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Relationship between interhemispheric inhibition and bimanual coordination in musicians

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Relationship between interhemispheric inhibition and bimanual coordination in musicians

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RELATIONSHIP BETWEEN INTERHEMISPHERIC INHIBITION AND
BIMANUAL COORDINATION IN MUSICIANS

By

Yi-Ling (Irene) Kuo

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOKINESIOLOGY)

August 2018

Copyright 2018 Yi-Ling Kuo
ii
ACKNOWLEDGMENTS

My Ph.D. journey at USC has been a wonderful adventure. Five years ago, I came to the US
on my own to pursue a Ph.D. and had a dream about advancing science and starting a career
in academia. This adventure enriched my life experience and the pleasing and delightful
memories I had in LA is truly unforgettable. As Ph.D. students and researchers, we constantly
received all kinds of critiques and rejections. Sometimes, the progress in our own work could
be slow, and the finish line seems to be so far away. However, I am very fortunate to have
many families and friends around me to help and encourage me throughout these five years –
I could not have completed this Ph.D. dissertation by myself.

First, I would like to thank my all-time best advisor and teammate, Dr. Beth Fisher. We have
faced so many challenges together, but the best thing is that we always stand by each other
and conquer those challenges as a team. I still remember as an international student who was
not a native speaker, the difficulty I had a few years ago using English to express myself
clearly both in speaking and writing. You have always been patient and selfless in helping me
in becoming an independent and trustworthy researcher, and your influence on me, both
scientifically and personally, is invaluable. There are countless brainstorming and intellectual
discussions in our meetings, and working together with you is such a pleasure. I will never
forget how much joy and laugh we had together and I expect more to come in our future, as
colleagues and as lifetime friends.

Here I would also like to express my gratefulness to all of my committee members. Dr. Jim
Gordon, the division chair, thank you for leading such a wonderful and friendly learning
environment for the students to concentrate on our research. You always provide wise advice
to my research and encourage me to continue exploring the unknown. Dr. Carolee Winstein,
iii
you joined my Ph.D. journey in early days since we have lab meetings together. I always get
insightful opinions from you and think outside the box after talking to you every time. Thank
you for being around and available all these years with me moving to different stages in my
Ph.D. life and I always keep your advice in mind that we are here to advance the field. Dr.
Jason Kutch, we had a great time together playing with numbers and codes, and
brainstorming about verifying the results in a scientifically solid way. You inspire me to be
creative in our research and I really appreciate your help. Dr. Shailesh Kantak, thank you for
your remote guidance through skype and be flexible in accommodating the time difference
between LA and Philadelphia. You can understand my struggle, as you are very experienced
and still active in conducting brain stimulation research, and timely provide valuable input.
With you all on my committee, I always know that I have some brilliant minds to reach out
and never need to worry about seeking for help.

I am very lucky to have families beside me throughout this mission. Mom, Dad, my sister,
Stephanie, grandpa and grandma, I love you all very much and cannot thank you enough for
being so supportive unconditionally, and for having faith in me that I can achieve this goal.
There were many moments worth cherishing that I was absent these years. Although I am so
far away from home, I know our hearts are united and no matter where I am, you are always
there, warmly welcoming me to come home.

I met many good friends and colleagues here, who make my life in LA fun and memorable.
Lab members from the Neuroplasticity and Imaging Laboratory as well as the Motor
Behavior and Neurorehabilitation Laboratory, friends from other labs and the Division of
Biokinesiology and Physical Therapy, the study participants, as well as my badminton
buddies, LA really becomes my second hometown because of you all.
iv
Finally yet importantly, I want to express my love and lifelong commitment to Evan, my
newlywed husband. The distance between Taipei and LA, although beyond my imagination,
did not do us apart. I certainly cannot finish a Ph.D. without you listening to me talking about
the stressful life as a Ph.D. student, and telling me how much you support me wholeheartedly,
either in front of skype or during the few reunions while we held each other’s hands. You
have seen how I chose and worked hard on becoming a scientist all these years, and I am so
blessed to have your trust. Future is unpredictable and challenging, but I am not afraid – I
know we will face it together; our post-Ph.D. life journey just began!

v
TABLE OF CONTENTS

ACKNOWLEDGMENTS....................................................................................................... ii
LIST OF FIGURES ............................................................................................................... vii
LIST OF TABLES .................................................................................................................. ix
ABSTRACT ............................................................................................................................. 1
CHAPTER ONE: Overview ................................................................................................... 3
Statement of Problem ...................................................................................................... 3
Specific Aims .................................................................................................................... 5
Background ...................................................................................................................... 6
Significance of Research ................................................................................................ 11
CHAPTER TWO: Measuring Ipsilateral Silent Period: Effects of Muscle Contraction
Levels and Quantification Methods ..................................................................................... 14
CHAPTER THREE: Distinct Relationship between Interhemispheric Inhibition and
Bimanual Coordination Following Intensive Musical Training ......................................... 31
CHAPTER FOUR: Relationship between Interhemispheric Inhibition and Bimanual
Coordination in Musicians: Is There an Instrument-dependent Effect? .......................... 56
CHAPTER FIVE: Summary and General Discussion ........................................................ 82
Summary of Main Results ............................................................................................. 82
Interhemispheric Inhibition as a Feature of Neuroplasticity Following Musical
Training .......................................................................................................................... 83
Generalization of Hand Motor Function Following Musical Training ..................... 84
Distinct Reorganization of Interhemispheric Inhibition and Adaptation in Bimanual
vi
Coordination Following Musical Training .................................................................. 85
Clinical Implications and Future Directions ............................................................... 86
REFERENCES ...................................................................................................................... 88
APPENDIX: Measuring Ipsilateral Silent Period: Effects of Muscle Contraction Levels
and Quantification Methods ............................................................................................... 102
vii
LIST OF FIGURES

Figure 2-1. A sampling trial recorded during 50% MVC ........................................................ 21
Figure 2-2. A processed EMG trial .......................................................................................... 22
Figure 2-3. Exemplary EMG traces from 30%, 50% and 100% of maximum voluntary
contraction (MVC) of a representative participant ............................................... 24
Figure 3-1. Finger sequence task ............................................................................................. 36
Figure 3-2. Purdue pegboard task ............................................................................................ 37
Figure 3-3. Results of the finger sequence task in musicians and controls ............................. 42
Figure 3-4. Results of the ipsilateral silent period (iSP) in musicians and controls ................ 43
Figure 3-5. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes across all participants ............................... 45
Figure 3-6. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes after variable reduction in musicians ....... 47
Figure 3-7. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes in musicians after variable reduction:
comparisons across instrument types (keyboard, string, and woodwind
players) ................................................................................................................ 49
Figure 4-1. A representative trial of the force tracking task .................................................... 64
Figure 4-2. Results of the finger sequence task with tones (FST-T) and silence (FST-S)
conditions in keyboard and string players ............................................................ 70
Figure 4-3. Results of the force tracking task in left hand sine wave (FTT-L sine) and right
hand sine wave (FTT-R sine) conditions in keyboard and string players ............ 71
Figure 4-4. Results of the ipsilateral silent period (iSP) in keyboard and string players ........ 72
Figure 4-5. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes in all musicians ........................................ 74
viii
Figure 4-6. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes after variable reduction ............................ 76

ix
LIST OF TABLES

Table 2-1. iSP obtained in the three muscle contraction conditions and analyzed by three
quantification methods .......................................................................................... 25
Table 3-1. Demographics in the musician and control groups ................................................ 40
Table 3-2. Group comparisons of bimanual coordination variables ....................................... 41
Table 3-3. Group comparisons of interhemispheric inhibition variables ................................ 43
Table 4-1. Demographics in the keyboard and string players ................................................. 67
Table 4-2. Group comparisons of the results of finger sequence task (FST) .......................... 68
Table 4-3. Group comparisons of the results of force tracking task (FTT) ............................. 70
Table 4-4. Group comparisons of interhemispheric inhibition variables ................................ 72

1

ABSTRACT

Musical expertise provides an opportunity to explore science through art. A musician’s brain
is shaped by long-term skill acquisition, and the neuroplastic changes are expressed by the
complex coordinated movements between the two hands. This dissertation aimed to
investigate how the communication between the two cerebral hemispheres through
interhemispheric inhibition (IHI) contributes to bimanual coordination in skilled musicians,
and to understand the instrument-dependent effect on this brain-behavior relationship.

We first systematically determined the optimal methodology (ideal muscle contraction level
and quantification method) to measure ipsilateral silent period (iSP), which is an indicator of
IHI. The iSP measure was then applied to the following investigations to compare IHI in
musicians and non-musicians. Musicians demonstrated significantly better bimanual
coordination compared to non-musicians. Increased IHI from the left to the right hemisphere
was found to significantly correlate with increased key pressing consistency, but with
reduced speed during a bimanual task in skilled musicians. This IHI-bimanual coordination
relationship was only evident in musicians, but not in non-musicians. We then designed both
symmetric and asymmetric bimanual coordination tasks to address whether the IHI-bimanual
coordination relationship in a musician was dependent on instrument type. Moreover, we
investigated whether auditory sound impacts motor performance in musicians. Keyboard and
string players were recruited as representatives of symmetric hand use and asymmetric hand
use, respectively. Overall, there were no differences in performance of either symmetric or
asymmetric tasks between keyboard and string players. There were no differences in motor
performance with or without auditory feedback in either group. A similar IHI-bimanual
coordination relationship was found in both keyboard and string players. Specifically,
2
increased left to right as well as decreased right to left IHI were associated with increased
speed as well as increased accuracy (i.e. better bimanual coordination) in both groups.

The findings of this dissertation provided strong evidence of experience-dependent
neuroplasticity associated with the brain-behavior relationship following intensive musical
training. Future studies would investigate the beneficial effects of instrument playing in
individuals with impaired bimanual coordination. Moreover, modulation of IHI using non-
invasive brain stimulation with an individualized and instrument-specific approach may be a
promising intervention for Musician’s dystonia in order to regain bimanual coordination.

3
CHAPTER ONE
Overview

Statement of Problem
One of the most influential discoveries of the 21
st
century is the brains capability to rewire
itself, termed neuroplasticity. Since the Presidential Proclamation that dedicated the 1990’s as
the Decade of the Brain, the incredible capability for the brain to remodel itself with practice
has had far-reaching impact on human endeavor. The two-hemisphere architecture inherent to
all mammalian brains provides the conduit by which communication between the
hemispheres occurs and elegant movement emerges. With exceptional proficiency in
coordinating bimanual movements, musicians are an ideal population for the scientists to
understand experience-dependent neuroplasticity in the motor system. Enriched
communication between the two cerebral hemispheres enables musicians to acquire high
levels of skill in delivering their art. A wealth of studies have shown the beneficial impact of
musical training on cognitive function, language, memory, creativity, and attention, as well as
treatment effects from children to the elderly, and from non-disabled individuals to patients
(Habib and Besson, 2009)(Wan and Schlaug, 2010)(Barrett et al., 2013)(Altenmüller and
Schlaug, 2015). However, even though musical performance is fundamentally a motor task,
the remodeling of the brain and motor system by means of musical training to achieve
coordination in hand movements has not been well understood.

We used transcranial magnetic stimulation (TMS), a form of non-invasive brain imaging to
study the nature of interhemispheric communication (balance between processes of inhibition
and facilitation) (Ferbert et al., 1992) in elite musicians. Specifically, interhemispheric
inhibition (IHI) is a cortical mechanism underlying hand motor control, and IHI has
demonstrated to account for fine motor control and hand dexterity (Harris-Love et al.,
4
2007b)(Perez and Cohen, 2008)(Morishita et al., 2012)(Wahl et al., 2015). Proficiency in
musical instrument opens a window to advance our knowledge in experience-dependent
remodeling between the two hemispheres in reaching the frontier of human motor
performance. It is established that skillful movements require activation of the inhibitory
circuitry in the brain to confront the demand of fine motor control (Harris-Love et al.,
2007b)(Morishita et al., 2012). A greater IHI observed in trained musicians can be a
neurophysiologic feature which differentiates them from non-musicians (Vollmann et al.,
2014)(Bäumer et al., 2016)(Chieffo et al., 2016). However, it is not known to what degree
this enhanced IHI is a task-specific phenomenon associated with the use of a particular
instrument (e.g., violin or piano) or instead a more task-independent phenomenon associated
with other motor tasks that require some form of bimanual coordination.

There is growing evidence that an imbalance in IHI is observed in musicians who use both
hands relatively asymmetrically (e.g. string players), whereas a more balanced IHI is
observed in musicians that play instruments with symmetric hand use (e.g. pianists).
Specifically, it has been shown in violinists that there is greater IHI from the left (L)
hemisphere (controlling the bow hand) towards the right (R) hemisphere (controlling the
string hand) compared to pianists in which IHI is relatively equal between the two
hemispheres (Vollmann et al., 2014). Given that the specific instrument a musician plays can
be shown to directly impact IHI, one might assume that this unique brain organization may
impact other similar forms of bimanual movements. However, the influence of instrument-
specificity (with its known effect on IHI proposed by Vollmann et al., 2014) and its
association with bimanual motor skill level is not well understood.

There are two TMS paradigms to measure IHI – (1) the paired pulse paradigm: one test
stimulus preceded by one conditioning stimulus (i.e. double stimulation) over both
5
hemispheres to measure the change of corticospinal excitability in the test hemisphere in
response to the conditioning stimulus over another hemisphere; and (2) the single pulse
paradigm: providing single stimulation ipsilateral to an active muscle to measure the
temporary interruption of muscle activity (termed ipsilateral silent period, iSP). Whereas the
paired pulse paradigm is more frequently used to examine IHI, the methodology of the single
pulse paradigm has not well been systematically explored. Unstandardized measures of iSP
in the single pulse paradigm leaves the results inconsistent and unable to be compared
between different studies. Accordingly, we first determined the methodology for optimal
acquisition and measurement of iSP to index IHI. We then subsequently used iSP to measure
the functional neuroplastic changes in musicians. The specific aims were addressed as
follows.

Specific Aims
Aim 1: To determine the optimal voluntary contraction level and iSP analysis methods
for achieving measurement consistency. This aim determined the ideal methodology of the
single pulse TMS paradigm to measure iSP. We hypothesized that iSP will be more reliable
under sub-maximal contraction conditions during measurement, with a quantification method
taking both duration and amplitude into account.
Aim 2: To investigate the relationship between IHI and bimanual coordination in
musicians compared with non-musicians. We hypothesized that the relationship between
IHI and bimanual coordination will be stronger in musicians compared with non-musicians.
Aim 3: To investigate instrument-specific effects on the relationship between IHI and
bimanual coordination in keyboard players (symmetric musical skill) compared with
string players (asymmetric musical skill). We hypothesized that keyboard players will
6
exhibit a relationship between IHI and symmetric bimanual coordination, while the string
players will exhibit a relationship between IHI and asymmetric bimanual coordination.

Background
Aim 1:
iSP acquisition: muscle contraction level effect
Studies that have used iSP to infer IHI have employed a wide range of muscle activation
levels, or have not documented the contraction level (Boroojerdi et al., 1996)(Chen et al.,
2003a)(Giovannelli et al., 2009)(Bradnam et al., 2010)(Fling and Seidler, 2012)(Reid and
Serrien, 2012)(McGregor et al., 2013)(Davidson and Tremblay, 2013a)(Perez et al., 2014).
Maximum muscle contraction has largely been used as the target level of muscle contraction.
However, maximum muscle contraction has a higher risk of inducing muscle fatigue.
Consequently, less reliable results and difficulty in generating maximum contraction,
especially in patients with motor control deficits, were anticipated. A significant aspect of
Aim 1 will be determination of which contraction level is ideal to achieve a reliable and
consistent iSP measure.

iSP quantification
Previous studies have used various methods (duration, amplitude, or both) to analyze the
EMG signal from the contracting muscles to determine iSP, including duration (time
difference between onset and offset of EMG), area (EMG reduction integral from onset to
offset), or area normalized to pre-TMS EMG which takes the muscle contraction level into
consideration (Cincotta et al., 2006)(Jung and Ziemann, 2006)(Avanzino et al.,
2007)(Giovannelli et al., 2009)(Harris-Love et al., 2011)(Fling and Seidler, 2012)(Tazoe and
Perez, 2013)(Davidson and Tremblay, 2013b). However, it is often not possible to compare
the iSP results from one study to another given that various analysis methods with different
7
units could be implemented. It was unclear which quantification method leads to the most
reliable iSP results.

Reliable acquisition and measurement of iSP to indicate IHI would afford a greater
understanding of how the two primary cortices interact to accomplish various tasks requiring
bimanual coordination. Also, the promotion of this proposed iSP method would enable
researchers to compare the results of IHI changes in various studies with different
populations.

Aim 2 and Aim 3:
Musicians’ motor behaviors
Musicians typically commence practice early in life when the central nervous system is
perhaps most plastic and capable of profound functional brain reorganization (Steele et al.,
2013). Music making is a multisensory and motor experience and musicianship requires
acquisition and maintenance of multiple musical skills. These experts are characterized by
superior hand motor control to precisely and rapidly reproduce tones in temporal sequences
(Wan and Schlaug, 2010). Musicians are equipped with superior fine motor performance due
to the long-term training of cognitive-sensorimotor processes. In hand motor ability
assessments, musicians were able to react faster to an external stimulus and move faster, had
reduced error rate and movement variability, and demonstrated better synchrony in bimanual
motor tasks as opposed to non-musicians (Hughes and Franz, 2007)(Pau et al., 2013)(Rüber
et al., 2013)(Haslinger et al., 2004). There were also studies that characterized the hand motor
ability associated with certain types of instruments. String players showed more organized
and articulated cross-joint movements with reducing bow variability to achieve quick bow
reversals compared to non-musicians (Verrel et al., 2014). Pianists were more able to
dissociate finger movements with precise force generation while playing keyboard, whereas
8
amateurs show uncoordinated finger movements and generate excessive force (Parlitz et al.,
1998). It was also found that pianists were able to tap individual fingers faster and more
evenly (less inter-tap interval variability) compared to non-musicians (Aoki et al., 2005).
However, most of the studies investigated musicians’ motor behaviors with unilateral tasks or
simple bilateral tasks (e.g. rhythmic tapping with different fingers of each hand), which may
not be sufficient to address differences in bimanual coordination between musicians and non-
musicians. It is also not clear how musical training can be transferred to daily functional hand
motor skills. Furthermore, there was no study which specifically compared the impact of
instrument type on musicians’ bimanual performance.

Neuroplastic reorganization in musicians
Structural adaptations have been found in musicians resulting from intensive musical
training. Compared to non-musicians, musicians have significantly increased gray matter
volume in sensorimotor, auditory and cognitive networks. These are areas critical to
movement preparation, planning, and execution of bimanual sequential finger tasks (Gaser
and Schlaug, 2003). Other anatomical changes, including increased size of the corpus
callosum and a higher density of descending motor tract axon myelination, are triggered by
bimanual practice (Lee et al., 2003)(Rüber et al., 2013). Such structural adaptations appear to
be instrument-dependent: in pianists, increased white matter myelination was observed in the
L hemisphere corticospinal tract compared to string players. Moreover, more myelination in
the motor tracts was correlated with better unimanual motor performance. The different
structural adaptations found in musicians playing different instruments were thought to be
attributed to the fact that string players’ R hands (bow hand) are engaged in less complicated
musical tasks compared to the pianists’ R hand (primarily engaged in themes). Thus the axon
myelination of the L hemisphere corticospinal tract in string players was not as evident as
that in the pianists (Rüber et al., 2013).
9

Functional adaptations have been found in musicians and such adaptations may also be
attributed to instrument type. Mapping of somatosensory evoked potentials showed enlarged
hand representation in R primary somatosensory cortex after peripheral nerve electrical
stimulation in violin players compared to controls. Mapping of motor evoked potentials
(MEP) assessed by TMS showed higher corticomotor excitability with pronounced right-
larger-than-left asymmetry in the primary motor cortex (M1) as well (Schwenkreis et al.,
2007). This is possibly due to the fact that string players’ L hands (string hand) are engaged
in more complex motor tasks to induce a functional adaptation in the R hemisphere. Finally,
resting-state functional connectivity of musicians was enhanced in several networks which
are involved in musical experience, such as auditory, sensorimotor, and emotional networks,
compared to non-musicians (Fauvel et al., 2014)(Palomar-Garcí a et al., 2016). This suggests
that musical skill training strengthens the functional communication between different brain
areas.

Interhemispheric interaction in musicians
The functional connectivity between the two hemispheres with respect to the excitability of
the inhibitory and facilitatory corticomotor circuits can be determined using TMS.
Specifically, interhemispheric inhibition (IHI), largely mediated by transcallosal fibers
between bilateral M1s, is more dominant compared to facilitation (Ferbert et al.,
1992)(Meyer et al., 1995), and is a crucial cortical mechanism underlying normal motor
control. TMS is a neurophysiological investigational tool that measures excitability of circuits
connecting the two hemispheres and is the only tool informs us about the “direction” of
interhemispheric communication (e.g. L inhibits R hemisphere), which potentially shows
which hemisphere is more active (Rossini et al., 2015). IHI has been considered crucial to
suppression of unwanted movements and controlling fine motor behaviors (Hübers et al.,
10
2008)(Fling and Seidler, 2012)(Harris-Love et al., 2007b)(Morishita et al., 2012). These
functional features of the inhibitory circuitry seem to be relevant to motor skills involved in
instrument playing. A few studies have investigated IHI in musicians (Ridding et al.,
2000)(Vollmann et al., 2014)(Bäumer et al., 2016)(Chieffo et al., 2016). Ridding et al. found
that musicians (5 pianists, 1 guitarist) had reduced IHI to both hemispheres compared to non-
musicians (Ridding et al., 2000). In contrast, other studies observed increased IHI in
musicians. Vollmann et al. (2014) found more IHI toward the R hemisphere only in string
players, compared to pianists and non-musicians. The increased IHI (i.e. more activated
transcallosal inhibitory circuits) was significantly associated with corpus callosum structural
integrity in both non-musicians and musicians. Specifically, this structure-function
relationship within musicians was impacted by instrument type: the correlation between
increased IHI and enhanced corpus callosum structural integrity was only significant in string
players but not in pianists. Since the L hand was involved in more complicated movements
for musicians who play string instruments, this structure-function relationship may be driven
by intensive instrument training with lateralized hand use. (Vollmann et al., 2014). Bäumer et
al. also found increased IHI in musicians (woodwind, string, brass and plucking players) in
contrast to non-musicians, yet the type of instrument did not impact the IHI results (Bäumer
et al., 2016). Similarly, Chieffo et al. found increased IHI in pianists compared to non-
musicians. An asymmetry index was used to highlight the side difference of outcomes and it
was found that lateralization of IHI correlated with lateralization of hand dexterity measured
by unimanual hand motor tasks. The pianists showed more symmetric mutual inhibition with
symmetric hand dexterity, while the non-musicians were skewed toward worse left-hand
dexterity that corresponded to more IHI from the L toward R hemisphere (Chieffo et al.,
2016). Prolonged musical training of an instrument requiring relatively symmetric hand use
(e.g. piano) balances transcallosal inhibition from a naturally lateralized interaction – a state
that favors the dominant L hemisphere.
11

The gap in our understanding pertains to the nature of the relationship between
interhemispheric interaction and bimanual coordination in musicians
Musical training has been shown to induce neuroplastic changes. The majority of research
has focused on differences between musicians and non-musicians studied separately from
hand motor control (Ridding et al., 2000)(Schwenkreis et al., 2007)(Fauvel et al.,
2014)(Vollmann et al., 2014)(Bäumer et al., 2016). Research that has shown differences
between musicians and non-musicians in hand motor control has not examined IHI nor has
the research examined bimanual coordination capability (Parlitz et al., 1998)(Hughes and
Franz, 2007)(Pau et al., 2013)(Verrel et al., 2014). The one exception which used both IHI
and behavioral hand function measures in musicians, investigated the impact of IHI
asymmetry on unimanual hand function (measured by the Nine hole peg test and index finger
tapping task) (Chieffo et al., 2016). A logical next step would be to determine if changes in
IHI in musicians are associated more generally with bimanual motor coordination in daily
activities including asymmetric activities such as opening up a jar and symmetric activities
such as typing on a computer keyboard.

Significance of Research
The ipsilateral silent period is a relatively simple way to measure IHI using the single pulse
TMS paradigm. It makes the IHI measurement more practical in both research and clinical
settings compared to the paired pulse paradigm. The expe rimental equipment of the single
pulse paradigm consists of only one TMS stimulator and one coil, as opposed to two
stimulators and two coils required for the paired pulse paradigm. The time required for the
preparation and procedures of iSP investigation is accordingly much less for the single pulse
paradigm compared to the paired pulse paradigm. From a perspective of methodological
concerns, the test-retest and inter-rater reliability of the iSP measure has been shown to be
12
higher compared to the that of the paired pulse paradigm (De Gennaro et al., 2003).
Additionally, for a population with neuropathology, such as stroke survivors, it is often
difficult to elicit a motor response in the affected hemisphere and makes it difficult to capture
IHI changes in these patients using the paired pulse paradigm. On the contrary, measuring
iSP with the single pulse paradigm still allows us to measure IHI by stimulating the
unaffected hemisphere in these neurologic patients. By developing a methodology which
yields consistent iSP results, we are advancing the application of TMS in the assessment of
IHI to answer different questions regarding interhemispheric interactions in different
populations, such as what we propose in the current study: to investigate IHI in musicians.

It is well established that the brain modifies its organization with practice (i.e.
neuroplasticity) and that communication between the two hemispheres of the brain is what
largely accounts for precise hand function. Instrument playing is a unique form of bimanual
coordination and is unlike strictly controlled laboratory motor assessments. There is a
tremendous demand for temporal, spatial and force coupling of the two hands to rapidly
move in a relatively independent yet coordinated manner. It is of interest to understand
whether the bimanual motor ability resulting from prolonged training with a musical
instrument can be transferred to functional daily activities, and whether such bimanual motor
ability transfer is driven by the adapted interhemispheric communication with musical
expertise. To date, there is no study investigating the relationship between musicians’
bimanual motor behavior and IHI. Moreover, although imbalanced IHI was observed in
musicians who use both hands relatively asymmetrically (e.g. string players) (V ollmann et al.,
2014), it is unknown whether instrument type influence the relationship between different
types of bimanual coordination (symmetric versus asymmetric) and the balance of IHI
between the two hemispheres (balanced versus imbalanced). Two important concepts mark
the innovative nature of this study: 1) providing evidence that transfer of bimanual motor
13
skills to everyday tasks may be associated with musical training-induced modifications in
interhemispheric interaction and 2) that the relationship between IHI and bimanual
coordination may be influenced by experience with different types of musical instrument.
Investigating the impact of instrument on the relationship between bimanual coordination
and IHI in musicians sheds lights on the brain-behavior interaction from a perspective of
practice-induced neuroplasticity.

Overall, the proposed research – to understand the relationship between changes in IHI and
bimanual coordination in those with skilled musical ability is significant for the following
reasons: 1) it pushes the boundary of what is known. To date, it is unknown whether changes
in IHI in musicians afford greater ability to perform bimanual tasks in general. 2) If our
hypotheses are supported, it would open up new dimensions for evidence-based therapies
with musical instrument training for children and adults afflicted with neuropathologies of
neuromuscular control (e.g., cerebral palsy, stroke, Parkinson’s Disease).

14
CHAPTER TWO
Measuring Ipsilateral Silent Period: Effects of Muscle Contraction Levels and
Quantification Methods
Published in Brain Research
Kuo YL, Dubuc T, Boufadel DF, Fisher BE, 2017. Measuring Ipsilateral Silent Period:
Effects of Muscle Contraction Levels and Quantification Methods. Brain Res 1674:77-83.
doi: 10.1016/j.brainres.2017.08.015.

Abstract

Background: Ipsilateral silent period (iSP) is a frequently measured index of
interhemispheric inhibition. However, the methodology used across studies has been
inconsistent and variable. We investigated the optimal contraction level and quantification
methods for achieving iSP measurement consistency.

Methods: Twenty-five healthy adults performed right isometric thumb abduction under three
conditions (30%, 50%, and 100% of maximal voluntary contraction) while transcranial
magnetic stimulation was applied over the primary motor cortex representational area of the
abductor pollicis brevis. iSP was quantified by: iSP duration, iSP area and normalized iSP.
Measurement consistency was determined by the homogeneity of variance test and by the
coefficient of variation.

Results: iSP was consistent across all contraction levels when measured by iSP duration and
normalized iSP. Normalized iSP showed the least measurement variability.
15
Discussion: We propose that future investigations examining interhemispheric inhibition use
normalized iSP for measurement consistency and the ability to compare results across
studies.

Keywords: interhemispheric inhibition, measurement consistency, variability, transcranial
magnetic stimulation, electromyography
16
Introduction
The left and right cerebral hemispheres are connected by the corpus callosum, which transfers
essential information between both hemispheres to control movements. The ability to
precisely perform coordinated movements requires interhemispheric interaction to enable
humans to complete both unimanual and bimanual tasks (Beaulé et al., 2012)(Wahl and
Ziemann, 2008)(Takeuchi et al., 2012). Transcranial magnetic stimulation (TMS), a non-
invasive brain stimulation technique can be used to study the nature of interhemispheric
communication (balance between processes of inhibition and facilitation) (Ferbert et al.,
1992). Inhibitory processes are measured with TMS as interhemispheric inhibition (IHI)
which is specifically a measurement of the transcallosal connections and processing between
bilateral primary motor cortices (M1s) (Ferbert et al., 1992)(Meyer et al., 1995).
Interhemispheric inhibition is considered an essential feature of fine dexterous motor control
(Harris-Love et al., 2007a)(Perez and Cohen, 2008)(Morishita et al., 2012)(Wahl et al., 2015).
For example, in a unimanual task requiring fine control, it is thought that unwanted mirror
movements of the resting hand are curtailed by means of IHI from the active hemisphere
(controlling the active hand) toward the less active hemisphere (Hübers et al., 2008).
Furthermore, it has been demonstrated that transfer of motor performance to the unskilled
hand after learning a unimanual task is closely related to IHI modulation between both
hemispheres (Perez et al., 2007)(Camus et al., 2009)(Hortobágyi et al., 2011). As IHI is an
important mechanism associated with human movement control, precise measurement would
afford a greater understanding of the interaction between the two M1s in accomplishing
various tasks.

There are two TMS paradigms to measure IHI – (1) the paired pulse paradigm: providing
double stimulation (one test stimulus preceded by one conditioning stimulus) over both
hemispheres to measure the change of corticospinal excitability in the test hemisphere due to
17
the conditioning stimulus applied to the opposite hemisphere; and (2) the single pulse
paradigm: providing stimulation to the hemisphere ipsilateral to an actively contracted
muscle to measure the temporary disruption in the electromyographic signal (EMG), termed
ipsilateral silent period (iSP) (Ferbert et al., 1992)(Meyer et al., 1995)(Jung and Ziemann,
2006)(Chen et al., 2008). Whereas the investigations employing the paired pulse paradigm
have used more consistent parameters (i.e. stimulation intensities, inter-stimulus interval) to
examine IHI, the methodology of the single pulse paradigm has not been systematically
established. Measures of iSP in the single pulse paradigm have been derived with inconsistent
methods such that comparisons of the results between different studies are problematic.
Studies that have measured iSP to index IHI have employed a wide variety of muscle
activation levels of the active hand anywhere from 15% to 100% maximum voluntary
contraction (MVC) (Davidson and Tremblay, 2013)(McGregor et al., 2013)(Perez et al.,
2014)(Chen et al., 2003b)(Niehaus et al., 2001)(Cincotta et al., 2006)(Giovannelli et al.,
2009)(Houdayer et al., 2016), or have not reported the contraction level at all (Boroojerdi et
al., 1996)(Bradnam et al., 2010). Previous work has largely employed 100% MVC as the
required muscle activation level for the participants (Niehaus et al., 2001)(Cincotta et al.,
2006)(Giovannelli et al., 2009)(Hoeppner et al., 2012)(Wegrzyn et al., 2013)(Houdayer et al.,
2016). However, 100% MVC is likely to lead to fatigue, and thus limits the number of
measured trials. Therefore, less reliable results, especially in pathological populations with
difficulty generating maximum contraction, could result from 100% MVC paradigms. The
first aim of the current study then was to determine whether muscle contraction level (30%,
50%, and 100% MVC) impacted the variability and measurement consistency of iSP.

iSP outcome measures quantify the degree of IHI and the physiological interpretation is that
larger values indicate more inhibition. Previous studies have determined iSP using various
methods to analyze the EMG from the contracting muscle (Cincotta et al., 2006)(Trompetto
18
et al., 2004)(Avanzino et al., 2007). These methods include 1) the duration of the disrupted
EMG signal (iSP duration) (Meyer et al., 1995)(Jung and Ziemann, 2006)(McGregor et al.,
2013)(Daskalakis et al., 2003)(Petitjean and Ko, 2013)(Spagnolo et al., 2013), 2) area of the
disrupted EMG signal (iSP area) (Giovannelli et al., 2009)(Bradnam et al., 2010)(Tazoe et
al., 2013), or 3) the area normalized to pre-TMS EMG which takes the muscle contraction
level into consideration (normalized iSP) (Houdayer et al., 2016)(Harris-Love et al.,
2011)(Reid and Serrien, 2012)(Tazoe and Perez, 2013)(Long et al., 2016). However, it is
difficult to compare the iSP results of different studies when one study measures duration
whereas another measures area, given that the units are different. It remains unknown which
quantification method is more consistent for quantifying iSP. Therefore the second aim of this
study was to determine which iSP analysis method yielded the most consistent results.

Methods
Participants
Twenty-five healthy right-handed individuals (13 females) with a mean age of 27.16 ± 3.92
years (20-34) participated in this study. Each participant was screened using a TMS safety
questionnaire. Based on criteria suggested by Wasserman (Wassermann, 1998), participants
were excluded if they had a history of neurological disorders which contraindicated TMS
procedures. The study protocol was approved by the Institutional Review Board of the
University of Southern California. Once screening was completed, the study protocol and
risks were described to each participant who then provided written informed consent.

Experimental Set-Up
A single-pulse magnetic stimulator (Magstim 200
2
; The Magstim Company Ltd, Whitland,
UK) with a figure of eight coil (outer-wing diameter of 50 mm) was used for all TMS
assessments. The abductor pollicis brevis (APB) was the targeted muscle. Not only is thumb
19
abduction a critical part of hand function but it has been established that iSP obtained in APB
leads to obvious onset and offset of EMG disruption (Jung and Ziemann, 2006). The skin of
bilateral APB muscles was cleaned with abrasive gel and alcohol to decrease skin impedance
for the surface EMG electrodes (inter-electrode distance, 20 mm; Motion Lab Systems Inc,
Baton Rouge, LA). Surface EMG electrodes were placed in a belly-tendon fashion and
secured with tape. The EMG signal was sampled at 14,992 Hz, band-pass filtered at 10 to
2,000 Hz, and amplified with a gain of 2,000 using a 1401 analog-to-digital unit and Signal 6
software (Cambridge Electronic Design, Cambridge, UK). To determine the stimulation
hotspot on the right hemisphere, surface EMG electrodes were used on the left hand to record
a consistent MEP. The surface EMG electrode on the right hand recorded raw data for iSP
analysis. Each trial consisted of 300 ms of data with the first 100 ms occurring before the
TMS pulse.

Participants were seated at a table with both feet firmly planted on the ground and a pillow
used for lumbar support. The table height was adjusted to allow for approximately 100° of
elbow flexion and slight shoulder flexion. The right arm was secured in this position using a
forearm strap. The wrist was braced in a neutral position to control for activation of hand
muscles other than APB (Johnston et al., 2010). The participants were asked to push the right
thumb against a firm stick to allow for isometric thumb abduction.

Preparation for Data Collection
A lycra cap with a 1 cm grid was placed on each participant’s head to estimate the position of
the vertex and systematically identify the hotspot of the APB representational area in M1
(Fisher et al., 2016)(Rossini et al., 2015). The hotspot was defined as the location in right M1
which produced the largest and most consistent MEP amplitude (Fisher et al., 2016)(Rossini
et al., 2015). The coil was placed tangential to the scalp with the handle pointing backwards
20
and 45° away from midline (Rossini et al., 2015). Resting motor threshold (RMT) was
defined as the lowest TMS intensity required to produce at least 5 out of 10 MEP’s in
consecutive trials with amplitudes greater than 50 microvolts with the muscle relaxed
(Rothwell et al., 1999).

Experimental Protocol
The average EMG activity of three maximal contractions was used to calculate the MVC.
The mean MVC was used to calculate the testing conditions at 30%, 50%, and 100% of MVC
as the independent variables. Real time visual feedback seen on the computer screen as a
thick blue line was provided for the participants to maintain EMG output within the specified
range for each condition (+/- 10%) (Figure 2-1). Participants were asked to abduct the right
thumb isometrically within the specified range while maintaining a relaxed left hand. A
supra-threshold TMS pulse of 130% of RMT was then applied to the ipsilateral M1 once the
EMG signal was consistently maintained within the specified range. The participants
performed a total of 15 trials for each MVC condition (total of 45 trials). However, rather
than performing all 15 trials at once, three trials for each MVC condition were performed in
order over five blocks to avoid potential fatigue associated with performing all 15 trials of
100% MVC. A two-minute rest period was given between each trial.

21

Figure 2-1. A sampling trial recorded during 50% MVC. A: TMS pulse; B: EMG of MEP
recorded on the left hand; C: EMG of iSP recorded on the right hand. iSP was the primary
outcome (occurred in the pink circle); D: processed virtual EMG activity (root mean square
of raw EMG) from C to provide biofeedback of muscle activity level (blue line).

Outcome Measures and Data Analysis
The raw EMG data was processed in Matlab (The MathWorks Inc., Natick, MA, USA) using
an objective graphical method to determine iSP onset, offset, and duration from the EMG
signal of the right APB (Garvey et al., 2001). The fifteen trials of each MVC condition were
averaged and rectified to generate a processed EMG trial. A hundred milliseconds of EMG
activity prior to the TMS pulse was averaged as the pre-stimulus EMG value. The lower and
upper variation limits of the pre-stimulus EMG were calculated according to the formula:
mean pre-stimulus EMG ± (MCD × 2.66), where MCD is the mean consecutive difference of
individual pre-stimulus EMG data points. The iSP onset was defined as the first of 5
consecutive data points to fall below the lower variation limit. The iSP offset was defined as
22
the first data point that fell above the lower variation limit if 50% or more of the data points
in the 5-millisecond window following the designated iSP offset were also above the lower
variation limit (Garvey et al., 2001). The iSP duration was calculated by subtracting the iSP
offset from iSP onset (Figure 2-2).

Figure 2-2. A processed EMG trial. Horizontal straight gray line: mean pre-TMS EMG.
Horizontal dotted pink line: threshold. Vertical continuous black line: TMS pulse. Vertical
straight red line and green line: iSP onset and offset, respectively. Vertical dotted red and
green lines: 5 ms window following the iSP onset and iSP offset, respectively. Vertical dotted
cyan line: time equal to iSP duration before TMS pulse. A: iSP duration, time difference
between onset and offset. B: Area between the threshold and the depth of EMG reduction. C:
Area under the reduced ongoing EMG activity within iSP duration. D. Area under the pre-
TMS baseline muscle activity.

Three different quantification measurements of iSP were compared. One method quantified
iSP duration (Figure 2-2: A) in milliseconds as the time difference between onset and offset
23
as described above (Jung and Ziemann, 2006)(Fling and Seidler, 2012); and two methods
quantified iSP amplitude: i) iSP area (Figure 2-2: B): area between the threshold and the
depth of EMG reduction (Giovannelli et al., 2009), and ii) normalized iSP (Figure 2-2: (1 -
C/D)*100): area under the reduced EMG activity, normalized to pre-stimulus EMG area over
an equal duration of EMG reduction (Trompetto et al., 2004)(Harris-Love et al., 2011)(Tazoe
and Perez, 2013). iSP was quantified by each method described above in the processed EMG
data.

Statistical Analysis
All of the outcomes (i.e. three iSP measurements) were compared between the three MVC
conditions (30%, 50%, and 100% MVC). Repeated-measures ANOVA was used to compare
group means across the three MVC conditions and the Bonferroni test was used for post hoc
analysis. Measurement consistency as determined by the homogeneity of variance test and by
the CV (calculated by SD/mean) was compared across all quantification methods. SPSS
Version 20 (SPSS Inc, Chicago, IL) was used to perform statistical analyses with a
significance level of p < 0.05.

Results
Background EMG activity
As expected, the average EMG levels of the contracting right APB in the three MVC
conditions were distinctly different from each other. For 30% MVC, the average pre-stimulus
EMG value for the right APB was 0.19 ± 0.06 mV; for 50% MVC, the value was 0.33 ± 0.10
mV; and for 100% MVC, the value was 0.56 ± 0.15 mV . On the other hand, the average
baseline EMG values of the resting left APB were less than 0.02 mV in all three MVC
conditions.

24
Contraction level
An example of EMG traces obtained from all MVC conditions of a representative participant
is shown in Figure 2-3. Repeated-measures analysis of variance (ANOV A) showed a
significant effect of contraction level [F(2,23) = 26.28, p < 0.001] only in iSP area. Post hoc
analysis showed that the means of iSP area measured under all MVC conditions were
significantly different from each other. For the remaining iSP quantification methods,
repeated-measures ANOV A did not show significantly different means across the three MVC
conditions (iSP duration: F(2,23) = 1.23, p = 0.30; normalized iSP: F(2,23) = 1.29, p = 0.29)
(Table 2-1).

Figure 2-3. Exemplary EMG traces from 30%, 50% and 100% of maximum voluntary
contraction (MVC) of a representative participant. Noted that higher contraction level led to
deeper EMG suppression. Vertical continuous black line at 100 ms: TMS pulse. Horizontal
dotted lines: mean pre-TMS EMG of the first 100 ms in each MVC condition.
25

Variance of iSP
Homogeneity of variance test showed significantly different variance across the three MVC
conditions in only iSP area (p < 0.01) (Table 2-1). Different muscle contraction levels did not
influence the variance of the other two iSP quantification methods (iSP duration and
normalized iSP).

Quantification methods
Normalized iSP resulted in overall less measurement variability (coefficient of variation, CV
= 22.04%, 27.39%, 35.08% in 30%, 50%, 100% MVC, respectively) compared to iSP
duration and iSP area (Table 2-1).

Discussion
Reproducibility is critical in neurophysiological investigations to avoid weakening the
reliability of the dependent variables. The objective of this study was to determine the
Table 2-1. iSP obtained in the three muscle contraction conditions and analyzed by three
quantification methods
Outcomes 30% MVC 50% MVC 100% MVC
Homogeneity of
variance test
iSP duration
(ms)
Values
CV
24.49 ± 7.62
31.13 %
26.18 ± 10.27
39.23 %
23.56 ± 8.37
35.52 %
p = 0.48
iSP area
(mV*ms)
Values
CV
21.82 ± 14.98
†§
68.65 %
42.12 ± 33.19
†
78.80 %
62.08 ± 41.78
67.30 %
p < 0.01
*

Normalized iSP
(%)
Values
CV
44.33 ± 9.77
22.04 %
43.55 ± 11.93
27.39 %
40.78 ± 14.31
35.08 %
p = 0.21
Data were shown in values ± SD
CV: coefficient of variation; iSP: ipsilateral silent period; MVC: maximum voluntary contraction
*significantly different variance across all MVC conditions
†significantly different from 100%MVC
§significantly different from 50%MVC
26
optimal parameters for consistently determining IHI using the iSP method. We first
determined whether muscle activity level affected iSP measures to assess IHI; and secondly,
different iSP analysis methods were compared to quantify iSP reliably and consistently. The
results showed that normalized iSP was the analysis method which provided least
measurement variability, and the amount of IHI was not significantly different across
different MVC conditions. Accordingly, normalized iSP would be the quantification method
of choice. Additionally, of the three quantification methods, iSP area is the least optimal
choice given the effect of contraction level on the measurement as well as the large
measurement variability across all conditions (i.e. large CV’s).

While there was no difference between the three contraction levels when quantified by iSP
duration or normalized iSP, anecdotally, most of the participants reported that 50% MVC was
the easiest condition to obtain. Since the impact of fatigue on iSP was not part of the research
question in the current study, we suggest that future studies investigate fatigue associated
with different contraction levels (especially 100% MVC). Nonetheless, 50% MVC was
reported as the easiest to achieve and likely can be generated repeatedly without fatigue and
thus may be more ideal when using iSP to determine IHI.

TMS has been used widely to understand how the two hemispheres communicate (through
inhibition or facilitation) with each other. The iSP is a relatively simple way to measure IHI
using the single pulse TMS paradigm. The iSP method of measuring IHI is more practical in
both research and clinical settings compared to the paired pulse paradigm. For one, the
experimental equipment of the single pulse paradigm consists of only one TMS stimulator
and one coil, as opposed to two stimulators and two coils required for the paired pulse
paradigm. Secondly, the time required for the preparation and procedures of an iSP
investigation is accordingly much less for the single pulse paradigm compared to the paired
27
pulse paradigm. From a perspective of reliable methodology, the test-retest and inter-rater
reliability of the iSP measure was high (Fleming and Newham, 2017) while the test-retest and
inter-rater reliability demonstrated with the paired pulse paradigm was moderate (De
Gennaro et al., 2003). However, the objective of the above referenced studies was not to
compare the reliability of IHI between the two paradigms. Additionally, for a pathologic
population, the iSP method affords the investigation of IHI even with difficulties obtaining a
motor evoked potential (MEP). For example, difficulty in obtaining an MEP with stimulation
over the lesioned hemisphere has been demonstrated in the stroke population (Byrnes et al.,
1999). Measuring iSP with the single pulse paradigm still allows us to measure IHI by
stimulating the unaffected hemisphere in these neurologic patients. By developing a
methodology which yields consistent iSP results, we are advancing the application of TMS in
the assessment of IHI to answer different questions regarding interhemispheric interactions in
different populations.

All three quantification methods require determination of iSP onset and offset which can be
challenging given the oscillatory nature of the EMG signal. As iSP duration indicates IHI
solely on the basis of EMG onset and offset without considering EMG amplitude reduction, it
may be susceptible to subjective bias. Furthermore, by not including an index of EMG
reduction, the amount of IHI with a measurement that is confined to the temporal domain is
limiting. While the objective of the current study was to determine the most consistent iSP
measure for comparison of IHI across studies, we acknowledge that specific research
questions may necessitate the use of iSP duration as the most appropriate measurement. As
an aside, we did not specifically address the issue of using the graphical method with
statistical criteria proposed by Garvey et al. compared to visual determination of iSP onset
and offset. However, it seems reasonable that the graphical method with statistical criteria to
28
determine silent period would increase the precision of the data and avoid subjective bias
from the investigators (Garvey et al., 2001)(Fisch, 1998).

We found that IHI changed with different contraction levels as a function of the selected
method to analyze iSP. In the current study, when iSP was measured as area, the variance
(determined by the homogeneity of variance test) and mean (determined by repeated-
measures ANOVA) were significantly different across the three MVC conditions. It has
previously been shown that the magnitude of EMG amplitude reduction also increased with
increasing contraction level of the first dorsal interosseous (FDI) (Ferbert et al., 1992)(Long
et al., 2016). Here we demonstrate that with APB the area under the curve is similarly
impacted by different contraction levels (Figure 2-3). A possible explanation for the
modulation of iSP area with contraction level may be due to the fact that at higher contraction
levels there is greater pyramidal neuron discharge resulting in greater magnitude of EMG
output (Ferbert et al., 1992). While this is mere speculation and it is currently not understood
why iSP area modulates with different contraction levels, this observation appears to be a
consistent across hand muscles. The advantage of using APB compared to FDI is that two
phases of iSP have been observed more often in FDI than in APB (i.e. reduction of EMG
followed by an increase and then a second reduction) (Jung and Ziemann, 2006). Thus, by
using APB the measurement of iSP is ‘cleaner’ by virtue of the fact that commonly there is
only one iSP phase. However, regardless of the muscle studied, iSP area is not an ideal
method to quantify the amount of IHI given the variability associated with contraction level.
As such, comparison across studies that have used different contraction levels and quantified
iSP as area, would not be possible.

Normalized iSP, conversely, normalizes pre-stimulus contraction level and can thus cancel
out the effect of muscle contraction level (Ferbert et al., 1992). However, this was not the
29
case in Long et al. (2016) in which normalized iSP was compared at 10, 40, 70% MVC (Long
et al., 2016). The normalized iSP values during unilateral 70% MVC significantly increased
compared with 40% and 10% of MVC, whereas the current study found that normalized iSP
was independent of contraction level. Additionally, the normalized iSP values in 10% and
40% MVC in Long et al. (2016) were similar to our data; however, the normalized iSP value
for 70% MVC was higher (50%) compared to the present study in which the normalized iSP
ranged from 40%-44% across all MVC levels. It is unclear physiologically what accounts for
the differences found by Long et al. (2016) in 70% MVC given that the duration of iSP
measured under 10, 40, 70% MVC was similar to the iSP duration values measured in the
current study (about 26-29 ms). However, our results parallel those of Ferbert et al. (1992)
demonstrating no difference in normalized iSP at different contraction levels. Future work
utilizing both the muscle used in the current study (APB) compared with the muscle used in
Long et al. (FDI) at 70% MVC will enable an unbiased comparison. Since different phases of
EMG suppression can be observed in different hand muscles and therefore impact the
quantification of iSP (Jung and Ziemann, 2006), whether normalized iSP is also the most
consistent measure in other muscles (e.g. other hand muscles or upper-arm muscles) requires
future investigations.

Conclusion
Muscle contraction level in this study was independent of IHI measurement consistency when
measured as iSP duration and normalized iSP. With a methodology which yields consistent
results, iSP is a practical tool to investigate IHI. We suggest that future studies utilize
normalized iSP, which takes all critical parameters of the acquired EMG data (duration,
amplitude, and contraction level) into consideration and shows the least variability, to
determine the role of IHI in complex task performance. Normalized iSP is a quantification
method less prone to variability resulting from oscillatory EMG activity and different
30
contraction levels. Using normalized iSP therefore allows for comparison of the amount of
IHI across studies. It is also worthwhile to report the values of duration and area while using
the normalized iSP method as duration and area are both critical parameters to determine
normalized iSP. Even though muscle contraction level was not a factor in determining IHI,
we propose using 50%MVC based on the ease with which participants appear to be able to
repeatedly achieve this level of contraction without fatigue.

31
CHAPTER THREE
Distinct Relationship between Interhemispheric between Interhemispheric Inhibition
and Bimanual Coordination Following Intensive Musical Training

Abstract

Background: As interhemispheric inhibition (IHI) is essential for dexterous motor control,
bimanual skill developed with instrument playing may result in increased IHI in musicians.
However, it is unclear whether there is any difference in the relationship between
interhemispheric inhibitory circuits and bimanual motor skills in skilled musicians compared
to non-trained individuals. Therefore, the current study aimed to compare the relationship
between IHI and bimanual coordination in skilled musicians compared to non-musicians.

Methods: Thirty-six musicians and 36 non-musicians participated. An 8-element finger
sequence task (FST) was used to test bimanual coordination. Speed, accuracy, and evenness
of key pressing interval were recorded. The Purdue pegboard test was used to test asymmetric
coordination, by measuring the number of assembled objects. Using transcranial magnetic
stimulation, IHI was measured as ipsilateral silent period (iSP), both in left (L) and right (R)
hemispheres. Canonical correlation analysis (CCA) was used to identify linear relationships
between the IHI and bimanual coordination measures. A general linear model was used to
compare the IHI-bimanual coordination relationship between musicians and non-musicians.
Permutation testing was used to validate whether the observed differences of IHI-bimanual
coordination relationship between musicians and non-musicians occurred by chance. Variable
reduction CCA was performed with only the highly-contributing variables based on multiple
regression analysis.
32
Results: Compared to non-musicians, musicians demonstrated significantly better bimanual
coordination (faster speed, higher accuracy, and more evenness). No differences in iSP were
observed between the two groups. However, canonical correlation analysis (CCA) showed
that the composite IHI variables (iSP-L, iSP-R) were significantly related to bimanual
coordination in musicians (r = 0.52), but not in non-musicians (r = -0.06). The strength of the
relationship was significantly greater in musicians than in non-musicians (p = 0.016).
Variable reduction CCA showed that greater L-to-R IHI was related to increased evenness
but at the cost of reduced speed in musicians.

Discussion: Musicians demonstrated superior bimanual coordination in an instrument-like
task compared to non-musicians. Prolonged musical training strengthened the relationship
between interhemispheric inhibitory circuits and bimanual motor skills. The trade-off
between evenness and speed was dependent on IHI and may result from long-term musical
training as reducing variability is an important skill in instrument playing.

Keywords: interhemispheric inhibition, bimanual coordination, musician, canonical
correlation analysis, speed-evenness trade-off

33
Introduction
Musical expertise serves as a distinctive model to study practice-induced neuroplasticity in
the context of exceptional bimanual hand coordination. Communication between the two
hemispheres is essential for the musicians to acquire high levels of skill in expressing their
artistry. However, the translation of musical training-induced brain remodeling to more
general skills that require coordinated movements between the hands has not been
determined. There is strong evidence that musical training generalizes to cognitive
development in children (e.g. language, working memory, intelligence) and for maintaining
cognitive function in aging adults (Loui et al., 2011)(Ott et al., 2011)(Patel and Iversen,
2007)(Hanna-Pladdy and Gajewski, 2012)(Schellenberg, 2004). This study asks whether the
interhemispheric interactions and bimanual motor coordination acquired by musicians who
have trained intensively with a musical instrument generalizes to other bimanual skills.

Interhemispheric inhibition (IHI) is an essential cortical mechanism underlying most forms of
motor control, but is considered a crucial feature of fine dexterous motor control (Harris-
Love et al., 2007b)(Perez and Cohen, 2008)(Morishita et al., 2012)(Wahl et al., 2015). The
sophisticated and finely tuned bimanual coordination required of an expert musician offers an
unprecedented opportunity to explore human performance limits and to advance our
understanding of experience-dependent interhemispheric remodeling. For example, we know
that the brain’s inhibitory circuitry has a greater role in the execution of dexterous hand
movements and in the performance of tasks with a high skill demand (Harris-Love et al.,
2007b)(Morishita et al., 2012). The extraordinary skill level we have come to associate with
highly trained musicians appears to have a specific neurophysiologic ‘signature’ that includes
greater IHI compared with non-musicians (Vollmann et al., 2014)(Bäumer et al.,
2016)(Chieffo et al., 2016). However, it is not known to what degree this enhanced IHI is a
task-specific phenomenon associated with playing an instrument or instead a more task-
34
independent phenomenon associated with other motor skills that require some form of
coordination between the hands. Given that bimanual coordination may require performance
of skills in which both hands spatially do the same thing (i.e. symmetric bimanual
coordination) or skills in which each hand performs a different action at the same time (i.e.
asymmetric bimanual coordination), both types of bimanual coordination tasks were
investigated.

Musical training has been shown to induce neuroplastic changes. The majority of research
has focused on either brain differences between musicians and non-musicians without
consideration of hand motor control (Ridding et al., 2000)(Schwenkreis et al., 2007)(Fauvel
et al., 2014)(Bäumer et al., 2016) or differences between musicians and non-musicians in
hand motor control without examination of any brain measures. Additionally, those studies
that have evaluated motor function in musicians compared to non-musicians have mostly
utilized simple unimanual tasks. (Parlitz et al., 1998)(Hughes and Franz, 2007)(Pau et al.,
2013)(Verrel et al., 2014). The single study that used both IHI and behavioral hand function
measures in musicians, investigated the impact of IHI asymmetry on unimanual hand
function (Chieffo et al., 2016). It is yet to be determined if changes in IHI in musicians are
associated with bimanual motor coordination. Therefore, the current study aimed to
investigate the relationship between IHI and bimanual coordination in musicians compared
with non-musicians. We hypothesize that a stronger relationship between IHI and bimanual
coordination (i.e. both symmetric and asymmetric tasks) in musicians as compared with non-
musicians will be observed. An alternative hypothesis, however, is that a stronger
relationship between IHI and bimanual coordination in musicians will be evident in only one
form of bimanual coordination, either a symmetric or asymmetric task, in accordance with
the skill acquired from symmetric (e.g. piano) or asymmetric (e.g. violin) instrument training.

35
Methods
Participants
Thirty-six musicians (keyboard: 14, percussion: 1, woodwind: 7, string: 8, brass: 2, plucking:
4) and 36 age-matched non-musician controls participated in this study. The musicians were
classically-trained professionals or music-major college students who regularly and
intensively practiced musical instruments. All of the musicians had been practicing and
performing since early childhood. The age-matched non-musicians were not engaged in
intensive fine motor activities such as video game-playing. The participants were screened
using a transcranial magnetic stimulation (TMS) safety questionnaire and were excluded if
they had a history of neurological disorders which contraindicated TMS procedures
(Wassermann, 1998). Their handedness was measured by the Edinburgh Handedness
Inventory (Oldfield, 1971). The current study was approved by the Institutional Review
Board of the University of Southern California.

Bimanual Coordination Assessment
Finger sequence task (FST): for testing symmetric hand coordination and agility (Lee et al.,
2016)(Pascual-Leone et al., 1995). The participants performed an 8-element sequence of
finger movements on a computer keyboard (Figure 3-1). Participants were instructed to press
the keys in an ascending order: 1-2-3-4-5-6-7-8. Three sets of sequences were used and the
participants had 10 minutes to practice all of the assigned sequences before the start of the
test. The participants were asked to perform the finger sequence task as fast and as accurately
as possible while maintaining an even key pressing interval (i.e. reduce variability). In other
words, the task goals were to maximize speed, accuracy, and evenness. The three sequences
were presented in a pseudorandom order and there were a total of nine trials (3 trials per
sequence) for testing. Feedback in the form of time to complete each sequence and the
accuracy of the sequence were provided at the end of each trial. Total time (task completion
36
time), accuracy, and variability of key pressing interval (the standard deviation (SD) of the
time difference between two consecutive key presses) were recorded as motor performance.
Total time was further decomposed into reaction time (between the presentation of the
sequence and first key press) and movement time (between the first and final key press) to
determine movement planning and movement execution respectively.

Figure 3-1. Finger sequence task. Participants were instructed to put the index, middle, ring
and little fingers of both hands on the designated keys of an enlarged computer keyboard.
Each 8-element sequence was shown on the computer screen and the participants pressed the
corresponding keys in a sequential order as fast, accurately, and evenly as possible.

Purdue pegboard test (PPT): for testing hand dexterity during performance of an
asymmetric task (Tiffin and Asher, 1948). The PPT is a bimanual coordination assessment
tool using a functional daily activity (picking up small objects) and is widely used in clinical
settings and research. This is a task in which small objects are picked up by both hands and
37
are placed consecutively into holes embedded in a pegboard. There are three types of objects
(pins, washers, and collars), which the participants pick up from different cups. Participants
were instructed to assemble the objects into an identical hole using the two hands alternately
in one minute. The sequence was pin, washer, collar, and washer (Figure 3-2), with one hand
pinch-grasping one object at a time. The number of pins successfully put into the holes was
the motor performance outcome.

Figure 3-2. Purdue pegboard task. A pegboard was placed in front of the participants; they
used both hands to pick up the objects (pins, washers, and collars) in the cups and alternately
assembled the objects into the holes embedded in the pegboard. The designated sequence was
pin, washer, collar, and washer, starting with the dominant hand.

Interhemispheric Inhibition Assessment
Ipsilateral silent period (iSP) was measured by transcranial magnetic stimulation (TMS) to
index interhemispheric inhibition. The methods used for iSP acquisition and quantification
were identical to those detailed in a previous study (Chapter 2) (Kuo et al., 2017). First, the
maximum voluntary contraction (MVC) of the abductor pollicis brevis (APB) for both hands
38
was measured. The participants were instructed to abduct the thumb to 50% of MVC while a
single TMS pulse was applied to the representational area of the APB in the ipsilateral
primary motor cortex. The temporary reduction of muscle activity recorded by
electromyography (EMG) observed in the contracting thumb is termed ipsilateral silent
period. Fifteen trials were obtained for the left hemisphere APB representational area with
left hand activation and 15 trials were obtained for the right hemisphere APB representational
area with right hand activation. Ipsilateral silent period (iSP) was quantified using the
normalized iSP analysis: the area of EMG reduction following TMS, normalized to pre-
stimulation muscle contraction level. For delineating the results of the study, iSP-L is
indicative of L to R inhibition and iSP-R indicative of R to L inhibition (Chieffo et al., 2016).

Statistical Analyses
Independent t tests were used to compare differences in bimanual coordination outcomes and
differences in IHI outcomes between musicians and non-musicians. Canonical correlation
analysis (CCA) was used to identify a linear relationship between combinations of multiple
variables (bimanual coordination outcomes and IHI outcomes). As part of the analysis, each
variable was weighted, demonstrating the magnitude and direction of the contribution of each
outcome in the linear relationship (Drysdale et al., 2017)(Tsvetanov et al., 2016)(Misaki et
al., 2012). All of the bimanual coordination and IHI outcomes were converted into z scores to
allow unit-less comparisons across different measures. The bimanual coordination outcomes
included: speed (total time × (-1)), accuracy, and evenness (variability × (-1)) for the FST,
and number for the PPT, such that higher z scores indicated better motor performance. The
IHI outcomes included iSP-L and iSP-R, with higher z scores indicating more IHI. CCA was
performed with all participants’ z scores (musicians and controls, N = 72), resulting in a
canonical variate U (a weighted bimanual coordination score) and a canonical variate V (a
weighted IHI score) for each participant, as well as the correlation between all U and V
39
values. Group membership for each participant was then identified and two additional
correlations were calculated for musicians and non-musicians, respectively.

A general linear model (GLM) was then used to compare group differences (36 musicians
versus 36 controls) in the strength of the linear association between canonical variates (i.e. U
and V). To validate that the observed group differences in the strength of the linear
relationship between the canonical variates were not by chance, permutation testing was
performed. The z values were randomly re-assigned to each participant and the same CCA
and GLM procedures were repeated 10,000 times. Validation of the determined group
differences occurred as follows: the number of times that the difference between the two
correlation coefficients after random re-assignment of the z scores exceeded the original
coefficient difference was determined (10,000 coefficient differences compared to the
original difference). If the permutated correlation coefficient differences were equal to or less
than the original difference 500 times out of 10,000, than the probability of the original group
differences in the correlation coefficients occurring by chance was less than 5% (i.e. two-
tailed p value < 0.05) indicating statistically significant group differences in the original
linear relationship.

Following the results and validation of the CCA, multiple regression analysis was performed
in order to determine which of the original variables in the CCA were most explanatory to the
brain-behavior relationship. Variable selection using enter method determined the variance in
each dependent IHI variable (i.e., iSP-L and iSP-R) explained by the independent bimanual
coordination variables. We selected the highly-contributing variables according to the
absolute Beta values (i.e. standardized coefficients) and performed CCA a second time using
only the bimanual coordination and IHI variables with the most robust relationship.

40
Results
Participants’ demographic data are summarized in Table 3-1. There was no significant group
difference in age [t(70) = -1.87, p = 0.07, d = 0.54] and handedness as quantified by the
laterality quotients of the Edinburgh Handedness Inventory [t(70) = -0.89, p = 0.38, d = 0.21].
There were four left-handed and four mix-handed participants in the musician group, as well
as three left-handed and four mix-handed participants in the control group.

Bimanual Coordination Outcomes
Bimanual coordination outcomes of the two groups are shown in Table 3-2. For the FST,
musicians were significantly faster in total time (Figure 3-3 A) and more accurate (Figure 3-3
B) compared to the controls. The variability of key pressing interval was significantly less in
musicians than in controls (Figure 3-3 C). Therefore, musicians performed the sequences
more evenly and more accurately with less movement variability, compared to the controls.
In decomposing total time into reaction time and movement time, controls demonstrated a
shorter reaction time, compared to musicians (Figure 3-3 D). However, the musicians had
significantly shorter movement time (Figure 3-3 E). No between-group differences were
Table 3-1. Demographics in the Musician and Control groups
Musicians Controls Independent t test
Number 36 36
Age (years) 25.0 ± 7.0 28.0 ± 3.5 t (70) = -1.87, p = 0.07, d = 0.54
Handedness (laterality quotients, LQ) 54.1 ± 51.4 64.1 ± 43.1 t (70) = -0.89, p = 0.38, d = 0.21
Training start age (years) 6.5 ± 3.2 NA
Total training time (years) 18.5 ± 7.4 NA
Average daily practice time (hours) 3.1 ± 1.3 NA
Total practice time past week (hours) 19 ± 11.8 NA
Values are group means ± SD
Left handed: LQ < -40; Mix handed: -40  LQ  40; Right handed: LQ > 40
NA: not available. Cohen’s d: effect size

41
found in the PPT.

Table 3-2. Group comparisons of bimanual coordination variables
Musicians Controls Independent t test
Finger sequence task
Total time (ms) 3111.0 ± 547.5 3646.5 ± 889.6 t (70) = -3.08, p = 0.003, d = 0.72
Reaction time (ms) 1363.6 ±333.4 1125.7 ± 265.2 t (70) = 3.35, p = 0.001, d = 0.79
Movement time (ms) 1747.4 ± 563.5 2520.7 ± 881.1 t (70) = -4.44, p < 0.001, d = 1.05
Accuracy (%) 86.6 ± 12.9 79.3 ± 15.9 t (70) = 2.12, p = 0.041, d = 0.50
Variability (ms) 100.0 ± 48.5 158.2 ± 75.0 t (70) = -3.91, p < 0.001, d = 0.92
Purdue pegboard test
Number 37.7 ± 6.6 37.9 ± 6.5 t (70) = -0.09, p = 0.93, d = 0.02
42

Figure 3-3. Results of the finger sequence task in musicians and controls. A: Total time; B:
Accuracy; C: Variability (standard deviation, SD); D: Reaction time; E: Movement time.
Group data are shown in box plots: blue indicates musicians; red indicates controls; upper to
lower limit of the box: interquartile range (IQR); whiskers above and below the box:
1.5×IQR; middle horizontal black line: median; middle horizontal yellow line: mean;
individual data points: values exceeding 1.5×IQR. * p < 0.5; ** p < 0.005.
Musicians Controls Musicians Controls Musicians Controls
A
B
C
Musicians Controls Musicians Controls
D E
** * **
** **
43
Interhemispheric Inhibition Outcomes
Table 3-3 shows the results of ipsilateral silent period. There were no between-group
differences observed in the iSP outcomes measured in either L or R hemisphere (iSP-L,
Figure 3-4 A; iSP-R, Figure 3-4 B).

Figure 3-4. Results of the ipsilateral silent period (iSP) in musicians and controls. A: iSP-L:
iSP measured in the left hemisphere; B: iSP-R: iSP measured in the right hemisphere. Figure
convention is the same as Figure 3-3.
Table 3-3. Group comparisons of interhemispheric inhibition variables
Musicians Controls Independent t test
iSP-L (%) 52.7 ± 12.0 50.1 ± 17.5 t (70) = 0.75, p = 0.459, d = 0.18
iSP-R (%) 49.5 ± 13.7 48.7 ± 14.7 t (70) = 0.23, p = 0.817, d = 0.05
iSP: ipsilateral silent period; L: left; R: right
A B
Musicians Controls Musicians Controls
44
Canonical Correlation
Canonical correlation analysis generated a first order and a second order linear relationship
with two IHI outcome variables and four bimanual coordination outcome variables. The first-
order canonical correlation (r = 0.25, Wilk’s lambda = 0.902, p = 0.542) for all participants
(N = 72) with corresponding canonical variates (U: bimanual coordination outcomes; V:
interhemispheric inhibition outcomes) is shown in Figure 4. Of the bimanual coordination
outcomes, speed (coefficient: 1.116) and evenness (coefficient: -0.875) demonstrated larger
coefficients than accuracy (coefficient: 0.218) and number (coefficient: -0.391). Of the IHI
outcomes, iSP-L (coefficient: -1.024) demonstrated a larger coefficient compared to iSP-R
(coefficient: 0.034). Given the larger coefficients, it appears that speed, evenness and iSP-L
are more explanatory variables in the IHI-bimanual coordination relationship compared to
accuracy, number, and iSP-R.

Following CCA of all participants, post-group membership labeling was conducted. Two
linear relationships (canonical vectors) were then calculated separately for musicians and
controls (Figure 3-5). A significant relationship between the canonical variates (U and V) was
only evident in musicians (r = 0.52, p = 0.001) but not in controls (r = -0.059, p = 0.732).
GLM showed that the canonical relationship between bimanual coordination outcomes and
IHI outcomes observed in musicians was significantly stronger compared to controls [t(68) = -
2.47, p = 0.016]. The permutation testing with 10,000 repetitions showed that there was only
a 0.6% chance that the linear relationship difference between musicians and controls was
larger than the original difference (r value difference: 0.52 - (-0.059) = 0.579). This indicates
that the observed between-group difference in the canonical relationship was statistically
significant and was not by chance (i.e. < 5% probability of making type I error).

For the second-order canonical correlation (r = 0.25, Wilk’s lambda = 0.96, p = 0.369), there
45
was not a significant relationship between IHI and bimanual coordination in either group
(musicians: r = 0.31, p = 0.066; controls: r = 0.05, p = 0.776).

Figure 3-5. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes across all participants. Canonical variate U: speed,
accuracy, evenness, and number in the x axis. Canonical variate V: iSP-L and iSP-R in the y
axis. Correlation equation: -1.024 × iSP-L + 0.034 × iSP-R = 1.116 × Speed + 0.218 ×
Accuracy - 0.875 × Evenness - 0.391 × Number. Musicians were marked in blue (N = 36),
with significant canonical relationship (blue solid line, r = 0.52, p = 0.001) between bimanual
coordination and IHI outcomes. Controls were marked in red (N = 36), with no significant
canonical relationship (red dashed line, r = -0.059, p = 0.732).

● Musicians
○ Controls
1.116 × Speed + 0.218 × Accuracy - 0.875 × Evenness - 0.391 × Number
-1.024 × iSP-L + 0.034 × iSP-R
46
The secondary analysis of multiple regression followed by a reduced variable CCA was
calculated for musicians only given that the IHI-bimanual coordination relationship was only
observed in musicians. Multiple regression showed that evenness (Beta: -0.606) and speed
(Beta: 0.428) were the variables with a higher weighting in explaining the variance of iSP-L,
compared to accuracy (Beta: -0.275) and number (Beta: 0.264). Conversely, the variance of
iSP-R was not largely explained by any of the bimanual coordination outcomes (all absolute
Beta values < 0.275). Given that speed, evenness, and iSP-L were the highly-contributing
variables in the canonical relationship for musicians, a variable reduction CCA was
performed to show the effects of these three variables on the relationship between IHI (iSP-L)
and bimanual coordination (speed and evenness). Accuracy, number and iSP-R were dropped
in this secondary analysis. Canonical correlation analysis still showed a significant
relationship between bimanual coordination and IHI (r = 0.45, Wilk’s lambda = 0.79, p =
0.023) in musicians (Figure 3-6), with the same positive/negative signs for the coefficients of
the variables. Taking the original model and the variable reduction model together, increased
IHI from L to R hemisphere is associated with increased evenness and reduced speed.

47

Figure 3-6. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes after variable reduction in musicians. Canonical variate
U: speed and evenness in the x axis. Canonical variate V: iSP-L in the y axis. Correlation
equation: -1 × iSP-L = 0.561 × Speed - 1.051 × Evenness. For the purpose of demonstration,
the equation was multiplied by negative one and plotted as: iSP-L = -0.561 × Speed + 1.051 ×
Evenness. There was a significant canonical relationship (blue line, r = 0.45, p = 0.023)
between the remaining IHI and bimanual coordination variables.

Given the significant canonical relationship in musicians between iSP-L and bimanual
coordination measures exclusively related to the finger sequence task (symmetric), we sought
to understand whether the relationship between IHI and bimanual coordination was
dependent on instrument type. Specifically, we wanted to determine the degree to which the
IHI bimanual coordination relationship was a task-specific phenomenon associated with
playing a specific type of musical instrument. In order to answer this question, keyboard
● Musicians
-0.561 × Speed + 1.051 × Evenness
iSP-L
48
players (N = 14), string players (N = 8) and woodwind players (N = 7) were post-labeled to
calculate the variable reduction canonical relationships of each instrument type (Figure 3-7).
A significant relationship between the canonical variates was only evident in keyboard
players (r = 0.67, p = 0.008). The relationship in string players (r = 0.51, p = 0.193) and
woodwind players (r = 0.52, p = 0.224) were not significant. No between-group difference in
the strength of correlation was found among these three instrument types. The relationship
between bimanual coordination outcomes and IHI outcomes were mainly driven by the
keyboard players.

49

Figure 3-7. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes in musicians after variable reduction: comparisons
across instrument types (keyboard, string, and woodwind players). Canonical variate U:
speed and accuracy in the x axis. Canonical variate V: iSP-L in the y axis. Correlation
equation: -1 × iSP-L = 0.561 × Speed - 1.051 × Evenness. For the purpose of demonstration,
the equation was multiplied by negative one and plotted as: iSP-L = -0.561 × Speed + 1.051 ×
Evenness. Grouping: pink square: keyboard players; green diamond: string players; black
triangle: woodwind players. Only the keyboard players showed significant canonical
relationship (pink solid line, r = 0.73). No significant relationship was found in string players
(green dashed line, r = 0.29) or woodwind players (black dotted line, r = 0.58).

 Keyboard
 String
▲ Woodwind
-0.561 × Speed + 1.051 × Evenness
iSP-L
50
Discussion
The current study aimed to investigate whether modifications in interhemispheric
communication as a function of prolonged musical training accounts for skilled bimanual
coordination. We used two bimanual motor tasks to test the participants’ bimanual
coordination abilities with respect to movement speed, accuracy, and consistency
simultaneously. The musicians appeared to utilize direction-specific interhemispheric
inhibition (i.e., L to R IHI) to achieve superior bimanual coordination, whereas this was not
observed in the non-musicians.

As IHI is essential for dexterous motor control, the communication between bilateral
hemispheres may have been strengthened and facilitated in musicians in order to contend
with the demands of coordinating the two hands for rigorous instrument playing. It has been
well established that musical expertise leads to extensive morphological and functional
interhemispheric connectivity in cortical and subcortical regions (Fauvel et al., 2014)(Klein et
al., 2016)(Tanaka and Kirino, 2016). The current study provides evidence that enhanced
interhemispheric interaction may give rise to exceptional bimanual performance only in
individuals with extensive hand motor skill training.

We were first interested in identifying whether the communication between the two cerebral
hemispheres had been modified by long-term instrument training by comparing IHI in
musicians and non-musicians. Additionally, we separately analyzed if bimanual coordination
capability was different between the two groups.

We did not find a group difference in iSP independent of the bimanual coordination measures.
This result is contrary to what has been reported (Chieffo et al., 2016). Chieffo et al., (2016)
found that iSP was significantly different between musicians (all pianists) and non-musicians.
51
While the absolute amount of IHI in both musicians and non-musicians was similar to our
results, we did not detect any group differences. Given the common iSP methodology
between the two studies, the explanation for the discrepant results is unclear. A larger sample
size (current study: N = 72; Chieffo et al.: N = 30), both left and right hand dominant
participants compared to right hand only, highly skilled musicians compared to amateurs and
various instrument types compared to keyboard only marked the main differences between
the current study and Chieffo et al., (2016) respectively. For the current study, we would
conclude that differences in IHI are not revealed independent of behavior.

With respect to the assessment of bimanual coordination independent of IHI, we uniquely
employed a task that required accurate performance of three randomly presented, 8-element
sequences. Additionally, the finger sequence task used in the current study requires both
temporal and spatial precision between the two hands. Previous studies used only a simple
reaction time paradigm (one finger to press a button in response to the same stimuli as fast as
possible) and showed reduced reaction time in musicians compared to non-musicians
(Hughes and Franz, 2007)(Chang et al., 2014). Using essentially a ‘choice’ reaction time
paradigm that required motor planning prior to execution of movement, our data revealed a
longer reaction time in musicians compared to non-musicians. Conversely, we found that
once planned, musicians were significantly faster in performing the sequence compared to
non-musicians. It is possible that the musicians and non-musicians used different strategies to
perform the FST. The musicians considered the 8-element sequence as a “whole sequence”
and used more time for planning before movement initiation. Once the musicians started to
move, they were able to finish the movement quickly. The non-musicians may not “chunk”
the 8-element sequence into a single motor plan and thus spent additional time planning
during movement execution. As musical training involves acquisition of various musical note
sequences, the strategy adapted by the musicians for the FST may result from inherent task
52
features that are similar to instrument playing. The distinct strategy used by the musicians
whereby they demonstrated faster movement and more consistent inter-tapping interval than
controls, while spending more time in motor planning, may result from their instrument
training (e.g. planning ahead and playing the melody with consistent rhythm).

It was in the analysis of the relationship between IHI and bimanual coordination that unique
differences between musicians and non-musicians were revealed. The inhibition from L to R
hemisphere (i.e. iSP-L), as well as evenness and speed, were the dominant variables in the
canonical relationship, given the larger absolute values of the coefficients both in the CCA
and multiple regression analyses. The variable reduction analysis further confirmed that
increased communication from the L to R hemisphere was associated with enhanced
evenness in the key pressing interval (i.e. reduced variability), with a tradeoff of slower
movement speed.

These results can be viewed from a hemispheric specialization perspective. It is well
established that the L hemisphere is involved in feedforward control for pre-planned
intersegmental coordination and speed (Schaefer et al., 2012)(Christopoulos et al.,
2012)(Serrien and Sovijärvi-Spapé, 2016)(Pflug et al., 2017). On the other hand, the R
hemisphere is more specialized for feedback control, such as online correction of movement
error (Schaefer et al., 2012)(Christopoulos et al., 2012). Since evenness requires ongoing
adjustment of finger movements based on the previous key strokes, it is possible that R
hemisphere activation accounted for maintaining consistency of key pressing intervals in the
FST. Therefore, long-term musical training may rewire the musicians’ brain to allow greater
recruitment of the R brain (i.e., increased L to R iSP) in order to decrease movement
variability at the expense of speed. Moreover, reducing movement variability is a critical
feature in instrument playing, yet is usually not required in daily bimanual activities.
53
Increasing R hemisphere activation via transcallosal inhibitory circuits to achieve movement
consistency was not evident in non-musicians as there is no such need in daily tasks. Hence
the reorganization of the brain-behavior relationship observed in musicians may be attributed
to experience-dependent neuroplasticity.

We used CCA as a multivariate statistical model to address the linear relationships between
combinations of IHI and bimanual coordination variables. Variables possibly contributing to
this brain-behavior relationship were all introduced into CCA to determine the weighting of
each IHI and bimanual coordination variable (Lin et al., 2018). Both iSP-L and iSP-R were
introduced into the model given that there is ongoing mutual communication via inhibitory
circuits between the two hemispheres for fine-tuning dexterous finger movements. The
outcomes of FST and PPT were introduced into the model given that these variables address
different aspects of bimanual coordination. Canonical correlation analysis allows us to
comprehensively investigate the relationship between the two IHI variables as well as the
multiple measures of coordination (spatial and temporal optimization of movement speed,
accuracy, and evenness). Importantly, there were no pre-defined groups when all of the data
were included in CCA. Without an a priori hypothesis that any of the variables would be
more important than the others, CCA is an ideal approach to address the overall association
between IHI and bimanual coordination and the weighting of each variable informs its
importance in this IHI-bimanual coordination relationship. Remarkably, two groups
(musicians and non-musicians) emerged from the single analysis as having a distinct IHI-
bimanual coordination relationship.

That the CCA identified elements of the FST as being critical to the IHI-bimanual
coordination relationship and that these elements (evenness and speed) are similar to
instrument playing led to a post-hoc analysis of instrument type. In the instrument-specific
54
analysis, the IHI-bimanual coordination relationship was most evident in the keyboard
players. It is possible that the similarity between the FST and piano keyboard links the
increased interhemispheric communication to this non-musical bimanual task. Woodwind
instruments also require similar use of both hands (i.e. symmetric hand use, similar to
keyboard) and a moderate correlation between IHI and bimanual coordination was observed.
The weakest association was found in the string players, which supports the argument that the
strengthened association between IHI and bimanual coordination could be task-specific, due
to the fact that string instruments require asymmetric movement of the two hands (left hand
pressing strings while the right hand draws the bow). Therefore, the enhanced communication
between bilateral primary motor cortices concomitant with modifications in bimanual
coordination could be a task-specific phenomenon, and not a relationship that generalizes to
other forms of hand use.

One limitation of this study is that there was not an equal distribution of types of musical
instruments played, which limits the generalization of the current results to all kinds of
musicians playing different instruments. Second, to better address the instrument-specific
effect on the relationship between IHI and bimanual coordination, an asymmetric task which
resembles instruments with asymmetric hand use would be necessary. The PPT employed in
the current study required only temporal asymmetry of the two hands. Studies investigating
bimanual coordination have utilized one hand tracking a sine wave while the other hand
generates a given amount of force as a precise form of asymmetric hand use and one that
would be similar to string instrument playing (Fling and Seidler, 2012a)(Fling and Seidler,
2012b).

Conclusion
Musical expertise induces neuroplastic changes, which can be observed in the central nervous
55
system as well as in skilled bimanual motor behavior. The enhanced interhemispheric
communication is a vehicle in the central nervous system allowing the trained musician to
achieve the high skill demand essential for instrument playing. Increased inhibitory
processing from the L to R hemisphere accounts for reduced movement variability, possibly
through activating the R feedback-based hemisphere. Utilizing this IHI-dependent strategy,
greater movement consistency is achieved while speed is modulated. This relationship is
highly driven by keyboard players, which suggests that the brain reorganization following
musical training is task-specific and only revealed in behaviors requiring similar hand use as
instrument playing.

56
CHAPTER FOUR
Relationship between Interhemispheric Inhibition and Bimanual Coordination in
Musicians: Is There an Instrument-dependent Effect?

Abstract

Background: Functional reorganization in musician’s brain has long been considered strong
evidence of experience-dependent neuroplasticity. Strengthened connectivity between the
neural substrates of auditory and motor networks has been found in musicians compared to
non-musicians. However, it is not clear whether musician’s motor performance was driven
by, or independent of auditory process. Highly coordinated movements between the two
hands stem from intensive instrument training require abundant communication between
bilateral hemispheres. Interhemispheric inhibition (IHI) is one form of normal
communication between hemispheres and changes with instrument type. Instrument type may
also impact bimanual coordination given the dissimilar hand use with different instrument
performance. However, it was yet to know whether bimanual skill developed with musical
training in association with adapted IHI in musicians was dependent on instrument type.
Therefore, the current study aimed to 1) investigate the impact of sound on the bimanual
coordination in skilled musicians; and 2) To investigate instrument-dependent effects on the
relationship between IHI and bimanual coordination in keyboard players compared with
string players.

Methods: Thirteen keyboard players and 11 string players participated. An 8-element finger
sequence task (FST) with and without auditory tones were used to test symmetric bimanual
coordination. Speed, accuracy, and evenness of the key pressing interval were recorded as
performance outcomes. Asymmetric bimanual coordination was measured by a force tracking
57
task (FTT) with one hand performing sine wave tracking, while the other hand performed
constant contraction. Error was measured as the performance outcome for the FTT. Ipsilateral
silent period (iSP) was obtained using transcranial magnetic stimulation to index IHI in both
left (L) and right (R) hemispheres. Canonical correlation analysis (CCA) with variable
reduction was performed to identify linear relationships between the IHI and bimanual
coordination outcomes.

Results: Keyboard and string players did not show significantly different performance in
FST and FTT, or in the iSP outcome measures. In FTT, better performance with the R hand
tracking sine wave while the L hand performed constant contraction was observed in both
groups, compared to the L hand tracking sine wave and the R hand performing constant
contraction. Canonical correlation analysis (CCA) showed that better bimanual coordination
performance was associated with increased IHI from the L hemisphere to the R hemisphere
as well as decreased IHI from the R to the L hemisphere in both groups (keyboard players: r
= 0.32; string players: r = 0.45).

Discussion: Musicians were able to transfer their bimanual coordination acquired from
instrument training to novel laboratory motor tasks without the presence of auditory tones as
part of the performance outcome. Instrument effects were not evident in either symmetric or
asymmetric bimanual coordination performance. Adding both symmetric/asymmetric tasks
washed out differences in the IHI-bimanual coordination relationship between instruments.
Better L hand motor control through enhanced communication from the L to the R
hemisphere via inhibitory pathways may be critical for advanced bimanual motor control
following long-term musical training.

58
Keywords: experience-dependent neuroplasticity, symmetric and asymmetric bimanual
coordination, interhemispheric inhibition, keyboard, string

59
Introduction
Expedience-dependent neuroplasticity in the human central nervous system (CNS) has been
characterized as structural and functional reorganization with prolonged exposure to external
stimuli (Pascual-Leone et al., 2005)(May, 2011). Musicians are an ideal population for
studying expedience-dependent neuroplasticity due to the effects of sensorimotor skill
acquisition on neuroplastic adaptation and behavioral manifestation (Wan and Schlaug,
2010). As instrument playing requires coordination between two hands to execute complex
motor sequences, profuse interhemispheric communication via the corpus callosum must
contribute to such demanding bimanual control (Schlaug et al., 1995)(Hyde et al., 2009).
Given that different instruments require the two hands do similar or dissimilar movements,
adaptations in the CNS have been found to be influenced by instrument types. For example,
increased white matter myelination in the left (L) hemisphere corticospinal tract has been
observed in pianists compared with string players, possibly since the right (R) hand of a
string player is involved in bowing and less engaged in dexterous finger movements (Rüber
et al., 2013). Additionally, increased grey matter folding of the hand area in the precentral
gyrus was found in the L hemisphere in pianists, whereas the string players showed increased
folding in the R hemisphere. These results suggest that dependent on instrument type, the
hemisphere which is controlling the hand with a higher fine motor skill demand shows
greater morphological adaptation (Bangert and Schlaug, 2006). However, while instrument-
dependent adaptations in the CNS have been established, it is not known whether bimanual
coordination in a non-musical task following explicit musical training is also experience-
dependent (e.g. pianist perform better in a symmetric task, while violinists perform better in
an asymmetric task).

Sound is the result of instrument playing and the auditory delivery is a direct outcome of their
performance level. Auditory-motor coupling in the brain networks has been shown to be
60
strengthened in musicians compared to non-musicians given the sensory input as a result of
body movements (Münte et al., 2002)(van Vugt and Tillmann, 2014)(Altenmüller and
Schlaug, 2015). During imagined musical performance, cortical networks involving the
motor and auditory regions were activated in musicians, whereas no motor regions were
engaged in the amateurs. This suggested that the auditory-motor coupling in musicians was
strongly wired even though they were not actually playing a musical instrument (Lotze et al.,
2003). Although the wired motor and auditory networks in the brain of a musician is
indicative of experience-dependent neuroplasticity, to date, it is unknown whether hand
motor ability in musicians would be reinforced by acoustic processing accompanying hand
movements. Therefore, we modified the finger sequence task by adding auditory tones. This
enabled us to test a musician’s bimanual motor behavior within the context of instrument
playing.

As a conduit for transferring information between the two hemispheres, interhemispheric
inhibition (IHI) is a functional measurement of the communication between bilateral primary
motor cortices. Interhemispheric inhibition has been shown to play a critical role in
unimanual and bimanual motor performance, as well as in dexterous hand motor control in
both non-disabled adults and individuals with stroke (Harris-Love et al., 2007b)(Perez and
Cohen, 2008)(Morishita et al., 2012)(Wahl et al., 2015). As musicians have extraordinary
hand motor control acquired from instrument training, any observed IHI adaptation would be
an expression of experience-dependent neuroplasticity (Ridding et al., 2000)(V ollmann et al.,
2014)(Bäumer et al., 2016)(Chieffo et al., 2016). Instrument type has been shown to also
affect IHI, as more IHI from the L to the R hemisphere was found only in string players,
compared to pianists and non-musicians. This is possibly due to greater activation of the R
hemisphere through inhibitory circuits to facilitate L hand control (V ollmann et al., 2014).
Our previous study (Chapter 3) determined that L-to-R IHI is associated with bimanual
61
coordination performance. However, the influence of instrument specificity (with its known
effect on IHI) and its association with bimanual motor skill level is not well understood.

Therefore, the current study aimed 1) to investigate the impact of auditory tones on the
bimanual coordination ability in skilled musicians; and 2) to investigate instrument-
dependent effects on the relationship between IHI and bimanual coordination in keyboard
players (symmetric musical skill) compared with string players (asymmetric musical skill).
We hypothesized that first, musicians will show better motor performance when the tasks
were accompanied by auditory feedback similar to real instrument playing. Second, we
hypothesized that the keyboard players will exhibit a relationship between IHI and symmetric
bimanual coordination, while the string players will exhibit a relationship between IHI and
asymmetric bimanual coordination.

Methods
Participants
Thirteen keyboard players (piano: 13) and 11 string players (violin: 5, viola: 3, cello: 2,
double bass: 1) participated in this study. The musicians were classically trained professionals
or college students majoring in keyboard or string performance. Participants were actively
engaged in instrument performance since early childhood. A transcranial magnetic
stimulation (TMS) safety questionnaire was used to screen eligibility to undergo TMS
procedures. Participants were excluded if they had a history of neurological disorders
(Wassermann, 1998). Age and handedness (measured by the Edinburgh Handedness
Inventory (Oldfield, 1971)) were matched between the two groups. The current study was
approved by the Institutional Review Board of the University of Southern California.

62
Bimanual Coordination Assessment
Finger sequence task (FST): for testing symmetric hand coordination and agility (Lee et al.,
2016)(Pascual-Leone et al., 1995). The FST used in the current study was modified from the
previous study (Chapter 3). The participants performed an 8-element sequence of finger
movements on a computer keyboard. Participants were instructed to press the keys in a
sequential order from 1 to 8 according to the associated location of the key. Four sets of
sequences were used and the participants had 15 minutes to practice all of the assigned
sequences before the start of the test. The participants were asked to perform the finger
sequence task as fast and as accurately as possible while maintaining an even key pressing
interval in each finger (i.e. reduce variability). The task goals were thus to maximize speed,
accuracy, and evenness. The sequences were performed under two conditions: with and
without auditory tones associated with key pressing. The FST with auditory tones (FST-T)
was similar to piano playing (Furuya and Soechting, 2010)(Horvath et al., 2015). The FST
without auditory tones (FST-S) provided no auditory feedback. In each condition (with or
without auditory tones), four sequences were presented in a pseudorandom order for 28 trials
(7 trials per sequence). Accordingly, a total of 56 trials were recorded. The order of testing in
these two conditions was counterbalanced across participants. In the FST-T, total time spent
to complete each sequence and the auditory sound associated with each key were presented as
feedback. In FST-S, total time spent to complete each sequence and accuracy of the whole
sequence (i.e. correct or incorrect) were provided as feedback. Total time (task completion
time), accuracy, and variability of the key pressing interval (the standard deviation (SD) of
the time difference between two consecutive key presses) were recorded as motor
performance. Total time was further decomposed into reaction time (between the presentation
of the sequence and first key press) and movement time (between the first and final key
press) to determine movement planning and movement execution respectively.

63
Force tracking task (FTT): for testing asymmetric force regulation of the two hands (Fling
and Seidler, 2012; Tazoe et al., 2013). In the previous study (Chapter 3), the assessments of
bimanual coordination included finger sequence task (instrument-like task, similar to piano
playing) and Purdue pegboard test (clinical task, not similar to any instrument playing). We
did a preliminary analysis of the instrument-dependent effect on IHI-bimanual coordination
relationship and found that the keyboard players showed a stronger relationship between IHI
and performance of the finger sequence task, compared to the string players. To better
address the instrument-specific effect on the relationship between IHI and bimanual
coordination, it is necessary to introduce an asymmetric task which resembles instruments
with asymmetric hand use. As a result, we added this force tracking task as a measure of
asymmetric bimanual coordination.

This is a task in which one hand generates rhythmic force to match a 1.06 Hz sine wave,
while the other hand maintains constant contraction at a target force level (Figure 4-1). There
were two conditions: left (L) hand tracking sine wave (FTT-L sine) and right (R) hand
tracking since wave (FTT-R sine). For the hand which was tracking the sine wave, the
participants pinched a force transducer (PCB Piezotronics Inc., New York) with their thumb
and index finger to track the sine wave. Abductor pollicis brevis (APB) was the agonist to
abduct the thumb and generate force. For the other hand which was maintaining constant
contraction, the target force level was scaled to 30% of maximum voluntary contraction
(MVC). Real-time visual feedback was provided during task performance. Force data were
sampled at 2000 Hz with a low pass filter of 20 Hz. Each trial lasted 15 seconds and data of
the middle 12 seconds were used for analysis to exclude the ramp-up and ramp-down phases.
Between each trial, a 15-second break was provided to avoid fatigue. The order of testing in
these two conditions (FTT-L sine and FTT-R sine) was counterbalanced across participants.
Prior to collection of performance measures, the participants had 15 minutes of
64
(ms)
(ms)
Newton (N)
Newton (N)
familiarization time to practice the tasks. The outcomes for each condition included error
(measured by root mean square error, RMSE) of the hand performing the sine wave tracking
(Fling and Seidler, 2012). RMSE-L sine was the error of the left hand while tracking the sine
wave, and RMSE-R sine was the error of the right hand while tracking the sine wave. Four
trials were collected for each condition and the outcomes were the average values from all
trials.

Figure 4-1. A representative trial of the force tracking task. Upper panel: sine wave tracking
with the left hand. Sine wave: blue line; force trace: red line. Lower panel: constant
contraction with the right hand. Target level: blue line; force trace: pink line.

Interhemispheric Inhibition Assessment
The measures and analyses of IHI were the same as in the first study (Chapter 2) (Kuo et al.,
2017). Ipsilateral silent period (iSP) was measured by transcranial magnetic stimulation
65
(TMS) to index interhemispheric inhibition. First, the MVC of the APB (the same muscle
used in the FTT) for both hands was measured. The participants were instructed to abduct the
thumb to 50% of MVC while a single TMS pulse was applied to the representational area of
the APB in the ipsilateral primary motor cortex. The temporary reduction of muscle activity
recorded by electromyography (EMG) observed in the contracting thumb is the ipsilateral
silent period. Fifteen trials were obtained for the left hemisphere ABP representational area
with left hand activation and 15 trials were obtained for the right hemisphere ABP
representational area with right hand activation. Ipsilateral silent period (iSP) was quantified
using normalized iSP analysis: the area of EMG reduction following the TMS pulse,
normalized to pre-stimulation muscle contraction level. For delineating the results of the
study, iSP-L is indicative of L to R inhibition and iSP-R indicative of R to L inhibition
(Chieffo et al., 2016). The difference of inhibition between the two hemispheres (iSP-D) was
calculated by iSP-R minus iSP-L. A positive value indicates more inhibition from the R
hemisphere, whereas a negative value indicates more inhibition from the L hemisphere.

Statistical Analyses
The outcomes of FST, including total time, reaction time, movement time, accuracy and key
pressing variability were analyzed using a 2 group (keyboard vs. string) × 2 condition (with
tones, FST-T vs. without tones, FST-S) mixed effect repeated measures ANOV A. The
outcomes of FTT, including variability and error, were analyzed using a 2 group (keyboard
vs. string) × 2 condition (left hand tracking sine wave and right hand producing constant
force, FTT-L sine vs. right hand tracking sine wave and left hand producing constant force
FTT-R sine) mixed effect repeated measures ANOV A. Bonferroni correction was applied as
post-hoc analysis.

Independent t tests were used to compare differences in IHI outcomes (iSP-L, iSP-R, iSP-D)
66
between keyboard and string players. A one sample t test was used to compare whether iSP-D
was different from zero (i.e. imbalanced IHI).

Canonical correlation analysis (CCA) was used to identify a linear relationship between
combinations of multiple variables (bimanual coordination outcomes and IHI outcomes), and
the procedures were the same as described in the previous study (Chapter 3). As part of the
analysis, each variable was weighted, demonstrating the magnitude and direction of the
contribution of each outcome in the linear relationship (Drysdale et al., 2017)(Tsvetanov et
al., 2016)(Misaki et al., 2012). All of the bimanual coordination and IHI outcomes were
converted into z scores to allow unit-less comparisons across different measures. The
variables generated from the FST and FTT were included in CCA as bimanual coordination
outcomes. In FST, speed (total time × (-1)) and evenness (variability × (-1)) were selected,
with higher z scores indicative of better motor performance. In the FTT, accuracy-L (RMSE-
L sine × (-1)) and accuracy-R (RMSE-R sine × (-1)) were selected, with higher z scores
indicative of better motor performance. The IHI outcomes included iSP-L and iSP-R, with
higher z scores indicating more IHI. CCA was performed with the z scores of all participants
(keyboard and string, N = 24), resulting in a canonical variate U (a weighted bimanual
coordination score) and a canonical variate V (a weighted IHI score) for each participant, as
well as the correlation between all U and V values. Group membership for each participant
was then identified and two additional correlations were calculated for keyboard and string
players, respectively. Then the strength of correlation was compared between the two groups.

A general linear model (GLM) was then used to compare group differences (13 keyboard
players versus 11 string players) in the strength of the linear association between canonical
variates (i.e. U and V). Permutation testing was performed to validate that the observed group
differences in the strength of the linear relationship between the canonical variates were not
67
by chance. Following the results and validation of the CCA, multiple regression analysis
(enter method) was performed in order to select the most explanatory variables in the CCA
based on the absolute Beta values (i.e. standardized coefficients). Moreover, the absolute
values of coefficient greater than 0.45 were saved in the model (Sherry and Henson, 2005).
We then performed CCA a second time using only the remaining and highly-contributing
bimanual coordination and IHI variables.

Results
Participants’ demographic data are summarized in Table 4-1. There was no significant group
difference in age [t(22) = -0.69, p = 0.50, d = 0.30] and handedness [t(22) = 0.69, p = 0.50, d =
0.30]. There were two left-handed and three mix-handed participants in each group.

Bimanual Coordination Outcomes
Outcomes of the FST in two groups are shown in Table 4-2. There was no significant group
by condition interaction in any of the FST outcomes. No significant main effect of group, nor
condition, was found in FST (Figure 4-2). Only a marginal main effect of group was found in
Table 4-1. Demographics in the keyboard and string players
Keyboard String Independent t test
Number 13 11
Age (years) 26.2 ± 11.0 28.8 ± 6.3 t (22) = -0.69, p = 0.50, d = 0.30
Handedness (laterality quotients, LQ) 46.8 ± 43.8 30.4 ± 64.0 t (22) = 0.69, p = 0.50, d = 0.30
Training start age (years) 6.3 ± 3.2 6.2 ± 2.9 t (22) = 0.07, p = 0.95, d = 0.03
Total training time (years)
20.0 ± 11.2 22.6 ± 7.3
t (22) = 0.07, p = 0.95, d = 0.03
Average daily practice time (hours) 2.8 ± 2.0 3.6 ± 1.8 t (22) = -1.0, p = 0.33, d = 0.42
Total practice time past week (hours) 13.9 ± 14.2 25.2 ± 14.4 t (22) = -2.0, p = 0.06, d = 0.79
Values are group means ± SD
Left handed: LQ < -40; Mix handed: -40  LQ  40; Right handed: LQ > 40
Cohen’s d: effect size

68
reaction time: the string players tended to show reduced reaction time in the FST.
Specifically, in FST-S, string players showed significantly reduced reaction time compared to
keyboard players.

Table 4-2. Group comparisons of the results of finger sequence task (FST)
Keyboard String Mixed effect ANOVA
FST Tones (FST-T)
Total time (ms) 3395.2 ± 729.6 3422.8 ± 767.4 F (1, 22) = 0.02, p = 0.88, η p
2
< 0.01
Reaction time (ms) 1417.5 ± 261.8 1237.3 ± 252.6 F (1, 22) = 0.75, p = 0.40, η p
2
= 0.03
Movement time (ms) 1961.9 ± 631.0 2171.4 ± 631.4 F (1, 22) = 0.08, p = 0.78, η p
2
< 0.01
Accuracy (%) 87.4 ± 12.4 85.1 ± 12.0 F (1, 22) = 0.29, p = 0.59, η p
2
= 0.01
Variability (ms) 90.1 ± 49.5 102.0 ± 32.5 F (1, 22) < 0.01, p = 0.97, η p
2
< 0.01
FST Silence (FST-S)
Total time (ms) 3360.0 ± 646.2 3371.8 ± 800.5
Reaction time (ms) 1458.3 ± 302.4 1232.7 ± 192.6
Movement time (ms) 1901.7 ± 611.6 2139.1 ± 715.9
Accuracy (%) 87.6 ± 7.1 87.7 ± 8.0
Variability (ms) 101.9 ± 53.5 113.4 ± 54.0
Values are group means ± SD
Results of the 2 group (keyboard vs. string) × 2 condition (FST-T vs. FST-S) repeated measures
ANOVA were shown in the last column; partial eta squared (η p
2
): effect size
69

A
Keyboard String Keyboard String
Total Time
FST-T FST-S
B
Keyboard String Keyboard String
Accuracy
FST-T

FST-S

C
Keyboard String Keyboard String
Variability
FST-T

FST-S

D
Keyboard String Keyboard String
Reaction Time
FST-T

FST-S

E
Keyboard String Keyboard String
Movement Time
FST-T

FST-S
*
70
Figure 4-2. Results of the finger sequence task with tones (FST-T) and silence (FST-S)
conditions in keyboard and string players. A: Total time; B: Accuracy; C: Variability
(standard deviation, SD); D: Reaction time; E: Movement time. Group data are shown in box
plots: pink indicates keyboard players; green indicates string players; upper to lower limit of
the box: interquartile range (IQR); whiskers above and below the box: 1.5×IQR; middle
horizontal black line: median; middle horizontal yellow line: mean; individual data points:
values exceeding 1.5×IQR. * p < 0.5.

Outcomes of the FTT in two groups are shown in Table 4-3. There was no significant group
by condition interaction in either FTT outcome. A significant main effect of condition was
shown in error [F(1, 22) = 78.55, p < 0.001, ηp
2
= 0.78] (Figure 4-3). The error of force tracking
was significantly higher in the FTT-L sine condition in both groups.

Table 4-3. Group comparisons of the results of force tracking task (FTT)
Keyboard String Mixed effect ANOVA
L hand sine wave and R hand constant contraction (FTT-L sine)
RMSE-L sine (N) 1.28 ± 0.52 1.29 ± 0.33 F (1, 22) = 0.01, p = 0.91,
η p
2
< 0.01

R hand sine wave and L hand constant contraction (FTT-R sine)
RMSE-R sine (N) 0.62 ± 0.24 0.61 ± 0.20
Values are group means ± SD
Results of the 2 group (keyboard vs. string) × 2 condition (FTT-L sine vs. FTT-R sine) repeated
measures ANOVA were shown in the last column
RMSE: root mean squared error, indicating error; N: Newton
71

Figure 4-3. Results of the force tracking task in left hand sine wave (FTT-L sine) and right
hand sine wave (FTT-R sine) conditions in keyboard and string players. Error was measured
by root mean square error, RMSE). Group data are shown in box plots: pink indicates
keyboard players; green indicates string players; upper to lower limit of the box: interquartile
range (IQR); whiskers above and below the box: 1.5×IQR; middle horizontal black line:
median; middle horizontal yellow line: mean; individual data points: values exceeding
1.5×IQR; L: left, R: right; ** significant main effect of condition, p < 0.005.

Interhemispheric Inhibition Outcomes
Table 4-4 shows the results of ipsilateral silent period. There were no between-group
differences observed in all three iSP outcomes (Figure 4-4). The iSP-D was not significantly
different from zero in both groups, although string players demonstrated more inhibition from
the L hemisphere (iSP-D: -3.28 ± 10.64 %), while keyboard players showed a more balanced
IHI (iSP-D: 0.80 ± 14.64 %).
FTT-L sine FTT-R sine
**
Error
Keyboard String Keyboard String
72

Figure 4-4. Results of the ipsilateral silent period (iSP) in keyboard and string players. A:
iSP-L: iSP measured in the left hemisphere; B: iSP-R: iSP measured in the right hemisphere.
C: iSP-D: iSP difference (iSP-R - iSP-L). Figure convention is the same as Figure 4-3.
Table 4-4. Group comparisons of interhemispheric inhibition variables
Keyboard String Independent t test
iSP-L (%) 38.7 ± 9.7 43.8 ± 10.7 t (22) = -1.21, p = 0.24, d = 0.50
iSP-R (%) 39.5 ± 12.1 40.5 ± 14.6 t (22) = -0.18, p = 0.86, d = 0.07
iSP-D (%) 0.8 ± 14.6 -3.3 ± 10.6 t (22) = 0.77, p = 0.45, d = 0.32
iSP: ipsilateral silent period; L: left; R: right; D: difference
Inhibition (%)
Keyboard String
A
iSP-L

Inhibition (%)
Keyboard String
B
iSP-R

Inhibition (%)
Keyboard String
C iSP-D

73
Canonical Correlation
Canonical correlation analysis generated a first order and a second order linear relationship
with two IHI outcome variables (iSP-L and iSP-R) and four bimanual coordination outcome
variables (FST-S: speed and evenness; accuracy-L; accuracy-R). Since there was no
difference between the two conditions of FST, only the outcomes in the FST-S condition were
added to avoid over-fitting the model. The first-order canonical correlation (r = 0.38, Wilk’s
lambda = 0.85, p = 0.92) for all participants (N = 24) with corresponding canonical variates
(U: bimanual coordination outcomes; V: interhemispheric inhibition outcomes) is shown in
Figure 4-5. Of the bimanual coordination outcomes, speed (coefficient: 0.689) and accuracy-
L (coefficient: 0.894) demonstrated large coefficients, which considerably outweighed other
bimanual coordination outcomes (all absolute coefficients < 0.27). Of the IHI outcomes, iSP-
L (coefficient: 0.431) and iSP-R (coefficient: -1.096) contributed similarly to the observed
IHI-bimanual coordination relationship. Post-group membership labeling showed the IHI-
bimanual coordination relationship was similar in keyboard players (r = 0.34, p = 0.26) and
string players (r = 0.46, p = 0.16), and no between-group differences in correlation strength
was detected by GLM (p = 0.63). Increased IHI from the L hemisphere along with reduced
IHI from the R hemisphere is explanatory to the performance of increased speed in a
symmetric task (i.e. FST) and increased accuracy in an asymmetric task (i.e. FTT) in both
groups.

For the second-order canonical correlation (r = 0.12, Wilk’s lambda = 0.98, p = 0.94), no
significant relationship between IHI and bimanual coordination was found in either group
(keyboard: r = 0.13, p = 0.68; string: r = 0.14, p = 0.68).

74

Figure 4-5. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes in all musicians. Canonical variate U: speed and
evenness of the FST-S; accuracy-L (error of the FTT-L sine); accuracy-R (error of the FTT-R
sine) in the x axis. Canonical variate V: iSP-L and iSP-R in the y axis. Correlation equation:
0.431 × iSP-L - 1.096 × iSP-R = 0.689 × Speed + 0.021 × Evenness + 0.894 × Accuracy-L -
0.274 × Accuracy-R. Keyboard players were marked in pink (N = 13) and string players were
marked in green (N = 11), with canonical relationship between bimanual coordination and
IHI outcomes: r = 0.34, p = 0.26 (pink solid line), r = 0.46, p = 0.16 (green dashed line),
respectively.

Multiple regression showed that speed (Beta: 0.10) and accuracy-L (Beta: -0.09) were the
variables with a higher weighting in explaining the variance of iSP-L, compared to other
 Keyboard
 String

0.689 × Speed + 0.021 × Evenness + 0.894 × Accuracy-L - 0.274 × Accuracy-R
0.431 × iSP-L - 1.096 × iSP-R
75
bimanual coordination variables (all absolute Beta values < 0.05). Likewise, speed (Beta:
0.28) and accuracy-L (Beta: 0.27) were highly explanatory of iSP-R, compared to other
bimanual coordination variables (all absolute Beta values < 0.08). Given the large absolute
coefficients in the first-order CCA and the results of multiple regression analyses, a variable
reduction CCA was performed to show the relationship among these four variables: iSP-L
and iSP-R (IHI outcomes) as well as speed and accuracy-L (bimanual coordination
outcomes). The CCA with variable reduction was performed again with all musicians to
investigate if there was an instrument-dependent IHI contribution to bimanual coordination.

The CCA with variable reduction still showed a similar relationship between IHI and
bimanual coordination as the CCA results before variable reduction(r = 0.37, Wilk’s lambda
= 0.85, p = 0.52, Figure 4-6). Post-group membership labeling showed the IHI-bimanual
coordination relationship was similar in keyboard players (r = 0.32, p = 0.28) and string
players (r = 0.45, p = 0.16), and no group differences in correlation strength was detected by
GLM (p = 0.61). Increased IHI from the L hemisphere and reduced IHI from the R
hemisphere is associated with better symmetric as well as asymmetric bimanual coordination
performance in both instrument groups.

76

Figure 4-6. Canonical relationship between bimanual coordination outcomes and
interhemispheric inhibition outcomes after variable reduction. Canonical variate U: Speed
and Accuracy-L in the x axis. Canonical variate V: iSP-L and iSP-R in the y axis. Correlation
equation: 0.401 × iSP-L - 1.095 × iSP-R = 0.714 × Speed + 0.747 × Accuracy-L. Canonical
relationships between IHI and bimanual coordination outcomes: r = 0.32, p = 0.28 for the
keyboard players (pink solid line); and r = 0.45, p = 0.16 (green dashed line) for the string
players.

Discussion
There is compelling evidence for practice-induced neuroplastic changes in the brains of
musicians, and to investigate the impact of instrument type on musicians’ CNS advances the
knowledge of practice-induced neuroplasticity. There is beginning evidence that an
imbalance in IHI is observed in musicians who use both hands relatively asymmetrically, and
0.714 × Speed + 0.747 × Accuracy-L
0.401 × iSP-L - 1.095× iSP-R

 Keyboard
 String

77
we asked whether the alteration in IHI in musicians was associated with behavioral capability
in tasks that were similar to the instrument they specialized in. Results of the current study
showed that with both symmetric and asymmetric tasks included in the bimanual
coordination assessments, no instrument-dependent brain-behavior relationship was
observed. Increased L to R IHI and decreased R to L IHI jointly account for better bimanual
coordination and is universally found in both keyboard and string players.

We first assessed whether the musicians’ bimanual motor capability was wired and facilitated
by auditory processing as music-making requires multi-sensory integration (Altenmüller and
Schlaug, 2015). Adding tones to the FST made this task very similar to instrument playing: a
motor sequence task in which each key is accompanied by a specific tone (Furuya and
Soechting, 2010)(Horvath et al., 2015). Sound as a key component in instrument playing
serves as part of the kinesthetic representation of a musician’s finger movement during
instrument playing (Altenmüller et al., 2000) and it has been shown that the functional
connectivity between motor and auditory areas were strengthened during performance of a
musical task (Lotze et al., 2003). For example, while performing a trumpet-related task,
greater activation in the motor and auditory networks was found in trumpeters compared to
pianists (Gebel et al., 2013). Surprisingly, musicians in both groups performed equally well
in the two FST conditions, even though key pressing is similar to a piano keyboard. In a non-
musical behavioral task, instrument type did not differentiate the musicians’ motor
performance. The hand motor ability acquired from musical training can be generalized to
non-musical tasks, independent of instrument type.

Bimanual coordination can be approached in various ways and the FST measures the spatial
and temporal aspects of bimanual coordination (Pascual-Leone et al., 1995). The other
behavioral assessment, the FTT, addresses force regulation as also part of the bimanual
78
coordination capability (Shim et al., 2005). Force adjustment with the fingers is particularly
important for certain instrument types, such as keyboard and string instruments, while most
of the wind instruments do not require force regulation in the hands. The FTT in the current
study was designed to be a task requiring asymmetric hand use. There was a main effect of
condition in FTT: increased error was observed during the FTT-L sine condition in both
groups. Bimanual tasks in daily activities usually require the dominant hand performing more
dexterous movements while the non-dominant hand is responsible for stabilization (e.g. a
right handed person opens the lid of a jar with the right hand while holding the jar with the
left hand). The FTT with the left hand tracking the sine wave and the right hand maintaining
constant contraction is opposite to the common roles of both hands in daily activities. This
could explain the worse performance (i.e. increased error) observed in FTT-L sine compared
to FTT-R sine in both groups. Even though the FTT-L sine resembled string instruments,
again, instrument type did not differentiate musicians’ motor performance assessed by a non-
musical behavioral task.

The current study did not support IHI by itself as a sensitive measure of instrument
specificity. Using a paired pulse paradigm to measure IHI (one test stimulus preceded by one
conditioning stimulus over bilateral M1s to measure the change of corticospinal excitability),
Vollmann et al. (2014) found increased L to R IHI in string players compared to keyboard
players (Vollmann et al., 2014). The current study, instead, used the single pulse paradigm to
measure IHI (iSP measured by providing single stimulation ipsilateral to an active muscle to
measure the change of EMG). We did not find significantly different iSP between the
keyboard and string players, although string players also showed a trend of increased L to R
inhibition (both in iSP-L and iSP-D). It is still unclear whether the single pulse and paired
pulse paradigms are equivalent in measuring IHI. IHI measured by the paired pulse method is
primarily inferred by changes in motor evoked potential (MEP) amplitude, a summation of
79
corticospinal excitability of the inhibitory and facilitatory circuits, which primarily takes only
magnitude into account. On the contrary, iSP measures the temporary interruption of ongoing
EMG activity, and the amount of inhibition can be inferred by duration alone or magnitude of
reduction over time. “Time” is then the feature that is considered in iSP quantification
(Chapter 2), yet is often not included in the outcome of the paired pulse paradigm (i.e. MEP
amplitude). The interpretation of a temporal measure compared with magnitude is
challenging given that these outcomes are fundamentally different and may explain the
inconsistent results observed in the current study and in Vollmann et al. (2014).

Inconsistent with our hypothesis, there was no instrument-dependent effect on the IHI-
bimanual coordination relationship. In addition to FST (symmetric bimanual task), the
current study introduced FTT as an asymmetric task to eliminate the bias of task specificity in
the second study (Chapter 3). In the second study, the strongest IHI-bimanual coordination
relationship was observed in the keyboard players. However, this might result from the fact
that the FST resembles piano keyboard playing. Accordingly, FTT was an asymmetric task
designed to resemble string instruments, especially the FTT-L sine condition: left hand
performing force fine-tune (similar to string pressing) while the right hand maintained
constant force regulation (similar to bow drawing). Thus, there were two forms of bimanual
coordination assessments to impartially represent both instrument types: FST is keyboard-like
and FTT is string-like. However, adding both symmetric and asymmetric bimanual tasks
simultaneously washed out the differences in a brain-behavior relationship between
instrument types. Therefore, the results of the current study further confirmed that the
strongest IHI-bimanual coordination relationship observed in keyboard players in the second
study may well be due to an instrument-dependent effect. It is interesting that even though the
FTT-L sine appears to closely mirror string instrument playing, the string player’s sampled in
the current study did not outperform the keyboard players. In the CCA results, increased L to
80
R IHI along with decreased R to L IHI was associated with better bimanual coordination (i.e.
increased speed and accuracy). It is possible that enhanced interhemispheric communication
with the R hemisphere being activated through the inhibitory transcallosal fibers (Daskalakis
et al., 2002) accounts for better motor control of the less dexterous L hand. A better L hand
(in particularly right hand dominant individuals) may be essential for superior bimanual
coordination. In FTT for both groups, when performing finely-tuned and more complicated
force adjustment (i.e. sine wave tracking), worse performance of the L hand was observed
compared to the R hand performing sine wave tracking. This also suggested that in a non-
musical bimanual task, the L hand demonstrated poor coordination, even in string players
who have received intensive training in using the L hand to perform highly skillful
movements. Long term musical training reorganizes the brain to enhance L hand performance
for accommodating the demand of complex bimanual movements regardless of primary
instrument expertise.

The limitation of the current study was that first, given the number of variables (two IHI
outcomes and seven bimanual coordination outcomes) introduced into CCA, the number of
observation (a total of 24 participants) is relatively small. A variable reduction approach may
help reduce over-fitting of the data with too many variables. However, a larger sample size
would still be more favorable (Sherry and Henson, 2005)(Sadoughi et al., 2016). Second,
auditory-motor coupling was not evident in musicians’ hand motor behavior as motor
performance was not improved by adding auditory tones. However, some of the participants
reported that the auditory tones generated with key pressing did not resemble an authentic
musical melody, and therefore the FST with tones was not similar to instrument playing. This
could be the alternative explanation of why the musician’s motor performance was not
enhanced by auditory processing in a laboratory setting.

81
Conclusion
Human central nervous system can be shaped by experience and this study specifically asked
the question whether musicians demonstrate divergent adaptations in the brain as well as in
bimanual coordination in response to prolonged intensive skill training with different
instruments. The musician’s motor performance was not improved with auditory feedback in
the laboratory testing. The keyboard and string players showed equal performance in two
non-musical tasks, in which one simulated keyboard instrument playing (finger sequence
task) and the other string instrument playing (force tracking task). Bimanual motor capability
acquired with instrument practice generalizes to non-musical tasks and is independent of
instrument type. With both symmetric and asymmetric tasks being used to comprehensively
measure bimanual coordination, there was no instrument-dependent effect on the IHI-
bimanual coordination relationship.
82
CHAPTER FIVE
Summary and General Discussion

The overall goal of this dissertation was to investigate the relationship between
interhemispheric inhibition (IHI) and bimanual coordination in skilled musicians. We
specifically compared the IHI-bimanual coordination relationship between musicians and
non-musicians, as well as examined whether there was an instrument-dependent effect on this
brain-behavior relationship in musicians who play two different instruments. Motor
assessments requiring symmetric and asymmetric hand use were implemented to understand
bimanual coordination in both instrument-similar and -dissimilar tasks. Transcranial magnetic
stimulation (TMS) was used to measure IHI, as indexed by ipsilateral silent period (iSP) and
a methodology study was conducted to establish ideal measures and data analysis methods
for later application of IHI measurements in musicians.

Summary of Main Results
The first study (Chapter 2) was a systematic investigation of muscle contraction level and
quantification methods in yielding consistent iSP results. iSP was consistent across all
contraction levels. However, given subject preference, 50% MVC would be recommended
for future studies. Normalized iSP, that incorporates duration, magnitude and baseline muscle
contraction, was the quantification method which provided the best, most comprehensive
measurement consistency.

The second study (Chapter 3) aimed to compare the relationship between IHI and bimanual
coordination between musicians and non-musicians. No difference in the amount of IHI was
observed between the two groups. Musicians demonstrated superior bimanual coordination in
a complex instrument-like task (faster speed, higher accuracy, more evenness), but not a
83
clinical measurement of hand dexterity. Increased left (L) to right (R) IHI was found to
correlate with enhanced evenness, but reduced speed, only in musicians. This result suggests
that the adaptation of IHI is specifically related to behavior. By categorizing the musicians
according to instrument type, the strength of the IHI-bimanual coordination relationship was
strongest in keyboard players and weakest in string players. Hence the observed brain-
behavior relationship could be task-specific, which was mainly driven by keyboard players,
possibly due to the similarity between the finger sequence task (FST) and the piano keyboard.

The third study (Chapter 4) investigated the impact of sound on a musician’s motor
performance. Second, this study aimed to explore the instrument-dependent effect on the
relationship between IHI and bimanual coordination in keyboard and string players that
perform differential motor skills during instrument play (symmetric versus asymmetric,
respectively). Keyboard players and string players performed equally well in all of the
bimanual coordination assessments. Auditory tones did not benefit the musician’s motor
performance. Increased speed and accuracy were associated with increased L to R IHI as well
as decreased R to L IHI in both keyboard and strings players. Using both symmetric and
asymmetric bimanual coordination assessments, no instrument-dependent effect was
observed in the IHI-bimanual coordination relationship between keyboard (representing
symmetric hand use) and string (representing asymmetric hand use) players.

Interhemispheric Inhibition as a Feature of Neuroplasticity Following Musical Training
Using a consistent method for iSP measurement and index of IHI, our results were conflicted
with those from previous studies. We did not show increased IHI in musicians compared with
non-musicians. The hypothesis that balanced IHI will be observed in keyboard players,
whereas imbalanced IHI will be observed in string players was also not supported by our data
(only a trend of more L to R IHI in string players). Given that there was only one previous
84
study which used iSP to measure IHI (Chieffo et al., 2016), it is not possible to hypothesize
why we did not observe differences in iSP between musicians and non-musicians compared
with those results. Additionally, only one study demonstrated increased L to R inhibition in
string players compared with keyboard players (Vollmann et al., 2014). However, given that
this previous work used the paired pulse paradigm to measure IHI with the target muscle at
rest, the results of our study and Vollmann et al. (2014) are not directly comparable. To
ensure homogeneity, the musicians involved in our study were intensively engaged in
instrument practice and performance. We only recruited professionals and music-major
students to exclude the confound of amateur players. We also ensured that the recruited non-
musicians were currently not intensively involved bimanual motor tasks (e.g. video gaming).
However, some non-musicians did report limited experiences in instrument playing in
childhood. Potentially, this might introduce noise into the non-musicians data and dilute any
between-group difference.

Generalization of Hand Motor Function Following Musical Training
Transfer of musical skills to similar tasks employed in daily activities has significant clinical
application in patients with coordination deficits, such as children with developmental delay,
as well as preventing decline of bimanual coordination in the aging population. However, the
pegboard test, as a clinical assessment of asymmetric bimanual coordination, was not
sensitive enough to distinguish musicians from non-musicians. Instead, FST was a sensitive
measure of symmetric bimanual coordination as musicians outperformed the non-musicians
in maximizing their speed, accuracy, and evenness.

Sound is the most important outcome of instrument playing and making pleasant sounds is
the ultimate goal. Musicians judge their performance by the sound generated from
movement-obligatory instrument playing and it has been shown that their auditory and motor
85
networks are strengthened (Lotze et al., 2003)(Zatorre et al., 2007)(van Vugt and Tillmann,
2014). Our data showed that presenting auditory tones in the FST as performance feedback
did not enhance performance beyond that achieved with the FST without auditory tones. One
possible interpretation of this result could be that musicians can successfully generalize their
impressive motor capabilities to any non-musical motor task. Alternatively, it is possible that
the musicians did not show better motor performance in the FST plus auditory feedback, due
to the fact that this laboratory-designed task does not wholly resemble real instrument
playing.

Distinct Reorganization of Interhemispheric Inhibition and Adaptation in Bimanual
Coordination Following Musical Training
This dissertation is the first study to address bimanual coordination (symmetric versus
asymmetric hand use) as a result of musical expertise in instrument playing (symmetric
versus asymmetric types of instrument). A significant relationship between IHI and bimanual
coordination was evident only in musicians, as increased L to R IHI was related to bimanual
coordination (increased evenness but decreased speed). In the second study (Chapter 3),
keyboard players showed a stronger association compared to woodwind and string players in
the FST, which assessed symmetric bimanual coordination. In the third study (Chapter 4),
with the bimanual coordination assessments including both symmetric and asymmetric tasks,
increased L to R IHI as well as decreased R to L IHI were associated with better bimanual
coordination (increased speed and accuracy). This IHI-bimanual coordination relationship
was similar in both keyboard and string players, which possibly was the result of
implementing both symmetric and asymmetric bimanual coordination assessments. The
instrument-specific effect observed in the second study was washed out after introducing an
asymmetric task (i.e. force tracking task) in the third study. This further establishes that the
strongest IHI-bimanual coordination association observed in the keyboard players in the
86
second study was instrument-dependent (finger sequence task requires symmetric hand use,
similar to keyboard playing). Overall, the rewired motor network in the brain of a musician
may link to the instrument-specific, repetitive form of hand use. This result suggests that
musical training leads to experience-dependent neuroplasticity. The brain and hand of a
musician may be shaped simultaneously following intensive musical exposure, and they
jointly reflect the influence of long-term skill acquisition on the human motor system.

Clinical Implications and Future Directions
The results of this dissertation inform neuroscientists who study experience-dependent
neuroplasticity. We expect the results to advance the field of neuroscience in understanding
how the two hemispheres of the brain communicate to influence bimanual coordination. The
knowledge of this brain-behavior relationship will help us understand how musical training-
induced neuroplasticity may facilitate bimanual motor control. Importantly, this knowledge
can be translated to the development of bimanual coordination in children as well as the
preservation of bimanual function in patients with motor control impairments. In addition to
artistic advancement, instrument playing may be revealed to impact health and physical
capability.

Finally, greater understanding of brain reorganization and coordination capability in non-
disabled musicians can potentially provide valuable criterion data for clinicians who work
with musicians with focal hand dystonia. Abnormal communication between the two
hemispheres of the brain is hypothesized to be a potential cause of Musicians’ dystonia.
Compelling evidence suggests that interhemispheric inhibition, crucial for dexterous fine
motor control, is reduced in patients with focal dystonia (Beck et al., 2009)(Nelson et al.,
2010)(Sattler et al., 2014). As there is no structural abnormality in the brain, scientists have
been attempting to identify the neuropathological processes that may underlie this debilitating
87
disorder. Abnormal interhemispheric communication and reduced transcallosal excitability of
inhibitory circuits could be the potential mechanism underlying impaired hand control during
musical performance. Given this hypothesized pathophysiology, neuro-modulatory
techniques, such as repetitive transcranial magnetic stimulation, may be promising
intervention for Musician’s dystonia. Understanding instrument-specific effects on
interhemispheric communication enables the development of individualized treatment in
modulating the cortical networks. Motor reeducation can also be individualized given the
symmetric versus asymmetric nature of the preferred instrument as IHI adaptation is
associated with instrument-specific bimanual coordination. Effective clinical intervention
may develop from a thorough understanding of practice-induced ‘negative’ neuroplasticity
that disrupts the normal interaction between the two hemispheres for precise bimanual motor
control.

88
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Research report
Measuring ipsilateral silent period: Effects of muscle contraction levels
and quantification methods
Yi-Ling Kuo, Tobin Dubuc, Danielle F. Boufadel, Beth E. Fisher
⇑
Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA, United States
article info
Article history:
Received 12 March 2017
Received in revised form 20 July 2017
Accepted 12 August 2017
Available online 18 August 2017
Keywords:
Interhemispheric inhibition
Measurement consistency
Variability
Transcranial magnetic stimulation
Electromyography
abstract
Ipsilateralsilentperiod(iSP)isafrequentlymeasuredindexofinterhemisphericinhibition.However,the
methodology used across studies has been inconsistent and variable. We investigated the optimal con-
traction level and quantification methods for achieving iSP measurement consistency. Twenty-five
healthy adults performed right isometric thumb abduction under three conditions (30%, 50%, and 100%
ofmaximalvoluntarycontraction)whiletranscranialmagneticstimulationwasappliedovertheprimary
motorcortexrepresentationalareaoftheabductorpollicisbrevis.iSPwasquantifiedby:iSPduration,iSP
area andnormalized iSP.Measurement consistency was determined by thehomogeneity of variance test
andby thecoefficient of variation. iSPwas consistent across allcontraction levelswhen measuredby iSP
durationandnormalizediSP.NormalizediSPshowedtheleastmeasurementvariability.Weproposethat
futureinvestigationsexamininginterhemisphericinhibitionusenormalizediSPformeasurementconsis-
tency and the ability to compare results across studies.
2017 Elsevier B.V. All rights reserved.
1. Introduction
The left and right cerebral hemispheres are connected by the
corpus callosum, which transfers essential information between
both hemispheres to control movements. The ability to precisely
perform coordinated movements requires interhemispheric inter-
action to enable humans to complete both unimanual and biman-
ual tasks (Beaulé et al., 2012; Wahl and Ziemann, 2008; Takeuchi
et al., 2012). Transcranial magnetic stimulation (TMS), a non-
invasive brain stimulation technique can be used to study the nat-
ure of interhemispheric communication (balance between pro-
cesses of inhibition and facilitation) (Ferbert et al., 1992).
Inhibitory processes are measured with TMS as interhemispheric
inhibition (IHI) which is specifically a measurement of the tran-
scallosal connections and processing between bilateral primary
motor cortices (M1s) (Ferbert et al., 1992; Meyer et al., 1995).
Interhemispheric inhibition is considered an essential feature of
fine dexterous motor control (Harris-Love et al., 2007; Perez and
Cohen, 2008; Morishita et al., 2012; Wahl et al., 2015). For exam-
ple, in a unimanual task requiring fine control, it is thought that
unwanted mirror movements of the resting hand are curtailed by
means of IHI from the active hemisphere (controlling the active
hand)towardthelessactivehemisphere(Hübersetal.,2008).Fur-
thermore, it has been demonstrated that transfer of motor perfor-
mance to the unskilled hand after learning a unimanual task is
closely related to IHI modulation between both hemispheres
(Perez et al., 2007; Camus et al., 2009; Hortobágyi et al., 2011).
As IHI is an important mechanism associated with human move-
ment control, precise measurement would afford a greater under-
standingof the interactionbetweenthe two M1sin accomplishing
various tasks.
There are two TMS paradigms to measure IHI – (1) the paired
pulse paradigm: providing double stimulation (one test stimulus
preceded by one conditioning stimulus) over both hemispheres
to measure the change of corticospinal excitability in the test
hemisphere due to the conditioning stimulus applied to the oppo-
site hemisphere; and (2) the single pulse paradigm: providing
stimulation to the hemisphere ipsilateral to an actively contracted
muscle to measure the temporary disruption in the electromyo-
graphic signal (EMG), termed ipsilateral silent period (iSP)
(Ferbert et al., 1992; Meyer et al., 1995; Jung and Ziemann, 2006;
Chen et al., 2008). Whereas the investigations employing the
http://dx.doi.org/10.1016/j.brainres.2017.08.015
0006-8993/ 2017 Elsevier B.V. All rights reserved.
Abbreviations: ANOVA, analysis of variance; APB, abductor pollicis brevis; CV,
coefficient of variation; EMG, electromyographic signal; IHI, interhemispheric
inhibition; iSP, ipsilateral silent period; M1, primary motor cortex; MCD, mean
consecutive difference; MEP, motor evoked potential; MVC, maximal voluntary
contraction;RMT,restingmotorthreshold;TMS,transcranialmagneticstimulation.
⇑
Corresponding author at: Division of Biokinesiology and Physical Therapy,
University of Southern California, 1540 East Alcazar Street, Los Angeles, CA 90033,
United States.
E-mail addresses: yiling.kuo@usc.edu (Y.-L. Kuo), dubuc@usc.edu (T. Dubuc),
dfenning@gmail.com (D.F. Boufadel), bfisher@usc.edu (B.E. Fisher).
Brain Research 1674 (2017) 77–83
Contents lists available at ScienceDirect
Brain Research
journal homepage: www.elsevier.com/locate/bres
APPENDIX
Paper Published in Brain Research
102
paired pulse paradigm have used more consistent parameters (i.e.
stimulationintensities,inter-stimulusinterval)toexamineIHI,the
methodology of the single pulse paradigm has not been systemat-
ically established. Measures of iSP in the single pulse paradigm
have been derived with inconsistent methods such that compar-
isons of the results between different studies are problematic.
Studiesthathavemeasured iSPtoindexIHIhaveemployedawide
variety of muscle activation levels of the active hand anywhere
from 15% to 100% maximum voluntary contraction (MVC)
(Davidson and Tremblay, 2013; McGregor et al., 2013; Perez
et al., 2014; Chen et al., 2003; Niehaus et al., 2001; Cincotta
etal.,2006;Giovannellietal.,2009;Houdayeretal.,2016),orhave
not reported the contraction level at all (Boroojerdi et al., 1996;
Bradnam et al., 2010). Previous work has largely employed 100%
MVC as the required muscle activation level for the participants
(Niehaus et al., 2001; Cincotta et al., 2006; Giovannelli et al.,
2009; Hoeppner et al., 2012; Wegrzyn et al., 2013; Houdayer
et al., 2016). However, 100% MVC is likely to lead to fatigue, and
thus limits the number of measured trials. Therefore, less reliable
results, especially in pathological populations with difficulty gen-
erating maximum contraction, could result from 100% MVC para-
digms. The first aim of the current study then was to determine
whether muscle contraction level (30%, 50%, and 100% MVC)
impacted the variability and measurement consistency of iSP.
iSP outcome measures quantify the degree of IHI and the phys-
iological interpretation is that larger values indicate more inhibi-
tion. Previous studies have determined iSP using various
methods to analyze the EMG from the contracting muscle
(Cincotta et al., 2006; Trompetto et al., 2004; Avanzino et al.,
2007). These methods include 1) the duration of the disrupted
EMG signal (iSP duration)(Meyer et al., 1995; Jung and
Ziemann, 2006; McGregor et al., 2013; Daskalakis et al., 2003;
Petitjean and Ko, 2013; Spagnolo et al., 2013), 2) area of the dis-
rupted EMG signal (iSP area)(Giovannelli et al., 2009; Bradnam
et al., 2010; Tazoe et al., 2013), or 3) the area normalized to pre-
TMS EMG which takes the muscle contraction level into consider-
ation (normalized iSP)(Houdayer et al., 2016; Harris-Love et al.,
2011; Reid and Serrien, 2012; Tazoe and Perez, 2013; Long et al.,
2016).However,itisdifficulttocomparetheiSPresultsofdifferent
studies when one study measures duration whereas another mea-
sures area, given that the units are different. It remains unknown
which quantification method is more consistent for quantifying
iSP.Thereforethesecondaimofthisstudywastodeterminewhich
iSP analysis method yielded the most consistent results.
2. Results
2.1. Background EMG activity
As expected, the average EMG levels of the contracting right
abductor pollicis brevis (APB) in the three MVC conditions were
distinctly different from each other. For 30% MVC, the average
pre-stimulus EMG value for the right APB was 0.19±0.06mV; for
50% MVC, the value was 0.33±0.10mV; and for 100% MVC, the
value was 0.56±0.15mV. On the other hand, the average baseline
EMG values of the resting left APB were less than 0.02mV in all
three MVC conditions.
2.2. Contraction level
An example of EMG traces obtained from all MVC conditions of
a representative participant is shown in Fig. 1. Repeated-measures
analysis of variance (ANOVA) showed a significant effect of con-
traction level [F
(2,23)
=26.28, p<0.001] only in iSP area. Post hoc
analysis showed that the means of iSP area measured under all
MVC conditions were significantly different from each other. For
the remaining iSP quantification methods, repeated-measures
ANOVAdidnotshowsignificantlydifferentmeansacrossthethree
MVC conditions (iSP duration: F
(2,23)
=1.23, p=0.30; normalized
iSP: F
(2,23)
=1.29, p=0.29) (Table 1).
2.3. Variance of iSP
Homogeneity of variance test showed significantly different
variance across the three MVC conditions in only iSP area
(p<0.01) (Table 1). Different muscle contraction levels did not
influence the variance of the other two iSP quantification methods
(iSP duration and normalized iSP).
Fig. 1. Exemplary EMG traces from 30%, 50% and 100% of maximum voluntary contraction (MVC) of a representative participant. Noted that higher contraction level led to
deeper EMG suppression. Vertical continuous black line at 100ms: TMS pulse. Horizontal dotted lines: mean pre-TMS EMG of the first 100ms in each MVC condition.
78 Y.-L. Kuo et al./Brain Research 1674 (2017) 77–83
103
2.4. Quantification methods
Normalized iSPresultedinoveralllessmeasurementvariability
(coefficient of variation, CV=22.04%, 27.39%, 35.08% in 30%, 50%,
100% MVC, respectively) compared to iSP duration and iSP area
(Table 1).
3. Discussion
Reproducibility is critical in neurophysiological investigations
to avoid weakening the reliability of the dependent variables.
The objective of this study was to determine the optimal parame-
tersforconsistentlydeterminingIHIusingtheiSPmethod.Wefirst
determinedwhethermuscleactivitylevelaffectediSPmeasuresto
assessIHI;andsecondly,differentiSPanalysismethodswerecom-
pared to quantify iSPreliably and consistently. The resultsshowed
that normalizediSP was the analysis methodwhich provided least
measurement variability, and the amount of IHI was not signifi-
cantly different across different MVC conditions. Accordingly, nor-
malized iSP would be the quantification method of choice.
Additionally, of the three quantification methods, iSP area is the
least optimal choice given the effect of contraction level on the
measurement as well as the large measurement variability across
all conditions (i.e. large CV’s).
While there was no difference between the three contraction
levels when quantified by iSP duration or normalized iSP, anecdo-
tally,mostoftheparticipantsreportedthat50%MVCwastheeasi-
est condition to obtain. Since the impact of fatigue on iSP was not
part of the research question in the current study, we suggest that
futurestudiesinvestigatefatigueassociatedwithdifferentcontrac-
tion levels (especially 100% MVC). Nonetheless, 50% MVC was
reported as the easiest to achieve and likely can be generated
repeatedlywithoutfatigueandthusmaybemoreidealwhenusing
iSP to determine IHI.
TMS has been used widely to understand how the two hemi-
spherescommunicate(throughinhibitionorfacilitation)witheach
other. The iSP is a relatively simple way to measure IHI using the
single pulse TMS paradigm. The iSP method of measuring IHI is
more practical in both research and clinical settings compared to
the paired pulse paradigm. For one, the experimental equipment
of the single pulse paradigm consists of only one TMS stimulator
and one coil, as opposed to two stimulators and two coils required
for the paired pulse paradigm. Secondly, the time required for the
preparation and procedures of an iSP investigation is accordingly
much less for the single pulse paradigm compared to the paired
pulse paradigm. From a perspective of reliable methodology, the
test-retest and inter-rater reliability of the iSP measure was high
(Fleming and Newham, 2017) while the test-retest and inter-
rater reliability demonstrated with the paired pulse paradigm
was moderate (DeGennaro et al., 2003). However, the objective
of the above referenced studies was not to compare the reliability
of IHI between the two paradigms. Additionally, for a pathologic
population, the iSP method affords the investigation of IHI even
with difficulties obtaining a motor evoked potential (MEP). For
example, difficulty in obtaining an MEP with stimulation over
thelesionedhemispherehasbeendemonstratedinthestrokepop-
ulation (Byrnes et al., 1999). Measuring iSP with the single pulse
paradigm still allows us to measure IHI by stimulating the unaf-
fected hemisphere in these neurologic patients. By developing a
methodologywhichyieldsconsistentiSPresults,weareadvancing
theapplicationofTMSintheassessmentofIHItoanswerdifferent
questions regarding interhemispheric interactions in different
populations.
All three quantification methods require determination of iSP
onsetandoffsetwhichcanbechallenginggiventheoscillatorynat-
ure of the EMG signal. As iSP duration indicates IHI solely on the
basis of EMG onset and offset withoutconsidering EMG amplitude
reduction,itmaybesusceptibletosubjectivebias.Furthermore,by
not including an index of EMG reduction, the amount of IHI with a
measurement that is confined to the temporal domain is limiting.
Whiletheobjectiveofthecurrentstudywastodeterminethemost
consistent iSP measure for comparison of IHI across studies, we
acknowledge that specific research questions may necessitate the
use of iSP duration as the most appropriate measurement. As an
aside, we did not specifically address the issue of using the graph-
icalmethodwithstatisticalcriteriaproposedbyGarveyetal.com-
pared to visual determination of iSP onset and offset. However, it
seems reasonable that the graphical method with statistical crite-
ria to determine silent period would increase the precision of the
data and avoid subjective bias from the investigators (Garvey
et al., 2001; Fisch, 1998).
WefoundthatIHIchangedwithdifferentcontractionlevelsasa
function of the selected method to analyze iSP. In the current
study, when iSP was measured as area, the variance (determined
by the homogeneity of variance test) and mean (determined by
repeated-measures ANOVA) were significantly different across
the three MVC conditions. It has previously been shown that the
magnitude of EMG amplitude reduction also increased with
increasing contraction level of the first dorsal interosseous (FDI)
(Ferbert et al., 1992; Long et al., 2016). Here we demonstrate that
with APB the area under the curve is similarly impacted by differ-
ent contraction levels (Fig. 1). A possible explanation for the mod-
ulation of iSP area with contraction level may be due to the fact
that at higher contraction levels there is greater pyramidal neuron
discharge resulting in greater magnitude of EMG output (Ferbert
et al., 1992). While this is mere speculation and it is currently
not understood why iSP area modulates with different contraction
levels, this observation appears to be a consistent across hand
muscles. The advantage of using APB compared to FDI is that two
phases of iSP have been observed more often in FDI than in APB
(i.e. reduction of EMG followed by an increase and then a second
reduction) (Jung and Ziemann, 2006). Thus, by using APB the
Table 1
iSP obtained in the three muscle contraction conditions and analyzed by three quantification methods.
Outcomes 30% MVC 50% MVC 100% MVC Homogeneity of variance test
iSP duration (ms) Values
CV
24.49±7.62
31.13%
26.18±10.27
39.23%
23.56±8.37
35.52%
p=0.48
iSP area (mV*ms) Values
CV
21.82±14.98
y§
68.65%
42.12±33.19
y
78.80%
62.08±41.78
67.30%
p<0.01
*
Normalized iSP (%) Values
CV
44.33±9.77
22.04%
43.55±11.93
27.39%
40.78±14.31
35.08%
p=0.21
Data were shown in values±SD.
CV: coefficient of variation; iSP: ipsilateral silent period; MVC: maximum voluntary contraction.
*
Significantly different variance across all MVC conditions.
y
Significantly different from 100% MVC.
§
Significantly different from 50% MVC.
Y.-L. Kuo et al./Brain Research 1674 (2017) 77–83 79
104
measurementofiSPis‘cleaner’byvirtueofthefactthatcommonly
there is only one iSP phase. However, regardless of the muscle
studied, iSP area is not an ideal method to quantify the amount
of IHI given the variability associated with contraction level. As
such, comparison across studies that have used different contrac-
tion levels and quantified iSP as area, would not be possible.
Normalized iSP, conversely, normalizes pre-stimulus contrac-
tion level and can thus cancel out the effect of muscle contraction
level (Ferbert et al., 1992). However, this was not the case in Long
et al. (2016) in which normalized iSP was compared at 10, 40, 70%
MVC (Long et al., 2016). The normalized iSP values during unilat-
eral 70% MVC significantly increased compared with 40% and 10%
of MVC, whereas the current study found that normalized iSP was
independent of contraction level. Additionally, the normalized iSP
values in 10% and 40% MVC in Long et al. (2016) were similar to
our data; however, the normalized iSP value for 70% MVC was
higher (50%) compared to the present study in which the normal-
ized iSPrangedfrom 40%to44% acrossall MVClevels. It isunclear
physiologically what accounts for the differences found by Long
et al. (2016) in 70% MVC given that the duration of iSP measured
under10,40,70%MVCwassimilartotheiSPdurationvaluesmea-
sured in the current study (about 26–29ms). However, our results
parallel those of Ferbert et al. (1992) demonstrating no difference
in normalized iSP at different contraction levels. Future work uti-
lizing both the muscle used in the current study (APB) compared
with the muscle used in Long et al. (FDI) at 70% MVC will enable
an unbiased comparison. Since different phases of EMG suppres-
sion can be observed in different hand muscles and therefore
impactthequantificationofiSP(JungandZiemann,2006),whether
normalized iSP is also the most consistent measure in other mus-
cles (e.g. other hand muscles or upper-arm muscles) requires
future investigations.
In conclusion, muscle contraction level in this study was inde-
pendent of IHI measurement consistency when measured as iSP
duration and normalized iSP. With a methodology which yields
consistentresults, iSP is a practicaltool to investigate IHI. We sug-
gestthatfuturestudiesutilizenormalizediSP,whichtakesall crit-
ical parameters of the acquired EMG data (duration, amplitude,
and contraction level) into consideration and shows the least vari-
ability, to determine the role of IHI in complex task performance.
Normalized iSP is a quantification method less prone to variability
resulting from oscillatory EMG activity and different contraction
levels. Using normalized iSP therefore allows for comparison of
the amount of IHI across studies. It is also worthwhile to report
the values of duration and area while using the normalized iSP
methodas duration and area are both criticalparameters to deter-
mine normalized iSP. Even though muscle contraction level was
not a factor in determining IHI, we propose using 50%MVC based
ontheeasewithwhichparticipantsappeartobeabletorepeatedly
achieve this level of contraction without fatigue.
4. Experimental procedure
4.1. Participants
Twenty-fivehealthyright-handedindividuals(13females)with
ameanageof27.16±3.92years(20–34)participatedinthisstudy.
Each participant was screened using a TMS safety questionnaire.
Based on criteria suggested by Wasserman (Wassermann, 1998),
participants were excluded if they had a history of neurological
disorderswhichcontraindicatedTMSprocedures.Thestudyproto-
col was approved by the Institutional Review Board of the Univer-
sity of Southern California. Once screening was completed, the
study protocol and risks were described to each participant who
then provided written informed consent.
4.2. Experimental Set-Up
A single-pulse magnetic stimulator (Magstim 200
2
; The Mag-
stim Company Ltd, Whitland, UK) with a figure of eight coil
(outer-wingdiameterof50mm)wasusedforallTMSassessments.
The APB was the targeted muscle. Not only is thumb abduction a
critical part of hand function but it has been established that iSP
obtained in APB leads to obvious onset and offset of EMG disrup-
tion (Jung and Ziemann, 2006). The skin of bilateral APB muscles
was cleaned with abrasive gel and alcohol to decrease skin impe-
dance for the surface EMG electrodes (inter-electrode distance,
20mm; Motion Lab Systems Inc, Baton Rouge, LA). Surface EMG
electrodes were placed in a belly-tendon fashion and secured with
tape.TheEMGsignalwassampledat14,992Hz,band-passfiltered
at 10–2000Hz, and amplified with a gain of 2000 using a 1401
analog-to-digital unit and Signal 6 software (Cambridge Electronic
Design, Cambridge, UK). To determine the stimulation hotspot on
therighthemisphere,surfaceEMGelectrodeswereusedontheleft
handtorecordaconsistentMEP.ThesurfaceEMGelectrodeonthe
right hand recorded raw data for iSP analysis. Each trial consisted
of 300ms of data with the first 100ms occurring before the TMS
pulse.
Participantswereseatedatatablewithbothfeetfirmlyplanted
on the ground and a pillow used for lumbar support. The table
heightwasadjustedtoallowforapproximately100ofelbowflex-
ion and slight shoulder flexion. The right arm was secured in this
position using a forearm strap. The wrist was braced in a neutral
position to control for activation of hand muscles other than APB
(Johnston et al., 2010). The participants were asked to push the
right thumb against a firm stick to allow for isometric thumb
abduction.
4.3. Preparation for data collection
A lycra cap with a 1cm grid was placed on each participant’s
head to estimate the position of the vertex and systematically
identify the hotspot of the APB representational area in M1
(Fisher et al., 2016; Rossini et al., 2015). The hotspot was defined
as the location in right M1 which produced the largest and most
consistent MEP amplitude (Fisher et al., 2016; Rossini et al.,
2015). The coil was placed tangential to the scalp with the handle
pointing backwards and 45 away from midline (Rossini et al.,
2015). Resting motor threshold (RMT) was defined as the lowest
TMSintensityrequiredtoproduceatleast5outof10MEP’sincon-
secutivetrialswithamplitudesgreaterthan50microvoltswiththe
muscle relaxed (Rothwell et al., 1999).
4.4. Experimental protocol
The average EMG activity of three maximal contractions was
used to calculate the MVC. The mean MVC was used to calculate
the testing conditions at 30%, 50%, and 100% of MVC as the inde-
pendentvariables.Realtimevisualfeedbackseenonthecomputer
screen as a thick blue line was provided for the participants to
maintainEMGoutputwithinthespecifiedrangeforeachcondition
(±10%) (Fig. 2). Participants were asked to abduct the right thumb
isometrically within the specified range while maintaining a
relaxed left hand. A supra-threshold TMS pulse of 130% of RMT
was then applied to the ipsilateral M1 once the EMG signal was
consistently maintained within the specified range. The partici-
pants performed a total of 15 trials for each MVC condition (total
of 45 trials). However, rather than performing all 15 trials at once,
three trials for each MVC condition were performed in order over
five blocks to avoid potential fatigue associated with performing
all 15 trials of 100% MVC. A two minute rest period was given
between each trial.
80 Y.-L. Kuo et al./Brain Research 1674 (2017) 77–83
105
4.5. Outcome measures and data analysis
The raw EMG data was processed in Matlab (The MathWorks
Inc., Natick, MA, USA) using an objective graphical method to
determine iSP onset, offset, and duration from the EMG signal of
the right APB (Garvey et al., 2001). The fifteen trials of each MVC
condition were averaged and rectified to generate a processed
EMG trial. A hundred milliseconds of EMG activity prior to the
TMSpulsewasaveragedasthepre-stimulusEMGvalue.Thelower
and upper variation limits of the pre-stimulus EMG were calcu-
lated according to the formula: mean pre-stimulus
EMG±(MCD2.66), where MCD is the mean consecutive differ-
ence of individual pre-stimulus EMG data points. The iSP onset
was defined as the first of 5 consecutive data points to fall below
thelowervariationlimit.TheiSPoffsetwasdefinedasthefirstdata
pointthat fellabove thelowervariationlimitif 50%ormore ofthe
data points in the 5-millisecond window following the designated
iSP offset were also above the lower variation limit (Garvey et al.,
2001).TheiSPdurationwascalculatedbysubtractingtheiSPoffset
from iSP onset (Fig. 3).
Three different quantification measurements of iSP were com-
pared. One method quantified iSP duration (Fig. 3: A) in millisec-
onds as the time difference between onset and offset as described
above (Jung and Ziemann, 2006; Fling and Seidler, 2012); and two
methods quantified iSP amplitude: i) iSP area (Fig. 3: B): area
between the threshold and the depth of EMG reduction
(Giovannelli et al., 2009), and ii) normalized iSP (Fig. 3:(1C/
D)⁄100): area under the reduced EMG activity, normalized to
Fig. 2. A sampling trial recorded during 50% MVC. A: TMS pulse; B: EMG of MEP recorded on the left hand; C: EMG of iSP recorded on the right hand. iSP was the primary
outcome (occurred in the pink circle); D: processed virtual EMG activity (root mean square of raw EMG) from C to provide biofeedback of muscle activity level (blue line).
Fig.3. Aprocessedtrial.Horizontalstraightgrayline:meanpre-TMSEMG.Horizontaldottedpinkline:threshold.Verticalcontinuousblackline:TMSpulse.Verticalstraight
redlineandgreenline:iSPonsetandoffset,respectively.Verticaldottedredandgreenlines:5mswindowfollowingtheiSPonsetandiSPoffset,respectively.Verticaldotted
cyan line: time equal to iSP duration before TMS pulse. A: iSP duration, time difference between onset and offset. B: Area between the threshold and the depth of EMG
reduction. C: Area under the reduced ongoing EMG activity within iSP duration. D. Area under the pre-TMS baseline muscle activity.
Y.-L. Kuo et al./Brain Research 1674 (2017) 77–83 81
106
pre-stimulus EMG area over an equal duration of EMG reduction
(Trompetto et al., 2004; Harris-Love et al., 2011; Tazoe and Perez,
2013). iSP was quantified by each method described above in the
processed EMG data.
4.6. Statistical analysis
All of the outcomes (i.e. three iSP measurements) were com-
pared between the three MVC conditions (30%, 50%, and 100%
MVC). Repeated-measures ANOVA was used to compare group
means across the three MVC conditions and the Bonferroni test
wasusedforposthocanalysis.Measurementconsistencyasdeter-
mined by the homogeneity of variance test and by the CV (calcu-
lated by SD/mean) was compared across all quantification
methods. SPSS Version 20 (SPSS Inc, Chicago, IL) was used to per-
form statistical analyses with a significance level of p<0.05.
Acknowledgements
We thank Dr. Clarisa Martinez’s help in study design.
Funding
Thisresearchdidnotreceiveanyspecificgrantfromfundingagen-
cies in the public, commercial, or not-for-profit sectors.
Conflict of interest
None of the authors have potential conflicts of interest to be
disclosed.
Author contributions
YLK: studydesign,datacollection,dataanalysisandinterpreta-
tion, manuscript drafting and revision; TD: data collection, manu-
script drafting; DFB: data collection, manuscript drafting; BEF:
studydesign,dataanalysisandinterpretation,manuscriptdrafting
and revision.
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Abstract (if available)

Abstract Musical expertise provides an opportunity to explore science through art. A musician’s brain is shaped by long-term skill acquisition, and the neuroplastic changes are expressed by the complex coordinated movements between the two hands. This dissertation aimed to investigate how the communication between the two cerebral hemispheres through interhemispheric inhibition (IHI) contributes to bimanual coordination in skilled musicians, and to understand the instrument-dependent effect on this brain-behavior relationship. ❧ We first systematically determined the optimal methodology (ideal muscle contraction level and quantification method) to measure ipsilateral silent period (iSP), which is an indicator of IHI. The iSP measure was then applied to the following investigations to compare IHI in musicians and non-musicians. Musicians demonstrated significantly better bimanual coordination compared to non-musicians. Increased IHI from the left to the right hemisphere was found to significantly correlate with increased key pressing consistency, but with reduced speed during a bimanual task in skilled musicians. This IHI-bimanual coordination relationship was only evident in musicians, but not in non-musicians. We then designed both symmetric and asymmetric bimanual coordination tasks to address whether the IHI-bimanual coordination relationship in a musician was dependent on instrument type. Moreover, we investigated whether auditory sound impacts motor performance in musicians. Keyboard and string players were recruited as representatives of symmetric hand use and asymmetric hand use, respectively. Overall, there were no differences in performance of either symmetric or asymmetric tasks between keyboard and string players. There were no differences in motor performance with or without auditory feedback in either group. A similar IHI-bimanual coordination relationship was found in both keyboard and string players. Specifically, increased left to right as well as decreased right to left IHI were associated with increased speed as well as increased accuracy (i.e. better bimanual coordination) in both groups. ❧ The findings of this dissertation provided strong evidence of experience-dependent neuroplasticity associated with the brain-behavior relationship following intensive musical training. Future studies would investigate the beneficial effects of instrument playing in individuals with impaired bimanual coordination. Moreover, modulation of IHI using non-invasive brain stimulation with an individualized and instrument-specific approach may be a promising intervention for Musician’s dystonia in order to regain bimanual coordination.

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Asset Metadata

Creator Kuo, Yi-Ling (Irene) (author)

Core Title Relationship between interhemispheric inhibition and bimanual coordination in musicians

School School of Dentistry

Degree Doctor of Philosophy

Degree Program Biokinesiology

Publication Date 08/05/2018

Defense Date 04/20/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag bimanual motor control,instrument training,interhemispheric communication,ipsilateral silent period,OAI-PMH Harvest,transcranial magnetic stimulation

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Fisher, Beth E. (committee chair), Gordon, James (committee member), Kantak, Shailesh S. (committee member), Kutch, Jason J. (committee member), Winstein, Carolee J. (committee member)

Creator Email yiling.kuo@pt.usc.edu,yilingku@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-53039

Unique identifier UC11670531

Identifier etd-KuoYiLingI-6645.pdf (filename),usctheses-c89-53039 (legacy record id)

Legacy Identifier etd-KuoYiLingI-6645.pdf

Dmrecord 53039

Document Type Dissertation

Format application/pdf (imt)

Rights Kuo, Yi-Ling (Irene)

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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