Adult Demographic Information (Experiment 3) 98

Table 12: Stepwise Multiple Linear Regression for Syncopated Accuracy 113

Table 13: Stepwise Multiple Linear Regression for Syncopated Accuracy Difference 114

Table 14: Healthy Young Adult Demographic Information (Experiment 4) 126

Table 15: Healthy Older Adult Demographic Information (Experiment 4) 126

Table 16: Stepwise Multiple Linear Regression for Behavioral Cognitive Inhibition 130

Table 17: Stepwise Multiple Linear Regression for Neural Cognitive Inhibition 131

Table 18: Stepwise Multiple Linear Regression for Neural Cognitive Inhibition 131

Table 19: Stepwise Multiple Linear Regression for Neural Cognitive Inhibition 132

Table 20: Stepwise Multiple Linear Regression for Neural Cognitive Inhibition 132

Table 21: Stepwise Multiple Linear Regression for Neural Cognitive Inhibition 132

Table 22: Stepwise Multiple Linear Regression for Behavioral Motor Inhibition 133

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Table 23: Stepwise Multiple Linear Regression for Behavioral Cognitive Inhibition 133

Table 24: Predictors Associated with Behavioral Cognitive Inhibition 133

Table 25: Predictors Associated with Neural Cognitive Inhibition 134

Table 26: Predictors Associated with Neural Cognitive Inhibition 134

Table 27: Predictors Associated with Neural Cognitive Inhibition 134

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ACKNOWLEDGEMENTS

It truly takes a village to help a human achieve their dreams. This cannot be more evident for a dissertation. I’m the luckiest person in the world with the best village in the world.

First, I would like to thank my major professor, Dr. Elizabeth Stegemöller, and my committee members, Dr. Mack Shelley, Dr. Ann Smiley, Dr. Christina Svec, Dr. Auriel Willette, for their time, efforts, and guidance through this journey. Thank you for allowing me the space and support to explore my interests and ideas in the arts and sciences. Dr. Stegemöller, I am especially grateful to you for your unlimited and exemplary kindness, patience, and generosity for the last five years.

Second, I would like to thank my family. Monika and Krzysztof Izbicki, you instilled in me the work ethic, compassion, and humanity needed to thrive as a first generation Polish-

American. Your sacrifices have allowed me to grow up in the land of opportunity and pursue my academic, musical, and athletic passions. Words cannot describe my eternal gratefulness. Dr.

Hedi Salanki-Rubardt, I never would have considered completing a degree in Music

Performance and a PhD in the field of neuroscience and music without your mentorship for the last 12 years. You will always be my role model as a scholar, harpsichordist, pianist, and, most importantly, an incredible human being. My First Baptist Church family, especially Mindy

Radke Phomvisay, Julie Johnston, Susan Russell, and Dr. David Russell, thank you for loving me in my best and worst moments and letting me express and share my love for music.

Third, I would like to thank all my supportive colleagues and friends who are like family to me. Dr. Sondra Ashmore, I never would have had the confidence and financial support to complete my PhD without your constant advocacy and mentorship on my behalf. Thank you for showing me the importance of being a great mentor and mentee. Dr. Matthew Jefferson, you ix were kind to me from the very first moment I arrived at Iowa State University. I am grateful our friendship has surpassed the test of time. Kasandra Diaz, your resilience in the face of adversity has inspired me to constantly strive to be better. Your advice and support as a lab mate and as a best friend have been incredible. Dr. Katheryn Friesen, after spending three weeks in Stockholm,

Sweden teaching global leadership, I knew we were long lost sisters. I couldn’t have gotten through my last year of my PhD without you. Dr. Anthony Andenoro, thank you for instilling in me the importance of exceptional leadership and teaching, providing me with extraordinary leadership opportunities, and helping me take a step back to realize that life is a celebration and full of possibility every corner. Bra dag!

Fourth, I would like to collectively thank all the incredible students I have had the pleasure and honor of teaching and mentoring, especially Cortney Elkin, Emma Gettes, Ella

Gustafson, Tessa Mendoza, Jon Mennecke, Allison Meyer, and Molly Norman, all my former piano students, and the students I had the opportunity to teach and mentor in Stockholm,

Sweden. Your fresh perspective, love of learning, incredible accomplishments, humor, and kindness got me through extremely low parts of my PhD journey. I will forever be grateful to you all and can’t wait to see you make your mark on the world.

Lastly and most importantly, I want to thank my fiancée Dr. Andrew Zaman. Never in this world did I believe that I would be lucky enough to be doing my doctoral work alongside an insanely brilliant yet compassionate and generous human being. Thank you for being my academic and life soulmate and providing me with limitless love and laughter. I am eternally grateful to you for showing me how to live a more fulfilling life. I cannot wait to see where our next adventure takes us.

ABSTRACT

Older adults experience a decline in inhibitory control, specifically cognitive and motor inhibition. These declines have been associated with poorer performance in instrumental activities of daily living. However, studies have revealed that older musicians have behavioral and neurophysiological enhancements in various cognitive and motor domains as compared to non-musicians. This suggests that music training may delay the decline in cognitive and motor inhibition with aging. Yet, cognitive and motor inhibition has not been studied across the lifespan in musicians and non-musicians. Thus, the aim of this study was to investigate the behavioral and neurophysiological differences in cognitive and motor inhibition in aging musicians and non-musicians.

Twenty healthy young adult musicians and non-musicians (between ages 18 and 35) and twenty healthy older adult musicians and non-musicians (between ages 65 and 80) were recruited for the study. To measure cognitive inhibition, the Stroop task was performed while electroencephalography (specifically P300 waveform) was recorded. Participants were asked to name the color of a word presented in either red, green, yellow, or blue. Three conditions were presented randomly: neutral (infrequent words sol, helot, eft, and abjure presented in different colors), congruent (color of word matches the word), and incongruent (color of the word does not match the word itself). Accuracy and response time were recorded using E-Prime 2.0

(Psychology Software Tools, Pittsburgh, PA). P300 amplitude and latency were recorded, processed, and analyzed using ActiveTwo Bio Semi system (BioSemi, Amsterdam, NL) and

MATLAB. To measure motor inhibition, participants were asked to perform an index finger flexion-extension movement (i.e., finger tap) in sync with an auditory tone (i.e., synchronized) and between auditory tones (i.e., syncopated) presented at 1 Hz. The forearm, wrist, thumb, and xi fingers 2-4 were supported with a brace maintaining the forearm in a pronated position with the elbow flexed at 90 degrees. The index finger remained unconstrained to allow for full range of motion without touching a surface. Transcranial magnetic stimulation (Magstim Model 200,

Magstim, Whiland, Carmarthenshire) single-pulse and short interval cortical inhibition were performed at rest and between synchronized and syncopated finger taps. Finger tap accuracy was recorded using a goniometer. Motor evoked potential amplitude was recorded from electromyography (Delsys, Boston, MA, USA) and analyzed in The Motion Monitor (Chicago,

IL, USA).

Results revealed musicians overall display enhanced cognitive inhibition, motor inhibition, and processing speed. We observed that musicians displayed a decrease in incongruent response time and earlier P300 latencies and greater P300 amplitudes. This indicates better cognitive inhibition in musicians. We observed that music practice, decreased short interval cortical inhibition motor evoked potential, and decreased inhibition percent predict better syncopation timing during syncopation. This indicates better motor inhibition in musicians.

Furthermore, exploratory analyses revealed that musicians contain more refined rather than overlapping inhibitory pathways. In closing, results suggest that engaging in music practice and music training throughout life may maintain or enhance inhibitory control and associated brain regions. Since decrements in inhibitory control are associated with lower quality of life, these results reveal that playing music may improve quality of life for older adults and serve as a guide to future design of music training/practice interventions in older adults.

CHAPTER 1: BACKGROUND

1.1 General Introduction

Inhibitory control (the suppression of goal-irrelevant stimuli and behavioral responses) declines in the aging process, which leads to disruptions in activities of daily living that impact independent living. Current pharmacological or behavioral strategies do not fully restore the loss of inhibition among aging adults. Music training (practicing an instrument) may be an alternative strategy. However, there is a lack of studies addressing the behavioral and neurophysiological effects of Figure 1. Pilot data revealed that musicians demonstrated quicker response time on the Stroop task which was associated with a music training throughout the lifespan. Thus, the objective of larger electroencephalography P300 response (i.e., more cognitive inhibition). this study was to determine the differences in cognitive Musicians also demonstrated better accuracy on a finger syncopation task, which was associated with a smaller MEP inhibition (i.e., suppression of goal-irrelevant stimuli) and (i.e. more motor inhibition) using SICI transcranial magnetic stimulation. However, while older adult musicians motor inhibition (i.e., suppression of excessive movements) demonstrated more inhibition than older non-musicians, inhibition was not fully among older adult musicians and non-musicians. restored to the levels of young adults.

Aging musicians have shown superior performance in filtering out irrelevant stimuli compared to non-musicians, and cortical activity associated with inhibition has differed between aging musicians and non-musicians. Understanding the differences in behavioral measures and the underlying neural mechanisms of inhibition between musicians and non-musicians is critical in advancement of music training as an effective intervention to delay loss of inhibition in older adults. As a first step toward understanding these differences, the following specific aims were proposed:

Aim 1: Determine the differences in behavioral and associated neurophysiological measures of cognitive inhibition in older and young adult musicians and non-musicians.

Cognitive inhibition was assessed using the Stroop task. Cortical activity associated with cognitive inhibition was assessed using the P300 response recorded with electroencephalography over the frontal/central/parietal, and dorsolateral prefrontal cortices during the Stroop task. I hypothesized that (1) musicians will demonstrate greater accuracy and reduced response time on the Stroop task than non-musicians, (2) musicians will demonstrate reduced latency and increased amplitude of the P300 response compared to non-musicians, and (3) young adult musicians and non-musicians will demonstrate greater accuracy and reduced response time on the Stroop task and reduced latency and increased amplitude of the P300 response.

Aim 2: Determine the differences in behavioral and associated neurophysiological measures of motor inhibition in older and young adult musicians and non-musicians. Motor inhibition was assessed using a finger syncopation task. Cortical activity associated with motor inhibition was assessed with short-interval cortical inhibition (SICI) transcranial magnetic stimulation applied over the hand area of the primary motor cortex during the finger syncopation task. I hypothesized that (1) musicians will demonstrate greater accuracy in finger syncopation than non-musicians, (2) musicians will demonstrate decreased MEP amplitude compared to non- musicians, and (3) young adult musicians and non-musicians will demonstrate greater accuracy in finger syncopation than older adult musicians and non-musicians and decreased MEP amplitude compared to older adult musicians and non-musicians.

1.2 Inhibitory Control – A Brief History

Inhibitory control is involved in a variety of activities in daily life. According to the father of psychology, William James, inhibition is “…not an occasional accident; it is an essential and unremitting element of our cerebral life” (James, 1890; Vol. I, p. 583). Although anecdotally and intuitively, inhibition has been around and considered for centuries, according to

Bari & Robbins (2012) and Smith (1992), the concept of inhibitory control became used and recognized in the field of neuroscience only beginning in the 1950s. Its first international recognition was in neurophysiology with Sir Charles Scott Sherrington and Edgar Douglas

Adrian being awarded the Nobel Prize in 1932 for Physiology or Medicine for their work on neural inhibition as a fundamental principle in the organization of the central nervous system

(Bari & Robbins, 2013; NobelPrize.org). This was followed by foundational work by Samuel

James Meltzer, Ernest Burton Skaggs, and Horace B. English & Ava Champney English in the fields of physiology and psychology. Meltzer published classification of forms of inhibition for physiology. Skaggs presented a systematic description of major categories of inhibition and defining the distinction between voluntary and involuntary forms of inhibition (Skaggs, 1929).

English and English followed up over thirty years later, listing 22 types of inhibition (English &

English, 1958). In short, inhibitory control has been a behavioral concept of study for over a century.

Alongside behavioral phenomenon, over a century of research in psychology and neuroscience has been conducted to attempt to discover the neural locus or correlates of inhibition (Bari & Robbins, 2013). In 1863, Ivan Sechenov was the first to discuss that the brain stem was the only brain region involved in inhibitory control. In contrast, Goltz (1869) argued that there were no specific brain regions involved. Instead, higher brain centers repress the lower 4 ones. Since then, the advent of new technologies and plethora of generation of theories on the neural basis of inhibition have emerged.

The earliest theory that emerged that mirrors modern day understanding of the neural basis of inhibition was from Sir David Ferrier (Ferrier, 1876; Bari & Robbins, 2013). Ferrier was one of the first to propose that the frontal regions of the brain were the “inhibitory-motor centers”. In the next century, human studies using magnetoencephalography (MEG), electroencephalography (EEG), and positron emission tomography (PET) have all implicated the role of frontal brain areas in inhibition. Furthermore, lesion and electrical stimulation studies in both animal models and humans have shown a complex network of brain areas involved including the basal ganglia, hippocampus, septal nuclei, and cerebellum (Bari & Robbins, 2013).

Thus, we have evidence for both the behavior and neural basis of inhibitory control.

Although progress in the field of inhibitory control has increased tremendously, the concept of inhibition has been under scrutiny and debate for decades. As perfectly stated by

Conway and Engle (1994), "Unfortunately, the term inhibition is a nebulous one that connotes a multitude of meanings”. It has been an elephant in the room of cognition so to speak. There are vast groups of scientists who believe that inhibition is a fundamental and unifying component of executive control while others propose that there is no specific domain of inhibition or an inhibition-specific factor (Bari & Robbins, 2013; MacLeod et al., 2003; Aron, 2007; Munakata et al., 2011). The position in this dissertation is consistent with the former and defines inhibition as the ability to filter or prevent unwanted behaviors to achieve a goal (Bari & Robbins, 2013).

Furthermore, inhibitory control is a complex, multifaceted construct featuring a variety of subdomains (Hung et al., 2018; Stahl et al., 2014; Noree & MacLeod, 2015). Thus, inhibition 5 will be divided into two recognized domains: cognitive and behavioral (i.e., response) inhibition

(Bari & Robbins, 2013; MacLeod, 2007) (Figure 2).

Figure 2. Separation of inhibition into cognitive and behavioral inhibition (Bari & Robbins, 2013). Graphic reproduced with permission from the authors.

1.3 Cognitive Inhibition

Cognitive inhibition has been referred to in previous literature as interference control, interference suppression, and resistance to distractor interference (Tiego et al., 2018; MacLeod et al., 2003; Stroop, 1935). There are various similar definitions albeit slightly nuanced definitions of cognitive inhibition. Nigg (2000), Harnishfeger (1995), and Rafal and Henik (1994) refer to cognitive inhibition/interference control as “suppressing a stimulus that pulls for a competing response, suppressing distractors that might slow the primary response, or suppressing internal stimuli that may interfere with the current operations of working memory.” More recent definitions build upon the older proposed ones. Tiego et al. (2018) described cognitive inhibition as attentional inhibition or “the ability to resist interference from distracting stimuli.” Diamond

(2013) states it as “suppressing prepotent mental representations” and “resisting extraneous or unwanted thoughts or memories, resisting proactive interference from information acquired earlier, and resisting retroactive interference from items presented later.” For this dissertation, cognitive inhibition is defined according to MacLeod (2007) as “stopping or overriding of a mental process, in whole or in part, with or without intention.” In this study, it is assumed that 6 the stopping of the process is intentional. This definition is used because it succinctly condenses both past and current descriptions in the literature of cognition inhibition.

1.3.1 Behavior and Brain

Cognitive inhibition has been studied using a variety of tasks. These include the Stroop, flanker tasks, priming tasks, and dual-task interference (Nigg, 2000; MacLeod, 2007). For this dissertation, the focus of the literature review will be on the Stroop task. It is a standard and one of the most widely cited methods of eliciting and measuring cognitive inhibition in the brain

(Stroop, 1935; MacLeod, 1991; Nigg, 2000; MacLeod, 2007; Aron, 2007; Dillon & Pizzagalli,

2007; Bernal & Altman, 2009; Diamond, 2013; Tiego et al., 2018). During the Stroop task, the participant is asked to participate in congruent (i.e. color of word and the word itself match) and incongruent (i.e. color of the word and the word itself do not match) conditions (Figure 3). In the incongruent condition, the participant is attempting to filter the interfering word (i.e. stimuli) to name the color successfully (i.e., task). This elicits the activity of cognitive inhibition. Response time is typically slower for the incongruent than for the congruent or neutral condition due to the interference effect (i.e., the time needed to overcome conflict between automatic word-reading tendency and the controlled color naming response (Botvinick et al., 2001; Kerns et al., 2004;

Ridderinkhof et al., 2011; MacLeod, 1991; MacLeod & Dunbar, 1988). The Stroop task has been suggested to be the most sensitive for not only testing cognitive inhibition (Monsell et al., 2001;

West & Alain, 2000) but also overall cognition as age increases (Van Hooren et al., 2007). See the Aging and Cognitive Inhibition section of this dissertation for further discussion.

Figure 3. Example of incongruent stimuli in the Stroop task (Oakley and Halligan, 2013). Graphic reproduced with permission from the authors.

Additionally, there are specific neural correlates of cognitive inhibition. Nigg (2000),

Harnishfeger (1995), and Rafal and Henik (1994) were early supporters of the neural correlates of following simple, yet accurate schema of cognitive inhibition: anterior cingulate dorsolateral prefrontal/premotor cortex basal ganglia. A meta-analysis completed by Laird et al. (2005) confirmed the earlier schema but also found that the lateral prefrontal cortex, insula, and lateral parietal cortex are involved during the Stroop task. Similar regions were also identified in a meta-analysis of the go/nogo task (Simmonds et al., 2008) and other cognitive inhibition tasks (Nee et al., 2007) (Figure 4). Furthermore, a recent meta-analysis of 66 studies containing 1,447 participants and 987 brain regions revealed that tasks involving cognitive inhibition activated the dorsal frontal inhibitory system (i.e. dorsal anterior cingulate, dorsolateral prefrontal cortex, and parietal areas) (Hung et al., 2018).

Figure 4. Meta-analysis results for cognitive inhibition dimension (Hung et al., 2018). Cognitive inhibition shows significant clusters in the dorsal frontal system and left anterior insula. Dorsal regions include the dorsal anterior cingulate cortex (dACC) extended to the medial frontal surface, and bilateral dorsolateral prefrontal cortex (dlPFC), as well as the parietal lobe regions. Image coordinates are in Talairach space. Graphic reproduced with permission from the authors.

With further research, focus moved away from particular brain regions associated with cognitive inhibition to functionality and connectivity of networks involving these brain regions.

Spielberg et al. (2015) identified specific networks associated with Stroop task performance. A greater demand for cognitive inhibition was associated with global brain connectivity change. In other words, brain regions displayed increased communication efficiency and resilience against disruption. Furthermore, the central “network hubs” responsible for the increased efficiency and 9 resilience were the right inferior frontal gyrus and anterior insula while the dorsal anterior cingulate cortex became more interconnected with other brain local regions.

In addition to the neuroimaging, a neural correlate associated with cognitive inhibition using the Stroop task is the latency and amplitude of the P300 event-related potential (ERP) (Ila

& Polich, 1999). Biologically, an ERP is an average of postsynaptic potentials (PSP) from the firing of thousands of neurons called pyramidal cells before, during, or after an event or stimuli is presented. A PSP occurs when ion channels open and close in a neuron in response to neurotransmitters binding with receptors on the postsynaptic membrane. The opening and closing of ion channels initiates change on the outside of the neuron. Thus, the ERP is a summation of primarily postsynaptic potentials around the dendrites of pyramidal neurons near the surface of the skull that can be recorded using electroencephalography (EEG) (Biasiucci et al., 2019; Cohen, 2017, Luck, 2014; Tivadar & Murray, 2019).

To get an accurate ERP, the stimuli (in this case the neutral, congruent, and incongruent conditions in the Stroop task) must be shown between 50 to 100 trials for each participant and then averaged (Polich & Herbst, 2000; Luck, 2014) although recent studies reveal that there is no

“magic number” and researchers need to consider sample size, anticipated effect magnitude, and signal-to-noise ratio (Boudewyn et al., 2018). After averaging activity, results should show a component similar to Figure 5. To find differences between groups, the differences between latencies and amplitudes of the ERP from one group was compared to the response of another group (Luck, 2014).

A specific component of ERPs that has been linked with cognitive inhibition is the P300 component. P300 simply means there is a positive (i.e. P) deflection (or voltage amplitude) of 10 electrical brain activity that occurs over a specific area of the cortex approximately 300 (i.e. 250

– 400) milliseconds after presentation of the stimuli (Figure 5).

After almost 50 years of intensive research with over 12,000 publications on the P300, it has still not been possible to link the P300 to a single, specific cognitive process. Likely, it is reflecting multiple cognitive processes simultaneously (Dinteren et al., 2014). Overall, the P300 is proposed to reflect cognitive speed and capacity. More specifically, data support the position that the P300 latency indicates neural speed or brain efficiency while the amplitude indicates neural power or cognitive resources available. Various studies show that shorter P300 latencies and increased amplitudes are associated with superior information processing (Polich 1996;

Polich 2004; Shajahan et al., 1997; Fjell & Walhovd, 2003; Sachs et al., 2004). The modulation of the P300 is observed when inhibition is required to complete a task such as the Stroop task. A reduction of the P300 amplitude suggests a reduction in cognitive reserve, and an increase in

P300 latency suggests slowing of processing speed (Polich & Martin, 1992, Polich & Herbst,

2000; Hämmerer et al., 2010; Dinteren et al., 2014). Taken together, these studies show that neurophysiological measures associated with performance on the Stroop task and the P300 waveform are good indicators of changes in brain activity associated with cognitive inhibition with music training and aging.

Figure 5. Example of P300 (P3) waveform occurring after exposure to a stimulus (Guth et al, 2018). Graphic reproduced with permission from the authors.

1.4 Motor Inhibition

Motor inhibition has been referred to in previous literature as behavioral inhibition and response inhibition (Tiego et al., 2018). There are various similar definitions albeit slightly nuanced definitions of motor inhibition. Examples include from Nigg (2000) referring to motor inhibition as “deliberate control of a primary motor response in compliance with changing context cues,” whereas Harnishfeger (1995) defined it as “automatic or intentional delay of an overt motor response.” The definition for motor inhibition is agreed upon more than cognitive inhibition due to the ability to observe the output via movement (MacLeod, 2007). For this dissertation, motor inhibition is defined as “the suppression of unwanted movement.” It will be assumed that the stopping of the process is with intention. Other definitions in the literature are similar to this definition (MacLeod, 2007; Nigg, 2000; Tiego et al., 2018). This definition will be used because it succinctly condenses both past and current descriptions in the literature of motor inhibition.

1.4.1 Behavior and Brain

Motor inhibition has been studied using a variety of tasks. These include the stop-signal reaction-time task and go/nogo task (MacLeod, 2007; Nigg, 2000). For this dissertation, the focus of the literature review will be using the stop-signal reaction-time task, go/nogo task, and finger syncopation. Stop-signal reaction-time task and go/nogo task are standard and two of the most widely cited method of eliciting and measuring motor inhibition in the brain (Aron, 2007;

Bari & Robbins, 2013; Bernal & Altman, 2009; Diamond, 2013; Dillon & Pizzagalli, 2007;

MacLeod, 1991; MacLeod, 2007; Nigg, 2000; Stroop, 1935; Tiego et al., 2018). Furthermore, both tasks activate similar brain regions across studies. During the stop-signal reaction-time task, one is required to stop or change a movement during its execution. During the go/nogo task, 12 participants respond to certain stimuli and not to others. During finger syncopation, a participant is asked to tap in between tones occurring at a certain rate. Although not used commonly as a motor inhibition task, syncopated finger tapping is being proposed for two reasons in this dissertation: 1) limb movements and key presses are the actions most commonly used in motor inhibition experiments (Bari & Robbins, 2013) and 2) go/nogo task and Stroop task activate similar brain regions making it difficult to distinguish between true motor inhibition and cognitive inhibition, respectively (Nee et al., 2007; Simmonds et al., 2008).

Additionally, there are specific neural correlates of motor inhibition. Current research and models show the importance of prefrontal and premotor areas along with deeper brain regions in motor inhibition in both the stop-signal reaction-time task and go/nogo task. The cortical areas most often involved (according to functional magnetic resonance imaging, lesion, and brain stimulation studies) are prefrontal and frontal lobes (Bari & Robbins, 2013; Luria, 1966; Drewe,

1975; Mishkin, 1964; Stanley & Jaynes, 1949). Specifically, these are the pre-supplementary motor area (Juan & Muggleton, 2012), supplementary motor area (Hung et al., 2018; Mostofsky et al., 2003; Simmonds et al., 2008), pre-motor cortex (Picton et al., 2008; Wantanabe et al.,

2002), primary motor cortex (Bari & Robbins, 2013), ventrolateral prefrontal cortex (Hung et al.,

2018), dorsolateral prefrontal cortex (Fassbender et al., 2004; Garavan et la., 2006; Hester et al.,

2004; Hung et al., 2018; Menon et al., 2001), anterior insula (Boehler et al., 2010; ; Hung et al.,

2018; Swick et al., 2008), right inferior gyrus (Benarroch, 2008; Juan & Muggleton, 2012;

Munakata, 2012; Nambu et al., 2002). Additional areas highly connected to the frontal and prefrontal areas include parietal areas (Menone et al., 2001; Rubia et al., 2001; Hung et al.,

2018), basal ganglia (Hung et al., 2018), and thalamus (Hung et al., 2018) (Figure 6).

Figure 6. Meta-analysis results for motor (i.e., response) inhibition dimension (Hung et al., 2018). Motor inhibition shows significant clusters in the anterior insula and fronto-striatal brain regions, including the dorsal anterior cingulate cortex (dACC). Bilateral supplementary motor areas (SMA), right dorsal lateral prefrontal cortex (dlPFC) and ventral lateral prefrontal cortex (vlPFC), basal ganglia (BG), thalamus, anterior insula (AI), and parietal regions. Image coordinates are in Talairach space. Graphic reproduced with permission from the authors.

The proposed connectivity for motor inhibition involves the subthalamic nucleus providing global inhibition over output of the basal ganglia with importance in pausing a motor output until an adequate motor plan has been decided on by the frontal motor areas (Aron, 2007;

Frank et al., 2007; Munakata, 2011). Various prefrontal areas project to subthalamic nucleus along with the anterior cingulate cortex and pre-supplementary area (Munakata, 2012). In short, a complex circuit is involved in motor inhibition. A proposed model suggests there is functional communication and connectivity between cortical and subcortical areas during motor inhibition, 14 specifically between the inferior frontal cortex, pre-supplementary motor area, subthalamic nucleus, caudate, and primary motor cortex (Figure 7) (Aron, 2010; Duann et al., 2009).

Figure 7. Functional connectivity between cortical and subcortical areas during motor inhibition (Duann et al., 2009). Abbreviations: preSMA, pre-supplementary motor area; IFC, inferior frontal cortex; STN, subthalamic nucleus; PMC, primary motor cortex. Graphic reproduced with permission from the authors.

The prominent schema of motor inhibition includes cortical areas sending a stop command to the basal ganglia structures that ‘intercept’ the go response, thus increasing the excitability of the primary motor cortex (Greenhouse et al., 2011). An important area involving the cortical stopping command is the subthalamic nucleus which is directly connected to the pre- supplementary motor area and inferior frontal gyrus via the direct and indirect pathway (Aron et al., 2007; Inase et al., 1999) (Figure 8).

More specifically, cortico-basal ganglia projections to the subthalamic nucleus are part of the direct pathway (Nambu et al., 2002) allowing for fast inhibition of ongoing actions via increasing inhibitory signals from the globus pallidus (Alexander & Crutcher 1990; Parent &

Hazrati, 1995) (Figure 8). Various studies have shown the involvement of subthalamic nucleus in motor inhibition (Aron & Poldrack, 2006; Forstmann et al., 2012; Frank 2006; Hikosask & 15

Isoda, 2010; Li et al., 2008; Munakata et al., 2012) (Figure 9) including during intra-or post- surgical electrophysiological recordings (Ballanger et al., 2008; Mirabella et al., 2012; Obeso et al., 2001; van den Wildenberg et al., 2006).

Figure 8. Motor inhibition schema involving fronto-striatal connections. Before movement initiation, the globus pallidus pars interna (GPi) inhibits the thalamus and thus suppresses or inhibits thalamocortical targets. When a movement is initiated, the cortex excites the striatal neurons which inhibit cells in the pallidum causing disinhibition (or removal of inhibition) on thalamocortical output. Ultimately, this activates the particular movement or motor command. Open arrow = excitatory connection. Filled lines/circles = inhibitory connections (Aron & Poldrack, 2006; Aron, 2007). Graphic reproduced with permission from the authors.

The involvement of the indirect pathway is more controversial (Bari & Robbins, 2013).

Although the striatum is an important and vital part of the frontal-striatal loop involved in motor inhibition (Alexander & Crutcher, 1990; Boehler et al., 2010; Ghahremani et al., 2012; Jahfari et al., 2010; Li et al., 2008) neuroimaging studies are somewhat inconsistent in these predictions depending on the type of response preparation (i.e. reactive or proactive) (Greenhouse et al.,

2011). Recent research has shown that reactive mechanisms would stop all ongoing responses through the direct pathway whereas proactive inhibition would act more selectively through the 16 involvement of the striatum (Aron, 2010) via dopaminergic modulation in this area (Boehler et al., 2011; Eagle et al., 2011). Unfortunately, there are still many unanswered questions about motor inhibition. Although better defined than cognitive inhibition, the brain circuitry involved in motor inhibition still is not clear as well as its application in different tasks.

In addition to the neuroimaging, a technique used to measure the behavior and neural correlates of motor inhibition is syncopated finger tapping and transcranial magnetic stimulation

(TMS). During finger syncopation, a participant is asked to tap in between auditory tones occurring at a certain rate. To reiterate, although syncopated finger tapping is not used commonly as a motor inhibition task, it is being proposed for two reasons in this dissertation: 1) limb movements and key presses are the actions most commonly used in motor inhibition experiments (Bari & Robbins, 2013) and 2) go/nogo task and Stroop task activate similar brain regions making it difficult to distinguish between true motor inhibition and cognitive inhibition, respectively (Nee et al., 2007; Simmonds et al., 2008). In previous studies in young adults have shown an increase in motor cortical inhibition during syncopated finger movements (Byblow et al., 2006).

To measure motor inhibition using TMS, a motor-evoked potential (MEP) needs to be elicited. TMS uses a coil that emits a pulse that produces a magnetic field via stored energy in its capacitors (Figure 9). Since the brain acts as a conductor, the magnetic field creates an electrical current in the brain tissue causing depolarization of neuronal cell membranes and initiation of an action potential (i.e., firing of neurons). Very simply, the coil is placed over the primary motor cortex (i.e., the area of the brain responsible for initiating movement). After the pulse is emitted from the coil, it will induce an action potential in the area that will travel down the corticospinal tract, eventually producing a movement. However, the type of motor behavior depends on where 17 the coil is placed over the primary motor cortex. For example, if the coil is placed over the hand area of the brain, the resulting movement will be a hand twitch (Hallett, 2007) (Figure 9).

Figure 9. Transcranial magnetic stimulation (TMS), specifically short-interval intracortical inhibition (SICI) measures the level of inhibition in the motor cortex. The level of inhibition of the motor cortex can be demonstrated by comparing the MEP for a standard pulse alone with the MEP to a standard pulse preconditioned by a pulse a few milliseconds earlier. The MEP is reduced in this latter condition, an effect called (SICI) an effect attributed to the activation of cortical inhibitory neurons (Aron, 2007). Graphic reproduced with permission from the authors.

To specifically measure levels of motor inhibition, a TMS technique called short-interval cortical inhibition (SICI) is used. SICI represents GABAA inhibitory neurotransmission in interneurons (Bhandari et al., 2016). To induce SICI in the brain, a pair of TMS pulses to the same area in the brain are emitted. The first pulse, the conditioning stimulus, activates the GABA inhibitory interneurons but not enough to illicit a motor response. The second pulse, the test stimulus, occurs at suprathreshold level 3-5 milliseconds after the conditioning stimulus. This activates the pyramidal neurons, which elicits a motor response (e.g., a hand twitch). This hand twitch is measured by recording electrical muscle activity (or electromyography) (Figure 9). The smaller the motor response, the greater the levels of motor inhibition in the primary motor cortex

(Hallett, 2007). 18

The modulation of motor inhibition using TMS is observed when inhibition is required to complete a task such as the stop-signal reaction time task, go/nogo task, and finger syncopation.

Stimulation of the prefrontal cortex during nogo condition reduced electrical activity in the motor cortex during the suppressed or cancelled movement in the monkey (Aron et al., 2004).

Using TMS during a stop-signal reaction time task decreased motor excitability after the response was stopped (Cai et al., 2011; Duque & Ivry, 2009). Waldvogel et al. (2000) used a go/nogo task paired with SICI TMS. They showed increased SICI (i.e., increased motor inhibition) on nogo trials than go trials (also see Coxon et al., 2006). This establishes that motor inhibition (nogo) produces increased gamma-Aminobutryic acid (GABA) mediated neural inhibition (Aron, 2007). Furthermore, Byblow et al. (2006) showed increased SICI (i.e., increased motor inhibition) during finger syncopation (i.e., tapping between the auditory tones) as compared with finger synchronization (i.e., tapping to the auditory tones). Taken together, these studies suggest that neurophysiological measures associated with performance on the finger syncopation and SICI may be good indicators of changes in brain activity associated with motor inhibition in musicians and in aging.

1.5 Relevance of Studying Cognitive and Motor Inhibition

We need to inhibit constantly, whether it is critical thoughts about a partner, reaching for seconds at dinner, or fear while boarding a plane (Munakata et al., 2011). Without it, we would be unable to control impulses, old habits of thought or action, or stimuli in the environment not relevant to current goals or tasks (Diamond, 2013). As Ursin wrote, ‘‘We have to stop whatever we are doing in order to start any new action’’ and ‘‘Inhibition is indeed a most important faculty for our abilities to make choices, and for our freedom of choice’’ (p. 1064). Inhibition has also been associated with normal social development as well as career development (Fuster, 2008; 19

Gable et al., 2000). Furthermore, disorders of both cognitive and motor inhibition lead to impulsivity which is a character train of many neurological and psychiatric conditions such as attention-deficit hyperactivity disorder (Nigg, 2001), drug addiction (Jentsch & Taylor, 1999), schizophrenia (Gut-Fayand et al., 2001; Schmitz, 2017), and obsessive compulsive disorder

(Chamberlain et al., 2005). In short, cognitive and motor inhibition are extremely relevant areas of study because they affect the lives of all human beings, both clinical populations and healthy adults throughout the lifespan.

1.6 Aging

Individuals age 60 and above will double between 2015 and 2050 (World Health

Organization, 2015). With the aging population increasing, there is a great need to understand behavior and brain of healthy aging, which has been ignored in the past. Recent studies have found that cognitive decline can negatively affect medication adherence (i.e., failure to take medicines as prescribed) (Insel, 2006), financial decision-making (Bangma et al., 2017), and performance-based instrumental activities of daily living (IADLs) (i.e., managing money, managing home and transportation, health and safety, and social adjustment) (Cahn-Weiner et al., 2002; Vaughan & Giovanello, 2010). In short, decline in cognition and movement in aging is associated with a lower overall quality of life.

Observations of cognitive and motor aging go back at least 1,000 years (Baars, 2012).

Quantitative measurements began in the 1930s. Walter R. Miles was one of the first to empirically evaluate cognitive aging in 1,600 people between 6 and 95 years old. He discovered declines after age 40 in perceptual, motor, and cognitive abilities (Miles, 1933). This was followed by Irving Lorge’s finding that speed of processing declines with age (Lorge, 1940), which later was central to Salthouse’s theory and that of the field of aging that age-related 20 changes in speed correspond to several cognitive domains (Anderson & Craik, 2017; Salthouse,

1996).

Although progress in the field of aging has grown tremendously, the behavior and brain mechanisms of inhibition, specifically cognitive and motor inhibition, have not been clearly elucidated. Thus, the following sections will focus on a review of the literature in aging and cognition, motor control, neurophysiology, and cognitive and motor inhibition.

1.6.1 Aging and Cognition

Behavioral research in aging and cognition has found patterns of decline in specific domains. Executive function performance predicts the ability to carry out a number of important daily activities during aging such as individual activities of daily living, medication adherence, and complex financial decision making (Bangma et al., 2017; Bell‐McGinty et al., 2002; Cahn-

Weiner et al., 2002; Insel et al., 2006). Encoding of new memories (episodic and semantic), executive functioning, and processing speed worsen during aging, but short-term memory, autobiographical memory, semantic knowledge, and emotion processing remain stable (Craik,

1994; Hedden & Gabrieli, 2004; Reuter-Lorenz et al., 2016). Park et al. (2002) demonstrated that working memory and speed of processing decline steadily from the 20s to 80s. Recently, results from a cross-sectional and quasi-longitudinal comparison of a large older adult cohort found linear declines from early adulthood in speed and accelerating declines in memory and reasoning

(Hartshorne & Germine, 2015; Salthouse, 2019). Although interindividual variability (i.e., genetics, education, physical activity) plays an important and striking role in aging cognition

(Cabeza et al., 2018; Hedden & Gabrieli, 2004), overall there are decrements in cognition throughout the aging process. 21

A particularly important part of cognition and aging is cognitive inhibition. It regulates attention and the contents of working memory, thereby affecting cognitive performance broadly, including the ability to focus attention, comprehend and produce language, solve problems, and learn new information (West, 1996; Zacks & Hasher, 1994). Cognitive operations that do not involve inhibitory processes or operating with little cognitive control are thought to be well maintained in old age (Zacks & Hasher, 1997). This makes cognitive inhibition an important aspect of cognition to study in healthy aging. It will be further discussed in the Aging and

Cognitive Inhibition section of this dissertation.

1.6.2 Aging and Motor Control

Behavioral research in aging and motor control has found patterns of decline in a variety of motor domains. Coordination difficulty (Siedler et al., 2002), increased spatial and temporal variability of movement (Contreras-Vidal et al., 1998; Darling et al., 1989; Siedler et al., 2010), slowing of movement (Diggles-Buckles, 1993; Morrison & Newell, 2015), and difficulties with balance and gait emerge (Hunter et al., 2016; Tang & Woollacott, 1996). Issues in coordination of bimanual and multi-joint movements arise (Siedler et al., 2010). Furthermore, there is deterioration in psychomotor speed and fine motor skills (Salthouse, 1996; Salthouse, 2000;

Seidler et al., 2010; Michely et al., 2018; Oliveira et al., 2018). Taken in combination, these declines in motor control with the aging process increase the risk for life threatening injuries with adverse consequences.

A particularly important part of aging and motor control is motor inhibition. This process suppresses unwanted movements thereby allowing for the accurate performance of movements allowing for completion of daily living and maintenance of independence (Siedler et al., 2010;

Oliveira et al., 2018). Healthy older adults have shown behavioral deficits in tasks implicating 22 motor inhibition (i.e. response inhibition and go/nogo tasks) (Hsieh et al., 2016). Dual-task motor and cognitive paradigms that involve a component of motor inhibition (e.g., walking while counting backwards by nines) have shown greater motor performance declines in older adults

(Huxhold et al., 2006; Lindenberger et al., 2000; Lövdén et al., 2008; Oliveira et al., 2018) indicating an increased reliance on cognitive resources during motor inhibition. Decrements in dual-task performance have also been implicated in decline in performance of daily activities of living in aging (Oliveira et al., 2018). Overall, this makes cognitive inhibition an important aspect of cognition to study in healthy aging. It will be further discussed in the Aging and Motor

Inhibition section of this dissertation.

1.6.3 Aging and Neurophysiology

Although cognitive and motor performance decrements in aging have been observed for decades, a shift to connecting cognitive and motor declines to age-related changes in brain structure and function didn’t begin until the late 1990s early 2000s (Anderson & Craik, 2017;

Raz, 2000). Overall, cellular, cortical, and network level brain differences have been observed through the normative aging process. Furthermore, these neurophysiological modulations have been associated with declines in cognitive and motor function. The following paragraphs will describe in further detail the evidence for these changes and associations.

First, alterations in aging neurophysiology have been observed at the cellular level.

Specifically, neuronal structure and neurochemistry are modulated during the aging process.

Post-mortem and in vivo studies revealed that older adults display lower volumes of gray matter than younger adults due to lower synaptic densities (Cabeza et al., 2018; Haug & Eggers, 1991;

Hedden & Gabrieli, 2004; Resnick et al., 2003; Spreng & Turner, 2019; Terry, 2000). Activated spinal microglia and loss of spinal motor neurons accompany the decreased synaptic densities 23

(Buchman et al., 2019). Furthermore, decreases in neurotransmitters dopamine, noradrenaline, and serotonin are associated with declines in volume and function of the prefrontal cortex

(Damoiseaux et al., 2007; Griffanti et al., 2018; Giller & Beste, 2019; Mohan et al., 2016; Raz et al., 2004; Seidler et al., 2010; Volkow et al., 1996). Jointly, these studies demonstrate that, during aging, neuronal and neurochemical changes arise at the cellular level.

Second, alterations have been observed at the cortical and network level. Intracortical circuits have been found to be impaired in old age (Oliviero et al., 2006). Specifically, studies have shown that the frontostriatal system (i.e., the frontal lobes and the basal ganglia) is affected during the aging process (Volkow et al., 1996; Raz et al., 2004; Damoiseaux et al., 2007; Seidler et al., 2010; Mohan et al., 2016; Griffanti et al., 2018; Giller & Beste, 2019). There is a steady volumetric decline of the prefrontal cortex and medial temporal cortex across the lifespan

(Hedden & Gabrieli, 2004; Raz, 2000; Raz et al., 2003; Raz et al., 2004; Resnick et al., 2003;

Spreng & Turner, 2019; West, 1996). Additionally, there are brain-wide reductions in white and gray matter during the aging process (Driscoll et al., 2009). Overall, these studies demonstrate that cortical and network level neuroanatomical changes occur as we age.

Third, the neuroanatomical changes detected during aging have been associated with cognitive declines. One longitudinal cross section study found that in normal aging, cognitive functioning declines as cortical gray matter and hippocampal volume decreases (Kramer et al.,

2007). Another study found that decrease in frontal white matter tract integrity was associated with decrease in performance of memory measures (Fan et al., 2019; Head et al., 2004;

Kaskikallio et al., 2019). Yuan and Raz (2014) found that larger prefrontal cortex volume and thickness is associated with better executive function. In all, brain atrophy has been associated with various cognitive declines. 24

Fourth, changes in electrophysiological activity in the brain (i.e., brain activity) has also been linked to both cognitive and motor declines in aging. PET and fMRI studies show that poorer performing older adults display greater prefrontal cortical activity while completing executive function tasks than younger adults (Cabeza, 2002; Davis et al., 2008; Hedden &

Gabrieli, 2014; Logan et al., 2002; Rypma & D’Esposito, 2000; Stebbins et al., 2002). While completing perceptual tasks, older adults show greater dorsolateral prefrontal cortex, anterior insular, and frontal opercular activation (Spreng et al., 2010; Spreng & Turner, 2019; Turner &

Spreng, 2012). While performing various motor tasks, older adults display enhanced and widespread brain activity (Li & Lindenberger, 1999; Logan et al., 2002; Mattay et al., 2002;

Michely et al., 2018; Riecker et al., 2006; Rowe et al., 2006). In other words, there is enhanced motor network activity and connectivity/coupling and greater prefrontal influences on the motor system (Boudrias et al., 2012; Heitger et al., 2013; Michely et al., 2018; Rowe et al., 2006).

Additionally, alternating hand and foot movements were positively correlated with the degree of activation in the prefrontal cortex in older adults (Heuninckx et al., 2008). To summarize, evidence suggests that alterations in electrophysiological activity in the brain are associated with cognitive and motor declines during aging.

In addition to neuroanatomical decrements and global electrophysiological changes, the

P300 has been used as a neurophysiological biomarker of age. During normal aging, the P300 latency increases (i.e. occurs later after seeing the stimulus) and P300 amplitude decreases indicating slower neural speed or less brain efficiency and decreased neural power or cognitive resources in different brain regions (Ashford et al., 2011; Kuba et al., 2012; Polich, 1996;

Walhovd et al., 2008; van Dinteren et al., 2014). The P300 has also been found to shift anteriorly

(i.e., larger amplitudes over the frontal scalp than in parietal scalp) which has been thought to 25 indicate an increase reliance on prefrontal structures (Friedman et al., 1997; Li et al., 2013;

O’Connell, et al., 2012). Motor-evoked potential (MEPs) have also been used as a neurophysiological biomarker of age. A recent meta-analysis has found that there are age- dependent reductions in cortical excitability in the primary motor cortex (Bhandar et al., 2016).

Along with being biomarkers of age, the P300 latency and amplitude and MEPs are also neural correlates of cognitive and motor inhibition, respectively. It will be further discussed in

Aging and Cognitive Inhibition and Aging and Motor Inhibition sections of this dissertation.

1.6.4 Aging and Cognitive Inhibition

Rabbitt (1965) was one of the first researchers to demonstrate that older adults are worse at ignoring irrelevant information when selecting targets, implicating declines in inhibition during the aging process (Anderson & Craik, 2017). This was followed and supported by studies from Hasher and Zacks (1988), Kramer et al. (1994), Zacks et al. (1995), Hasher et al. (2007), and Lusting et al. (2007). Thus, it seems that older adults either produce more irrelevant information and/or have a reduced ability to downregulate irrelevant information.

Throughout the lifespan, differences in behavioral cognitive inhibition emerge, specifically between healthy young adults and healthy older adults. Older adults have shown deficits in tasks measuring cognitive inhibition including the Stroop task, sustained attention, and vigilance tasks (Augustinova et al., 2018; Braver et al., 2001; Noreen & MacLeod, 2015; Parsons

& Barnett, 2019). The Stroop task (e.g., identifying the color of the word; word “red” colored in green) has been suggested to be the most sensitive for testing not only cognitive inhibition

(Monsell et al., 2001; West & Alain, 2000) but also overall cognition as age increases (Van

Hooren et al., 2007). In a group of 578 healthy older adults, a multivariate analysis indicated cognitive inhibition using the Stroop task is most affected of all cognitive tasks tested in the 26 study (i.e., Concept Shifting Task, Letter Digit Substitution Test, Visual Verbal Learning Test, and Verbal Fluency Test) as age increases (Van Hooren et al., 2007). Research has also shown that older adults exhibit an increased response time (i.e., increase in Stroop effect) and lower accuracy than young adults (Milham et al., 2002; West & Alain, 2000; West, 2004). Overall, the literature suggests that older adults demonstrate worse cognitive inhibition than young adults.

Previous research suggests three neurophysiological mechanisms contribute to the decline in cognitive inhibition across the lifespan: 1) structural deficits (i.e, brain atrophy), 2) degradation of connectivity and activity between brain regions (i.e. brain atrophy of dendritic and axonal projections between brain regions), and 3) a combination of both (Cabeza et al.,

1997; Dennis & Cabeza, 2008). In 2014, Hu et al. used magnetic resonance imaging and demonstrated reduction in gray matter volume (i.e. brain atrophy) in healthy older adults in areas of the brain correlated with cognitive inhibition, specifically in prefrontal/frontal regions.

Additionally, the researchers found that blood oxygenation levels in prefrontal/frontal regions, specifically the supplementary motor area, pre-supplementary motor area, anterior cingulate cortex, and dorsolateral prefrontal cortex were negatively correlated with age (i.e. neural activity decreases with age). Furthermore, West and Alain (2000) demonstrated age-related deficits in connectivity and activity between regions involved in cognitive inhibition. They used evoked- related potentials (ERP) paired with Stroop task in healthy young adults versus healthy older adults. Healthy older adults demonstrated greater reduction of amplitude in phasic negativity (or the negative voltage response 500 milliseconds after exposure to the Stroop task) over electrodes

Fc1/Fc2 (i.e., frontocentral region), F11/F12 (i.e., the bilateral frontal regions), and P3 (i.e., left parietal region). The reduction of amplitude over these cortical areas implicates age-related 27 deficits in cognitive inhibition possibly due to the degradation of the neuronal connectivity and activity between brain regions.

Wolf et al. (2014) provide additional evidence supporting age-related deficits in neuroanatomical connectivity and activity. They paired diffusion tensor imaging (DTI) with the

Stroop task to associate the topography of neuronal networks (i.e., white matter connectivity) with cognitive inhibition. Participants consisted of healthy young adults (22-37 years of age), healthy older adults (60-70 years of age), and healthy advanced older adults (71-85 years of age).

Results demonstrated that white matter integrity of connections between the bilateral dorsolateral prefrontal cortex with frontal and subcortical regions or the anterior cingulate cortex with frontal and subcortical regions was strongly associated with performance on the Stroop task.

Furthermore, white matter connectivity and performance on the Stroop task were negatively associated with age. In other words, cognitive inhibition declines in aging populations via decrements in connectivity and activity in brain regions involved with cognitive inhibition.

Milham et al. (2002) used fMRI to compare brain oxygenation (a measure of brain activity) in healthy young adults and healthy older adults while completing the Stroop task.

Results indicated a decrease in the oxygenation response in the dorsolateral prefrontal cortex and parietal cortex in healthy older adults vs. healthy young adults, which is linked to attentional impairments. In addition, there was an increase in the oxygenation response in ventral visual processing regions in healthy older adults that was associated with cognitive inhibitory impairments in processing task-irrelevant information. Additionally, previous research has shown a decrease in the oxygenation response in the dorsolateral prefrontal cortex while completing the Stroop task (Milham et al., 2002) and a negative correlation between white matter connectivity, Stroop task performance, and age (Wolf et al., 2014). Overall, older adults perform 28 worse on the Stroop task than young adults potentially due to brain atrophy and altered connectivity and functionality of the frontal cortices (specifically the dorsolateral prefrontal cortex) and the parietal cortices.

As discussed in Aging and Neurophysiology, the P300 has been used as a neurophysiological biomarker of age. During normal aging, the P300 latency increases (i.e. occurs later after seeing the stimulus) and P300 amplitude decreases indicating slower neural speed or less brain efficiency and decreased neural power or cognitive resources in different brain regions, respectively (Ashford et al., 2011; Hämmerer et al., 2010; Kuba et al., 2012;

Polich, 1996; Tucker & Stern, 2011; van Dinteren et al., 2014; Walhovd et al., 2008). This has also been found in tasks requiring use of cognitive inhibition (Bokura et al., 2002; Kropotov et al., 2016; West & Alain, 2000). In short, during the process of aging, there is a combination of declines in behavior, brain volume, brain activity, and connectivity associated with cognitive inhibition.

1.6.5 Aging and Motor Inhibition

There are differences in motor inhibition throughout the lifespan. Healthy older adults have shown behavioral deficits in tasks implicating motor inhibition (i.e. response inhibition and go/nogo tasks) (Hsieh et al., 2016). Nielson et al. (2002) examined healthy young adults and healthy older adults using a motor inhibition task requiring participants to withhold a response based on alternating presentations of letters. There was a significant difference in response time between the older group and younger group, with the elderly group being significantly slower than the young group. In addition, a correlational analysis confirmed a negative relationship between number of correct responses/inhibitions and age. In another study, Langenecker and

Nielson (2003) used the go/nogo task with a heathy older adult group and a healthy young adult 29 group. Independent sample t-tests indicated older adults scored significantly lower in correct responses in the go/nogo task as compared to younger adults, suggesting healthy older adults have more difficulty in inhibiting motor responses. Furthermore, a recent meta-analysis has shown that in the go/nogo and stop-signal tasks, older adults show poorer performance than young adults (Rey-Mermet & Gade, 2019). In short, older adults seem to have worse motor inhibition than young adults.

While these studies support the notion that older adults have declines in motor inhibition, the tasks used may also involve cognitive function. There is limited research using simpler motor tasks, such as finger syncopation, in healthy adults to examine changes in motor inhibition.

However, previous studies in young adults have shown an increase in motor cortical inhibition during syncopated (i.e., finger tapping between auditory tones) finger movements in young adults (Byblow & Stinear, 2006). Moreover, research has revealed that older adults demonstrate a decline in the performance of syncopated finger movement (Stegemöller et al., 2009). Thus, studies suggest that syncopated finger movement needs to be further explored in terms of motor inhibition in aging adults.

Neurophysiological changes that may support observed behavioral motor inhibition deficits include 1) reduction in gray matter brain volume, 2) reduction in white matter volume and connectivity, and/or 3) biochemical changes in the brain (Coxon et al., 2012; Fujiyama et al.,

2016; Mattay et al., 2002; Seidler et al, 2010). Hu et al. (2014) demonstrated correlations between declines in gray matter volumes in the inferior parietal lobule, anterior cingulate cortex, and dorsal lateral prefrontal cortex and decline in motor inhibitory control. Dorsal lateral prefrontal cortical activation in older adults is increased while preparing to inhibit an action

(Fernandez-Ruiz et al., 2018). Inter-and intrahemispheric inhibition during motor tasks have also 30 been shown to decline during healthy aging (Ruitenberg et al., 2019). Decreased integrity of white matter tracts in the fronto-basal-ganglia network have been associated with declines in motor inhibition in older adults (Coxon et al., 2012). Neurotransmitter GABA levels in cortical brain regions associated with motor inhibition have shown that older adults contain less GABA.

Specifically, lower GABA levels in the pre-supplementary motor area were associated with poor motor inhibition using the stop-signal task (Hermans et al., 2018; Pauwels et al., 2018).

Furthermore, greater distributed network connectivity seems to take on a larger role as we age

(Tsvetanov et al., 2018). Overall, changes in neurophysiology, specifically reduction in gray and white matter brain volume/connectivity and decrease of neurotransmitter GABA, may be influencing motor inhibition deficits during the aging process.

As discussed in Aging and Neurophysiology, the use of TMS has been shown to be an effective tool for understanding changes in motor cortical activity in older adults. Oliviero et al.

(2006) showed that single-pulse MEP (i.e., hand twitch) amplitudes at rest are reduced in older adults compared to young adults indicating a reduction in motor cortical excitability possibly due to loss of cortical and spinal motor neurons in older adults. This has also been shown during movement preparation (Davranche et al., 2007; Duque & Ivry, 2009; Levin et al., 2014).

Research measuring SICI at rest revealed a decrease in SICI (i.e., decrease in inhibition) is associated with poor performance in motor inhibition, possibly due to a reduction in inhibitory neurotransmitters (i.e., GABA) and loss of corticospinal motor neurons during aging (Peinemann et al. 2001; Heise et al., 2013; McGinley et. al, 2010). This has also been found in studies measuring SICI during movement (Fujiyama et al., 2011; Heise et al., 2013).

Taken together, these results suggest that motor inhibition is reduced in older adults.

However, there is limited research examining changes in motor inhibition during a simple 31 movement task in older adults. Previous research using TMS has shown that SICI is increased during the performance of syncopated finger tapping compared to synchronized finger tapping in young adults (Byblow & Stinear, 2006). However, Fujiyama et al. (2011) showed SICI did not differ between young adults and older adults during a go/nogo task. Thus, studies suggest that syncopated finger movement paired with SICI needs to be further explored in terms of motor inhibition in aging adults. In short, during the process of aging, there is a combination of declines in behavior, brain volume, brain activity, and connectivity associated with motor inhibition.

1.7 Musicians vs. Non-Musicians Throughout the Lifespan

Research on music and neuroscience goes back to the late-nineteenth century.

Researchers in the fields of psychology, musicology, and neurology began showing interest in how the brain processes music. Franz Joseph Gall (1758-1828) was one of the first scientists to examine exceptionally talented musicians and their processes for perception and memory. Within the last twenty years, the development and refinement of neuroimaging techniques has allowed for exploration of how the behavior and neurophysiology of musicians differs (if at all) from non-musicians. The following section is devoted to discussing behavioral and neurophysiological differences in younger and older musicians discovered in the two decades.

1.7.1 Behavioral & Neurophysiology in Young Adult Musicians

Music training has been shown to affect a wide range of cognitive and motor abilities associated with brain enhancement. Examples include verbal processing (Moreno et al., 2009;

Seither-Preisler et al., 2014), intelligence (Schellenberg, 2004), reading (Moreno et al., 2011), inhibitory processing (Moreno et al., 2011), auditory processing (Moreno & Farzan, 2015;

Tierney & Kraus, 2015; Swaminathan et al., 2015), decision-making (Smayda et al., 2018), gross motor ability (Rose et al., 2019), working memory (D’Souza et al., 2018), coordination (Jäncke 32 et al., 1997) and brain development (Hyde et al., 2009). Lower-order abilities such as fluid intelligence, crystallized intelligence, verbal fluency, and auditory discrimination are also enhanced (Silvia et al., 2016). Executive functions, specifically cognitive flexibility, working memory, and verbal fluency were enhanced in musicians (Zuk et al., 2014). Further, cognitive benefits proposed that take into account an active control group (i.e. all arts and sports) include enhancements in verbal memory (Jonides, 2008), listening skills and reading, executive functions, general IQ, and social skills (Miendlarzewska & Trost, 2014). In short, there is evidence to suggest that music training improves cognition in young adults.

Motor control has also been found to be enhanced by music training. Several studies have shown enhanced sensorimotor functions in young instrumental musicians (Hanna-Pladdy &

MacKay, 2011). Musicians have been found to perform sensorimotor synchronization tasks and timed motor sequence tasks with less variability (Watanabe et al., 2007; Krause et al., 2010).

Furthermore, musicians (specifically pianists) have been found to display equal independence of movements across fingers and over various rates (tempi) (Furuya & Altenmüller, 2015; Kang et al., 2013). Overall, evidence suggests that music training also improves motor control in young adults.

Music training has also been shown to induce neuroplastic changes and enhancements.

First, brain volume changes with music training. Increases in gray matter volume have been found in multiple brain areas, including the auditory cortex, primary motor cortex, somatosensory areas, premotor cortex, inferior/superior temporal area, posterior cingulate areas, right/middle/superior frontal regions, cerebellum, hippocampus, right insula, and supplementary motor area (Barrett et al., 2013; Miendlarzewska & Trost, 2014; Groussard et al., 2014). Playing a musical instrument was also associated with greater white matter thickness in motor, premotor, 33 and supplementary motor, prefrontal and parietal cortices (Fauvel et al., 2014; Hudziak et al.,

2015). Thus, these studies demonstrate brain volume changes with music training.

Second, music training changes connectivity and functional connectivity in the brain.

Longitudinal studies showed correlations between years of music training and amount of structural change in white matter tracts in the internal capsule, arcuate fasciculus, and corpus callosum (Bengtsson et al., 2005; Schlaug et al., 2005). Changes in the auditory-motor network were also found for young adults over 18 beginning music training (Li et al., 2018), suggesting that music training can influence brain plasticity even after brain maturation is almost complete.

This has also been observed by Tierney et al. (2015) and Penhune (2011). Furthermore, musicians have increased functional connectivity in sensorimotor network, default mode network

(specifically in the posterior cingulate cortex), salience network, auditory network, visual network, prefrontal regions, and interoceptive and emotional areas as compared to non-musicians

(Cantou et al., 2018; Luo et al., 2012; Zatorre et al., 2007; Zuk et al., 2014). Overall, there is evidence that brain connectivity and functional connectivity change as a result of music training.

These changes in brain volume and connectivity are supported by changes in event- related potentials (ERPs) recorded from EEG. Various ERPs, including the P300, were found to be modulated post music training in younger populations (Brydges et al., 2014; Moreno et al.,

2014; Shahin et al., 2003; Zendel & Alain, 2009). George and Coch (2011) demonstrated a positive correlation between musical experience and increased working memory performance in auditory and visual domains. ERP analysis in the same study showed that musicians had a shorter P300 latency in both domains and larger amplitude in the auditory domain. In conclusion, research demonstrates that ERPs, including the P300, are altered with music training. Taken all 34 together, the literature reveals that both behavior and neurophysiology are enhanced in young adult musicians as compared to young adult non-musicians.

1.7.2 Behavioral & Neurophysiological Differences in Cognitive Inhibition in

Young Adult Musicians

Music training has the ability to enhance cognitive inhibition. Travis et al. (2011) compared professional musicians and amateur musicians matched for age, gender, and education on response time during the Stroop task. Professional musicians displayed a significantly reduced response time than amateur musicians. In another study, Bialystok and DePape (2009) studied three groups of healthy young adults: bilinguals, musical instrumentalists (monolingual),

25 vocalists (monolingual), and 24 neither musician nor bilingual. A modified Stroop task was used involving auditory and linguistic conflict in four conditions: pitch control, word control, pitch conflict, and word conflict. In this modified Stroop task, musicians performed faster suggesting an enhanced cognitive inhibition response in healthy young adult musicians.

Enhanced performance in the Stroop task has also been found by Habibi et al. (2019). Joret et al.

(2016) found that cognitive inhibition (as measured by the Simon task) was increased in children with musical training.

There are limited studies that look at neurophysiological differences in cognitive inhibition is young adult musicians versus non-musicians. During the Stroop task, Sachs et al.

(2017) found greater bilateral activation in the cognitive inhibition network (i.e., pre- supplementary motor area, supplementary motor area, anterior cingulate cortex, and inferior frontal gyrus) in young musicians as compared with young athletes. However, a recent study building upon data from Sachs et al. (2017) showed that after four years of training, musicians displayed greater in brain activity in the right inferior frontal gyrus during the Stroop task. 35

Thus, evidence seems to suggest that cognitive inhibition is enhanced in young adult musicians as compared to young adult non-musicians. However, there are limited studies examining cognitive inhibition, specifically using the Stroop task and P300 in young adult musicians and non-musicians.

1.7.3. Behavioral & Neurophysiological Differences in Motor Inhibition in

Young Adult Musicians

Music training has been shown to alter adult motor inhibitory performance and neurophysiology (Hughes & Franz, 2007; Penhune et al., 2005; Rosenkranz et al., 2007). Slater et al. (2018) showed increased motor inhibition and decreased variability in motor inhibition in musicians (specifically drummers) during a rhythmic task. This was accompanied by an increase in the N300 component amplitude over Cz. Wright et al. (2012) found that 10 healthy non- musicians had a smaller movement-related cortical amplitude in the primary motor cortex (i.e. electrode C3) and the SMA (i.e. electrode Cz) after practicing a G major scale for five weeks as compared to before practice. This result suggests greater motor inhibition due to reduced motor cortical activity during motor learning. A visual go/nogo task that involved withholding of key presses to rare targets demonstrated no behavioral differences between musicians, bilinguals, and controls. However, the study did show that musicians showed an enhanced and early P2 response accompanied by a reduced N2 amplitude, demonstrating differences in brain activity of musicians during motor inhibition as compared to bilinguals and controls (Moreno et al., 2014).

Using TMS, Nordstrom and Butler (2002) showed reduced intracortical inhibition of corticospinal neurons in musicians. Rosenkranz et al. (2007) used SICI TMS to show an increase in motor inhibition using increasing stimulation intensities in 11 musicians as compared to non- musicians, which implicates a more sensitive inhibitory response to motor movements. Vaalto et 36 al. (2016) showed that musicians specifically display more motor inhibition in non-primary areas of the motor cortex as compared to non-musicians. Musicians also showed that proprioceptive stimuli exerted stronger inhibition effects on corticospinal excitability, suggesting greater motor inhibition to specific somatosensory inputs. Marquez et al. (2018) showed using SP TMS that there is greater motor cortical inhibition in musicians during movement preparation of isolated and complex finger movements. Furthermore, musicians have shown symmetrical motor homunculus maps as compared to non-musicians which was accompanied by greater interhemispheric inhibition during finger movement (Chieffo et al., 2016). Neurophysiological results suggest healthy young adult musicians display greater motor inhibition than their non- musician counterparts. Thus, the neurophysiology behind motor inhibition in musicians involves reduced motor cortical activity and reduced inhibition in motor corticospinal circuits.

Unfortunately, there are no studies revealing behavioral differences in motor inhibition in young musicians and non-musicians.

1.7.4 Behavior & Neurophysiology in Older Adult Musicians

Music training has shown both neuroenhansive and neuroprotective effects on cognition and motor control (Amer et al., 2013; Grassi et al., 2017; Moreno & Bidelman, 2014). Research indicates music training has benefits in a variety of cognitive domains throughout the lifespan

(Amer et al., 2013; Mansens et al., 2017; Strong & Mast, 2019). Older musicians performed better than musicians in auditory processing tasks such as speech perception in noise, gap detection and mistuned harmonic detection in auditory stimuli, and auditory working memory

(Amer et al., 2013; Alain et al., 2014; Grassi et al., 2017; Parbery-Clark et al., 2011; Zendel &

Alain, 2012; Zendel et al., 2019). Older adults with at least 10 years of musical experience in their lifespan displayed better performance in far-transfer tasks such as nonverbal memory, 37 naming, and executive functioning tests than non-musicians or musicians with less than 10 years of musical experience (Hanna-Pladdy & MacKay, 2011). Other benefits include reduced age- related decline of fluid intelligence in older musicians, increased inhibition, and increased executive functioning (Gray & Gow, 2019; Oechslin et al., 2013; Strong & Midden, 2018). In a recent review, Schneider et al. (2019) found that older musicians outperformed non-musicians in either speed or accuracy in cognitive batteries across studies. Furthermore, older adults who played a musical instrument frequently were less likely to develop dementia (Verghese et al.,

2003) and have been shown to have a better visual memory (Diaz Abrahan et al., 2019). Taken together, these studies suggest that music training has a neuroenhasive and neuroprotective effect on cognition.

Surprisingly, literature has suggested that people can benefit from music training at any age. Musically naïve adults displayed enhanced cognitive flexibility, working memory, and general processing speed after six months of piano training (Bugos et al., 2007). Another similar intervention found improvement post-training on executive function, inhibitory control, and divided attention (Seinfeld et al., 2013). A drumming intervention in older adults was found to increase verbal and visual memory (Degé & Kerkovius, 2018). Mansens et al. (2017) found that playing a musical instrument once every two weeks as an older adult is associated with better attention, episodic memory, and executive function. Furthermore, recent meta-analysis of 10 music training interventions in older adults showed overall improvements in memory, processing speed, and general cognition (Kim & Yoo, 2019). This suggests that learning an instrument at any age may improve cognition. This is fascinating given the critical period for the auditory cortex is 3-4 years of age, for language learning is 11-13 years of age, and for complete brain maturation is 30 years of age (Kral & Sharma, 2012; Miendlarzewska & Trost, 2014; Weber-Fox 38

& Neville, 2001; Wan & Schlaug, 2010). Yet, neuroplasticity is possible throughout the lifespan which may explain the benefits of music training late in life.

Motor control is also positively influenced by music training during aging. In older expert pianists, music-related motor abilities remained intact (Krampe & Ericsson, 1996). Another music training intervention found enhanced motor ability post training (Seinfeld et al., 2013).

Music training interventions have been shown to improve mobility in older adults. A six-month randomized control trial music training paradigm has shown to increase the equilibrium and regularity (i.e. reduce variability) of gait in older adults. This was accompanied by reduced falls, stance, and increased gait velocity (Trombetti et al., 2011). Similarly, music training has benefits for fine motor control in aging. Motor dexterity was increased in older musicians as compared to older non-musicians (Hanna-Pladdy & Gajewski, 2012). Recently, older adults participating in a

16-week piano and percussion training program demonstrated greater bimanual synchronization than the music listening instruction control group (Bugos, 2019). Just like with cognition, one can also gain benefits in motor control through music training late in life.

Furthermore, neural differences demonstrated in late-life musicians (Alain et al., 2019).

Brain changes associated with music training remain intact even when training has stopped for seven years (Skoe & Kraus, 2012). Lifelong engagement in music training has been suggested to maintain the brain in a younger state (Rogenmoser et al., 2018) and enhance neural functioning

(Alain et al., 2019; Bidelman et al., 2014; Zendel & Alain, 2014). Bidelman and Alain (2015) have shown that older musicians showed increased neuroplasticity in the brain stem and auditory cortex. A recent review highlights that music training results in sustained anatomical brain changes and cognitive advantages in older age (Jordan, 2019). However, neurophysiology in older adult musicians as compared to non-musicians is severely limited and must be subject to 39 further exploration. While behavior and neurophysiology seem to be enhanced in older adult musicians as compared to older adult non-musicians, there remains a limited number of studies.

Moreover, failure to replicate and lack of mechanistic studies also challenge this notion. More studies specifically focused on both behavior and associated neurophysiology are needed.

1.7.5 Behavioral & Neurophysiological Differences in Cognitive Inhibition in

Older Adult Musicians

Although limited, there have been a few studies demonstrating that music training may improve cognitive inhibition in older adults. Zendel and Alain (2013) showed that healthy older musicians displayed better performance on tuning out irrelevant sounds to perceive speech than healthy older non-musicians. Amer et al. (2013) found that older musicians had greater accuracy and response times in an auditory Stroop task than older non-musicians. Strong and Midden

(2018) showed that currently practicing older musicians performed better on the Stroop task than non-musicians. Interestingly, there were no differences in performance between former musicians and non-musicians. Furthermore, a recent meta-analysis showed that older adult musicians showed significant improvements in overall inhibition (Roman-Caballero et al., 2018).

Thus, these studies suggest that music training should be continued throughout the lifespan to maintain benefits in cognitive inhibition.

Improvements in cognitive inhibition have also been shown in a music training intervention with musically naïve older adults. Seinfeld et al. (2013) recruited 28 healthy older adults with no previous music experience. Sixteen participants were assigned to the piano group, and 15 participants were assigned to the age-matched control group that participated in other leisure activities (e.g. physical exercise, knitting). The Stroop task was used to measure cognitive inhibition. Compared to controls, the piano group increased the number of correct responses post 40 intervention suggesting increased cognitive inhibition after music training. In another study,

Alain et al. (2019) found that musically naïve older adults improved performance on the Stroop task post music training. This was not found in controls and musically naïve older adults who completed the visual arts training arm of the study. Taken together, this suggests that improvements in cognitive inhibition through music training can be gained at any age without previous music training.

In conclusion, healthy older musicians show enhancements in various cognitive domains as compared to non-musicians. Furthermore, healthy older adults that begin music training during their late age express enhancements in cognitive inhibition. Though one can argue that seeing behavioral benefits of music training in cognitive inhibition is of the utmost importance, understanding the neurophysiology underlying the change allows for a clearer understanding of the mechanism by which benefits of music training occur allowing for better manipulation and modulation of cognitive inhibition. However, there is a gap in literature addressing the effect of music training on both behavior and neurophysiology associated with cognitive inhibition in the process of aging.

1.7.6 Behavioral & Neurophysiological Differences in Motor Inhibition in

Older Adult Musicians

There are limited studies showing if music training alters motor inhibitory performance and neurophysiology in older adults. Using a stop-signal task, older musicians had a reduced number of missed responses than older non-musicians (Vromans &Postma-Nilsenov, 2016).

Furthermore, a recent meta-analysis showed that older adult musicians showed significant improvements in overall inhibition (Roman-Caballero et al., 2018). Another study measured

ERPs using EEG while healthy older musicians completed a visual go/nogo task. Older 41 musicians displayed fewer nogo errors as compared to older non-musicians. Furthermore, there was 1) an increase of P300 amplitude waveform in the frontal brain regions of musicians, 2) more anterior topography of the P300 response in musicians, and 3) P300 amplitude was correlated with years of music training in musicians. These results suggest enhanced preservation of motor inhibition in aging (Moussard et al., 2016). Furthermore, in the only high-quality intervention to date, Alain et al. (2019) found that older, musically naïve adults undergoing a music training intervention showed a greater response associated with motor inhibition over the right frontal scalp areas (i.e., electrode F2) as compared to the group in the visual art intervention. Taken together, this suggests that improvements in motor inhibition through music training can be gained at any age without previous music training.

In conclusion, healthy older musicians show enhancements in motor inhibition as compared to non-musicians. Although these results are promising, there are only two studies examining motor inhibition in older adult musicians. Thus, there remains a need for further research focusing on the behavior and neurophysiology associated with motor inhibition in music training across the lifespan.

1.8 Why Music Training as an Alternative Strategy to Combat Aging

Genetics account for approximately 25% of health and function in older age (Beard &

Bloom, 2015). There are lifestyle and environmental factors that influence a large portion of the aging process (Rowe & Kahn, 1997). Targeted intervention therapies can potentially enhance quality of life, functional independence, and lead to better health outcomes and reduction of reliance on medical care via slowing of age-related declines (Tan et al., 2019; Willis et al.,

2006). Targeting executive functions, such as inhibition, has been shown to be a reliable 42 predictor of everyday functioning in older adults (McAlister & Schmitter-Edgecombe, 2016;

Willis et al., 2006;) and to play a key role in the independence of older adults (Tan et al., 2019).

Although cognitive and motor interventions for healthy aging have shown promise, results have been mixed due to limited far transfer of interventions (Nguyen et al., 2019). However, there has been evidence to show that music training is effective in far transfer domains (Jordan,

2019). Music training (i.e., practicing an instrument) is an accessible intervention to potentially preserve and improve cognition and motor control in aging (Krampe et al., 1996; Mansens et al.,

2017). For example, older musicians scored better on cognitive functioning tasks than their matched non-musician counterparts (Bugos et al., 2007; Seinfeld et al., 2013). In addition, older adults with at least 10 years of music experience had better performance in nonverbal memory, naming, and executive functioning tests compared to non-musicians or musicians with less than

10 years of music experience (Hanna-Pladdy & MacKay, 2011).

Together, these studies demonstrate that healthy older musicians show improved performance in the cognitive domains compared to non-musicians. However, there is limited evidence on the benefits of music training on cognitive inhibition (stopping or overriding of a mental process, in whole or in part, with or without intention) (MacLeod, 2007) and little to no evidence on the benefits of music training on motor inhibition (i.e., the suppression of unwanted movement) (MacLeod, 2007) in healthy older adults. Moreover, there is limited research demonstrating the behavioral and neurophysiological effects of music training throughout the lifespan.

1.9 Why Does Music Training Work? Potential Mechanisms of Music Training

Music itself has shown to be a rewarding experience, specifically modulating activity in the mesolimbic network involved in reward processing, as well as pleasure and motivational 43 circuitry (Mas-Herrero et al., 2018; Menon & Levitin, 2005). Moreover, music training has been shown to induce long-term brain plasticity in the frontal and motor areas involved with cognitive and motor inhibition and offset declines in cognition and motor control in healthy adults (Amer et al., 2013; Moreno & Bidelman, 2014; Moreno & Farzan, 2015; Rosenkranz et al., 2007;

Vaalto et al., 2016; White-Schwoch et al., 2013). Furthermore, there seems to be a transfer effect of practicing music into nonmusical domains (Asaridou & McQueen, 2013; George & Coch,

2011; Hetland, 2000; Milovanov & Tervaniemi, 2011; Moreno & Bidelman, 2014; Moreno et al.,

2011; Schellenberg, 2004; Slevc & Miyake, 2006).

The rationales for these improvements are that playing an instrument is a complex motor skill and intense cognitive exercise unlike many other activities (Altenmüller et al., 2006; Brown et al., 2015; Chen et al., 2008; Drake & Palmer, 2000; Levitin, 2006). One must interpret the complex musical score, produce accurate movements in both hands and fingers based on the score interpretation, determine if the movement accurately portrays the written and emotional content, and memorize long musical passages (Zatorre et al., 2007). Music performance also involves focus of attention, integration of sensory and motor information, and careful planning and monitoring of performance (Amer et al., 2013). Moreover, one must suppress unwanted movements (i.e., motor inhibition) as well as stop or override irrelevant mental processes (i.e., cognitive inhibition) while playing. Thus, in combination with its pleasurable and motivational properties as well as neuroplastic changes in areas involved with inhibitory control, music training could be used as an intervention to potentially maintain and enhance cognitive and motor inhibition in aging. However, advancement of music training as an effective intervention is critically dependent upon first demonstrating differences in motor and cognitive inhibition and associated brain activity between healthy older adult musicians and non-musicians. 44

Thus, the overarching goal for this dissertation is to determine the differences in cognitive and motor inhibition between aging musicians and non-musicians. The data collected from this study will provide the initial neurophysiological evidence necessary to later refine and develop music training interventions and strategies to use to enhance inhibitory control in the aging process. In the absence of such information, the potential of using music training as an alternative, high-impact, low-risk, and low-cost treatment for improving inhibitory control and associated brain activity in aging will remain limited.

Aim 1: Determine the differences in behavioral and associated neurophysiological measures of cognitive inhibition in older and young adult musicians and non-musicians.

We hypothesize that (1) musicians will demonstrate greater accuracy and reduced response time on the Stroop task than non-musicians, (2) musicians will demonstrate reduced latency and increased amplitude of the P300 response compared to non-musicians, and (3) young adult musicians and non-musicians will demonstrate greater accuracy and reduced response time on the Stroop task and reduced latency and increased amplitude of the P300 response.

Aim 2: Determine the differences in behavioral and associated neurophysiological measures of motor inhibition in older and young adult musicians and non-musicians. We hypothesize that (1) musicians will demonstrate greater accuracy in finger syncopation than non- musicians, (2) musicians will demonstrate decreased MEP amplitude compared to non- musicians, and (3) young adult musicians and non-musicians will demonstrate greater accuracy in finger syncopation than older adult musicians and non-musicians and decreased MEP amplitude compared to older adult musicians and non-musician. 45

CHAPTER 2: EXPERIMENT 1

2.1 Background

A loss in ability to complete instrumental activities of daily living often results in the need for dependent care in older adults, which can be costly and can reduce quality of life

(Chatterji et al., 2015; Harper, 2014). With a projected increase to more than 2 billion in the global population of older adults over age 60 in 2050 (United Nations, 2013), there is a pressing need to develop effective strategies that improve quality of life and promote active engagement for older adults. One method is to target strategies that improve cognitive inhibition (stopping or overriding of a mental process, in whole or in part, with or without intention) (Tiego et al., 2018).

Indeed, decrements in inhibitory control in aging (Heise et al., 2013; Nielson et al., 2002; Van

Hooren et al., 2007; Wolf et al., 2014) lead to the loss of especially relevant ability in domains of shopping, laundry, transportation, and finances (Jefferson et al., 2006; Royall et al., 2004). This fact highlights the need to development effective strategies to maintain or improve inhibitory control during the aging process.

Genetics account for approximately 25% of health and function in older age (Beard et al.,

2015). There are lifestyle and environmental factors that influence a large portion of the aging process (Rowe & Kahn, 1997). Music training (i.e., practicing an instrument) is an accessible intervention to potentially preserve and improve cognition in aging (Krampe, 1996; Mansens et al., 2017). For example, older musicians scored better on cognitive functioning tasks than their matched non-musician counterparts (Bugos et al., 2007; Seinfeld et al., 2013). In addition, older adults with at least 10 years of music experience had better performance in nonverbal memory, naming, and executive functioning tests compared to non-musicians or musicians with less than

10 years of music experience (Hanna-Pladdy & MacKay, 2011). Thus, interventions using music 46 may be a viable, engaging, and cost-effective alternative strategy to also improve cognitive inhibition in older adults. (Walworth, 2005).

Older adults have shown deficits in tasks measuring cognitive inhibition (i.e. stopping or overriding of a mental process, in whole or in part, with or without intention) including the

Stroop task, sustained attention, and vigilance tasks (MacLeod, 2007; Noreen & MacLeod,

2015). The Stroop task (e.g., identifying the color of the word; word “red” colored in green) has been suggested to be the most sensitive for testing not only cognitive inhibition (Monsell et al.,

2001; West & Alain, 2000) but also overall cognition as age increases (Van Hooren et al., 2007).

Indeed, research has also shown that older adults exhibit an increased response time (i.e., increase in Stroop effect) and lower accuracy than young adults on the Stroop task (Milham et al., 2002). However, music training may attenuate this deficit in older adults. Alain et al. (2019) found that musically naïve older adults improved performance on the Stroop task post music training, and Strong and Midden (2018) also showed that currently practicing older musicians performed better on the Stroop task than non-musicians. Taken together, these studies suggest that the Stroop task is an appropriate measure to determine differences in cognitive inhibition among young and older adult musicians and non-musicians.

Older adults display changes in brain anatomy that have been associated with cognitive inhibition. Previous research has revealed reductions in gray and white matter brain volume, especially in the frontal lobes responsible for inhibitory control in older adults (Seidler et al.,

2010; Mattay, 2002). Moreover, there is a decrease in the oxygenation response in the dorsolateral prefrontal cortex while completing the Stroop task (Milham et al., 2002) and a negative correlation between white matter connectivity, Stroop task performance, and age (Wolf 47 et al., 2014). Thus, evidence demonstrates that older adults have declines in brain anatomy associated with cognitive inhibition.

Although the aforementioned studies primarily use magnetic resonance imaging to observe structural brain changes, electroencephalography (EEG) is also an effective measure of brain differences, specifically electrical brain activity, associated with cognitive inhibition in aging. In particular, the modulation of the EEG response P300 is observed when inhibition is required to complete a task. A reduction of the P300 amplitude suggests a reduction in cognitive reserve, and an increase in P300 latency suggests slowing of processing speed (Salthouse, 2000;

Hämmerer et al., 2010). Indeed, older adults demonstrated poorer performance and a decrease in amplitude of the P300 waveform during an inhibition task. (Bokura et al., 2002). These studies suggest that neurophysiological measures associated with performance on the Stroop task and the

P300 waveform may be good indicators of changes in brain activity associated with cognitive inhibition in older adults.

Together, these studies demonstrate that healthy older musicians show improved performance in the cognitive domains compared to non-musicians. However, there is limited evidence on the benefits of music training on cognitive inhibition (i.e., suppression of goal- irrelevant stimuli) (MacLeod, 2007) and to our knowledge in healthy older adults. Moreover, there is limited research demonstrating the neurophysiological and behavioral effects of music training throughout the lifespan. Thus, the aim of this study is to determine the differences in behavioral and associated neurophysiological measures of cognitive inhibition in older and young adult musicians and non-musicians. Cognitive inhibition was assessed using the Stroop task. Cortical activity associated with cognitive inhibition was assessed using the P300 response recorded with EEG over the frontal/central/parietal, and dorsolateral prefrontal cortices during 48 the Stroop task. We hypothesized that (1) musicians will demonstrate greater accuracy and reduced response time on the Stroop task than non-musicians, (2) musicians will demonstrate reduced latency and increased amplitude of the P300 response compared to non-musicians, and

(3) young adult musicians and non-musicians will demonstrate greater accuracy and reduced response time on the Stroop task and reduced latency and increased amplitude of the P300 response.

2.2 Methods

2.2.1 Participants

All participants provided written informed consent to participate in the study as approved by the Iowa State University Institutional Review Board. All procedures performed involving human participants were in accordance with the ethical standards of the institution and with the

1964 Helsinki declaration and its later amendments or comparable ethical standards (see

Appendix).

The inclusion criteria for all young and older adults included: (1) between ages of 18-35 and 65-80, (2) instrumental musician (defined as currently practicing) or non-musician, and (3) no neurological, musculoskeletal, and/or neuropsychiatric disorder. Exclusion criteria included:

(1) significant cognitive impairment (Mini Mental State Exam < 24), (2) major depression (Beck

Depression Inventory > 18), and/or color blindness (Ischihara Plate Test).

A total of 22 healthy young adult (HYA) musicians, 19 HYA non-musicians, 24 healthy older adult (HOA) musicians, and 20 HOA non-musicians completed the study. Demographic data collected included age, gender, ethnicity, education, handedness, marital status, yearly income, and hours of physical activity (Table 1 and 2). The Lubben Social Network Score was collected to control for variability in social engagement in participants (Lubben et al., 2006). 49

Shipley-2 (IQ assessment) was collected to control for existing variability in cognitive function

and ability (Kaya et al., 2012). Gordon’s Advanced Measures of Music Audiation (AMMA) was

collected to control for existing variability in music aptitude (Gordon, 1989). The Musical

Experience Questionnaire (MEQ) was collected to assess years of music experience, years of

formal training, and hours of current practice (Bailey & Penhune, 2012).

Table 1. Healthy young adult (HYA) demographic information. SD = standard deviation; GPA = grade point average.

Table 2. Healthy older adult (HOA) demographic information. SD = standard deviation; GPA = grade point average.

2.2.2 Stimuli

To measure cognitive inhibition, a computerized Stroop task was performed using E-

Prime 3.0 (Psychology Software Tools, Pittsburgh, PA). Participants were asked to name the color of a word presented in either red, green, yellow or blue. Three conditions were presented randomly: neutral (infrequent words sol, helot, eft and abjure presented in different colors), congruent (color of word matches the word itself e.g., the word green presented in green), and incongruent (color of the word does not match the word itself e.g. the word green presented in pink) (Ila & Polich, 1999). There were three blocks presented. Each block consisted of 48 trials across the 3 Stroop conditions and 4 display colors (for a total of 144 neutral, 144 congruent, and

144 incongruent). Stimuli randomly presented in the experiment to ensure adequate signal-to- noise ratio (Luck, 2014). Inter-stimulus interval varied between 2000 and 2500 ms to minimize expectation effects. Answers were inputted via a computer keyboard using the index finger on 5

(red), middle finger on 6 (green), ring finger on 7 (yellow), and pinky finger on 8 (blue) if the participant’s dominant hand was the right hand. Answers were inputted using the pinky finger on

5 (red), ring finger on 6 (green), middle finger on 7 (yellow), and index finger on 8 (blue) if the participant’s dominant hand was the left hand (Ila & Polich, 1999). Subjects completed one block of practice with XXXX being displayed in different colors. The task took between 20 to 30 minutes to complete.

2.2.3 Procedure

Data Collection

While participants completed the computerized Stroop task, electroencephalography

(EEG) was recorded at a sampling rate of 2000 Hz using a 64-channel unit conforming to the international 10-20 system (Biosemi, Amsterdam, Netherlands). 51

Data Analysis

E-Prime 3.0 E-Data Aid was used to find accuracy and response times. The response times were calculated only for accurate trials. Stroop interference refers to the additional time it takes to name the colors of the words in an incongruent relative to a congruent or neutral condition. To determine Stroop interference scores, accuracy and response time were found subtracting incongruent minus congruent for both accuracy and response time (Moussard et al.,

2016; Yadon et al., 2009).

Analysis of EEG data focused on mean latencies and peak-to-peak amplitudes of the

P300 response between musicians and non-musicians. Electrodes of focus were Fz, Cz, Pz, F3, and F4. These electrodes have been shown P300 differences in inhibitory tasks (Alain et al.,

2019; Beam et al., 2009; Moussard et al., 2016). Analysis was completed using MATLAB

(Version 7.14, Mathworks, Natick, USA). EEG data were downsampled to 250 Hz, filtered through a (0.1 Hz – 30 Hz pass band) phase shift-free Butterworth filter (60 Hz notch), and re- referenced to the common average reference. Data was epoched 200 ms before and 800 ms after the event onset. All trials were baseline corrected using the 200 ms pre-event window. Trials in which any channel exceeded 10 uV per sampling point were discarded. One HOA non-musician outlier was removed for Stroop incongruent accuracy (Figure 10). One outlier for each Stroop condition was removed for HYA musicians and HOA musicians during analysis of Pz amplitude

(Figure 21), and two outliers for each Stroop condition were removed for HOA non-musicians during analysis of Pz amplitude (Figure 21).

Statistical Analysis

Statistical analysis was completed in IBM SPSS Statistics for Windows, Version 25.0

(IBM Corp., Armonk, New York, USA). Independent t-tests were used to determine any 52 differences in demographics of young and older musicians and non-musicians. Significance was set to α = 0.05. A 2 (young adult, older adult) x 2 (musicians, non-musician) ANOVA was conducted for each condition to determine differences for the outcome measures of accuracy and response time. A 2 (young adult, older adult) x 2 (musicians, non-musician) ANOVA was conducted for each Stroop interference condition (incongruent relative to congruent) to determine differences among music conditions for the outcome measures of Stroop interference scores of accuracy and response time. A 2 (young adult, older adult) x 2 (musicians, non- musician) ANOVA was conducted for the P300 latencies and amplitudes and P300 latency and amplitude interference scores at electrodes Fz, Cz, Pz, F3, and F4. Post-hoc differences between means were assessed using Bonferroni Correction. Significance was set at α = 0.05. Post-hoc differences between groups (HYA musicians vs. HYA non-musicians, HOA musicians vs. HOA non-musicians) was assessed using Bonferroni Correction. Significance was set at α = 0.025. As exploratory analysis, stepwise multiple regressions were conducted to determine whether age, group, and/or P300 latency and amplitude predicted performance in the incongruent (i.e. inhibitory) condition on the Stroop task.

2.3 Results

Participants

The young adults did not differ in gender, age, handedness, ethnicity, physical activity per week, highest education, GPA, or marital status (Table 1). Social engagement as measured by the Lubben Social Network scale (p = 0.54), cognitive function and ability as measured by the

Shipley-2 vocabulary, abstraction, and pattern scores (p = 0.77, p = 0.41, p = 0.13), and parental education (mother and father) as measured by the MEQ (p = 0.36, p = 0.55) did not differ. The young adults did differ in years of education and yearly income (Table 1). They also differed in 53 music aptitude as measured by the AMMA (p < 0.01), instrumental start age (p = 0.03), and number of years playing instrument (p < 0.01). Family musical experience was also different between groups (p < 0.01). HYA musicians demonstrated a greater musical aptitude, later instrumental start age, and greater family musical experience. HYA musicians began playing at

11 (± 4) while HYA non-musicians began playing at 7 (± 7). HYA musicians played their instrument for 9 years (± 4) while HYA non-musicians played their instrument for 1 year (± 2).

77% of HYA musicians had one or more immediate family members that played a musical instrument while 26% of HYA non-musicians had one or more immediate family members that played a musical instrument.

The older adults did not differ in gender, age, handedness, ethnicity, physical activity per week, years of education, marital status, or yearly income (Table 2). Social engagement as measured by the Lubben Social Network scale (p = 0.80), cognitive function and ability as measured by the Shipley-2 abstraction and pattern scores (p = 0.37, p = 0.98), instrumental start age (p = 0.19), and parental education (mother) as measured by the MEQ (p = 0.19) did not differ. The older adults did differ in highest education achieved and GPA (Table 2). Cognitive function and ability of as measured by Shipley-2 vocabulary scores (p < 0.01), music aptitude as measured by AMMA (p = 0.02), number of years playing instrument (p < 0.01), family musical experience (p < 0.01), and level of education (of father) (p = 0.03) did differ. HOA musicians demonstrated a greater vocabulary performance, musical aptitude, number of years playing instrument, family musical experience, and parental education (father). HOA musicians began playing at 15 (± 17) while HOA non-musicians began playing at 10 (± 4). HOA musicians played their instrument for 53 years (± 17) while HOA non-musicians played their instrument for

7 years (± 8). 83% of HOA musicians had one or more immediate family members that played a 54 musical instrument while only 40% of HOA non-musicians had one or more immediate family members who played a musical instrument.

Stroop Task

For the Stroop task accuracy, no significant differences were revealed for the main effect of age (F(1,81) = 3.5, p = 0.064, ηp2 = 0.042 ; F (1,80) = 0.78, p = 0.38, ηp2 = 0.010), the main effect of group (F (1,81) = 1.3, p = 0.27, ηp2 = 0.015; F (1,80) = 2.4, p = 0.12, ηp2 = 0.030), or the age x group interaction (F (1,81) = 1.8, p = 0.18; ηp2 = 0.022; F (1,80) = 0.32, p = 0.58, ηp2 = 0.004) for congruent and incongruent condition accuracy, respectively. There was a significant main effect of age (F (1,81) = 14.2, p < 0.01, ηp2 = 0.15) for neutral accuracy. Older adults were more accurate

(98%) than younger adults (96%) However, there was no significant main effect of group (F (1,81)

= 0.70, p = 0.40; ηp2 = 0.009) or age x group interaction (F (1,81) = 1.3, p = 0.26, ηp2 = 0.015) for neutral condition accuracy (Figure 10).

100 99 + 98 97 96 95

94 Accuracy (%) Accuracy 93 92 91 90 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 10. Stroop results for accuracy (%) for each condition. Error bars reflect standard error. + = main effect of age p < 0.001

For the Stroop task response time, there was a significant main effect of age (F(1,81) =

0.17, p < 0.01, ηp2 = 0.23) and group (F (1,81) = 4.6, p = 0.034, ηp2 = 0.054) for the congruent condition. Older adults were slower (907.84 ms) than young adults (703.77 ms). Musicians were faster (760.51 ms) than non-musicians (851.10 ms). There was no significant difference revealed for congruent age x group interaction (F (1,81) = 0.17, p = 0.68, ηp2 = 0.002). For the incongruent condition, there was a significant main effect of age (F (1,81) = 20.0, p < 0.01, ηp2 = 0.20) and group (F(1,81) = 4.7, p = 0.033; ηp2 = 0.055). Older adults were slower (1134.42 ms) than young adults (838.51 ms). Musicians were faster (914.48 ms) than non-musicians (1058.44 ms).There was no significant difference for incongruent age x group interaction (F (1,81) = 0.12, p = 0.27, ηp2

= 0.015). For the neutral condition, there was a significant main effect of age (F (1,81) = 30.0, p <

0.01, ηp2 = 0.27) and group (F(1,81) = 7.9, p < 0.01, ηp2 = 0.089) for the neutral condition. Older adults were slower (944.51 ms) than young adults (737.00 ms). Musicians were faster (787.38 ms) than non-musicians (894.13 ms) there was no significant difference revealed for neutral age x group interaction (F(1,81) = 0.030, p = 0.86, ηp2 = 0.00) (Figure 11).

1400

1200 + △ + △ + △

1000

800

600

400 Response (ms)Time Response 200

0 Congruent Incongruent Neutral

HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 11. Stroop results for response time (ms) for each condition. Error bars reflect standard error. + = main effect of age p < 0.001; △ = main effect of group p < 0.05

For the Stroop interference accuracy score, there were no significant differences for main effect of age (F(1,81) = 1.3, p = 0.26, ηp2 = 0.016), group (F (1,81) = 2.06, p = 0.16, ηp2 = 0.025), or age x group interaction (F(1,81) = 1.3, p = 0.27, ηp2 = 0.015) for incongruent minus congruent condition (Figure 12). For Stroop interference response time score, there was a significant main effect of age (F(1,81) = 5.9, p = 0.017, ηp2 = 0.068) for incongruent minus congruent condition.

Older adults had greater interference (226.58 ms) than young adults (134.73 ms). There were no significant differences revealed for main effect of group (F(1,81) = 2.0, p = 0.16, ηp2 = 0.024) and age x group interaction (F(1,81) = 2.2, p = 0.15, ηp2 = 0.026) for incongruent minus congruent condition response time (Figure 13).

-1

-2

-3

-4

Accuracy Accuracy (%) -5

-6

-7

-8 Incongruent - Congruent HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 12. Stroop interference results for response time (ms). Error bars reflect standard error.

300 + 250

200

150

100 Response (ms)Time Response 50

0 Incongruent - Congruent HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 13. Stroop interference results for response time (ms) for each condition. Error bars reflect standard error. + = main effect of age p < 0.05

P300 Latency

For the P300 latency over Fz in the congruent condition, there was a main effect of age

(F(1,81) = 4.4, p = 0.038, ηp2 = 0.052). Older adults had an earlier amplitude (284 ms) compared to young adults (307 ms). There was no main effect of group (F(1,81) = 0.22, p = 0.64, ηp2 = 0.003) or age x group interaction (F(1,81) = 0.97, p = 0.33, ηp2 = 0.012). For Fz in the incongruent condition, there was no main effect of age (F(1,81) = 0.14, p = 0.71, ηp2 = 0.002), group (F(1,81) =

0.36, p = 0.55, ηp2 = 0.004), or age x group interaction (F(1,81) = 0.075, p = 0.78, ηp2 = 0.001). For

Fz in the neutral condition, there was no main effect of age (F(1,81) = 0.016, p = 0.90, ηp2 = 0.00), group (F(1,81) = 0.25, p = 0.62, ηp2 = 0.00), or age x group interaction (F(1,81) = 0.005, p = 0.94,

ηp2 = 0.003) (Figure 14).

Fz 340 + 320

300

280

260

P300 Latency (ms)Latency P300 240

220

200 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 14. P300 latency (ms) results for Fz for each condition. Error bars reflect standard error. + = main effect of age p < 0.05

For the P300 latency over Cz in the congruent condition, there was a main effect of age

(F(1,81) = 6.6, p = 0.012, ηp2 = 0.075). Older adults have a later amplitude (297 ms) compared to young adults (273 ms). There was no main effect of group (F(1,81) = 1.6, p = 0.22, ηp2 = 0.019) or age x group interaction (F(1,81) = 0.83, p = 0.37, ηp2 = 0.010). For Cz in the incongruent condition, there was a main effect of age (F(1,81) = 31.7, p < 0.01, ηp2 = 0.28) and group (F(1,81) =

4.09, p = 0.046, ηp2 = 0.048). Older adults had a later amplitude (319 ms) compared to young adults (274 ms). Musicians had an earlier amplitude (289 ms) than non-musicians (305 ms).

There was no age x group interaction (F(1,81) = 0.038, p = 0.85, ηp2 = 0.00). For Cz in the neutral condition, there was a main effect of age (F(1,81) = 25.6, p < 0.01, ηp2 = 0.24). Older adults had a later amplitude (323 ms) compared to young adults (281 ms). There was no main effect of group

(F(1,81) = 1.33, p = 0.25, ηp2 = 0.016) or age x group interaction (F(1,81) = 1.1, p = 0.29, ηp2 =

0.014) (Figure 15).

Cz 340

320 + + △ +

300

280

260

P300 Latency (ms)Latency P300 240

220

200 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 15. P300 latency (ms) results for Cz for each condition. Error bars reflect standard error. + = main effect of age p < 0.001; ∆ = main effect of group p < 0.05

For the P300 latency over Pz in the congruent condition, there was a main effect of age

(F(1,81) = 23.8, p < 0.01, ηp2 = 0.23). Older adults had a later amplitude (278 ms) compared to young adults (223 ms). There was no main effect of group (F(1,81) = 0.33, p = 0.57, ηp2 = 0.004) or age x group interaction (F(1,81) = 0.51, p = 0.48, ηp2 = 0.006). For Pz in the incongruent condition, there was a main effect age (F(1,81) = 63.5, p < 0.01, ηp2 = 0.44), main effect of group

(F(1,81) = 11.08, p < 0.01, ηp2 = 0.12), and age x group interaction (F(1,81) = 7.9 p < 0.01, ηp2 =

0.089). Older adults had a later amplitude (283 ms) compared to young adults (213 ms).

Musicians had an earlier amplitude (234 ms) than non-musicians (263 ms). Post-hoc differences revealed that older adult musicians displayed earlier P300 latency over Pz (257 ms) compared to older adult non-musicians (310 ms) (p < 0.01). For Pz in the neutral condition, there was a main effect of age (F(1,81) = 51.54, p < 0.01, ηp2 = 0.39), main effect of group (F(1,81) = 11.48, p < 0.01,

ηp2 = 0.13), and age x group interaction (F(1,81) = 7.9, p < 0.01, ηp2 = 0.089). Older adults had a later amplitude (291 ms) compared to young adults (221 ms). Musicians had an earlier amplitude 60

(239 ms) than non-musicians (272 ms). Post-hoc differences revealed that older adult musicians displayed earlier P300 latency over Pz (260 ms) compared to older adult non-musicians (321 ms)

(p < 0.01) (Figure 16).

Pz 340 * * 320 + + △ + △

300

280

260

P300 Latency (ms)Latency P300 240

220

200 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician Figure 16. P300 latency (ms) results for Pz for each condition. Error bars reflect standard error. + = main effect of age p < 0.001; ∆ = main effect of group p < 0.05; * = group x age p < 0.01

For the P300 latency over F3 in the congruent condition, there was no main effect of age

(F(1,81) = 0.061, p = 0.81, ηp2 = 0.01), main effect of group (F(1,81) = 1.7, p = 0.19, ηp2 = 0.021), and age x group interaction (F(1,81) = 0.10, p = 0.75, ηp2 = 0.001). For the incongruent condition, there was no main effect of age (F(1,81) = 0.003, p = 0.95, ηp2 < 0.01), main effect of group (F(1,81)

= 0.46, p = 0.50, ηp2 = 0.006), and age x group interaction (F(1,81) = 0.35, p = 0.55, ηp2 = 0.004).

For the neutral condition, there was no main effect of age (F(1,81) = 0.45, p = 0.50, ηp2 = 0.006), main effect of group (F(1,81) = 0.10, p = 0.75, ηp2 = 0.001), and age x group interaction (F(1,81) =

1.9, p = 0.18, ηp2 = 0.023) (Figure 17).

F3 340

320

300

280

260

240 P300 Latency (ms)Latency P300

220

200 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 17. P300 latency (ms) results for F3 for each condition. Error bars reflect standard error.

For the P300 latency over F4 in the congruent condition, there was no main effect of age

(F(1,81) = 0.29, p = 0.59, ηp2 = 0.004), main effect of group (F(1,81) = 1.6, p = 0.21, ηp2 = 0.019), and age x group interaction (F(1,81) = 0.002, p = 0.97, ηp2 = 0.00). For the incongruent condition, there was no main effect of age (F(1,81) = 0.24, p = 0.63, ηp2 = 0.003), main effect of group (F(1,81)

= 3.6, p = 0.061, ηp2 = 0.043), and age x group interaction (F(1,81) = 0.91, p = 0.34, ηp2 = 0.011).

For the neutral condition, there was no main effect of age (F(1,81) = 4.2, p = 0.062, ηp2 = 0.042), and age x group interaction (F(1,81) = 0.17, p = 0.68, ηp2 = 0.002). There was a main effect of group (F(1,81) = 3.6, p = 0.044, ηp2 = 0.049). Musicians had a greater latency (301 ms) than non- musicians (284 ms) (Figure 18).

F4 340

320 △

300

280

260

P300 Latency (ms)Latency P300 240

220

200 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician Figure 18. P300 amplitude (uV) results for F4 for each condition. Error bars reflect standard error. ∆ = main effect of group p < 0.05

P300 Amplitude

For the P300 amplitude over Fz in the congruent condition, there was a main effect of age

(F(1,81)= 29.6, p < 0.01, ηp2 = 0.27). Older adults had smaller P300 amplitudes than healthy young adults. There was no main effect of group (F(1,81) = 1.9, p = 0.17, ηp2 = 0.023) and age x group interaction (F(1,81) = 1.4, p = 0.23, ηp2 = 0.018). For the incongruent condition, there was a main effect of age (F(1,81) = 20.35, p < 0.01, ηp2 = 0.20). Older adults had a smaller P300 amplitude than healthy young adults. There was no main effect of group (F(1,81) = 0.36, p = 0.55, ηp2 =

0.004) and age x group interaction (F(1,81) = 1.6, p = 0.21, ηp2 = 0.020). For the neutral condition, there was a main effect of age (F(1,81) = 26.4, p < 0.01, ηp2 = 0.25). Older adults had a smaller

P300 amplitude than healthy young adults. There was no main effect of group (F(1,81) = 1.9, p =

0.17, ηp2 = 0.023) and age x group interaction (F(1,81) = 1.2, p = 0.27, ηp2 = 0.015) (Figure 19).

Fz 8 7 + + + 6

P300 (uV) AmplitudeP300 2

0 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 19. P300 amplitude (uV) results for Fz for each condition. Error bars reflect standard error. + = main effect of age p < 0.001

For the P300 amplitude over Cz in the congruent condition, there was no main effect of age (F(1,81) < 0.01, p = 0.99, ηp2 < 0.01), main effect of group (F(1,81) = 0.26, p = 0.61, ηp2 =

0.003), or age x group interaction (F(1,81) = 0.34 p = 0.56, ηp2 = 0.004). For the incongruent condition, there was no main effect of age (F(1,81) = 0.47, p = 0.49, ηp2 = 0.006), main effect of group (F(1,81) = 0.58, p = 0.45, ηp2 = 0.007), or age x group interaction (F(1,81) = 1.3, p = 0.25, ηp2

= 0.016). For the neutral condition, there was no main effect of age (F(1,81) = 0.018, p = 0.89, ηp2

< 0.01), main effect of group (F(1,81) = 0.32, p = 0.58, ηp2 = 0.004), or age x group interaction

(F(1,81) = 1.3, p = 0.25, ηp2 = 0.016) (Figure 20).

Cz 3.5

2.5

1.5

1 P300 (uV) Amplitude P300 0.5

0 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 20. P300 amplitude (uV) results for Cz for each condition. Error bars reflect standard error.

For the P300 amplitude over Pz in the congruent condition, there was main effect of age

(F(1,77) = 5.2, p = 0.025, ηp2 = 0.063). Older adults had a smaller P300 amplitude than young adults. There was no main effect of group (F(1,77) = 1.1, p = 0.30, ηp2 = 0.015) or age x group interaction (F(1,77) = 0.76 p = 0.39; ηp2 = 0.010). For the incongruent condition, there was a main effect of age (F(1,77) = 7.5, p < 0.01, ηp2 = 0.089). Older adults had a smaller P300 amplitude than young adults. There was no main effect of group (F(1,77) = 0.012, p = 0.91, ηp2 < 0.01) or age x group interaction (F(1,77) = 0.85, p = 0.36, ηp2 = 0.011). For the neutral condition, there was a main effect of age (F(1,77) = 6.6, p = 0.012, ηp2 = 0.079). Older adults had a smaller P300 amplitude than young adults. There was no main effect of group (F(1,77) = 0.058, p = 0.81, ηp2 =

0.001) or age x group interaction (F(1,77) = 0.077, p = 0.78, ηp2 = 0.001) (Figure 21).

Pz 1.5 + + + 1

0.5

-0.5 P300 (uV) AmplitudeP300 -1

-1.5 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician Figure 21. P300 amplitude (uV) results for Pz for each condition. Error bars reflect standard error. + = main effect of age p < 0.01

For the P300 amplitude over F3 in the congruent condition, there was a main effect of age (F(1,81) = 43.1, p < 0.01, ηp2 = 0.044). Older adults had a smaller P300 amplitude than healthy young adults. There was no main effect of group (F(1,81) = 2.8, p = 0.10, ηp2 = 0.033) or age x group interaction (F(1,81) = 3.8, p = 0.056, ηp2 = 0.044). For the incongruent condition, there was a main effect of age (F(1,81) = 39.6, p < 0.01, ηp2 = 0.33), no main effect of group (F(1,81) = 3.5, p

= 0.064, ηp2 = 0.042), and an age x group interaction (F(1,81) = 5.6, p = 0.020, ηp2 = 0.065). Older adults had a smaller P300 amplitude than healthy young adults. Post-hoc differences revealed that young adult musicians displayed greater P300 amplitude over F3 (6.7 uV) compared to young adult non-musicians (4.2 uV) (p = 0.018). For the neutral condition, there was a main effect of age (F(1,81) = 42.3, p < 0.01, ηp2 = 0.34), no main effect of group (F(1,81) = 3.7, p =

0.057, ηp2 = 0.044), and an age x group interaction (F(1,81) = 4.7, p = 0.034, ηp2 = 0.054). Older adults had a smaller P300 amplitude than healthy young adults. Post-hoc differences revealed 66 that young adult musicians displayed greater P300 amplitude over F3 (6.3 uV) compared to young adult non-musicians (3.9 uV) (p < 0.01) (Figure 22).

F3 8 + + + 7 * * 6

2 P300 (uV) AmplitudeP300

0 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 22. P300 amplitude (uV) results for F3 for each condition. Error bars reflect standard error. + = main effect of age p < 0.001; * = group x age p > 0.05

For the P300 amplitude over F4 in the congruent condition, there was a main effect of age (F(1,81) = 29.7, p < 0.01, ηp2 = 0.27). Older adults had a smaller P300 amplitude than healthy young adults. There was no main effect of group (F(1,81) = 1.5, p = 0.22, ηp2 = 0.018) or age x group interaction (F(1,81) = 0.69, p = 0.41, ηp2 = 0.008). For the incongruent condition, there was a main effect of age (F(1,81) = 40.9, p < 0.01, ηp2 = 0.34). Older adults had a smaller P300 amplitude than young adults. There was no main effect of group (F(1,81) = 3.4, p = 0.069, ηp2 =

0.040) or age x group interaction (F(1,81) = 0.002, p = 0.97, ηp2 = 0.00). For the neutral condition, there was a main effect of age (F(1,81) = 33.8, p < 0.01, ηp2 = 0.29) and group (F(1,81) = 4.0, p =

0.050, ηp2 = 0.047). Older adults had a smaller P300 amplitude than young adults. Musicians had 67

a larger P300 than non-musicians. There was no significant age x group interaction (F(1,81) = 1.4, p = 0.24, ηp2 = 0.017) (Figure 23).

F4 8 + △ 7 + +

P300 (uV) AmplitudeP300 2

0 Congruent Incongruent Neutral HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician Figure 23. P300 amplitude (uV) results for F4 for each condition. Error bars reflect standard error. + = main effect of age p < 0.001; ∆ = main effect of group p = 0.05

Figure 24 displays the summary of significant electrodes for the P300 latency and

amplitude in musicians across the lifespan in the incongruent condition (i.e., cognitive inhibition).

Figure 24. P300 latency (ms) and amplitude (uV) results for each group in the incongruent condition (i.e., inhibition). Musicians displayed a shorter latency over Cz (i.e, greater inhibition). Healthy older musicians displayed a shorter latency over Pz (i.e., greater inhibition). Healthy young musicians displayed a larger amplitude over F3 (i.e., greater inhibition).

Stepwise Multiple Linear Regression

To determine whether age, group, and/or incongruent P300 latency and amplitude predict

Stroop incongruent condition performance (i.e., does neural cognitive inhibition predict behavioral cognitive inhibition), a stepwise multiple regression model for Stroop accuracy and performance was estimated. Age and group were entered first and then the other variables (i.e. incongruent Fz P300 amplitude and latency, Cz P300 amplitude and latency, Pz P300 amplitude and latency, F3 P300 amplitude and latency, and F4 P300 amplitude and latency) were entered in using the stepwise method. For incongruent accuracy, the initial model did not attain significance. Age and group were not significant predictors of incongruent accuracy (F(2,81) =

1.6, R2 = 0.038, p = 0.21). In the second step the model did not reach significance (F(3,80) = 2.5,

R2 = 0.086, p = 0.065) (Table 3). For incongruent response time, the initial model reached significance. Age and group were significant predictors of incongruent response time and explained 24% of the variance in the model (F(2,82) = 12.6, R2 = 0.24, p < 0.01) (Table 4).

Table 3. Stepwise multiple linear regression for predictors associated with incongruent accuracy for the second step. Standardized Beta Coefficient (ß). *p < 0.05

Predictor ß t p Age 0.25 1.9 0.064 Group -0.13 -1.2 0.25 Fz Latency 0.16 1.5 0.15 Fz Amplitude -0.053 -0.35 0.73 Cz Latency 0.10 0.80 0.43 Cz Amplitude 0.002 0.019 0.96 Pz Latency -0.076 -0.52 0.61 Pz Amplitude 0.010 0.085 0.93 F3 Latency -0.027 -0.24 0.81 F3 Amplitude* 0.012 0.092 0.043 F4 Latency 0.038 0.34 0.73 F4 Amplitude 0.12 0.77 0.45

Table 4. Stepwise multiple linear regression for predictors associated with incongruent response time. Standardized Beta Coefficient (ß). *p < 0.05, ***p < 0.001

Predictor ß t p Age*** 0.44 4.6 0.000017 Group* 0.21 2.1 0.036 Fz Latency -0.063 -0.65 0.52 Fz Amplitude -0.043 -0.39 0.70 Cz Latency 0.11 0.94 0.35 Cz Amplitude -0.037 -0.38 0.71 Pz Latency -0.17 -1.3 0.19 Pz Amplitude -0.017 -0.18 0.86 F3 Latency 0.13 1.3 0.18 F3 Amplitude 0.072 0.61 0.55 F4 Latency 0.090 0.91 0.37 F4 Amplitude -0.11 -0.9 0.36

2.4 Discussion

To recap, the aim of this study was to determine the differences in behavioral and associated neurophysiological measures of cognitive inhibition in older and young adult musicians and non-musicians. Cognitive inhibition was assessed using the Stroop task. Cortical activity associated with cognitive inhibition was assessed using the P300 response recorded with electroencephalography over the frontal/central/parietal, and dorsolateral prefrontal cortices during the Stroop task. We hypothesized that (1) musicians will demonstrate greater accuracy and reduced response time on the Stroop task than non-musicians, (2) musicians will demonstrate reduced latency and increased amplitude of the P300 response compared to non- musicians, and (3) young adult musicians and non-musicians will demonstrate greater accuracy and reduced response time on the Stroop task and reduced latency and increased amplitude of the

P300 response.

Stroop Task and Stroop Task Interference

Hypothesis 1 was partially supported. Musicians did not demonstrate greater accuracy on the Stroop task, but did demonstrate reduced response time (914 ms) compared to non-musicians 70

(1058 ms) in the incongruent condition indicating enhanced cognitive inhibition. Furthermore, in the stepwise multiple linear regression we found that being a musician predicted the decrease in response time in the incongruent condition. Overall, the reduced response time may suggest increased cognitive inhibition in musicians.

For the behavioral results, hypothesis 3 was partially supported. Older adults only demonstrated increased response time (1134 ms) as compared to young adults (839 ms) in the incongruent condition. Age effects and age-related slowing on the Stroop task have been observed in previous studies. Overall, the reduced response time may suggest increased cognitive inhibition in young adults.

There have been mixed results regarding older musician and non-musician performance in the Stroop task (Alain et al., 2019; Amer et al., 2013; Strong & Mast, 2018; Strong & Midden,

2020). Amer et al. (2013) showed that, indeed, older musicians were faster and more accurate than older non-musicians on the congruent and incongruent Stroop trials. However, this was shown via an auditory Stroop task and not the traditional color-word Stroop task. Using the traditional Stroop task, Strong and Mast (2018) and Strong and Midden (2020) demonstrated that older musicians were faster in completing both the congruent and incongruent conditions of the

Stroop task. Furthermore, Alain et al. (2019) and Moussard et al. (2016) found that older adults were faster in the color naming speed subset of the Stroop task (i.e, naming the color of squares and naming color words in black font, respectively). Since older musicians demonstrate greater response times across different conditions (not only the incongruent condition), this collectively suggests that processing speed not cognitive inhibition may be enhanced in musicians.

The Stroop task has often been criticized to involve processing speed rather than cognitive inhibition (MacLeod, 2007). A common method to determine whether cognitive 71 inhibition or processing speed is the determining mechanism is calculating Stroop task interference scores (i.e., incongruent minus congruent) (Moussard et al., 2016; Yadon et al.,

2009). For Stroop interference accuracy in our study, there were no differences. However, for

Stroop interference response time, older adults demonstrated greater interference (i.e., worse inhibition) than young adults. But again, there were no differences in musicians compared to non-musicians. Thus, these results further support the conclusion that processing speed not cognitive inhibition is enhanced in musicians across the lifespan.

Although enhancements in cognitive inhibition between musicians and non-musicians were not detected, it is still meaningful and promising that differences in processing speed among the groups were found. Processing speed is a crucial part of daily activities. It facilitates crucial cognitive functions like learning and long-term memory, comprehension, decision making, and planning (Baddeley, 2012; West, 1996). Declines in processing speed have been associated with cognitive decline during aging (Salthouse, 1996; Salthouse, 2000). Since processing speed seems to be enhanced in musicians, music training may be an intervention that has broad improvements in overall cognition rather than a particular domain. Alain et al. (2019) has indeed shown a causal link between music training and improvement in processing speed in older adults. Taken together, our results support the notion that music training may benefit processing speed in musicians throughout the lifespan.

P300 Latency & P300 Amplitude

Hypothesis 2 for P300 latency and amplitude were fully supported. Musicians did indeed display a reduced latency for Cz and Pz and an increased amplitude at F3 as compared to non- musicians. Post-hoc analyses revealed that older musicians had a reduced latency at Pz compared to older non-musicians. Greater amplitude and earlier latency may indicate superior cognitive 72 inhibition. Thus, overall results reveal that musicians may have greater cognitive inhibition than non-musicians.

For the neurophysiology results, hypothesis 3 for P300 latency and amplitude were fully supported. Young adults demonstrated reduced latency for Cz and Pz and increased amplitude for Pz, F3, and F4 as compared to older adults. Again, Greater amplitude and earlier latency may indicate superior cognitive inhibition. Thus, overall results reveal that younger adults may have greater cognitive inhibition than older adults.

Overall musicians had a larger P300 amplitude on the right dorsolateral prefrontal cortex

(F4) only in the neutral condition. Additionally, only young adult musicians displayed greater

P300 amplitude than young adult non-musicians on the left dorsolateral prefrontal cortex (F3) in both the incongruent and neutral conditions (i.e., greater cognitive resources leading to increased cognitive inhibition).

However, the exploratory analysis revealed that only age and group were related to the behavioral measure of cognitive inhibition (i.e., incongruent Stroop response time). This is in contrast to previous research that has shown that the P300 response is significantly associated with Stroop performance (Hanslmayr et al., 2008; Pfefferbaum et al., 1983; Polich, 1999; West

& Alain, 2000). While P300 changes observed in our study support the stated hypotheses, they may represent changes in brain processes related to other behavior, such as processing speed.

Indeed, the behavioral results from our study tend to suggest that changes in processing speed rather than cognitive inhibition may be at play. Thus, it seems that the neurophysiological results may be representing changes in processing speed rather than cognitive inhibition.

What Does it All Mean for the Aging Brain?

Inhibition has been theorized to be inefficient in older adults. Due to the delayed P300 latency, decreased P300 amplitude, and reduced Stroop response time in older adults, especially in the incongruent condition, it could be interpreted that the inhibitory deficit theory is upheld in our study (Lustig, Hasher, Zacks, 2007). However, the fact that older adults displayed this in other Stroop conditions leads to the suggestion that the speed processing deficit theory is equally upheld. In general, measures of speed tend to share 75% of the age-related variance with a variety of cognitive measures. Overall, evidence suggests that processing speed may be driving the differences between aging musicians versus non-musicians (Jacoby, 1991; Lustig, Hasher,

Zacks, 2007).

However, brain regions showing differences in P300 latency and amplitude have all been implicated in cognitive inhibition. These include the dorsolateral prefrontal cortex and posterior parietal cortex (Cabeza & Nyberg, 1997; Lustig, Hasher, & Zacks, 2007; Nigg, 2000), which were both found to be enhanced in musicians versus non-musicians in our study. These brain regions have also been commonly found to be associated with cognitive inhibition both in single studies that test the same participants on multiple tasks (Sylvester et al., 2004; Wager et al.,

2005) and in meta-analyses (Nee et al., 2007). Furthermore, Schneider et al. (2019) demonstrated that music practice has benefits on cognitive inhibition and prevents cognitive decline among older adults. However, this review also showed that music practice has benefits on overall cognition not just cognitive inhibition. Additionally, since various studies with older musicians demonstrate faster response times across different Stroop conditions (not only the incongruent condition) (Alain et al., 2019; Moussard et al., 2016; Strong & Mast, 2018; Strong & Midden

2020), this collectively suggests that processing speed not cognitive inhibition may be 74 heightened in musicians. In short, we believe based on previous studies and our results that musicians across the lifespan have enhanced processing speed rather than enhanced cognitive inhibition.

There are several important limitations to consider. First, there were older adults in the non-musician category that received music training when they were young (between 0 to 7 years of training). The lack of differences seen between older musicians and non-musicians might be due to the fact that non-musicians did have exposure to music in early childhood. Various studies have shown that non-musicians with short-term training (i.e., 1-3 years) that occurred in early childhood do have some music training-related neuroplasticity later on in life (Merrett et al.,

2013). Second, our musician sample contained a mix of professional and amateur musicians.

Studies of older adults have shown differential effects on cognition of being a professional versus amateur musician (Hanna-Pladdy & MacKay, 2011; Hanna-Pladdy & Gajewski, 2012;

Rogenmoser et al., 2018; Strong & Mast, 2018). Third, our Stroop tasks involved motor responses, which require both cognitive and motor inhibition. Thus, it is difficult to separate whether the behavioral and neurophysiological differences observed are due to cognitive or motor inhibition (Bernal & Nolan, 2009). Future directions would be to test a complete pure sample of musicians and non-musicians especially for older adults, analyzing type of instrument and type of music training along with genetics, environment, and individual differences (Merrett et al., 2013). Dosage of current music practice for older adults would also be of interest since the degree of structural and functional change has been correlated with intensity and duration of practice (Miendlarzewska & Trost, 2014).

In conclusion, musicians overall seem to display enhanced processing speed rather than cognitive inhibition both in behavior and neurophysiology. Our results are the first to show that 75 music practice enhances processing speed but not cognitive inhibition throughout the lifespan using the Stroop task paired with the P300 response. Healthy lifestyles, including music training and music practice, have been shown to be more important than genes for predicting longevity

(Levitin, 2020). Furthermore, music practice and music training programs for older adults have been shown to improve overall wellbeing (MacRitchie et al., 2019). Ultimately, these results suggest that playing music may maintain or enhance processing speed throughout the aging process, improve quality of life for older adults, and serve as a guide to future design of music training/practice interventions in older adults.

CHAPTER 3: EXPERIMENT 2 3.1 Background

With a projected increase to more than 2 billion in the population of older adults over age

60 in 2050 (United Nations, 2013), there is a pressing need to develop effective interventions to improve quality of life and promote active engagement for older adults. One method is to target strategies that improve motor inhibition (the suppression of unwanted movement) (MacLeod,

2007; Nigg, 2000; Tiego et al., 2018). Indeed, decrements in inhibitory control in aging (Heise et al., 2013; Nielson et al., 2002; Van Hooren et al., 2007; Wolf et al., 2014) lead to the loss of ability to complete activities of daily living as well as diminished quality of life (Jefferson et al.,

2006; Royall et al., 2004).

Neurophysiological changes support observed behavioral motor inhibition deficits. These changes include 1) reduction in gray matter brain volume, 2) reduction in white matter volume, and/or 3) biochemical changes in the brain (Coxon et al., 2012; Fernandez-Ruiz et al., 2018;

Fujiyama et al., 2016; Hu et al., 2014; Mattay et al., 2002; Seidler et al, 2010). Decreased integrity of white matter tracts in the fronto-basal-ganglia network have been associated with declines in motor inhibition in older adults (Coxon et al., 2012). Inter-and intrahemispheric inhibition in the brain while completing motor tasks have also been shown to decline during healthy aging (Ruitenberg et al., 2019). Furthermore, lower gamma aminobutyric acid (GABA) levels in the pre-supplementary motor area were associated with poorer motor inhibition using the stop-signal task (Hermans et al., 2018; Pauwels et al., 2018). In short, motor inhibition is impaired in older individuals. However, there remains a paucity in research examining potential strategies to improve motor inhibition in older adults.

Music training may be a viable option and has been shown to alter healthy young adult motor inhibitory performance and neurophysiology (Hughes & Franz, 2007; Penhune et al., 77

2005; Rosenkranz et al., 2007). Slater et al. (2018) showed increased motor inhibition and decreased variability in motor inhibition in musicians (specifically drummers) during a rhythmic task. Yet, there is little research examining the effects of music training in older adults. Only one experiment has examined differences in motor inhibition of older musicians and non-musicians.

Moussard et al., 2016 demonstrated differences in brain activity of older adult musicians compared to non-musicians during motor inhibition using the go/nogo task (Moussard et al.,

2016). While these results are promising, there remains a critical need to address the behavior and neural correlates of motor inhibition in older adult musicians and non-musicians.

The use of transcranial magnetic stimulation has been shown to be an effective tool for understanding changes in motor cortical activity in older adults. Oliviero et al. (2006) showed that single-pulse motor evoked potential (MEP) (i.e., hand twitch) amplitudes at rest are reduced in older adults compared to young adults indicating a reduction in motor cortical excitability possibly due to loss of cortical and spinal motor neurons in older adults. Research measuring short interval intracortical inhibition (SICI) at rest has revealed that reduced SICI (i.e., reduced inhibition) is associated with poorer performance in motor inhibition in older adults (Heise et al.,

2013; McGinley et. al, 2010; Peinemann et al. 2001). Reduced SICI in older adults has also been found in studies measuring SICI during movement (Fujiyama et al., 2011; Heise et al., 2013).

Taken together, these results demonstrate that TMS is a valid method for looking at differences in aging musicians.

Using TMS, Nordstrom & Butler (2002) showed reduced intracortical inhibition of corticospinal neurons in musicians. Rosenkranz et al. (2007) used SICI to show an increase in motor inhibition in musicians as compared to non-musicians. Vaalto et al. (2016) showed that musicians specifically display more motor inhibition in non-primary areas of the motor cortex as 78 compared to non-musicians. Musicians also showed that proprioceptive stimuli exerted stronger inhibition effects on corticospinal excitability, suggesting greater motor inhibition to specific somatosensory inputs. Marquez et al. (2018) showed using single-pulse TMS that there is greater motor cortical inhibition in musicians during movement preparation of isolated and complex finger movements. Furthermore, musicians have shown greater interhemispheric inhibition during finger movement (Chieffo et al., 2016). In short, various studies using single-pulse and

SICI TMS demonstrate differences in motor inhibition between musicians and non-musicians.

To summarize, the neurophysiology behind motor inhibition in musicians involves reduced motor cortical activity and increased inhibition in motor corticospinal circuits.

Unfortunately, there are no studies revealing neurophysiological differences in motor inhibition in musicians and non-musicians across the lifespan. Thus, the aim of this study was to determine the differences in neurophysiological measures of motor inhibition at rest in older and young adult musicians and non-musicians. We hypothesize that (1) musicians will demonstrate decreased MEP amplitude compared to non-musicians and (2) young adult musicians and non- musicians will demonstrate decreased MEP amplitude compared to older adult musicians and non-musicians.

3.2 Methods

3.2.1 Participants

1964 Helsinki declaration and its later amendments or comparable ethical standards (see

Appendix). 79

(1) significant cognitive impairment (Mini Mental State Exam < 24), (2) major depression (Beck

Depression Inventory > 18), any previous adverse reactions to TMS, previous seizure, surgery on blood vessels, brain, or heart, previous stroke, severe vision or hearing loss, metal in head, implanted devices, severe headaches, previous brain-related conditions, brain injury, medications

(i.e. antibiotics, antifungal, antiviral, antidepressants, antipsychotics, chemotherapy, amphetamines, bronchodilators, anticholinergics, antihistamines, sympathomimetics), family history of epilepsy, pregnancy, alcohol consumption less than 24 hours before study, smoking, and illicit drug use.

A total of 19 healthy young adult (HYA) musicians, 16 HYA non-musicians, 13 healthy older adult (HOA) musicians, and 16 HOA non-musicians. Demographic data collected included age, gender, ethnicity, education, handedness, marital status, yearly income, and hours of physical activity (Table 5 and 6). The Lubben Social Network Score was collected to control for variability in social engagement in participants (Lubben et al., 2006). Shipley-2 (IQ assessment) was collected to control for existing variability in cognitive function and ability (Kaya et al.,

2012). Gordon’s Advanced Measures of Music Audiation (AMMA) was collected to control for existing variability in music aptitude (Gordon, 1989). The Musical Experience Questionnaire

(MEQ) was collected to assess years of music experience, years of formal training, and hours of current practice (Bailey & Penhune, 2012).

Table 5. Healthy young adult (HYA) demographic information. SD = standard deviation; GPA = grade point average.

Table 6. Healthy older adult (HOA) demographic information. SD = standard deviation; GPA = grade point average.

3.2.2 Procedure

Data Collection

For TMS, the motor hot spot, specifically the hand knob area in the primary motor cortex

(M1), was located on the contralateral hemisphere to the dominant hand. The location and coil

orientation (45 degrees to the left of the longitudinal fissure) was marked. Resting motor

threshold (RMT) (i.e. a MEP at an amplitude of at least 50 µV produced for 5 out of 10 trials or

50% of the time) was then found. RMT was completed in 20 minutes. Single-pulse TMS 81 intensity was set at 120% of RMT. SICI conditioning pulse was set at 80% of RMT, and SICI test pulse was set at 120% of RMT. The interstimulus interval was 3 ms.

Participants were seated in an armchair with their right forearm pronated and rested on the armrest. Participants were asked not to move during TMS. Single-pulse TMS was applied to the primary motor cortex dominant hand area using the Magstim Model 200 (Magstim,

Whitland, Carmarthenshire). The coil was figure-8 coil (7 cm outer diameter of wings). Coil current was induced approximately perpendicular to the motor homunculus and central sulcus.

The waveform was monophasic. Spike2 was used to trigger single-pulse and SICI stimulations via a Power 1401 data acquisition board and Spike2 software (Cambridge Electronic Design

(CED), Cambridge, UK).

Motor evoked potentials (MEPs) were recorded from the right first dorsal interosseous

(FDI) using bipolar surface electromyography (EMG) (Delsys, Boston, MA, USA). Ten single- pulse stimulations and ten SICI stimulations per participant were applied during rest. Single- pulses and SICI stimulations were applied approximately every 5 to 12 seconds (for a total of 83 to 85 seconds in each condition).

Data Analysis

EMG signals were notch filtered (60 Hz) and high-pass filtered (2nd-order dual-pass

Butterworth, 2 Hz cut-off). EMG signals were also DC shifted, and the root mean square of the

EMG signal was obtained. Peak-to-peak amplitude (µV) was obtained within 100 ms of the TMS pulse. Background EMG was determined for periods of 1.25s to 0.25s before the peak maximum amplitude and 0.25s to 1.25s after the peak maximum amplitude. Background EMG trials > 10

µV were discarded (Majid et al., 2015). For EMG activity before peak amplitude, one HOA non- musician was removed for single-pulse and SICI at rest; one HOA musician was removed for 82

SICI at rest. For EMG activity after peak amplitude, one HOA non-musician was removed; one

HOA musician was removed for SICI at rest. The primary outcome measure of MEP amplitude was obtained by averaging the 10 MEP trials for each condition (i.e. single-pulse & SICI). SICI was also expressed as a percentage using the formula: inhibition (%) = 100 – [rest SICI MEP

(conditioned pulse)/rest single-pulse MEP (non-conditioned pulse) x 100] (Byblow & Stinear,

2006). One HYA musician was removed for inhibition % due to being an outlier.

Statistical Analysis

Statistical analysis was completed in IBM SPSS Statistics for Windows, Version 25.0

(IBM Corp., Armonk, New York, USA). Independent t-tests were used to determine any differences in demographics of young and older musicians and non-musicians. Significance was set to α = 0.05. To confirm that potential differences in MEP amplitude are due to cortical mechanisms rather than an increase in drive to spinal mechanisms, a 2 (young adult, older adult) x 2 (musician, non-musician) ANOVA was conducted to compare 1.25s to 0.25s before the peak maximum amplitude among the three conditions as well as 0.25s to 1.25s after the peak maximum amplitude among the three conditions.

To examine differences in peak-to-peak amplitude and inhibition percent of the MEP at rest, a 2 (young adult, older adult) x 2 (musician, non-musician) ANOVA was completed.

Bonferroni correction was used for post- hoc analysis (HYA musicians vs. HYA non-musicians,

HOA musicians vs. HOA non-musicians). Significance was set at α = 0.025. As exploratory analyses, multiple linear regressions were conducted to determine whether age or group predict single-pulse MEP amplitude, SICI MEP amplitude, or inhibition percent.

3.3 Results

Participants

The young adults did not differ in gender, handedness, ethnicity, physical activity per week, highest education, GPA, or marital status (Table 3). Social engagement as measured by the

Lubben Social Network scale (p = 0.18), cognitive function and ability as measured by the

Shipley-2 vocabulary, abstraction, and pattern scores (p = 0.58, p = 0.14, p = 0.13), and parental education (mother and father) as measured by the MEQ (p = 0.17, p = 0.11) did not differ. The young adults did differ in years of education and yearly income (Table 3). They also differed in music aptitude as measured by the AMMA (p < 0.01), instrumental start age (p < 0.01), and number of years playing instrument (p < 0.01). Family musical experience was also different between groups (p = 0.03). HYA musicians demonstrated a greater musical aptitude, later instrumental start age, and greater family musical experience. HYA musicians began playing at

12 (± 4) while HYA non-musicians began playing at 6 (± 6). HYA musicians played their instrument for 8 years (± 4) while HYA non-musicians played their instrument for 1 year (± 2).

74% of HYA musicians had one or more immediate family members that played a musical instrument while 37% of HYA non-musicians had one or more immediate family members that played a musical instrument.

The older adults did not differ in gender, age, handedness, ethnicity, physical activity per week, years of education, marital status, or yearly income (Table 4). Social engagement as measured by the Lubben Social Network scale (p = 0.55), cognitive function and ability as measured by the Shipley-2 abstraction and pattern scores (p = 0.44, p = 0.45), instrumental start age (p = 0.27), and parental education (mother and father) as measured by the MEQ (p = 0.93; p

= 0.53) did not differ. The older adults did differ in highest education achieved and GPA (Table 84

4). Cognitive function and ability of as measured by Shipley-2 vocabulary scores (p = 0.02), music aptitude as measured by AMMA (p < 0.01), number of years playing instrument (p <

0.01), and family musical experience (p = 0.02) did differ. HOA musicians demonstrated a greater vocabulary performance, musical aptitude, greater number of years playing instrument, and greater family musical experience. HOA musicians began playing at 15 (± 16) while HOA non-musicians began playing at 10 (± 5). HOA musicians played their instrument for 53 years (±

16) while HOA non-musicians played their instrument for 6 years (± 9). 85% of HOA musicians had one or more immediate family members that played a musical instrument while only 56% of

HOA non-musicians had one or more immediate family members that played a musical instrument.

Pre-Background EMG

Results revealed no main effect of pre-background EMG activity for rest single-pulse in age (F(1,59) = 0.29, p = 0.59, ηp2 = 0.005), group (F(1,59) = 0.035, p = 0.85, ηp2 = 0.001), or age x group interaction (F(1,59) = 1.2, p = 0.27, ηp2 = 0.020). Results revealed a main effect of age

(F(1,59) = 5.6, p = 0.021, ηp2 = 0.089) for pre-background EMG activity for rest SICI. Older adults had more pre-background EMG activity (3.9 uV) than young adults (2.5 uV). There was no main effect of pre-background EMG for rest SICI for group (F(1,59) = 0.79, p = 0.38, ηp2 = 0.013) or age x group interaction (F(1,59) = 0.010, p = 0.92, ηp2 < 0.01) (Figure 25).

Post-Background EMG

Results revealed no main effect of post-background EMG activity for rest single-pulse in age (F(1,59) = 0.005, p = 0.94, ηp2 < 0.01), group (F(1,59) = 0.001, p = 0.98, ηp2 < 0.01), or age x group interaction (F(1,59) = 0.99, p = 0.32, ηp2 = 0.016). Results revealed a main effect of age

(F(1,58) = 5.6, p = 0.021, ηp2 = 0.089) for rest SICI. Older adults have greater post-background 85

EMG activity (3.9 uV) than young adults (2.6 uV). There was no main effect of post-background

EMG activity for rest SICI in group (F(1,58) = 0.79, p = 0.38, ηp2 = 0.013) or age x group interaction (F(1,58) = 0.010, p = 0.92, ηp2 < 0.01) (Figure 26).

5 +

Background (uV) EMG Background -

1 Pre

0 Rest SP Rest SICI HYA Musician HYA Non-Musician Figure 25. Pre-background EMG (uV) results for each condition and group. Error bars reflect standard error. + = main effect of age p < 0.02

+ 5

Background Background (uV) EMG -

Post 1

0 Rest SP Rest SICI HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 26. Post-background EMG activity for rest single-pulse and SICI. Error bars reflect standard error. + = main effect of age p < 0.05

MEP Peak to Peak

Results revealed no main effect of single-pulse MEP peak to peak in age (F(1,59) = 1.7, p

= 0.20, ηp2 = 0.028), group (F(1,59) = 1.3, p = 0.26, ηp2 = 0.022), or age x group interaction (F(1,59)

= 0.49, p = 0.49, ηp2 = 0.008). When background EMG activity (pre and post) were included as covariates, results further revealed no main effects of single-pulse MEP peak to peak in age

(F(1,57) = 3.0, p = 0.091, ηp2 = 0.049), group (F(1,57) = 2.2, p = 0.14, ηp2 = 0.037), or age x group interaction (F(1,57) = 0.00, p = 0.99, ηp2 = 0.00) (Figure 27).

Results revealed no main effect of SICI MEP peak to peak in age (F(1,58) = 0.18, p = 0.67,

ηp2 = 0.003), group (F(1,58) = 1.5, p = 0.22, ηp2 = 0.026), or age x group interaction (F(1,58) = 0.41, p = 0.52, ηp2 = 0.007). When background EMG activity (pre and post) were included as covariates, results further revealed no main effects of SICI MEP peak to peak in age (F(1,56) =

0.18, p = 0.68, ηp2 = 0.003), group (F(1,56) = 0.55, p = 0.46, ηp2 = 0.010), or age x group interaction (F(1,56) = 0.21, p = 0.65, ηp2 = 0.004) (Figure 27).

350

300

250

200

150 Evoked Potential Evoked (uV) Potential - 100

Motor 50

0 Rest SP Rest SICI HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 27. MEP peak-to-peak EMG activity for rest single-pulse and SICI. Error bars reflect standard error.

Inhibition Percent

Results revealed no main effect of inhibition percent in age (F(1,57) = 2.5, p = 0.12, ηp2 =

0.042), no main effect in group (F(1,57) = 2.9, p = 0.095, ηp2 = 0.048), and age x group interaction

(F(1,57) = 0.084, p = 0.77, ηp2 = 0.001). When background EMG activity (pre and post) were included as covariates, results further revealed no main effects of inhibition percent in age (F(1,55)

= 3.3, p = 0.075, ηp2 = 0.057), group (F(1,55) = 1.7, p = 0.20, ηp2 = 0.030), or age x group interaction (F(1,55) = 0.10, p = 0.75, ηp2 = 0. 002) (Figure 28).

Inhibition (%) Inhibition 30

0 HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 28. Inhibition (%) for each group. Error bars reflect standard error.

Multiple Linear Regression

To determine whether age or group predict single-pulse MEP, SICI MEP, or inhibition percent, a multiple linear regression model was estimated. For single-pulse MEP, the initial model did not reach significance (Table 7). Age and group were not significant predictors of single-pulse MEP (F(2,60) = 1.6, R2 = 0.049, p = 0.22). For SICI MEP, age, group, pre- background EMG activity, and post-background EMG activity were not significant predictors of 88

SICI MEP (F(2,59) = 1.01, R2 = 0.033, p = 0.37) (Table 8). For inhibition percent, the model, albeit close, did not reach significance (F(2,58) = 3.04, R2 = 0.095, p = 0.056) (Table 9).

Table 7. Multiple regression for predictors associated with single-pulse MEP. Standardized Beta Coefficient (ß).

Predictor ß t p Age 0.20 1.6 0.12 Group 0.22 1.8 0.084

Table 8. Multiple regression for predictors associated with SICI MEP. Standardized Beta Coefficient (ß).

Predictor ß t p Age 0.17 1.3 0.20 Group 0.14 1.1 0.28

Table 9. Multiple regression for predictors associated with inhibition percent. Standardized Beta Coefficient (ß). Predictor ß t p

Age -0.045 -0.35 0.73 Group -0.17 -1.3 0.19

3.4 Discussion

In short, the aim of this study was to determine the differences in neurophysiological measures of motor inhibition at rest in older and young adult musicians and non-musicians. We hypothesized that (1) musicians will demonstrate decreased MEP amplitude compared to non- musicians and (2) young adult musicians and non-musicians will demonstrate decreased MEP amplitude compared to older adult musicians and non-musicians. Neither of our hypotheses was supported. No differences in single-pulse or SICI MEP amplitude were observed. Furthermore, there were no differences in inhibition percent. However, descriptive results show a trend toward a decrease in inhibition percent for musicians.

What Does It All Mean for the Aging Brain?

Background EMG, specifically before and after SICI, was increased in older adults versus young adults. Background EMG activity has been shown to reflect spinal rather than cortical mechanisms while undergoing TMS (Kiers et al., 1993). Although pre- and post- background EMG activity was below the threshold used to determine EMG silence (< 10 uV)

(Majid et al., 2015), main effects of age remained. The fact that resting background EMG was higher for older adults than younger adults indicates an increase in drive to spinal mechanisms rather than cortical mechanisms for motor inhibition (Kiers et al., 1993; Majid et al., 2015). This might be due to atrophy of white and gray matter of the motor cortical regions, atrophy of the cerebellum, and alterations of the basal ganglia pathways in older adults (Dempster, 1992;

Rodriguez-Aranda & Sundet, 2006). Thus, the spinal mechanisms driving motor inhibition while at rest may reflect a compensatory mechanism for the inevitable brain atrophy that occurs in healthy aging.

Although there were no differences in single-pulse MEP in our study, there are studies that support our results. Fathi et al. (2010) examined single-pulse MEP at rest before and after paired associative stimulation (PAS) (a TMS technique that induces neuroplasticity). Although they showed differences in single-pulse MEP compared to young adults, it was only after PAS.

Similar results were seen in Freitas et al. (2011) in which there were no differences in single- pulse MEP for young, middle, and elder participants before PAS was applied. In other words, there was no difference between young and older adult single-pulse MEP before inducing neuroplasticity. Thus, it seems that motor circuitry itself isn’t affected in aging but rather neuroplasticity and functional connectivity. It also supports the notion that spinal mechanisms are also driving motor inhibition. 90

The lack of differences in SICI at rest is surprising. Studies have shown that underlying motor inhibition measured using SICI is different between older and younger adults (McGinley et al., 2010; Peinemann et al., 2001). An extensive body of literature suggests that SICI represents GABAA inhibitory neurotransmission (Bhandari et al., 2016). GABAA receptors are vulnerable to age-related deficits in GABAergic neurotransmission (Young-Bernier et al., 2012).

Furthermore, animal studies have shown a decline in the total number of GABAergic neurons

(Hua et al., 2008); alternations in GABAA receptor subunit composition and function (Caspary et al., 1999; Schmidt et al., 2010; Yu et al, 2006), and loss of amount of GABA neurotransmitter

(Ling et al., 2005) with aging. Our results indicate that via the SICI paradigm, GABAA receptor- mediated inhibitory neurotransmission seems to be intact in younger and older adults at rest.

Although studies have shown that underlying motor inhibition measured by SICI is different between older and younger adults (McGinley et al., 2010; Peinemann et al., 2001), there have been studies that are consistent with the results we obtained. A recent meta-analysis showed that older adults demonstrated non-significant SICI difference compared to young adults

(Bhandari et al., 2017). Other studies have shown no significant differences as well (Cirillo et al.,

2010; Opie et al., 2014; Rogasch et al., 2009; Smith et al., 2011). Thus, it seems that observed

SICI age differences are potentially due to individual differences as well as methodological differences. However, it seems unlikely that our lack of results is due to our methodology alone because other studies using similar methodologies haven’t found any significant differences

(Opie & Semmler, 2014; Smith et al., 2009). However, individual differences in our sample population, such as our older adults being both physically and cognitively active (see Table 4 and

Shipley-2 results) may contribute to intact inhibitory neurotransmission at rest. 91

There were no differences in rest single-pulse and SICI MEP in musicians versus non- musicians regardless of age. These results do not support previous literature that musicians and non-musicians’ brains appear to have differences in volume, morphology, density, connectivity, and function (Merrett et al., 2013). Rosenkranz et al. (2007) showed steeper recruitment of corticospinal excitatory and intracortical inhibitory projections in young musicians. However, the parameters of TMS stimulation and data analysis were different than in our paradigm. This could explain the difference why we weren’t able to see differences between musicians and non- musicians. Furthermore, Hirano et al. (2019) found identical results to our study. They showed no differences in resting single-pulse between musicians and non-musicians. In short, the motor cortical excitability and motor inhibitory circuitry seem to be intact at rest between aging musicians and non-musicians. However, studies have shown differences in single-pulse TMS in older adults (Opie & Semmler, 2014) and musicians (Hirano et al., 2019) performing movements such as finger tapping. Future studies should examine single-pulse and SICI during movement to investigate whether this elicits cortical differences in aging musicians versus non-musicians.

There were several limitations to this study that need to be considered. First, the sample consisted of a mix of past music experience in our non-musician groups. Young adult non- musicians had 0 to 3 years of training while older adult musicians had between 0 to 7 years of music training. Various studies have shown that non-musicians with short-term training (i.e., 1-3 years) that occurred in early childhood do have some music training-related neuroplasticity

(Merrett et al., 2013). Furthermore, in our musician group, we had a mix of professional and amateur musicians. Studies have shown differences in both of these populations in terms of executive function (Hanna-Pladdy & Mackay, 2011; Hanna-Pladdy & Gajewski, 2012). Future directions would be to test a controlled sample of musicians (profession and amateur separated) 92 and non-musicians (no formal music training experience), analyzing type of instrument and type of music training, genetics, and individual differences (Merrett et al., 2013).

In conclusion, we did not observe significant differences in resting single-pulse and SICI.

There was one significant background difference (pre and post SICI MEP) for older adults compared to younger adults. Thus, there seems to be more reliance on spinal mechanisms in older adults. Overall, despite the greater use of spinal mechanisms in older adults, results suggest that during rest motor circuitry remains intact and functional. However, daily life requires movement. Whether results will change while moving remains to be seen. Regardless, this is the first study to examine musicians and non-musicians across the lifespan using single-pulse and

SICI MEP TMS. Future studies will examine single-pulse and SICI MEP during movement across the lifespan and in musicians.

CHAPTER 4: EXPERIMENT 3

4.1 Background

We need to inhibit constantly, whether it is critical thoughts about a partner, reaching for seconds at dinner, or fear while boarding a plane (Munakata et al., 2011). Without inhibition, we would be unable to control impulses, old habits of thought or action, or stimuli in the environment not relevant to current goals or tasks (Diamond, 2013). As Ursin wrote, ‘‘We have to stop whatever we are doing in order to start any new action’’ and ‘‘Inhibition is indeed a most important faculty for our abilities to make choices, and for our freedom of choice’’ (Ursin,

2005). An important component of inhibitory control is motor inhibition. Motor inhibition is defined as the suppression of unwanted movement (MacLeod, 2007). Disorders of motor inhibition lead to impulsivity which is a character train of many neurological and psychiatric conditions such as attention-deficit hyperactivity disorder (Nigg, 2001), drug addiction (Jentsch

& Taylor, 1999), and schizophrenia (Gut-Fayand et al., 2001; Schmitz, 2017), and obsessive compulsive disorder (Chamberlain et al., 2005). In short, motor inhibition is an extremely relevant area of study because it affects the lives of all human beings, both clinical populations and aging adults.

There are differences in motor inhibition throughout the lifespan. Healthy older adults have shown behavioral deficits in tasks implicating motor inhibition (i.e., response inhibition and go/nogo tasks) (Hsieh et al., 2016). Nielson et al. (2002) examined healthy young adults and healthy older adults using a motor inhibition task requiring participants to withhold a response based on alternating presentations of letters. There was a significant difference in response time between the older group and younger group, with the elderly group being significantly slower than the young group. In addition, a correlational analysis confirmed a negative relationship 94 between number of correct responses/inhibitions and age. In another study, Langenecker and

Nielson (2003) used the go/nogo task with a heathy older adult group and a healthy young adult group. Independent sample t-tests indicated older adults scored significantly lower in correct responses in the go/nogo task compared to younger adults, thus suggesting healthy older adults have more difficulty in inhibiting motor responses. Furthermore, a recent meta-analysis has shown that in the go/nogo and stop-signal tasks, older adults show poorer performance than young adults (Rey-Mermet & Gade, 2019).

While these studies support the notion that older adults may have declines in motor inhibition, the tasks used may also involve cognitive function. There is limited research using simpler motor tasks, such as finger syncopation, in healthy adults to examine changes in motor inhibition. However, previous studies in young adults have shown an increase in motor cortical inhibition during syncopated (i.e., finger tapping between auditory tones) finger movements in young adults (Byblow & Stinear, 2006). Moreover, research has revealed that older adults demonstrate a decline in the performance of syncopated finger movement (Stegemöller et al.,

2009). Thus, studies suggest that syncopated finger movement may be a tool to understand better motor inhibition and the underlying neurophysiology than other inhibition tasks.

The use of transcranial magnetic stimulation (TMS) has been shown to be an effective tool for understanding changes in motor cortical activity in older adults. Oliviero et al. (2006) showed that single-pulse motor evoked potential (MEP) (i.e., hand twitch) amplitudes at rest are reduced in older adults compared to young adults indicating a reduction in motor cortical excitability possibly due to loss of cortical and spinal motor neurons in older adults. This has also been shown during movement preparation (Davranche et al., 2007; Duque & Ivry, 2009;

Levin et al., 2014). Research measuring short interval intracortical inhibition (SICI) at rest 95 revealed a decrease in SICI (i.e., decrease in inhibition) is associated with poor performance in motor inhibition, possibly due to a reduction in inhibitory neurotransmitters (i.e., GABA) and loss of corticospinal motor neurons during aging (Heise et al., 2013; McGinley et. al, 2010;

Peinemann et al. 2001;). This has also been found in studies measuring SICI during movement

(Fujiyama et al., 2011; Heise et al., 2013).

Taken together, these results suggest that motor inhibition is reduced in older adults.

However, there is limited research examining changes in motor inhibition during a simple movement task in older adults. Previous research using TMS has shown that SICI is increased during the performance of syncopated finger tapping compared to synchronized finger tapping in young adults (Byblow & Stinear, 2006). However, Fujiyama et al. (2011) showed SICI did not differ between young adults and older adults during a go/nogo task. Thus, studies suggest that syncopated finger movement paired with SICI needs to be further explored in terms of motor inhibition in aging adults.

Furthermore, there is limited evidence regarding whether music training influences motor inhibitory performance and neurophysiology in older adult musicians. Older musicians have shown enhanced performance on motor inhibition tasks (Moussard et al., 2016; Vromans

&Postma-Nilsenov, 2016). Furthermore, in the only high-quality intervention to date, Alain et al.

(2019) found that older, musically naïve adults undergoing a music training intervention showed a greater response associated with motor inhibition over the right frontal scalp areas as compared to the group in the visual art intervention. Otherwise, there are no studies examining motor inhibition in older adult musicians.

Thus, the aim of this study is to determine the behavioral and associated neurophysiological measures of motor inhibition in older and young adult musicians and non- 96 musicians. We hypothesize that (1) musicians will demonstrate greater accuracy in finger syncopation than non-musicians, (2) musicians will demonstrate decreased MEP amplitude compared to non-musicians, and (3) young adult musicians and non-musicians will demonstrate greater accuracy in finger syncopation than older adult musicians and non-musicians and decreased MEP amplitude compared to older adult musicians and non-musicians.

4.2 Methods

4.2.1 Participants

1964 Helsinki declaration and its later amendments or comparable ethical standards (see

Appendix).

(1) significant cognitive impairment (Mini Mental State Exam < 24), (2) major depression (Beck

A total of 19 healthy young adult (HYA) musicians, 16 HYA non-musicians, 13 healthy

older adult (HOA) musicians, and 16 HOA non-musicians. Demographic data collected included

age, gender, ethnicity, education, handedness, marital status, yearly income, and hours of

physical activity (Table 10 and 11). The Lubben Social Network Score was collected to control

for variability in social engagement in participants (Lubben et al., 2006). Shipley-2 (IQ

assessment) was collected in order to control for existing variability in cognitive function and

ability (Kaya et al., 2012). Gordon’s Advanced Measures of Music Audiation (AMMA) was

collected to control for existing variability in music aptitude (Gordon, 1989). The Musical

Experience Questionnaire (MEQ) was collected to assess years of music experience, years of

formal training, and hours of current practice (Bailey & Penhune, 2012).

Table 10 . Healthy young adult (HYA) demographic information. SD = standard deviation; GPA = grade point average.

98 Table 11. Healthy older adult (HOA) demographic information. SD = standard deviation; GPA = grade point average.

4.2.2 Stimuli

Participants were asked to perform an index finger flexion-extension movement (i.e.,

finger tap) in sync with an auditory tone (i.e. synchronized) and between auditory tones (i.e.,

syncopated) presented at 1 Hz (i.e. 1 beat per second). The forearm, wrist, thumb, and fingers 2-4

were supported with a brace maintaining the forearm in a pronated position with the elbow

flexed at 90 degrees. The index finger remained unconstrained to allow for full range of motion

without touching a surface. Each participant had a practice session of synchronized (85 trials)

and syncopated (83 trials) finger movements at 1 Hz. Participants completed both tasks in one

session. Order was randomized.

4.2.3 Procedure

Data Collection

For TMS, the motor hot spot, specifically the hand knob area in the primary motor cortex

(M1), was located on the contralateral hemisphere to the dominant hand. The location and coil

orientation (45 degrees to the left of the longitudinal fissure) was marked. Active motor

threshold (AMT) (i.e. a MEP at an amplitude of at least 200 µV produced for 5 out of 10 trials or

50% of the time) was then found. AMT was completed in 20 minutes. Single-pulse TMS 99 intensity was set at 120% of AMT. SICI conditioning pulse was set at 80% of AMT, and SICI test pulse was set at 120% of AMT. The interstimulus interval was 3 ms.

Participants were seated in an armchair with their right forearm pronated and rested on the armrest. Participants were asked not to move during TMS. Single-pulse TMS and SICI was applied to the M1 dominant hand area using the Magstim Model 200 (Magstim, Whitland,

Carmarthenshire). The coil was figure-8 coil (7 cm outer diameter of wings). Coil current was induced approximately perpendicular to the motor homunculus and central sulcus. The waveform was monophasic. Spike2 was used to trigger single-pulse and SICI stimulations via a Power

1401 data acquisition board and Spike2 software (Cambridge Electronic Design (CED),

Cambridge, UK).

Motor evoked potentials (MEPs) were recorded from the right first dorsal interosseous

(FDI) using bipolar surface electromyography (EMG) (Delsys, Boston, MA, USA). Ten single- pulse and SICI pulses were applied between synchronized and syncopated finger tapping (i.e., during muscle silence) over the hand area of the primary motor cortex (Byblow & Stinear, 2006).

The finger taps were in time with metronome pacing of 1.0 Hz. The presentation of the TMS conditions (rest, single-pulse, or SICI) and tapping conditions (synchronization or syncopation) was randomized for each participant.

Data Analysis

The primary behavioral outcome measure for motor inhibition was finger tapping accuracy and finger tapping accuracy difference of finger tapping (when the tap occurs in reference to tone) in the three different conditions (i.e., rest, single-pulse, and SICI). An accurate tap for synchronization will be defined as 150 ms before or 50 ms after the tone. An accurate tap for syncopation will be defined as 150 ms before or 50 ms after between tones (Byblow & 100

Stinear, 2006). The average for each condition (i.e., rest, single-pulse, and SICI) for synchronization and syncopation was calculated. Then, the conditions were averaged together.

For finger accuracy difference, one outlier was removed for HOA musician in the synchronization condition.

The primary neurophysiological outcome measure for motor inhibition will be MEP amplitude. EMG signals were notch filtered (60 Hz) and high-pass filtered (2nd-order dual-pass

Butterworth, 2 Hz cut-off). EMG signals were also DC shifted, and the root mean square of the

EMG signal was obtained Peak-to-peak amplitude (µV) was obtained within 100 ms of the TMS pulse. Background EMG was determined for periods of 1.25s to 0.25s before the peak maximum amplitude and 0.25s to 1.25s after the peak maximum amplitude. Background EMG trials > 10

µV were discarded (Majid et al., 2015). For EMG activity before peak amplitude, there were no trials discarded. For EMG activity after peak amplitude, there were no trials discarded. The primary outcome measure of MEP amplitude was obtained by averaging the 10 MEP trials for each condition (i.e. single-pulse & SICI). SICI was also expressed as a percentage using the formula: inhibition (%) = 100 – [synchronize or syncopate SICI MEP (conditioned pulse)/synchronize or syncopate SP MEP (non-conditioned pulse) x 100] (Byblow & Stinear,

2006). For inhibition percent, one outlier in the HYA musician and one outlier in the HYA non- musician synchronize condition were removed. Three outliers from inhibition percent in the

HYA musician syncopate condition were removed. Two outliers in the HOA musician syncopate condition were removed. Multiple regressions were conducted to determine whether age, group, and/or single-pulse MEP, SICI MEP, or inhibition percent predict performance in the syncopated finger tapping task.

101

Statistical Analysis

Statistical analysis was completed in IBM SPSS Statistics for Windows, Version 25.0

(IBM Corp., Armonk, New York, USA). Independent t-tests were used to determine any differences in demographics of young and older musicians and non-musicians. Significance was set to α = 0.05. To examine differences in finger tapping accuracy outcome measures, a 2 (young musicians and non-musicians) x 2 (older musicians and non-musicians) ANOVA was completed for each finger tapping condition (i.e., synchronize, syncopate). Bonferroni correction was used for post-hoc analysis. Significance was set at α = 0.05.

To confirm that potential differences in MEP amplitude are due to cortical mechanisms rather than an increase in drive to spinal mechanisms, a 2 (young adult, older adult) x 2

(musician, non-musician) ANOVA was conducted to compare 1.25s to 0.25s before the peak maximum amplitude among the three conditions as well as 0.25s to 1.25s after the peak maximum amplitude among the three conditions.

To examine differences in peak-to-peak amplitude and inhibition percent of the MEP at rest, a 2 (young adult, older adult) x 2 (musician, non-musician) in each TMS condition (i.e., single-pulse and paired-pulse) ANOVA was completed. Significance was set at α = 0.05.

Bonferroni correction was used for post-hoc analysis (HYA musicians vs. HYA non-musicians,

HOA musicians vs. HOA non-musicians). Significance was set at α = 0.025. As exploratory analyses, stepwise multiple linear regressions were conducted to determine whether age, group, background EMG, single-pulse MEP, SICI MEP, and inhibition percent predicted syncopated finger tapping accuracy and accuracy difference.

102

4.3 Results

Participants

The young adults did not differ in gender, handedness, ethnicity, physical activity per week, highest education, GPA, or marital status (Table 3). Social engagement as measured by the

Lubben Social Network scale (p = 0.18), cognitive function and ability as measured by the

12 (± 4) while HYA non-musicians began playing at 6 (± 6). HYA musicians played their instrument for 8 years (± 4) while HYA non-musicians played their instrument for 1 year (± 2).

= 0.53) did not differ. The older adults did differ in highest education achieved and GPA (Table 103

4). Cognitive function and ability of as measured by Shipley-2 vocabulary scores (p = 0.02), music aptitude as measured by AMMA (p < 0.01), number of years playing instrument (p <

0.01), and family musical experience (p = 0.02) did differ. HOA musicians demonstrated greater vocabulary performance, musical aptitude, number of years playing instrument, and family musical experience. HOA musicians began playing at 15 (± 16) while HOA non-musicians began playing at 10 (± 5). HOA musicians played their instrument for 53 years (± 16) while

HOA non-musicians played their instrument for 6 years (± 9). 85% of HOA musicians had one or more immediate family members that played a musical instrument while only 56% of HOA non-musicians had one or more immediate family members that played a musical instrument.

Finger Tapping Accuracy

Results for accuracy (150 ms before or 50 ms after the tone) of synchronized finger tapping revealed no main effect of group (F(1,60) = 1.1, p = 0.29, ηp2 = 0.018) or age x group interaction (F(1,60) = 0.21, p = 0.65, ηp2 = 0.004). There was a main effect of age (F(1,60) = 5.5, p =

0.023, ηp2 = 0.083). Older adults were less accurate (39%) in synchronized finger tapping than young adults (56%). Results for accuracy (150 ms before or 50 ms after the tone) in syncopated finger tapping revealed no main effect of age (F(1,60) = 1.8, p = 0.19, ηp2 = 0.029), group (F(1,60) =

0.076, p = 0.78, ηp2 = 0.001), or age x group interaction (F(1,60) = 1.03, p = 0.31, ηp2 = 0.001)

(Figure 29).

Finger Tapping Accuracy Difference

Results for accuracy difference (distance away from the correct movement at 0 ms) of synchronized finger tapping revealed no main effect of group (F(1,59) = 0.0010, p = 0.97, ηp2 =

0.00) or age x group interaction (F(1,59) = 3.1, p = 0.081, ηp2 = 0.051). There was a main effect of age (F(1,59) = 7.4, p < 0.01, ηp2 = 0.11). Older adults tapped on average 67 ms after the tone while 104 young adults tapped on average only 17 ms after the tone. Results for accuracy difference

(distance away from the correct movement at 500 ms) of syncopated finger tapping revealed no main effect of age (F(1,60) = 1.7, p = 0.20, ηp2 = 0.027) or age x group interaction (F(1,60) = 0.038, p = 0.85, ηp2 = 0.001). There was a main effect of group (F(1,60) = 5.8, p = 0.019, ηp2 = 0.088).

Musicians tapped on average 27 ms after the tone while non-musicians tapped on average 27 ms before the tone (Figure 30 & 31).

70 + 60 50

Accuracy Accuracy (%) 20

0 Synchronization Syncopation

HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 29. Accuracy (%) in each finger tapping condition. Error bars reflect standard error. + = main effect of age p < 0.05

120 + △ 100 80 60 40

20 0 -20

Accuracy (ms) Accuracy Difference -40 -60 -80 Synchronization Syncopation HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 30. Accuracy difference (ms) in each finger tapping condition. Error bars reflect standard error. + = main effect of age p < 0.01; ∆ = main effect of group p < 0.02

105

Figure 31. Spread of accuracy difference (ms) for each group in each finger tapping condition. The plot on the right represents the synchronized finger tapping condition. The plot of the left represents the syncopated finger tapping condition.

Pre-Background EMG

Results for synchronized single-pulse pre-background EMG activity revealed no main effect of group (F(1,60) = 0.004, p = 0.95, ηp2 = 0.00) or age x group interaction (F(1,60) = 0.65, p =

0.42, ηp2 = 0.011). There was a main effect of age (F(1,60) = 13.8, p < 0.01, ηp2 = 0.19). Older adults had a greater pre-background activity (59 uV) than young adults (21 uV). Results for syncopated single-pulse pre-background EMG activity revealed no main effect of group (F(1,60) =

0.048, p = 0.83, ηp2 = 0.001) or age x group interaction (F(1,60) = 1.01, p = 0.32, ηp2 = 0.017).

There was a main effect of age (F(1,60) = 14.2, p < 0.01, ηp2 = 0.19). Older adults had a greater pre-background activity (64 uV) than young adults (24 uV) (Figure 32). 106

Single-Pulse 90 + +

80 )

uV 70

Background Background ( EMG -

20 Pre 10

0 Synchronize Syncopate

HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 32. Pre-background EMG activity for single-pulse. Error bars reflect standard error. + = main effect of age p < 0.001

Results for synchronized SICI pre-background EMG activity revealed no main effect of group (F(1,60) = 0.27, p = 0.60, ηp2 = 0.005) or age x group interaction (F(1,60) = 1.5, p = 0.23, ηp2

= 0.024). There was a main effect of age (F(1,60) = 13.7, p < 0.01, ηp2 = 0.19). Older adults had a greater pre-background activity (50 uV) than young adults (24 uV). Results for syncopated SICI pre-background EMG activity revealed no main effect of group (F(1,60) = 0.16, p = 0.69, ηp2 =

0.003) or age x group interaction (F(1,60) = 2.0, p = 0.17, ηp2 = 0.032). There was a main effect of age (F(1,60) = 12.8, p < 0.01, ηp2 = 0.18). Older adults had a greater pre-background activity (56 uV) than young adults (25 uV) (Figure 33).

107

SICI 80 + + 70

Background Background (uV) EMG - 20

Pre 10

0 Synchronize Syncopate

HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 33. Pre-background EMG activity for SICI. Error bars reflect standard error. + = main effect of age p < 0.001

Post-Background EMG

Results for synchronized single-pulse post-background EMG activity revealed no main effect of group (F(1,60) = 0.048, p = 0.83, ηp2 = 0.001) or age x group interaction (F(1,60) = 0.84, p

= 0.36, ηp2 = 0.014). There was a main effect of age (F(1,60) = 14.3, p < 0.01, ηp2 = 0.19). Older adults had a greater pre-background activity (59 uV) than young adults (23 uV) (Figure 43).

Results for syncopated single-pulse post-background EMG activity revealed no main effect of group (F(1,60) = 0.065, p = 0.80, ηp2 = 0.001) or age x group interaction (F(1,60) = 1.5, p = 0.23, ηp2

= 0.23). There was a main effect of age (F(1,60) = 11.5, p < 0.01, ηp2 = 0.16). Older adults had a greater pre-background activity (61 uV) than young adults (26 uV) (Figure 34).

108

Single-Pulse 90

80 + + )

uV 70 60 50

Background Background ( EMG - 20

Post 10

0 Synchronize Syncopate HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 34. Post-background EMG activity for single-pulse. Error bars reflect standard error. + = main effect of age p < 0.05

Results for synchronized SICI post-background EMG activity revealed no main effect of group (F(1,60) = 0.11, p = 0.74, ηp2 = 0.002) or age x group interaction (F(1,60) = 0.28, p = 0.60, ηp2

= 0.005). There was a main effect of age (F(1,60) = 6.5, p = 0.014, ηp2 = 0.097). Older adults had a greater pre-background activity (49 uV) than young adults (28 uV). Results for syncopated SICI post-background EMG activity revealed no main effect of group (F(1,60) = 0.005, p = 0.94, ηp2 <

0.01) or age x group interaction (F(1,60) = 1.6, p = 0.21, ηp2 = 0.026). There was a main effect of age (F(1,60) = 11.5, p < 0.01, ηp2 = 0.16). Older adults had a greater post-background activity (53 uV) than young adults (24 uV) (Figure 35).

109

SICI 80 + +

) 70 uV 60 50

Background Background ( EMG -

20 Post

0 Synchronize Syncopate HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 35. Post-background EMG activity for SICI. Error bars reflect standard error. + = main effect of age p < 0.05

MEP Peak to Peak

Results for synchronized single-pulse MEP revealed no main effect of group (F(1,60) =

0.24, p = 0.63, ηp2 = 0.004) or age x group interaction (F(1,60) = 1.2, p = 0.27, ηp2 = 0.020). There was a main effect of age (F(1,60) = 13.5, p < 0.01, ηp2 = 0.19). Older adults had a greater MEP

(292 uV) than young adults (140 uV). Results for syncopated single-pulse MEP revealed no main effect of group (F(1,60) = 2.8, p = 0.097, ηp2 = 0.045) or age x group interaction (F(1,60) = 3.9, p = 0.052, ηp2 = 0.061). There was a main effect of age (F(1,60) = 10.5, p < 0.01, ηp2 = 0.15). Older adults had a greater MEP (288 uV) than young adults (161 uV). However, when including pre- and post-background EMG activity as covariates, there was no main effect of age (F(1,58) = 2.2, p

= 0.14, ηp2 = 0.037; F(1,58) = 2.5, p = 0.12, ηp2 = 0.041), no main effect of group (F(1,58) = 0.76, p

= 0.39, ηp2 = 0.013; F(1,58) = 3.3, p = 0.075, ηp2 = 0.054), or age x group interaction (F(1,58) = 110

0.41, p = 0.53, ηp2 = 0.007; F(1,58) = 2.6, p = 0.12, ηp2 = 0.042) for synchronized or syncopated single-pulse MEP (Figure 36).

Single-Pulse 450

400

350

300 250

200 Evoked Potential Evoked Potential (uV) - 150

Motor 100

50 0 Synchronize Syncopate HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 36. MEP (motor-evoked potential) peak to peak for single-pulse. Error bars reflect standard error.

Results for synchronized SICI MEP revealed no main effect of age (F(1,60) = 2.2, p =

0.15, ηp2 = 0.035), group (F(1,60) = 0.001, p = 0.97, ηp2 < 0.01), and age x group interaction

(F(1,60) = 0.002, p = 0.97, ηp2 < 0.01). Results for syncopated SICI MEP revealed no main effect of age (F(1,60) = 0.74, p = 0.39, ηp2 = 0.012), group (F(1,60) = 0.022, p = 0.88, ηp2 < 0.01), and age x group interaction (F(1,60) = 0.77, p = 0.39, ηp2 = 0.013). When including pre- and post- background EMG activity as covariates, there was no main effect of age (F(1,58) = 0.65, p = 0.42,

ηp2 = 0.011; F(1,58) = 0.12, p = 0.73, ηp2 = 0.002), no main effect of group (F(1,58) = 0.015, p =

0.90, ηp2 < 0.01; F(1,58) = 0.003, p = 0.95, ηp2 < 0.01), or age x group interaction (F(1,58) = 0.053, p = 0.82, ηp2 = 0.001; F(1,58) = 0.43, p = 0.51, ηp2 = 0.007) for synchronized or syncopated SICI

MEP (Figure 37). 111

SICI 160

140

120

100 80

Evoked Potential Evoked Potential (uV)

- 60

Motor 40

0 Synchronize Syncopate

HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 37. MEP (motor-evoked potential) peak to peak for SICI. Error bars reflect standard error.

Inhibition Percent

Results revealed no main effect of inhibition percent for synchronize in age (F(1,60) = 1.3, p = 0.25, ηp2 = 0.022 ), group (F(1,60) = 0.20, p = 0.66, ηp2 = 0.003), and age x group interaction

(F(1,60) = 0.48, p = 0.49, ηp2 = 0.008). When including pre- and post-background EMG activity as covariates, there was no main effect of age (F(1,56) = 0.028, p = 0.87, ηp2 = 0.001), no main effect of group (F(1,56) = 0.52, p = 0.47, ηp2 = 0.009), or age x group interaction (F(1,56) = 1.4, p = 0.24,

ηp2 = 0.024) on synchronized inhibition percent (Figure 38).

Results revealed no main effect of inhibition percent for syncopate in age (F(1,55) = 0.004, p = 0.95, ηp2 < 0.01), group (F(1,55) = 0.45, p = 0.51, ηp2 = 0.008), and age x group interaction

(F(1,55) = 1.9, p = 0.18, ηp2 = 0.033). When including pre- and post-background EMG activity as covariates, there was no main effect of age (F(1,53) = 0.009, p = 0.93, ηp2 < 0.01), no main effect of group (F(1,53) = 0.50, p = 0.48, ηp2 = 0.009), or age x group interaction (F(1,53) = 1.4, p = 0.24,

ηp2 = 0.026) (Figure 39). 112

Synchronization 100

50 Inhibition (%) Inhibition 40

0 HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 38. Inhibition percent for each group during synchronization. Error bars reflect standard error.

Syncopation 90

Inhibition (%) Inhibition 40 30

0 HYA Musician HYA Non-Musician HOA Musician HOA Non-Musician

Figure 39. Inhibition percent for each group during syncopation. Error bars reflect standard error.

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Stepwise Multiple Linear Regression

To determine whether age, group, background EMG activity, single-pulse and SICI MEP amplitude, and inhibition percent predict syncopated finger tapping accuracy and accuracy difference, a stepwise multiple regression was performed. Age and group were entered first and then the other variables (i.e., pre-single-pulse, post-single-pulse, single-pulse MEP, pre-SICI, post-SICI, SICI MEP, and inhibition percent) were entered in using the stepwise method. For syncopated finger tapping accuracy, the model did not reach significance (F(2,56) = 1.02, R2 =

0.035, p = 0.37). Age, group, background EMG activity, single-pulse and SICI MEP amplitude, and inhibition percent were not significant predictors of syncopated finger tapping accuracy.

(Table 12).

Table 12. Stepwise multiple linear regression for predictors associated with syncopated finger tapping accuracy.

Predictor ß t p Age -0.19 -1.4 0.16 Group -0.001 -0.004 1.0 Pre-Single-Pulse -0.099 -0.66 0.52 Post-Single-Pulse -0.094 -0.64 0.53 Single-Pulse MEP 0.079 0.54 0.59 Pre-SICI -0.13 -0.82 0.42 Post-SICI -0.14 -0.92 0.36 SICI MEP -0.035 -0.26 0.79 Inhibition Percent 0.15 1.2 0.25

For syncopated finger tapping accuracy difference, the initial model (age and group) reached significance (F(2,56) = 3.3, R2 = 0.11, p = 0.045). Group was a significant predictor of syncopated finger tapping accuracy difference. For the second step, inhibition percent was also a significant predictor and accounted for an additional 6% of variance explained (F(3,55) = 3.7, R2 =

0.17, p = 0.017). For the third step, single-pulse pre-background EMG activity was also a significant predictor and accounted for an additional 6% of variance explained (F(4,54) = 4.0, R2 = 114

0.23, p < 0.01). Age, post-background EMG activity during single-pulse, pre- and post- background EMG activity during SICI, and single-pulse and SICI MEP amplitude were not significant predictors of syncopated finger tapping accuracy difference (Table 13).

Table 13. Stepwise multiple linear regression for predictors associated with syncopated finger tapping accuracy difference for the third step. Standardized Beta Coefficient (ß). *p < 0.05, **p < 0.01 Predictor ß t p

Age 0.026 0.19 0.85 Group** -0.36 -2.9 0.005 Pre-Single-Pulse* 0.28 2.03 0.048 Post-Single-Pulse -0.010 -0.018 0.99 Single-Pulse MEP -0.041 -0.27 0.79 Pre-SICI -0.12 -0.47 0.23 Post-SICI -0.17 -0.68 0.50 SICI MEP* -0.24 -2.0 0.050 Inhibition Percent* -0.28 -2.3 0.025

4.4 Discussion

The aim of this study was to determine the behavioral and associated neurophysiological measures of motor inhibition in older and young adult musicians and non-musicians. We hypothesized that (1) musicians will demonstrate greater accuracy in finger syncopation than non-musicians, (2) musicians will demonstrate decreased MEP amplitude compared to non- musicians, and (3) young adult musicians and non-musicians will demonstrate greater accuracy in finger syncopation than older adult musicians and non-musicians and decreased MEP amplitude compared to older adult musicians and non-musicians.

Hypothesis 1 was partially supported. There no differences in syncopation accuracy between musicians and non-musicians. Interestingly, there were differences in terms of the accuracy difference (i.e., timing). Musicians were generally 27 ms late while non-musicians were

27 ms early. Hypothesis 2 was not upheld. Musicians demonstrated no differences in MEP amplitude as compared to non-musicians. Hypothesis 3 also not upheld. Young adults only 115 demonstrated greater accuracy and better timing only during synchronization compared to older adults.

What Does This All Mean for the Aging Brain?

Finger Tapping Accuracy

Our hypotheses were not supported in that older adults were less accurate (39%) in only synchronized finger tapping than young adults (56%). This supports general age-related slowing

(Salthouse, 1985; Krampe, 2002). However, there were no differences in syncopated finger tapping and no differences between musicians and non-musicians in accuracy. It is surprising that syncopated finger tapping did not differ via age or group. A reason why we might’ve not seen differences in both finger tasks is that bimanual arm and hand movements produce larger age-effects than unimanual tasks, especially if anti-phase movements or the coordination of non- homologous limbs are required (Krampe, 2002). Furthermore, it has been shown that higher movement rates (between 1.5 and 1.75 Hz) are less stable in older adults (Stegemöller et al.,

2010). In other words, if we used both fingers to tap at higher movement rates, we might’ve seen an age and group effect. However, that would have made it extremely difficult to produce the

TMS portion of the study. This is supported by the results that older adults have higher background EMG even at 1 Hz. Background EMG activity would only increase at higher rates, making accurate collection of TMS nearly impossible.

Another reason for a lack of differences is that the young and older adults completed repetitive, timed finger tapping. Other studies have shown that repetitive, timed finger tapping tasks have similar accuracies in young and old adults (Duchek et al., 1994; Greene & Williams,

1993). Krampe (2002) suggests that this might be due to lower-level timing processes remaining intact in normal aging. Finally, all three of these studies (Duchek et al., 1994; Greene & 116

Williams, 1993), including ours, used a single tempo. However, other studies using finger tapping with differing tempi and movement rates (between 1.5 and 1.75 Hz) have shown age related accuracy and timing deficiencies (Stegemöller et al., 2009). Syncopated finger movements usually are only successfully performed up to frequencies near 2 Hz, at which subjects then transition to a synchronized timing state (Kelso, 1992; Mayville et al., 1999;

Mayville et al., 2001; Mayville et al., 2002). Though, completing syncopation and TMS successfully at higher rates without any movement artifact would be difficult if nearly impossible.

We speculate that we didn’t see differences in musicians and non-musicians due to the simplicity of the task. Older adults have shown to produce more errors in complex sequence tasks (Krampe et al., 2005). Additionally, the task was not subject to only music training transfer. Both young and older adults complete complex finger movements commonly through keyboard typing. This could play a role in lack of group differences (Opie et al., 2015).

Furthermore, some of the young and older adult non-musicians in our sample did have early childhood music experience. Various studies have shown that non-musicians with short-term training (i.e., 1-3 years) that occurred in early childhood do have some music training-related neuroplasticity later on in life (Merrett et al., 2013). Second, our musician sample contained a mix of professional and amateur musicians. Studies of older adults have shown differential effects of being a professional versus amateur musician (Hanna-Pladdy & MacKay, 2011;

Hanna-Pladdy & Gajewski, 2012; Rogenmoser et al., 2018; Strong & Mast, 2018). Thus, advantages could be associated with long-term stability of timing (Krampe, 2002).

Finger Tapping Accuracy Difference 117

For synchronized finger tapping, older adults had worse timing. They tapped on average

67 ms after the tone while young adults tapped 17 ms after the tone. This result supports general age-related slowing (Krampe, 2002; Salthouse, 1985). This results also is supported by other studies using motor inhibition tasks with older adults such as the Flanker and go/nogo tasks

(Duque et al, 2016; Fujiyama et al., 2011).

For syncopated finger tapping, musicians on average tapped 27 ms after the tone while non-musicians tapped 27 ms before the tone. Timing plays a crucial role in perceiving and performing music (Franek et al., 1991; Green & Gibson, 1961). Not surprisingly, timing of finger tapping is found to be more accurate in young musicians than in non-musicians when tapping with an auditory tone sequence (Chen et al., 2008; Franek et al., 1991; Krause et al.,

2010). Longer previous musical training is also associated with enhanced timing accuracy

(Merten et al., 2019). Overall, results from our study and previous literature support our conclusion that musicians across the lifespan display better syncopation timing than non- musicians.

Additionally, similar to our study, non-musicians have been found to tap before tone onsets (Repp & Doggett, 2007). This is theorized to be due to non-musicians reacting rather than anticipating the tone (Repp & Doggett, 2007), which may be attributed to worse motor inhibition in non-musicians during syncopation (Gerloff & Hummel, 2006). Furthermore, older adults have impairments in controlling and coordinating unimanual and bimanual movements (Poston et al.,

2008; Vanneste et al., 2001). These decrements have been linked to degeneration of the basal ganglia during aging (De Keyser et al., 1990; Reeves et al., 2002). Thus, it may be that aging musicians have less degeneration of the basal ganglia than aging non-musicians, thereby, exhibiting better timing due to greater motor inhibition during syncopation. 118

Background EMG

For both pre and post background EMG, there was a main effect of age for synchronized and syncopated single-pulse and synchronized and syncopated SICI. The fact that background

EMG activity was higher for older adults than younger adults supports the theory that older adults have more “noise” in their peripheral motor system. These additional activations are often interpreted as reflecting compensation but there are several examples of greater activation being associated with poorer performance in older adults (Reuter-Lorenz & Lusting, 2005; Gazzaley et al., 2005).

Furthermore, background EMG activity has been shown to reflect spinal rather than cortical mechanisms while undergoing TMS (Kiers et al., 1993). Background EMG for both synchronization and syncopation was higher for older adults than younger adults. This indicates an increase in drive to spinal mechanisms rather than cortical mechanisms during movement

(Kiers et al., 1993; Majid et al., 2015). This compensation might be due to atrophy of white and gray matter of the motor cortical regions, atrophy of the cerebellum, and alterations of the basal ganglia pathways in older adults (Dempster, 1992; Rodriguez-Aranda & Sundet, 2006). Thus, the spinal mechanisms driving motor inhibition during movement may reflect a compensatory mechanism for the inevitable brain atrophy that occurs in healthy aging.

MEP Peak to Peak Single-Pulse

Our hypotheses were not supported. Results revealed no age or group differences. There have been studies that show that SP MEPs are greater in young adults during motor inhibition tasks (i.e., Flanker and go/nogo) (Fujiyama et al., 2011; Duque et al., 2016). However, there weren’t differences in musicians versus non-musicians. This may be due to the increased background EMG as well as the task being too simple to elicit neurophysiological differences. 119

MEP Peak to Peak SICI

Our hypotheses were not supported. No age or group differences were revealed. Although other studies have found differences in aging while older adults move, this was during static muscle activation (i.e. precision grip) rather isolated index finger abduction as in our study (Opie

& Semmler, 2014). Opie et al. (2015) found that when have a precision grip condition and an index finger abduction condition, SICI was reduced only in the precision grip condition.

Furthermore, SICI has been shown to be influenced by TMS intensity and test MEP amplitude

(i.e., paradigm and individual differences) (Opie & Semmler, 2014).

Overall, there may be several mechanisms in play with our results. First, successfully inhibiting an initiated response activates the hyperdirect pathway, which is critical for suppressing erroneous movement (Aron, 2007). Since we are stimulating after the movement has occurred, the pathway may not be active. Therefore, this could explain why we see no differences in SICI. In addition, several studies have shown that the differences in aging for inhibition occur during movement preparation (i.e. before the movement). For example, the go/nogo task can engage different inhibitory mechanisms that may co-occur with motor preparation, or follow it, depending on the task requirements and participants’ behavior

(Ficarella & Battelli, 2019). Fujiyama et al. (2011) showed that SICI for both young and older adults is reduced immediately prior to EMG onset not after. Since we recorded SICI MEP between movement, this may be the reason we do not see a difference. Furthermore, a recent study found that SICI during finger abduction produced no age-related difference in SICI during the movement but differences in grip. This shows that age-related differences are movement specific (i.e., single discrete versus repetitive movement) (Opie et al., 2015), rate of movement specific (Stegemöller et al., 2010), and task-specific (Littman & Takacs, 2017). 120

This study does not support previous literature that musicians and non-musicians’ brains appear to have differences across a range of brain regions and networks (Merrett et al., 2013).

There may be several reasons for this. First, the transfer of music training may not be relevant to this task, especially since the majority of the population types on a computer keyboard on a daily basis. The transfer of music practice and typing writing recruit similar processing requirements and brain regions (Schellenberg et al., 2011). Second, Hirano et al. (2019) found that tactile and proprioceptive stimuli exerted stronger inhibitory effects on corticospinal excitability in pianists than in non-musicians. In our study, we controlled for tactile and proprioceptive stimuli as a variable (i.e., participants were not touching anything while tapping). Perhaps a somatosensory component is crucial to see greater inhibition in musicians as was seen in Hirano et al. (2019) as well as in other response inhibition tasks (Alain et al., 2019; Moussard et al., 2016).

What is the most interesting result in this study is that group, background EMG activity,

SICI MEP, and inhibition percent significantly predict timing performance. Specifically, being a non-musician, greater background EMG activity, decreased SICI MEP, and decreased inhibition percent predicts a greater accuracy difference (i.e., worse timing). Older musicians have shown enhanced performance on motor inhibition tasks (Moussard et al., 2016; Vromans & Postma-

Nilsenov, 2016). Older musicians have also shown brain enhancements associated with better motor performance, specifically motor inhibition (Moussard et al., 2016; Roman-Caballero et al.,

2018). Furthermore, longer previous musical training is also associated with enhanced timing accuracy (Merten et al., 2019). Thus, it seems that musicianship and neurophysiological markers of motor inhibition (i.e., SICI MEP and inhibition percent) may play a role in timing in inhibitory control (i.e., syncopation). 121

This is the first study to examine musician and non-musician single-pulse and SICI MEPs across the lifespan while completing synchronized and syncopating finger tapping without tactile or proprioceptive feedback. Future directions would be to test a complete pure sample of musicians (profession and amateur separated) and non-musicians (no formal music training experience), analyze type of instrument and type of music training, genetics, environment, and individual differences, and use single-pulse and SICI MEP with and without tactile feedback during motor tasks (Merrett et al., 2013).

In conclusion, we did not observe a direct enhancement of motor inhibition in musicians.

There were no differences in syncopation accuracy albeit there being differences in timing between musicians and non-musicians. Furthermore, there were no MEP amplitude differences.

It does not seem that use of motor inhibition in a simple task is impaired in adults throughout the lifespan. However, the fact that music practice, decreased SICI MEP, and decreased inhibition percent predict better syncopation timing suggests that music practice could potentially maintain or improve motor inhibition across the lifespan resulting in better motor control during the aging process resulting in a prolonged quality of life.

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CHAPTER 5: EXPERIMENT 4

5.1 Background

Genetics account for approximately 25% of health and function in older age (Beard &

Bloom, 2015). Lifestyle and environmental factors influence a large portion of the aging process

(Rowe & Kahn, 1997). Music training (i.e., practicing an instrument) is an accessible intervention to potentially preserve and improve cognition and movement in aging (Krampe &

Ericsson 1996; Mansens et al., 2017). For example, older musicians scored better on cognitive functioning tasks than their matched non-musician counterparts (Bugos et al., 2007; Seinfeld et al., 2013). In addition, older adults with at least 10 years of music experience had better performance in nonverbal memory, naming, and executive functioning tests compared to non- musicians or musicians with less than 10 years of music experience (Hanna-Pladdy & MacKay,

2011).

Together, these studies demonstrate that healthy older musicians show improved performance in the cognitive domains compared to non-musicians. However, there is limited evidence on the benefits of music training on cognitive inhibition (i.e., stopping or overriding of a mental process, in whole or in part, with or without intention) and to our knowledge no evidence on the benefits of music training on motor inhibition (i.e., the suppression of unwanted movement) in healthy older adults (MacLeod, 2007). Previous results have shown that music practice may have the potential to improve both cognitive and motor inhibition.

and motor inhibition and offset declines in cognition and motor control in healthy adults (Amer et al., 2013; Moreno & Bidelman, 2014; Rosenkranz et al., 2007; Vaalto et al., 2016; White-

Schwoch et al., 2013). Rationale for these improvements are that playing an instrument is a complex motor skill and intense cognitive exercise (Altenmüller et al., 2006; Brown et al., 2015;

Chen et al., 2008; Drake & Palmer, 2000; Levitin, 2006). One must interpret the musical score, produce accurate movements in both hands and fingers based on the score interpretation, and then determine if the movement accurately portrays the written and emotional content (Zatorre et al., 2007). Moreover, one must suppress excessive movements (i.e., motor inhibition) (MacLeod,

2007; Ridderinkhof et al., 2004) as well as suppress goal-irrelevant stimuli (i.e., cognitive inhibition) while playing (Chen et al., 2008; Raver et al., 2001). Thus, in combination with its pleasurable and motivational properties as well as neuroplastic changes in areas involved with inhibitory control, music training could be used as an intervention to potentially maintain and enhance cognitive and motor inhibition in aging. However, advancement of music training as an effective intervention is critically dependent upon first demonstrating differences in motor and cognitive inhibition and associated brain activity between healthy older adult musicians and non- musicians.

To summarize, recent studies suggest that motor inhibition is associated with the dorsolateral and ventrolateral prefrontal cortex, parietal cortex, anterior cingulate, pre-SMA, primary motor cortex, SMA, premotor area, and the basal ganglia (Garavan et al., 2006; Mink,

1996; Rubia et al., 2003; Sanes & Donoghue, 2000). Additional studies show that the neuroanatomical correlates of cognitive inhibition include the anterior cingulate cortex, dorsal lateral prefrontal cortex, ventral lateral prefrontal cortex, orbitofrontal cortex, parietal cortex, extrastriate cortex, superior colliculus, thalamus, SMA, premotor area, primary motor cortex, and 124 basal ganglia (MacLeod, 2007; Ridderinkhof et al., 2004). Interestingly, both motor and cognitive inhibition use overlapping brain regions. Furthermore, music training elicits the use of both motor and cognitive inhibition (Levitin, 2006), and the overlapping regions have been found to be enhanced in musicians throughout the lifespan (Bugos et al., 2007; Chandrasekaran et al., 2010; Furuya et al., 2014; Seinfeld et al., 2013; Strait & Kraus, 2013; Wan and Schlaug,

2010). Collectively this suggests an association and enhancement of motor and cognitive inhibition across the lifespan. However, no models of associated motor and cognitive inhibitory pathways in musicians have been created such as the model of auditory-motor interactions in musicians (Zatorre et al., 2007). Additionally, there are no studies that have shown whether musicianship and behavioral/neurophysiological motor inhibition predict behavioral/neurophysiological cognitive inhibition. Thus, the overarching goal for this project was to determine whether musicianship and motor inhibition predict in cognitive inhibition.

The second aim was to examine the relationship between neural correlates of cognitive inhibition and motor cortical inhibition in musicians. We hypothesized that a decrease in background EMG activity, SICI MEP, inhibition percent, and musicianship will predict a decrease in P300 latency and increase in P300 amplitude.

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5.2 Methods

5.2.1 Participants

1964 Helsinki declaration and its later amendments or comparable ethical standards (see

Appendix).

(1) significant cognitive impairment (Mini Mental State Exam < 24), (2) major depression (Beck

A total of 17 healthy young adult (HYA) musicians, 15 HYA non-musicians, 13 healthy older adult (HOA) musicians, and 16 HOA non-musicians. Demographic data collected included age, gender, ethnicity, education, handedness, marital status, yearly income, and hours of physical activity (Table 14 and 15). The Lubben Social Network Score was collected to control for variability in social engagement in participants (Lubben et al., 2006). Shipley-2 (IQ 126 assessment) was collected in order to control for existing variability in cognitive function and ability (Kaya et al., 2012). Gordon’s Advanced Measures of Music Audiation (AMMA) was collected to control for existing variability in music aptitude (Gordon, 1989). The Musical

Experience Questionnaire (MEQ) was collected to assess years of music experience, years of formal training, and hours of current practice (Bailey & Penhune, 2012).

Table 14. Healthy young adult (HYA) demographic information. SD = standard deviation; GPA = grade point average.

Table 15. Healthy older adult (HOA) demographic information. SD = standard deviation; GPA = grade point average.

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5.2.2 Stimuli

The methods used for cognitive inhibition and motor inhibition (chapters 2 and 4 respectively) are the same here. As additional analyses to verify whether motor inhibition predicts cognitive inhibition, participants also completed the go/nogo task. Participants completed a computerized go/nogo task using E-Prime 3.0 (Psychology Software Tools,

Pittsburgh, PA). They were asked to press the B key whenever a green “O” was presented (go trial) and to press nothing whenever a red “X” was presented on the screen (nogo trial).

Participants first completed a practice session with 50% go trials and 50% no go trials (control)

(total of 80 stimuli). Then, participants completed either go/nogo trial 1 or go/nogo trial 2

(counterbalanced). Each trial had a total of 160 stimuli. In go/nogo trial 1, the first block is

50% go trials and 50% nogo trials (control). The second block is 30% go trials and 70% nogo trials (experimental). Go/nogo trial 2 is the reverse with the first block 30% go trials and

70% nogo and the second block 50 % go trials and 50% nogo trials. Two different versions of the test allowed for control of order effects.

5.2.3 Procedure

Data Collection

The methods used for cognitive inhibition and motor inhibition (chapters 2 and 4 respectively) are the same here.

Statistical Analysis

Statistical analysis was completed in IBM SPSS Statistics for Windows, Version 25.0

(IBM Corp., Armonk, New York, USA). Stepwise multiple linear regressions were conducted to determine whether age, group, and motor inhibition behavioral (syncopation accuracy and syncopation accuracy difference) and brain measures [background EMG activity (due to 128 significance in the previous two experiments), SICI MEP, inhibition percent] predict cognitive inhibition behavior (incongruent accuracy and response time) and brain activity (Fz, Cz, Pz, F3, and F3 amplitude/latency). Stepwise multiple linear regressions were also conducted to determine whether age, group, and motor inhibition via go/nogo accuracy and response time predict cognitive inhibition behavior (incongruent accuracy and response time) and motor inhibition behavior (syncopation accuracy and syncopation accuracy difference)

5.3 Results

Participants

The young adults did not differ in gender, age, handedness, ethnicity, physical activity per week, highest education, GPA, or marital status (Table 7). Social engagement as measured by the Lubben Social Network scale (p = 0.21), cognitive function and ability as measured by the

Shipley-2 vocabulary, abstraction, and pattern scores (p = 0.97, p = 0.16, p = 0.11), and parental education (mother and father) as measured by the MEQ (p = 0.15, p = 0.13) did not differ. The young adults did differ in yearly income (Table 7). They also differed in music aptitude as measured by the AMMA (p < 0.01), instrumental start age (p < 0.01), and number of years playing instrument (p < 0.01). Family musical experience was also different between groups (p =

0.01). HYA musicians demonstrated a greater musical aptitude, later instrumental start age, and greater family musical experience. HYA musicians began playing at 12 (± 4) while HYA non- musicians began playing at 6 (± 6). HYA musicians played their instrument for 8 years (± 4) while HYA non-musicians played their instrument for 2 years (± 2). 76% of HYA musicians had one or more immediate family members that played a musical instrument while 33% of HYA non-musicians had one or more immediate family members that played a musical instrument. 129

The older adults did not differ in gender, age, handedness, ethnicity, physical activity per week, years of education, marital status, or yearly income (Table 8). Social engagement as measured by the Lubben Social Network scale (p = 0.55), cognitive function and ability as measured by the Shipley-2 abstraction and pattern scores (p = 0.44, p = 0.45), instrumental start age (p = 0.27), and parental education (mother and father) as measured by the MEQ (p = 0.93; p

= 0.53) did not differ. The older adults did differ in highest education achieved and GPA (Table

8). Cognitive function and ability as measured by Shipley-2 vocabulary scores (p = 0.02), music aptitude as measured by AMMA (p < 0.01), number of years playing instrument (p < 0.01), and family musical experience (p = 0.02) did differ. HOA musicians demonstrated a greater vocabulary performance, musical aptitude, number of years playing instrument, and family musical experience. HOA musicians began playing at 15 (± 16) while HOA non-musicians began playing at 10 (± 5). HOA musicians played their instrument for 53 years (± 16) while

Stepwise Multiple Linear Regression

To determine whether age, group, syncopation accuracy, and syncopation accuracy difference (i.e., timing) predicted incongruent Stroop accuracy and incongruent Stroop response time, a stepwise multiple regression model was estimated. Age and group were entered first and then the other variables (syncopation accuracy and syncopation accuracy difference) were entered using the stepwise method. For incongruent accuracy, the model did not reach significance (F(2,58) = 1.1, R2 = 0.035, p = 0.36). For incongruent response time, the model 130

reached significance (F(2,58) = 9.6, R2 = 0.25, p < 0.01). Only age and group significantly predicted incongruent response time (Table 16).

Table 16. Stepwise multiple linear regression for predictors associated with incongruent response time. Standardized Beta Coefficient (ß). *p < 0.05, **p < 0.01

Predictor ß t p Age** 0.39 3.4 0.001 Group* 0.28 2.5 0.016 Syncopate Accuracy 0.17 1.5 0.14 Syncopate Accuracy Difference -0.19 -1.6 0.12

To determine whether age, group, background EMG activity pre- and post-SICI, SICI

MEP, and inhibition percent predicted P300 latency and amplitude (over Fz, Cz, Pz, F3, and F4) in the incongruent Stroop condition, a stepwise multiple regression model was estimated. Age and group were entered first and then the other variables (background EMG activity, SICI MEP, and inhibition percent) were entered in using the stepwise method.

For Fz latency in the incongruent condition, the model did not reach significance (F(2,53)

= 0.084, R2 = 0.003, p = 0.92). For Cz latency in the incongruent condition, the model did reach significance (F(2,53) = 11.6, R2 = 0.30, p < 0.01). Age was the only significant predictor (Table

17). For Pz latency in the incongruent condition, the initial model reached significance (F(2,53) =

29.2, R2 = 0.52, p < 0.01). Age and group were both significant predictors. The second model reached significance (F(3,52) = 30.5, R2 = 0.64, p < 0.01) and explained an additional 12% of the variance. Age, group, and post-background EMG activity after SICI were all significant predictors of Pz latency (Table 18). For F3 latency in the incongruent condition, the model did not reach significance (F(2,53) = 0.68, R2 = 0.025, p = 0.51). For F4 latency in the incongruent, the model did not reach significance (F(2,53) = 1.02, R2 = 0.037, p = 0.37).

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Table 17. Stepwise multiple linear regression for predictors associated with Cz latency in the second step. Standardized Beta Coefficient (ß). ***p < 0.001

Predictor ß t p Age*** 0.50 4.4 0.000057 Group 0.19 1.7 0.10 Pre-SICI 0.066 0.49 0.62 Post-SICI 0.11 0.83 0.41 SICI MEP 0.016 0.14 0.89 Inhibition Percent 0.094 0.81 0.42

Table 18. Stepwise multiple linear regression for predictors associated with Pz latency in second step. Standardized Beta Coefficient (ß). ***p < 0.001 Predictor ß t p

Age*** 0.43 4.5 0.000033 Group*** 0.32 3.8 0.00035 Pre-SICI 0.029 0.095 0.92 Post-SICI*** 0.38 4.04 0.000176 SICI MEP -0.036 -0.38 0.70 Inhibition Percent -0.026 -0.30 0.77

For Fz amplitude in the incongruent condition, the initial model reached significance

(F(2,53) = 4.1, R2 = 0.13, p = 0.022). Only age significantly predicted Fz amplitude. In the second model, pre-background EMG activity before SICI was a significant predictor and added 17% to the explained variance (F(3,52) = 7.3, R2 = 0.30, p < 0.01) (Table 19). For Cz amplitude incongruent condition, the initial model did not reach significance (F(3,52) = 0.47, R2 = 0.018, p =

0.63). The second model also did not reach significance (F(3,52) = 2.7, R2 = 0.13, p = 0.058). For

Pz amplitude incongruent condition, the model did not reach significance (F(3,52) = 0.73, R2 =

0.027, p = 0.49). For F3 amplitude incongruent condition, the model did reach significance

(F(2,53) = 11.3, R2 = 0.30, p < 0.01). Age was the only significant predictor of F3 amplitude

(Table 20). For F4 amplitude incongruent condition, the initial model reached significance

(F(2,53) = 10.2, R2 = 0.28, p < 0.01). Age was a significant predictor of F4 amplitude. For the second step, the model also reached significance (F(3,52) = 8.8, R2 = 0.34, p < 0.01). Inhibition percent explained an additional 6% of the variance (Table 21). 132

Table 19. Stepwise multiple linear regression for predictors associated with Fz amplitude. Standardized Beta Coefficient (ß). ***p < 0.001 Predictor ß t p

Age 0.14 1.03 0.29 Group -0.004 -0.33 0.97 Pre-SICI** 0.46 3.4 0.001 Post-SICI -0.36 -0.86 0.39 SICI MEP -0.064 -0.49 0.63 Inhibition Percent -0.008 -0.070 0.94

Table 20. Stepwise multiple linear regression for predictors associated with F3 amplitude. Standardized Beta Coefficient (ß). ***p < 0.001 Predictor ß t p

Age*** 0.52 4.5 0.000039 Group 0.14 1.2 0.22 Pre-SICI 0.20 1.6 0.12 Post-SICI 0.15 1.1 0.27 SICI MEP -0.011 -0.094 0.93 Inhibition Percent 0.14 1.2 0.23

Table 21. Stepwise multiple linear regression for predictors associated with F4 amplitude in the second step. Standardized Beta Coefficient (ß). *p < 0.05, ***p < 0.001 Predictor ß t p

Age*** 0.48 4.2 0.000095 Group 0.21 1.8 0.078 Pre-SICI 0.049 0.36 0.72 Post-SICI 0.034 0.26 0.80 SICI MEP -0.088 -0.70 0.49 Inhibition Percent* 0.25 2.2 0.036

To determine whether age, group, go accuracy, nogo accuracy, and go response time predicted incongruent Stroop accuracy, incongruent Stroop response time, syncopation accuracy, and syncopation accuracy difference, a stepwise multiple regression model was estimated. Age and group were entered first and then the other variables (i.e., go accuracy, nogo accuracy, and go response time) were entered in using the stepwise method. For syncopate accuracy, the model did not reach significance (F(2,53) = 1.4, R2 = 0.050, p = 0.26). For syncopate accuracy difference

(i.e., timing), the model reached significance (F(2,53) = 3.9, R2 = 0.13, p = 0.025) but only group predicted syncopation accuracy difference (Table 22). For incongruent accuracy, the model did 133

not reach significance (F(2,53) = 1.6, R2 = 0.056, p = 0.22). For incongruent response time, the

initial model did reach significance (F(2,53) = 7.7, R2 = 0.23, p < 0.01). Age and group were

significant predictors of incongruent response time. For the second step, the model also reached

significance (F(3,52) = 1.6, R2 = 0.41, p < 0.01). Only go response time predicted incongruent

response time but explained an additional 18% of the variance (Table 23).

Table 22. Stepwise multiple linear regression for predictors associated with syncopation accuracy difference in the second step. Standardized Beta Coefficient (ß). *p < 0.05 Predictor ß t p

Age* 0.15 1.1 0.27 Group -0.34 -2.6 0.011 Go Accuracy 0.004 0.030 0.98 Nogo Accuracy 0.20 1.5 0.13 Go Response Time -0.17 -0.11 0.91

Table 23. Stepwise multiple linear regression for predictors associated with incongruent response time in the second step. Standardized Beta Coefficient (ß). ***p < 0.001

Predictor ß t p Age 0.12 0.98 0.33 Group 0.16 1.4 0.16 Go Accuracy 0.069 0.63 0.53 Nogo Accuracy 0.031 0.28 0.78 Go Response Time*** 0.51 4.0 0.000197

Tables 24-27 display a summary of behavior and brain predictors for cognitive inhibition.

Table 24. Demographic and behavioral motor inhibition predictors associated with behavioral cognitive inhibition. (x) = non-predictor, (+) = positive predictor, (-) = negative predictor

Incongruent Accuracy Incongruent Response Time

Age x +

Group x + Syncopation Accuracy x x Syncopation Accuracy Difference x x

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Table 25. Demographic and neural motor inhibition predictors associated with neural cognitive inhibition. (x) = non-predictor, (+) = positive predictor, (-) = negative predictor

Fz Latency Cz Latency Pz Latency F3 Latency F4 Latency

Age x x x + + Group x x + x x

Pre-SICI x x x x x Post-SICI x x + x x SICI MEP x x x x x Inhibition Percent x x x x x

Table 26. Demographic and neural motor inhibition predictors associated with neural cognitive inhibition. (x) = non-predictor, (+) = positive predictor, (-) = negative predictor Fz Amplitude Cz Amplitude Pz Amplitude F3 Amplitude F4 Amplitude

Age x x x + + Group x x x x x

Pre-SICI + x x x x Post-SICI x x x x x SICI MEP x x x x x Inhibition Percent x x x x +

Table 27. Demographic and neural motor inhibition predictors associated with neural cognitive inhibition. (x) = non-predictor, (+) = positive predictor, (-) = negative predictor

Incongruent Incongruent Syncopation Syncopation Accuracy Accuracy Response Time Accuracy Difference Age x x x x

Group x x x + Go Accuracy x x x x Nogo Accuracy x x x x Go Response Time x + x x

5.4 Discussion

The first aim was to examine the relationship between behavioral correlates of motor inhibition and cognitive inhibition and whether musicianship plays. We hypothesized that an increase in syncopated finger tapping accuracy (increase in motor inhibition) and musicianship 135 would predict an increase in Stroop incongruent condition accuracy and a decrease in response time. This hypothesis was partially upheld. Only age and musicianship were significant predictors of incongruent response time performance. Musicianship and decrease in age predicted reduced incongruent response time.

The second aim was to examine the relationship between neural correlates of cognitive inhibition and motor cortical inhibition in musicians. We hypothesized that a decrease in background EMG activity, SICI MEP, and inhibition and musicianship will predict a decrease in

P300 latency and increase in P300 amplitude. This hypothesis was partially upheld.

Musicianship and decrease in post-background EMG activity along with decrease in age predicted a decrease in Pz latency.

What Does It All Mean for the Aging Brain?

Healthy older adults experience alterations in motor inhibition and cognitive inhibition

(Bugos et al., 2007), but aging musicians (and participants with previous music experience) have shown to be less susceptible to age-related brain reductions (Hanna-Pladdy & Mackay, 2011;

Wright et al., 2012). Preservation of neural circuits involved in motor and cognitive inhibition may explain why we see limited relationships between motor inhibition and cognitive inhibition, specifically no relationships in musicians for P300 amplitudes at Fz, Cz, Pz and latencies at Fz,

F3, and F4. In other words, neural circuits involved in motor and cognitive inhibition between musicians and non-musicians across the lifespan may be more refined and less overlapping than previously thought.

In previous studies, motor and cognitive inhibition have demonstrated moderately strong positive correlations and demonstrated closely related cognitive abilities (Tiego et al., 2018). To try to separate motor and cognitive inhibition, we used difference tasks. We only observed 136 limited relationships between behavioral and neural correlates of motor and cognitive inhibition.

This suggests that our tasks activate separate, non-overlapping neural circuits. This is also evidenced go response time predicting incongruent response time. This supports the research that go/nogo task and Stroop task activate similar brain regions making it difficult to distinguish between true motor inhibition and cognitive inhibition, respectively (Nee et al., 2007; Simmonds et al., 2008). Thus, our tasks may be better separators of motor inhibition and cognitive inhibition, which has been a challenge in the inhibitory control realm for decades. Likely though, it is an issue of EEG. A downside of using EEG is that you cannot reliably localize the source of the brain signal (Luck, 2015). In TMS, we have more accurate knowledge in control over what brain region is being stimulated (Hallett, 2007). Thus, the lack of knowledge of the source of the brain activity may be why we don’t see any predictors of cognitive inhibition.

Moreover, difficulty in controlling inhibitory control has been associated with hippocampal hyperactivity via dysfunctional GABAergic interneurons. These two mechanisms are tightly linked with the fronto-hippocampal inhibitory control pathway. Furthermore, response inhibition, cognitive inhibition, and emotional inhibition are supported by a right-lateralized fronto-basal ganglia circuitry, the orbitofrontal cortex, and interactions between the ventromedial prefrontal cortex and the amygdala, respectively (Dillon & Pizzagalli, 2007). The right ventrolateral prefrontal cortex also appears to be critically implicated in both motor inhibition and cognitive inhibition (Aron et al., 2004; Dillon & Pizzagalli, 2007; Konishi et al., 1999).

Altogether, this suggests that separate pathways not examined in our study are indeed involved in both motor and cognitive inhibition (Schmitz et al., 2017), which may explain why we didn’t see relationships for all motor and cognitive inhibition measures. 137

Limitations of this study include a mix of past music experience in our non-musician groups. Young adult non-musicians had 0 to 3 years of training while older adult musicians had between 0 to 7 years of music training. Various studies have shown that non-musicians with short-term training (i.e., 1-3 years) that occurred in early childhood do have some music training- related neuroplasticity (Merrett et al., 2013). Furthermore, in our musician group, we had a mix of professional and amateur musicians. Studies have shown differences in both of these populations in terms of executive function (Hanna-Pladdy & MacKay, 2011; Strong & Mast,

2018). Future directions would be to test a complete pure sample of musicians (profession and amateur separated) and non-musicians (no formal music training experience), analyzing type of instrument and type of music training, genetics, environment, and individual differences (Merrett et al., 2013).

In conclusion, musicianship and motor inhibition performance have limited predictability to cognitive inhibition performance. In the cases where musicianship and motor inhibition do predict cognitive inhibition, it is likely due to greater overlapping and less refinement of inhibitory pathways. Because there were limited relationships, it may mean that musicians may have a more refined rather than overlapping inhibitory pathways. Additionally, it may mean that the tasks used in the study target pure motor and cognitive inhibition, which has been a challenge for the field for decades. Nonetheless, this is the first study to examine the relationship of motor inhibition and cognitive inhibition in musicians and non-musicians across the lifespan and will help inform future studies of cognitive and motor control in older musicians and clinical populations.

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CHAPTER 6: GENERAL DISUCSSION

6.1 Summary

Music itself has shown to be a rewarding experience, specifically modulating activity in the mesolimbic network involved in reward processing, as well as pleasure and motivational circuitry (Mas-Herrero et al., 2018; Menon & Levitin, 2005;). Moreover, music training has been shown to induce long-term brain plasticity in the frontal and motor areas involved with cognitive and motor inhibition and offset declines in cognition and motor control in healthy adults (Amer et al., 2013; Moreno & Bidelman, 2014; Moreno & Farzan, 2015; Rosenkranz et al., 2007;

Vaalto et al., 2016; White-Schwoch et al., 2013). Furthermore, there seems to be a transfer effect of practicing music into nonmusical domains (Asaridou & McQueen, 2013; George & Coch,

2011; Hetland, 2000; Milovanov & Tervaniemi, 2011; Moreno & Bidelman, 2014; Moreno et al.,

2011; Schellenberg, 2004; Slevc & Miyake, 2006).

Rationale for these improvements are that playing an instrument is a complex motor skill and intense cognitive exercise unlike many other activities (Altenmüller et al., 2006; Brown et al., 2015; Chen et al., 2008; Drake & Palmer, 2000; Levitin, 2006). One must interpret the complex musical score, produce accurate movements in both hands and fingers based on the score interpretation, determine if the movement accurately portrays the written and emotional content, and memorize long musical passages (Zatorre et al., 2007). Music performance also involves focus of attention, integration of sensory and motor information, and careful planning and monitoring of performance (Amer et al., 2013). Moreover, one must suppress unwanted movements (i.e., motor inhibition) as well as stop or override irrelevant mental processes (i.e., cognitive inhibition) while playing (Schneider et al., 2019). Thus, in combination with its pleasurable and motivational properties as well as neuroplastic changes in areas involved with 139 inhibitory control, music training could be used as an intervention to potentially maintain and enhance cognitive and motor inhibition in aging. However, advancement of music training as an effective intervention is critically dependent upon first demonstrating differences in motor and cognitive inhibition and associated brain activity between healthy older adult musicians and non- musicians. Thus, the overarching goal for this dissertation was to determine the differences in cognitive and motor inhibition between aging musicians and non-musicians.

6.2 Aim 1

Aim 1: Determine the differences in behavioral and associated neurophysiological measures of cognitive inhibition in older and young adult musicians and non-musicians.

6.3 Aim 2

140

These set of experiments revealed the following results and insights:

• Experiment 1: Musicians across the lifespan seem to display enhanced processing speed

rather than cognitive inhibition.

• Experiment 2: Musicians and non-musicians across the lifespan seem to display intact

and functional motor inhibition circuitry while at rest.

• Experiment 3: Music practice and increased neural motor inhibition predict better

behavioral motor inhibition performance across the lifespan.

• Experiment 4: Musicians across the lifespan may have more refined rather than

overlapping motor and cognitive inhibitory brain pathways.

6.4 Implications for Music Training in Healthy Aging

Successful aging has been defined as intact cognitive and motor function (Hartley et al.,

2018) and continuation of high quality of life (Depp et al., 2006). Intact cognition and motor function can extend the lifespan and decrease the financial burden of impairment (Schneider et al., 2018). Previous studies have indeed shown that music training may provide greater cognitive and motor advantages during the aging process via activation of various brain areas and integration of multiple cognitive and motor processes (Bugos, 2019; Bugos et al., 2007; Hanna-

Pladdy & Mackay, 2011; Strong & Mast, 2019; Strong & Midden, 2020; Strong et al., 2019). In these experiments, it was observed that musicians had enhancements in both behavioral and neurophysiological components of cognitive and motor components across the lifespan. These results provide further evidence that components of cognition and motor function are enhanced through music training. Overall clinical recommendations are that older adults should continue music practice to maintain and potentially improve brain health.

141

6.5 Conclusions

Musicians overall seem to display enhanced cognition and motor function, specifically via enhancements in processing speed and motor inhibition. For experiment 1, we observed that musicians across the lifespan displayed enhancements in processing speed rather than cognitive inhibition. For experiment 2, we did not observe differences in resting single-pulse and SICI

MEP amplitudes. However, music practice, decreased SICI MEP, and decreased inhibition percent (i.e., greater neurophysiological motor inhibition) predicted better syncopation timing

(i.e., greater behavioral motor inhibition) suggesting that music practice could potentially maintain or improve motor inhibition across the lifespan. Furthermore, due to limited predictability of musicianship and motor inhibition on cognitive inhibition, musicians may contain more refined rather than overlapping inhibitory pathways. Additionally, it may mean that the tasks used in the study target pure motor and cognitive inhibition, which has been a challenge in the field for decades.

To summarize, results suggest that engaging in music training throughout life may maintain or enhance processing speed and inhibitory control. The information gained from this research is relevant because 1) it shows that aging musicians demonstrate more robust processing speed and inhibitory control, 2) it could aid in the design and implementation of future studies examining the effects of music training among clinical populations with abnormal inhibitory control, and 3) provides evidence of the benefits of music training in the aging process relevant to education and health policy. Ultimately, these results suggest that music training has the potential to improve quality of life for older adults and clinical populations with inhibitory control decrements. 142

Future directions will include identifying strategies for designing accessible and evidence-based music interventions promoting cognitive and motor health later in life. This includes studying cognitive and motor inhibition in musicians with neurological and psychiatric disorders and designing music training interventions for clinical populations. Disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, Alzheimer’s disease,

Parkinson’s disease, dystonia, Tourette syndrome, and compulsive disorders all have known and proposed deficits in cognitive and motor inhibition. Regardless of the results of these future directions, their outcomes will increase the validity of using music training as a novel alternative strategy to maintain and/or improve inhibition in older adults and all clinical populations in the hopes of preventing or maintaining brain health.

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