Master's Thesis Final-Xavier Readus-Tulane University

Abstract

Contemporary research has been examining potential links existing among

sensory, motor and attentional systems. Previous studies using TMS have shown that the

abrupt onset of sounds can both capture attention and modulate motor cortex excitability,

which may reflect the potential need for a behavioral response to the attended event.

TMS, however, only quantifies motor cortex excitability immediately following the

deliverance of a TMS pulse. Therefore, the temporal development of how the motor

cortex is modulated by sounds can’t be quantified using TMS. Thus, the purpose of the

present study is to use time frequency analysis of EEG to identify the time course of

cortical mechanisms underlying increased motor cortex excitability after sound onset.

Subjects sat in a sound attenuated booth with their hands outstretched at 45-degree angles

while frequency modulated sounds were intermittently presented from a speaker either in

the left and right hemispace. Our results indicated a transient reduction in EEG power

from 18-24 Hz (300-600 ms latency) and then a long lasting increase in EEG power that

began at ~800 ms and continued until at least 1.7 sec. The latency of EEG power

changes was shorter for sounds presented from the right speaker at both time periods.

When sounds were presented from the right speaker the contralateral hemisphere over

motor regions also showed greater power increases after 800 ms relative to the ipsilateral

hemisphere. In addition, power increases were greater in the left-handed subjects (8-12

Hz). Results showed that sounds increased EEG power at the time of a previously

observed increase in motor cortex excitability. Findings also suggest an increased

attentional salience to the right hemispace in neurologically normal subjects and

asymmetrical hemispheric activations in right and left-handers.

©Copyright by Xavier N. Readus, 2013

All Rights Reserved

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Acknowledgments

I would like to thank Dr. Edward J. Golob, Ph.D., for his direction in project

design and his continued support and assistance in completing this research.

I would also like to thank Dr. Jeffrey Mock, Ph.D., for his guidance and

assistance.

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Table of Contents

ACKNOWLEGDEMENTS……………………………………………...ii

LIST OF TABLES……………………………….………………………..iv

LIST OF FIGURES………………………………….…………………….v

Chapter

1. INTRODUCTION……………………………………………….1

2. HYPOTHESES/GOALS……………………………….………20

3. METHODS………………………………………..…………….22

4. RESULTS………………………………….……………………27

5. DISCUSSION………………………..………………………….39

Appendix

1. FIGURES...…………………………………………………………48

2. REFERENCES……………………………………………………..58

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LIST OF TABLES

TABLE 1: Inclusion & Exclusion Factors….…………………………...48

TABLE 2: Overview of Significant Results….………………………….57

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LIST OF FIGURES

FIGURE 1: Design Schematic………………………………..…………..49

FIGURE 2: Average Plot of ERP and ERSP Data...……………………50

FIGURE 3: ERSP Amplitude…………………………………………….51

FIGURE 4: Lower Beta ERSP Amplitude………………………………52

FIGURE 5: Electrode Site ERSP Data……...…………………………...53

FIGURE 6: Electrode Site ERSP Amplitude………………..…………..54

FIGURE 7: Handedness ERSP Data...…………………………………..55

FIGURE 8: Handedness and Power Perturbations Correlation………56

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Introduction

Sensory systems and Audition

Imagine a bee buzzing in close proximity to your hand. There has to be a

mechanism that transduces and integrates the sensory stimulus from the acoustic buzzing

sound made by the bee to the motor output of a fight-or-flight response, which means you

either swing at the bee or run away from the bee. Therefore, a link exists between

sensory, in this case a sound, and motor systems. This is further evidenced by the fact

that posterior sensory and premotor areas are anatomically connected (Petrides,

Tomaiuolo, Yeterian & Pandya, 2012). The present study aspires to examine some

feature of these auditory motor interactions. Visual properties of objects, such as a bee,

within our personal space can attract the attention of the observer (Makin, Holmes,

Brozzoli, Rossetti & Farnè, 2008). The abrupt onset of sounds, in the above case the

buzzing of the bee, can also orient attention to the source of sound (Serino, Annella &

Avenanti, 2009). The hair cells in the cochlea transduce sound pressure-waves into

neural action potentials, and subsequently convey sound information to the central

nervous system. The primary auditory cortex is the first region in the cerebral cortex to

receive auditory input. The secondary auditory cortex, or the association cortex,

interconnects the primary auditory cortex as well as many other prominent brain regions

and is also implicated in the processing of sound (Ghez & Krakauer, 2000). The primary

auditory cortex is located within the left and right temporal lobe, deep within the lateral

Sylvian fissure on the transverse gyrus of Heschl (Brodmann’s area 41) (Brodmann,

1909). The secondary auditory cortex is housed in the surrounding anatomic regions of

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the superior temporal gyrus (Brodmann’s area 21, 22, 42 and 52) (Brodmann, 1909;

Celesia, 1976).

The present study examines how auditory stimuli influence the motor cortex.

Auditory stimuli can be described in terms of frequency, intensity, and time. Frequency

is defined as number of cycles in a sound wave per second. Higher frequencies will have

more complete waves than lower frequency sound waves. Generally, frequency

determines pitch; the lower the frequency of the sound wave, the lower the perceived

pitch. Intensity (or amplitude) refers to the amount of energy in the sound wave.

Intensity is translated to changes in the height of the sound wave. Generally speaking,

greater intensity sounds are perceived to be louder than lesser intensity sounds. A change

in a sound feature can attract the attention of a passive listener (Yantis & Jonides, 1990).

Attending to an auditory stimulus molds perception and governs subsequent processing in

the auditory cortex (Welsh, 2011). Auditory input to the central auditory system

originates from both ears. Due to hemispheric asymmetries, the processing of

environmental sounds differs based on the ear the sound enters (Gonzalez & McLennan,

2009). That is, attenuation in priming was observed when environmental sounds were

presented to the left ear, but not when the same sounds were presented to the right ear.

Defining motor systems

The premotor cortex and primary motor cortex are the main components of the

motor system. The goal of this project is to examine how a component of the motor

system is modulated by sounds. The premotor cortex is located in the frontal lobe anterior

to primary motor cortex (M1) in Brodmann’s area 6 (Brodmann, 1909). The premotor

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cortex is the part of the brain responsible for the preparation and regulation of movement.

The premotor cortex receives information from other cortical regions and based on the

obtained information, determines the appropriate necessary movement. Subsequently,

the premotor cortex conveys the defined information to M1. Next, M1 readies the body

part for the coordinated movement based on the processed information from the premotor

cortex. M1 is located in the posterior portion of the frontal lobe on the precentral gyrus

and on the anterior paracentral lobule in Brodmann’s area 4 (Brodmann, 1909). Like the

somatosensory cortex, M1 is somatotopically organized. Intuitively, when the person

performs the coordinated movement, M1 is highly active. Thus, the premotor cortex

mediates movement preparation, and the M1 has a key role in the actual coordination of

the previously planned movement. M1 is divided into hemispheres. The right hemisphere

controls contralateral movement, meaning movement on the left side of the body, and the

left hemisphere controls movements on the right side of the body (Makin, et al., 2008).

Handedness

Motor cortex physiology is related to handedness (Bernard, Taylor & Seidler,

2011). As will be discussed below assessing the role of handedness in auditory motor

interactions is a major objective of this project. In a study using self-report to measure

handedness in the human populace, results indicated that about ninety percent of the

human population is right-handed, about ten percent is left-handed, and less than one

percent is ambidextrous (Perelle & Ehrman, 1994). The dominant hand is better at

performing fine motor tasks specifically, in executing tasks that require the use of

individual fingers (Barnsley & Rabinovitch, 1970; Fennell, 1986; Johnstone, Galin &

Herron, 1979; Palmer, 1974; Provins & Cunliffe, 1972). A magnetoencephalography

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(MEG) study suggests that a similar number of pyramidal neurons are active in the

dominant and non-dominant hemisphere, but the neurons are dispersed over a larger area

of motor cortex opposite the preferred hand (Volkmann et al., 1998). Multiple brain

regions have been correlated with handedness (Kertesz, Polk, Black & Howell, 1990;

Steinmetz, 1996; Amunts, et al., 1999; Galaburda, 1980). Additional evidence on the

importance of M1 in handedness is provided by TMS work showing lower threshold in

the dominant M1 for left and right-handed subjects (Triggs, Calvanio, Macdonell, Cros &

Chiappa, 1994). However, other brain regions have been associated with handedness.

For example, hand preference has been associated with neuroanatomical asymmetries in

the precentral gyrus (Amunts et al., 1996; Foundas, Leonard & Heilman, 1995). M1 is

the primary region of the brain activated during individual finger movement (Triggs, et

al., 1994). An fMRI study during a sequential finger movement task found that left-

handers showed less laterality, activating larger volumes and more association cortices

than did right-handers (Solodkin et al., 2001). A subsequent study by the same authors

revealed right-handers had left premotor activation with both left and right hand

movements, which indicates a left hemispheric dominance in motor function; whereas,

left-handers showed activation in the premotor cortex contralateral to the moving hand,

which indicates lack of asymmetry. Motor cortex physiology can be examined using

transcranial magnetic stimulation (TMS) (Mock, et al., 2011; Nikulin et al., 2003).

Transcranial magnetic stimulation (TMS) and motor cortex physiology

Auditory motor interactions have been studied previously employing a TMS

paradigm. TMS is a noninvasive technique that causes depolarization (positive increase

in voltage) or hyperpolarization (negative shift in voltage) in brain regions under the site

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of stimulation (Rosler, 2001). That is, TMS stimulates neurons to fire action potentials or

inhibits firing of action potentials by bringing cortical neurons away from threshold. A

brief magnetic pulse is delivered to the cortex using a plastic-enclosed copper coil, which

is capable of generating rapidly changing electric currents. Through electromagnetic

induction, a magnetic field perpendicular to the current flow is produced unhampered

through the skin and skull. The magnetic field produces an oppositely directed current

flow in the brain that activates neurons nearby. The shape of the coil determines magnetic

field patterns. Examples include round coil, figure-eight coil, double-cone coil and four-

leaf coil. If neurons in the motor cortex are stimulated using TMS, overt peripheral

muscular movements can be observed. The overt muscular activity observed from single

or paired TMS is termed motor evoked potentials (MEPs). MEPs are quantified by

electromyography (EMG) and have been correlated with corticospinal excitability

(Taylor, Walsh & Eimer, 2008; Rosler, 2001). Hence, the greater the MEP amplitude

elicited by TMS, the greater the corticospinal activity.

Motor cortex excitability in the presence of a sound

Two previous studies used TMS to assess motor cortex excitability as a function

of sound presentation. The first study presented sounds to subjects from near and far

locations (Serino, Annella, & Avenanti, 2009). A TMS pulse was delivered at 50 ms,

100 ms, 200 ms, and 300 ms after the presentation of the sound. The findings suggest

sounds emitted in both locations elicited similar motor cortex excitability but only in the

dominant M1 hemisphere. In another study, Mock et al., (2011) presented sounds

intermittently to subjects while watching a nature video in a similar experimental setup.

A TMS pulse that was delivered 1-second after sound onset assessed motor cortex

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excitability. The results showed that motor cortex excitability was significantly increased

for the M1 area of the dominant hand as measured by EMG. In contrast, the non-

dominant hand did not show increased excitability relative to baseline. Speakers located

on the same side as the non-dominant hand and speakers located on the opposite side of

dominant hand elicit similar corticospinal arousal (Mock, et al., 2011). Neither speaker

nor sound location influence corticospinal activity (Makin, et al., 2008; Mock, et al.,

2011). In addition, results indicated sounds presented within the space surrounding the

body that can be reached by the hands cause corticospinal excitability only when the

sound is presented to the dominant hand immediately following (Serino, et al., 2009) and

1-second post (Mock, Foundas & Golob, 2011) sound presentation. This could be the

case because the dominant hand should be more primed to action due to better dexterity.

That means brain processing of a sound is dependent on hand dominance (or preference)

and hand closeness to the speaker emitting the sound (Graziano & Cooke, 2006;

Rizzolatti, Fogassi & Gallese, 1997).

Limitations of past research and the present study:

The experimental setup of the two previous studies examining the response of the

motor cortex to sounds had several limitations. Although Mock et al., (2011) studied

both hemispheres, the use of TMS did not allow the measurement of motor cortex

activity in both hemispheres simultaneously or at multiple times after sound onset.

Therefore, a future study should examine both hemispheres simultaneously after

presentation of a sound. Secondly, TMS only collects data on motor cortex activation

immediately following the deliverance of the TMS pulse. This means information about

motor cortex activity is restricted to the point in time when the TMS pulse is delivered.

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Thus, TMS use makes it difficult to understand the different stages involved in the

processing of the sound in the brain and its influence on the motor cortices and

surrounding motor areas. Hence a future study should use a technique that allows the

assessment of the motor cortex throughout the entire time the sound is modulating the

motor cortex. Consequently, although Serino et al., (2009) and Mock et al., (2011)

results suggesting increased motor cortex activation, the cortical mechanisms producing

the larger MEP amplitudes and increased motor cortex activation cannot be understood

using TMS. Therefore, the present study will address the aforementioned limitations by

employing EEG, ERPs, and time-frequency analysis to assess motor cortex physiology

and it’s response to sounds (Ilmoniemi & Kicic, 2010).

Measures of cerebral cortex activity: EEG

The present study will employ EEG to study auditory motor interactions.

Discussed below is background information on EEG. Scientists have estimated the brain

has one hundred billion brain or nerve cells, which function to maintain the brain’s

electrical potential. Eighty-five percent of cortical neurons are excitatory while the

remaining fifteen percent are inhibitory (Braitenberg & Schüz, 1991). Each neuron has

an electrical charge or polarity, which is regulated by membrane transport proteins

pumping ions across the membrane (Kandel, Schwartz & Jessell, 2000). The membrane

potential is the electrical potential difference between the interior and exterior of the

neuron. Transmembrane ion pumps regulate membrane voltage. If positive ions (e.g.

Na2+, K+, Ca2+) enter or negative ions (e.g. Cl-) leave the interior membrane, intuitively,

the membrane voltage will become more positive, also called depolarization. However,

if negative ions influx or enter into the interior membrane or positive ions depart or efflux

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from the interior membrane, the membrane voltage will decrease, also termed

hyperpolarization. While at rest, neurons in the brain have a resting voltage of

approximately negative 70 millivolts (mVs) due mainly to the activity of potassium ions.

However, brain cells generate action potentials, which involve a transient, rapid

dissention from the resting membrane potential in a characteristic path of depolarization

and subsequent hyperpolarization (Kandel, Schwartz & Jessell, 2000). In some cases, the

membrane potential is not sufficiently depolarized to trigger the firing of an AP.

Therefore, a “graded” potential is produced, which could bring the neuron closer to its

firing threshold by a depolarization, termed excitatory postsynaptic potentials (EPSP), or

away from the threshold and less likely to fire an action potential or a hyperpolarization,

called inhibitory postsynaptic potential (IPSP).

Defining EEG and EEG use in measuring motor cortex physiology

Although attenuated, electrical potentials due to small currents in the cortex can

be measured on the surface of the scalp. A letter represents each electrode site, each

describing the brain region underlying the electrode (i.e. Fp-Frontopolar; F-Frontal; P-

Parietal; T-Temporal; O-Occipital). The numerical subscript identifies position relative

to the midline. Even and odd numbers represent electrodes on the right side and left side

of the midline, respectively. A z subscripts refers to an electrode positioned on the

midline. Recording of these voltage fluctuations over time is termed

electroencephalography (or EEG). Action potentials are not responsible for the waves

produced in the EEG. Instead EEG mostly reflects graded membrane potentials. Graded

IPSPs and EPSPs last longer than APs and are spread over more disperse surface area,

allowing an increased opportunity for summation (Kandel, Schwartz & Jessell, 2000).

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However, graded potentials for individual cells are weak, and as such are not readily

measurable by EEG. In order to generate an ample external signal recordable by EEG,

neurons within a volume of tissue must be spatially aligned and must be temporally

synchronized. Of all the neurons in the human brain, the most spatially aligned neuronal

network is the cortical (or pyramidal) neurons of the cerebral cortex, making them well

suited to obtain an externally measurable EEG signal. Thus, cortical neurons contribute

predominately to the amplitude of the EEG. Consequently, motor cortex physiology can

be quantified using EEG (Ilmoniemi & Kicic, 2010). Therefore, synchronous cortical

neurons that receive the same input or output information produce extracellular rhythmic

field potentials that can be measured using scalp EEG electrodes.

EEG measures are often quantified by decomposing the complex EEG waveform

into a set of sine waves of different frequencies. Sine waves are defined by three

characteristics: frequency, magnitude (or amplitude), and phase. The frequency of an

EEG sine wave is measured in Hertz (Hz) and is defined as the number of oscillations or

complete cycles per second (1 Hz equaling 1 cycle per second). There are four

characteristic EEG frequency bands: delta (δ) 0.5-4 Hz, theta (Θ) 4-8 Hz, alpha (α) 8-13

Hz, and beta (β) 13-30 Hz. Magnitude refers to the maximum height of the sine wave’s

peak or the maximal valley, both in regards to the x-axis. Therefore, the amplitude and

frequency of a normal EEG ranges from about -50 uV to +50 uV and 0 Hz to 40 or more

Hz, respectively. The phase refers to the location that specific time points fall within a

cycle, ranging from -180 degrees to +180 degrees or if expressed in radians, -π to +π.

Consequently, EEG is represented as sine waves of different frequencies and phase

angles that overlap in time with respect to sound onset.

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Volume conduction

The open electric field potentials are propagated throughout the body, decreasing

in strength as distance from the current sources increases. Stronger field potentials,

suggesting stronger neuronal activation, propagate further (Van den Broek, Reinders,

Donderwinkel & Peters, 1998). This propagation is termed volume conduction. Hence,

EEG data contains the summation of the volume conducted neural oscillations. EEG

electrode positioning does not correspond with a specific region of the brain. Current

source may be localized in a particular region; however, due to volume conduction, the

current is spread to many different regions. For example, currents generated in auditory

cortex within the temporal lobes also have substantial current density in frontal and

parietal cortex near the dorsal midline. Thus, it is unwise to assume location of the

electrode cap is measuring changes over the corresponding brain region. However,

volume conductance does have a discernible pattern. Although difficult, differences in

hemispheric activation can help in determining location of regional excitability, despite

volume conductance. Therefore, although EEG is limited in the accuracy in locating

cortical sources, it enables functional coupling to be analyzed across distant cortical areas

with a rather high temporal resolution.

EEG and event-related potentials (ERPs)

One derivative of EEG is event-related potentials (ERPs). ERPs are changes in

the EEG that arise in response to a specific stimulus (e.g. auditory, visual, or

somatosensory) (Wijers, Mulder, Gunter & Smid, 1996). The brain intuitively performs

numerous operations simultaneously. Therefore, the EEG recording reflects many

concordant brain processes. Thus, discriminating the brain’s response to a single event or

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stimulus of interest is difficult. Normally, the process of obtaining an ERP entails

recording EEG activity time-locked to multiple presentations of similar experimental

events subsequently averaging these traces together. ERP waveforms are comprised of

characteristic positive and negative deflections, termed components. In characterizing

ERP components, the letter N or P is used to define deflection direction, either negative

or positive, respectively. Usually the deflection direction or polarity alternates between N

and P. Immediately following the shift in direction, scientists add conventionally, either

a number that indicates the latency in milliseconds, (or the amount of time passed to see

the shift after the stimulus presentation) or number defining the sequential positioning of

the deflection. More precisely, ERPs are the positive or negative deflections in voltage

recorded by the ongoing EEG observed at the onset of a stimulus and up to 500 ms.

Scientists suspect that ERP production reflects discrete, high-level cognitive processes

(i.e. attention capture, expectation, or mental state changes) that occur in response to a

stimulus. EEG continuously records brain activity. Hence, scientists are able to discern

the effect a stimulus (or any other experimental manipulation) has on specific cognitive

stages even when no behavioral changes are observed. Thus, scientists can objectively

observe the influence a stimulus is having on specific stages in cognitive processing.

Defining characteristic ERPs

Neuroscientists have discovered a large set of auditory ERPs that are elicited from

subjects when sounds are presented. After covering the main auditory ERPs we will then

discuss the impact of attention on the ERPs. The latency of ERPs is believed to provide

information about the time needed to process a stimulus or the amount of time necessary

for brain regions to communicate. However, latencies vary in time relative to the

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stimulus. In the present study, speakers will deliver auditory stimuli to a subject who is

wearing an electrode cap to record EEG. Hence, certain ERP components are

characteristic of an auditory stimulus. Different cortical responses can result in similar

ERP component labels (i.e. N1 or P2).

The N100 or N1 peaks in adults in the brain region underlying the fronto-central

between 80-120 ms after the stimulus is presented. Albeit elicited by a number of stimuli

other than sound (e.g. visual, heat and pain), the auditory N100, as it is sometimes called,

is sensitive to several features of sound. The main influencing factor is predictability of

the sound; that is, the N100 is weaker when a stimulus is repetitive and stronger when a

stimulus is random. For example, the N100 amplitude is smaller when subjects control

when the sounds are given related to passive listening. Intuitively, warning a subject of

an ensuing sound additionally reduces the N100. Although the Mismatch negativity

(MMN) has similar properties to the N100, the MMN should not be confused with the

N100. The MMN is an evoked potential that appears at a later latency and may be

elicited in the absence of an expected auditory stimulus (Näätänen, Paavilaninen, Rinne

& Alho, 2007). A second ERP component is the P2 or P200 waveform measured at the

scalp. P2 is a positive shift in electrical potential that peaks around 200 ms (varying

between 150 and 275 ms) after the onset of the stimulus. As is the case for all evoked-

response potentials in appropriate paradigms, P2 is observed in the waveform of the EEG

by time-locking data from trials to the start of the stimulus. The P2 component is

distributed over electrodes underlying the centro-frontal and parieto-occipital regions

with a maximal around the vertex of the scalp. Characterizing the P2 is difficult because

it is modulated by a varying number of cognitive tasks. An enhanced P2 component is

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associated with auditory learning and repeated stimulus exposure (Ben-David et al.,

2011). Changes in P2 amplitude are sometimes seen independent of N1 amplitude

changes, suggesting some degree of independence between the two components (Sato,

Tremblay & Gracco, 2009). However, attention influences P2 and N1 amplitude

concurrently (Anllo-Vento and Hillyard, 1996; Eimer, 1994)

Attention

The auditory motor interactions studied in the present study have been known to

be modulated by attention. Recent evidence suggests that the neuronal characteristics of

selective attention depend on selective neuronal synchronization (Womelsdorf & Fries,

2006). The behavioral advantages of attentional selection are manifold and include

expedited reaction times and processing, higher efficiency rates, enhanced sensitivity to

fine changes, and increased apparent contrast (Womelsdorf & Fries, 2006). The

aforementioned behavioral returns are accomplished by selective neuronal

synchronization within and across cortical regions. First, the attended sensory input is

represented within local neuronal populations, tuning specifically to the attended spatial

or feature characteristics (Reynolds & Chelazzi, 2004; Maunsell & Treue, 2006). Second,

attention regulates the communication among spatially distant neuronal assemblies,

enhancing effective communication with groups that convey behaviorally relevant

information in a ‘top down’ manner (Miller & D’Esposito, 2005). Recent evidence

strongly suggests that the synchronization is important for promoting selective neuronal

communication and attention might be the substrate for such dynamic control of effective

neuronal interactions (Rose & Büchel, 2006; Pesaran, Pezaris, Sahani, Mitra & Andersen

2002). Studies on attention have shown that ERP component accurately reflect the

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differential processing of attended and unattended information (Wijers, Mulder, Gunter &

Smid, 1996). ERP studies have shown using voluntary attention tasks (e.g. sustained

attention and central cueing paradigms) that stimuli at an attended location elicit enlarged

P1 and/or N1 components at posterior recording sites contralateral to stimulus location

(Anllo-Vento and Hillyard, 1996; Eimer, 1994). This N1/P1 enhancement is associated

with faster reaction times and increased sensitivity to the targets (Eimer, 1994; Anllo-

Vento and Hillyard, 1996).

Defining frequency and amplitude characteristics of EEG

Communication among neuronal assemblies is readily detectable by EEG and

MEG signals. Neuronal networks can display differing states of synchrony, oscillating

at different characteristic frequencies (Pfurtscheller & Lopes da Silva, 1999). The

frequency of brain oscillations is inversely proportional to the amplitude of such

fluctuations, meaning as the amplitude decreases, the frequency of oscillatory activity

increases (Pfurtscheller & Lopes da Silva, 1999). The amplitude (or magnitude) of the

EEG sine wave is defined as the height of the waveform. The synchrony and number of

recruited neural elements determine the amplitude of the EEG waveform (Elul, 1972).

Therefore, as neural synchrony within a network increases, the EEG amplitude increases

and the frequency decreases (Pfurtscheller & Lopes da Silva, 1999b). The amplitude

provides some insight on the number of EPSPs reaching a neural assembly at a certain

point.

EEG time-frequency analysis

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While ERP analysis of the EEG data provide some indication of the serial or

sequential events occurring in the brain in response to a specific stimulus, an umbrella

term known as time-frequency analysis encompasses numerous methods that allow a

researcher to measure the various components of EEG magnitude and phase

relationships. The present study will utilize EEG time-frequency analysis to assess

auditory motor interactions. The magnitude relationships can be defined in terms of

power and the phase relationships can be defined in terms of synchronization. Therefore,

time-frequency analysis provides additional information about brain frequency in terms

of power and synchronization, respectively. Each frequency apparent in the EEG can be

characterized based on power and synchronization. These changes in power and

synchronization can be recording over time relative to the onset of the stimulus. Hence,

the magnitude of each frequency at a specific time can be quantified in terms of power

and the phases can provide information about the synchronization of the waves with

regard to time and space. Both ERP and time-frequency analysis examine the effect that

a stimulus (in the present study, a sound) has relative to a pre-stimulus baseline. The

main advantage of using time-frequency analysis is the ability to assess the brain’s

parallel processing by assessing the magnitude of the frequency wave (power) and neural

synchronization (based on phase angle assessment) throughout an experiment. That is,

ERPs are EEG response that are time and phase-locked with the onset of the stimulus and

typically last up to 500 milliseconds after stimulus onset. Time-frequency analysis, in

contrast, can assess frequency characteristics throughout the entire recording process

even if they are not phase-locked to the onset of the stimulus.

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Time-frequency analysis: Event-related Synchronization (ERS) and Event-related

Desynchronization (ERD)

The general assumption is that neuronal assembly dynamics are expressed in at

least two characteristic ways using a time-frequency analysis: (i) amplitude attenuation or

power decrease and (ii) amplitude enhancement or power increase of specific frequency

bands termed event-related desynchronization (ERD) and event-related synchronization

(ERS), respectively. As we will discuss below, ERS and ERS provide information about

the auditory motor cortical interactions occurring after sound onset. ERS is indicative of

cooperation or synchronization among a large number of neurons (Pfurtscheller, Stancak

Jr & Neuper, 1996). Increased cellular excitability in thalamo-cortical systems is

proceeded by a low amplitude EEG desynchronization (Steriade & Llinas, 1988). Thus,

ERD can be understood as an electrophysiological correlate of activated cortical areas

undergoing the processing of sensory or cognitive information or producing a motor

output (Pfurtscheller, 1992). An enhanced ERD is observed when task complexity is

increased, (Dujardin et al., 1993; Boiten et al., 1992) and/or more attention or effort is

given to a task (Defebvre et al., 1996; Neubauer, Freudenthaler & Pfurtscheller, 1995).

The terms ERS and ERD are only suggestive if there exist a meaningful shift in

percentage power relative to a referenced rhythmic baseline measured some seconds prior

to an event. (Pfurtscheller & Lopes da Silva, 1999). When referring to ERD/ERS of the

EEG/MEG, it is necessarily to specify a frequency band. Mathematically, ERD/ERS is

defined according to the expression ERD%=(A-R)/R X 100, in which A represents the

power within the frequency band of interest in the period after the event whereas R

signifies the power of the preceding baseline or reference point (and Pfurtscheller &

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Lopes da Silva, 1999). ERPs are assumed to represent the responses of cortical neurons

due to changes in afferent activity, while ERS/ERD reflect changes in the activity of the

neurons that control the frequency components of the ongoing EEG.

ERD/ERS in alpha band (8-12 Hz)

There are four characteristic frequency bands: delta (δ) 0.5-4 Hz, theta (Θ) 4-8

Hz, alpha (α) 8-13 Hz, and beta (β) 13-30 Hz. When ERS is not time-locked with

stimulus onset and assemblies of neurons undergo alpha ERS (or display coherent

behavior), an active processing of information is very unlikely and the underlying

corresponding network are in a deactivated state (Pfurtscheller & Lopes da Silva, 1999).

A recent findings suggests rhythmic alpha ERS might actively inhibit effective local

neuronal processing (Thut, Nietzel, Brandt & Pascual-Leone, 2006). Alpha can be

broken down into upper alpha and lower alpha. Lower alpha desynchronization (ranging

from 7-10 Hz) is topographically widespread over the scalp and is usually observed in

almost any type of experimental task. Desynchronization in the lower alpha region

reflects general task demands and attentional processing (Bauer, Oostenveld, Peeters &

Fries, 2006; Palva, Palva & Kaila, 2005). Upper alpha (mu) desynchronization (ranging

from 10-12 Hz), on the other hand, is usually topographically circumscribed to parieto-

occipital areas and develops during the processing of sensory-semantic information

((Klimesch, Schimke & Pfurtscheller, 1993; (Klimesch, Schimke & Schwaiger, 1994).

Voluntary movement induces upper alpha desynchronization topographically restricted

over scalp regions underlying sensorimotor areas (Pfurtscheller & Aranibar, 1979;

Pfurtscheller & Berghold, 1989). This mu desynchronization commences approximately

2 seconds before movement-onset over the contralateral Rolandic region and becomes

18

bilaterally symmetrical immediately before movement execution. Although brisk and

slow movements are quite different, the time course of the contralateral mu

desynchronization is similar (Pfurtscheller, Zalaudek & Neuper, 1998). The dominant

contralateral pre-movement mu ERD is independent of movement duration and is

identical with index finger, thumb, and hand movement. One interpretation of these

findings is that the contralateral pre-movement mu ERD reflects a relatively unspecific

pre-activation, priming or presetting of neurons in motor regions, which is fairly

independent of speed (e.g. brisk vs. slow) and the type of ensuing movement (e.g. wrist

vs. hand).

ERD/ERS in the beta band (12-30 Hz)

Previous studies have specifically linked the beta band to motor function. The

localization of the beta ERD is slightly anterior to the largest mu ERD with regard to

hand and foot movement. Central beta ERD (at least some portion of it) is located in the

pre-Rolandic motor area during movement. Analysis on a single trial basis also revealed

a slightly more anterior focus of the beta rhythm (Pfurtscheller, Flotzinger & Neuper,

1994). MEG measurements revealed the 20-Hz beta rhythm as being located in the

motor cortex and slightly more anterior to the 10-Hz alpha rhythm (Salmelin et al., 1995).

Imaginary right hand movement is accompanied by a contralateral beta ERD and a

corresponding ipsilateral beta ERS, both circumscribed and localized over the hand areas

(Pfurtscheller & Neuper, 1997). Voluntary movement induces lower beta ERD localized

close to sensorimotor areas (Stancak & Pfurtscheller, 1996; Leocani et al., 1997). Similar

to the mu desynchronization noted in response to voluntary movement, the observed

lower beta ERD starts about 2-seconds prior to movement initiation over the contralateral

19

Rolandic region, becoming symmetrical immediately preceding movement execution.

The pre-movement beta ERD was higher for movements performed in sequential order

when compared to a single ballistic movement (Manganotti et al., 1998; Alegre et al.,

2004). A beta ERS is present proceeding mechanical or somatosensory stimulation of the

limbs, even without movement (Pfurtscheller, Krausz & Neuper, 2001; Salenius,

Schnitzler, Salmelin, Jousmaki & Hari, 1997). In patients with neurological disorders,

the ERD is reduced or abolished over the affected hemisphere. In patients with

Parkinson’s disease, the pre-movement ERD is less lateralized over the contralateral

sensorimotor region and commences significantly later than in normal subjects (Magnani

et al., 1998).

EEG measures and the present study

In the present study time-frequency analysis will assess power in frequency bands

between 8-30 Hz for up to 1,700 ms after stimulus onset, with a specific time of interest

at 1,000 ± 200 milliseconds. This time period was chosen because that was the time

period where Mock et al (2011) found increased dominant motor cortex excitability in

response to a sound. Therefore, the full time-course of motor cortex response to sounds

can be assessed so that we might better understand the cortical mechanisms inducing the

greater MEP amplitudes seen in the aforementioned studies. Hence, the present study will

analyze neuronal activity with millisecond precision before, during and several seconds

after sound presentation.

20

Hypotheses/ Goals

Objectives of current study:

Goal: The overall purpose of the present study is to identify cortical mechanisms

associated with the production of larger MEP amplitudes following the presentation of a

sound using ERP and time-frequency analyses.

Objective 1: Compare EEG responses originating in dominant vs. non-dominant cortex in

response to sounds:

Hypothesis: We hypothesize that after the presentation of a sound, beta event-related

desynchronization (ERD) (13-30 Hz) will be observed only for the dominant hemisphere

as evidenced by changes in time-frequency analysis recordings.

The experimental setup of the previous two studies allowed Mock et al, (2011) to

observe the left and right motor cortices and Serino et al., (2009) to study only the left

motor cortex. Although Mock et al., (2011) studied both hemispheres; the use of TMS

did not allow the measurement of motor cortex activity in both hemispheres

simultaneously. Hence, the present study uses EEG, which allows neuronal activity in

both hemispheres to be assessed at the same time. In addition, EEG allows measurement

of cortical activity in regions of the brain surrounding the motor cortex such as the Pre-

frontal cortex (PFC), Premotor cortex, and Supplementary motor areas (SMAs).

Objective 2: Examine the time course of motor cortical responses to sounds, from

several hundred ms to beyond 1-second.

21

Hypothesis: Based on the results by Mock et al., (2011), we hypothesize that reductions

in beta power will occur over dominant hand M1 areas approximately 1-second after the

sound is initially presented.

TMS only collects data on motor cortex activation immediately following the

delivering of the TMS pulse. This means collection of data is restricted a few

milliseconds after the TMS pulse is delivered. Thus, TMS use makes it difficult to

understand the different stages involved in the processing of the sound in the brain and its

influence on the motor cortices and surrounding motor areas. EEG can infer changes in

the brain, several seconds before, during, and several seconds after the sound has been

presented from speaker. In the present study, time-frequency analysis will assess

frequency characteristics throughout the entire recording process, from -500 ms to 1700

ms. Therefore, changes in brain regions as the sound is processed can be recorded,

helping to explain possibly the mechanisms that caused the greater MEP amplitudes seen

in the aforementioned studies.

Objective 3: Compare left vs. right speaker locations for each hemisphere, relate to

dominant vs. non-dominant hemisphere:

Hypothesis: We hypothesize neither speaker (nor sound) location will influence

corticospinal activity (Mock, et al., 2011).

A sound presented within the peripersonal space (PPS), which is the space

surrounding the body reachable by the limbs, can cause increases in corticospinal

excitability as evidenced by the production of a MEP with greater amplitudes

immediately following (Serino, et al., 2009) and 1-second post (Mock, Foundas, Golob,

22

2011) sound presentation. Similar increased MEP amplitudes were seen when sounds

were coming from the left and the right speaker but only if the sound was being emitted

to the dominant hand (Mock, et al., 2011). Neither speaker (nor sound) location

influence corticospinal activity (Makin, et al., 2008; Mock, et al., 2011). This could be

the case because the dominant hand should be more primed to action due to better

dexterity. For example, if I wanted to swing at a bee, I would want to maneuver with my

most adept hand because of the potential dangers that could occur if I miss. That means

brain processing of a sound is dependent on hand dominance (or preference) and hand

closeness to the speaker emitting the sound (Graziano & Cooke, 2006; Rizzolatti,

Fogassi, & Gallese, 1997). Hence, cortical arousal is not controlled by only one variable.

Methods

Participants

A notice was posted on an online research system only accessible to Tulane

affiliates alongside other researcher studies that students could voluntarily participate in

until the desired number of participants was reached. Fifteen participants (8 females and

9 males) affiliated with Tulane University were examined. Subjects were between 18 and

37 years of age (mean 22 years old). Participation was open to all regardless of gender or

minority status. The Edinburgh inventory was administered to assess the handedness of

each participant (see section below for more details). 11 were classified as right-handed

and 6 were identified as left-handed. All subjects had completed high school and had

some form of secondary education. A Maico automated audiometer tested hearing

thresholds (0.5-8 kHz) for pure tones (Maico, Eden Prairie, MN, USA) in addition to

23

ascertaining the disparity in hearing thresholds between ears were <10 dB. All

participants had normal hearing levels. All participants reported no major neurologic or

psychiatric aberrations. Participants gave written consent to participate in the

experiment. All procedures obeyed the protocols outlined by Tulane University

Institutional Review Board, which is in accordance with the Declaration of Helsinki (for

inclusion and exclusion criteria see Table 1). Some participants have received course

credit (alternative methods of course credit were provided).

Experimental Design and Paradigm

Behavioral Tasks

All participants sat in a comfortable chair in an electrically attenuated sound

booth watching a nature video without audio. During the experiment, subjects extended

their arms toward speakers situated at about 45-degree angles ensuring the sound emitted

from the speakers was within the PPS (Figure 1). To ascertain the sound was within the

PPS, the hands of the participant was placed 3 cm from the speakers while pure tones

were intermittently presented from one of the two speakers. Participants were instructed

to minimize eye movements and blinking. In addition, subjects were required to maintain

complete muscle relaxation of the hands during all data acquiring periods. A total of 80

sounds were presented in each block with three total blocks per subject. Each block

consisted of the same sounds; however, the sounds were presented in a different order in

each block. To ensure muscle relaxation, background EMG activity was monitored

during the study. The experimental session lasted 45 minutes. Once the participant was

24

exposed to all three blocks and the pre and post 3-minute assessments, the participant was

given the option to be debriefed orally.

Handedness Quotient

Subjects were administered an inventory, originally developed by Annett and

subsequently modified by Briggs and Nebes, to quantify handedness, footedness, ear

preference, and eye preference. The questionnaire consisted of 15 prompts requiring

participant response by checking one of five boxes ‘always right,’ ‘usually right,’ ‘no

preference,’ ‘usually left,’ and ‘always left.’ One sample query in the handedness section

was the following: “To hold scissors to cut paper.” A subject would respond by selecting

the box that best describes their hand preference for the given task. Scores on the hand

section of the questionnaire ranged from -24 to +24, with positive values indicating a

right-handed preference and negative values a left-handed preference. Participants were

further subdivided into a category of moderately left or right-handed if the score on the

hand section of the questionnaire was ≥ -10 for left-handers and ≥ +10 for right-handers.

Footedness was measured because some research suggests it may be a better predictor of

functional cerebral asymmetries of higher cognitive functions (Elias & Bryden, 1998;

Searleman, 1980). One such question to assess footedness was the following: “Which

foot do you use to kick a ball?” Eye preference was assessed since it has been found to

modulate the influence of handedness on lateralization of non-spatial auditory functions

(Khalfa, Veuillet & Collet, 1998). In addition, ear preference was assessed as it is the

highest correlated with laterality in terms of direction and degree of asymmetry in

dichotic listening tasks (Strauss, 1986) One sample question was as follows: “Which ear

25

do you use to listen through a phone?” Footedness, ear preference, and eye preference

have been highly correlated with handedness (Reiss, Tymnik, Kögler & Reiss, 1999).

For each lateral preference, a laterality quotient was calculated according to the method

of Oldfield (1971). Scores on the laterality quotient ranged from -100 to +100, with

positive values indicating a right-sided preference and negative values a left-sided

preference.

Electrophysiological Recordings

Subjects wore a 64 Ag/AgCl EEG electrode cap with a reference electrode

between Cz and CPZ. Standard EEG system (Compumedics Neuroscan, Charlotte, NC)

and EOG recording procedures were employed. Bipolar horizontal EOG channels

affixed to the right (HEOGR) and left (HEOGL) canthi in addition to vertical EOG

bipolar channels attached to the upper (VEOGU) and lower (VEOGL) orbital ridges

documented eye movements. Also, EMGs were recorded bilaterally from the first dorsal

interosseous (FDI) and the other on the adductor pollicis trans using 0.9-cm-diameter

silver/silver chloride surface electrodes. Subjects were not permitted visual assess to

EMG and EOG data during testing, however, verbal feedback between blocks helped

ensure muscles were relaxed. Impedances of all electrode channels were kept below 10

kOkm. The EEG and EOG were continuously digitized at 500 Hz. The auditory

stimulus onset was automatically documented with inputted markers within the

continuous EEG file. APC-based Neuroscan recording system (Scan) with SynAmps

(biological amplifiers) were utilized to sample and attain the EEG and EOG data.

EEGLAB Matlab Toolbox was utilized for visualization of neuronal excitability and

filtering intentions. EEG permits analysis of non-phase locked changes in electrical

26

activity. Data from each subject was stored on a magnetic optical disk for subsequent

processing offline.

Data Analysis

The software program EEGLAB, a program within MATLAB (The Mathworks,

Inc., Natick, MA, USA) was used to analyze the EEG data. The number of electrodes

was reduced to 41, ascertaining there are an equal number of central scalp, temporal-

parietal, and frontal electrodes. A high-pass filter attenuates or blocks signals that are too

low while a low-pass filter attenuates frequency bands that are deemed too high. A high-

pass filter at 1 Hz and a low-pass filter of 50 Hz with a digital finite-impulse-response

filter were applied to the continuous EEG file to extract linear trends and remove high

frequency line noise. Subsequently, the sampling rate was attenuated to 250 Hz with a

32-bit resolution. For improved temporal, salient data analysis, the data were cut or

epoched into 1200-ms segments or sweeps. ‘Bad’ electrodes, defined as sites with

potential values that exceeded a predefined value or some level of noise, were excised.

Afterwards, an average reference was computed in order to sum the outward and inward

electric fields to zero. All sweeps containing eye blinks, based on visual inspection, were

corrected employing a blind algorithm paradigm (Gratton, Coles & Donchin, 1983) and

corrected for DC drift and offset. Trials contaminated with hand muscle movements

(without muscle relaxation) were discarded from subsequent analysis. Epochs with large

artifacts were excised from successive analysis. Additionally, noisy channels were

excluded from analysis and corrected trial sweeps were averaged. In event-related

experimental setups, individual epochs correspond to one or more trials that are

experimentally phased locked temporally with the presentation of the auditory stimulus.

27

Results

General profile of ERD/ERS to auditory stimuli

Alpha was characterized as being from 8-12 Hz. Beta was separated into lower

beta (12-18 Hz), central beta (18-24 Hz), and upper beta (24-30 Hz). Overall, the

acoustic stimuli led to an increase in delta (0-4 Hz), theta (4-8 Hz), alpha, lower beta,

central beta and upper beta power (synchronization) that lasted about 500 ms (See Figure

2). This early power increase corresponded to the ERP. This was followed by alpha and

beta desynchronization that lasted up to 600 ms. Subsequently, alpha and beta

synchronization then occurred around 1000 ms and lasted until the end of data

acquisition, which was 1700 ms. We analyzed all significant ERSP perturbations after the

ERP separately. Here we focus on ERSP data because we are interested in longer

latencies. Based on these observations, we categorized the perturbations in three separate

time intervals 300-600 ms, 800-1200, and 1200-1700, parsed into 100 ms blocks. FC5

corresponds to the left hemisphere and FC6 corresponds to the right hemisphere. We

focused a separate analysis between 800-1200 because this time range corresponded with

the production of larger MEPs seen in a previous study (Mock et al., 2011).

T-tests were performed to determine whether synchronization or

desynchronization was significantly above or below a 0 baseline, respectively for the

three different time intervals using 100 ms blocks. Subsequently, speaker location,

handedness, electrode site and interactions were analyzed using a repeated measures

ANOVA that was a 2 (Location: right speaker, left speaker) x 2 (Site: FC5, FC6) x 3

(Time: 300 ms-400 ms, 400-500 ms, 500-600 ms) for the 300-600 ms time interval, a 2

28

(Location: right speaker, left speaker) x 2 (Site: FC5, FC6) x 4 (Time: 800 ms-900 ms,

900-1000 ms, 1000-1100 ms, 1100-1200 ms) for the 800-1200 ms time interval, or a 2

(Location: right speaker, left speaker) x 2 (site: FC5, FC6) x 5 (Time: 1200 ms-1300,

1300-1400 ms, 1400-1500 ms, 1500-1600 ms, 1600-1700 ms) for the 1200-1700 time

interval. Stimulus-induced changes in EEG power were assessed using T-tests and

speaker location, handedness, and electrode site were assessed using ANOVAs.

300-600 ms time window

Alpha (8-12 Hz)

Stimulus-induced changes in EEG power

The left speaker location became desynchronized from 500-600 ms (p < .04), while the

right speaker never showed significant power perturbations.

Left vs. Right Speaker Location

There was a main effect of time for alpha (F(1,15) = 5.4, p < .02). On average, during this

time, motor cortical areas became more desynchronized over time (See Figures 3).

Right Handers vs. Left Handers

There was no main effect for handedness in the alpha frequency band between 300-600

ms.

FC5 vs. FC6

There was no main effect for electrode site in the alpha frequency range between 300-600

ms.

29

Interactions

There were no interactions in alpha between 300-600 ms.

Lower beta band (12-18 Hz)


The left speaker showed trends of becoming desynchronized between 400-500 ms while

the right speaker never became significantly synchronized or desynchronized.


There was a main effect of time for lower beta (F(2,30) = 4.5, p < .02). On average, during

this time, motor cortical areas became more desynchronized over time (See Figure 3).


There was no main effect of handedness in the lower beta band for 300-600 ms.

FC5 vs. FC6

There was no main effect of electrode site in the lower beta band for 300-600 ms.

Interactions

There were no interactions for lower beta in the 300-600 ms time interval.

Central beta Band (18-24 Hz)


30

The left speaker location became significantly desynchronized between 500-600 ms (p <

.04) while the right speaker location showed trends between 300-400 ms.


There was a main effect of time for central beta (F(2,30) = 2.5, p < .02). On average, during

this time, motor cortical areas became more desynchronized over time (See Figure 3).


There were no main effects of handedness for the central beta band between 300-600 ms.

FC5 vs. FC6

There was no main effect of electrode site for central beta between 300-600 ms.

Interactions

In the central beta band, there was an interaction between location and time (F(2,30) = 4.5,

p < .01). The left speaker location became desynchronized at a later time than the right

speaker location (See Figure 3).

Upper beta band (24-30 Hz)


The left speaker location became significantly desynchronized between 500-600 ms (p <

.04) while the right speaker location never showed any ERS or ERD changes relative to

baseline.


31

There was a main effect of time for upper beta bands (F(2,30) = 5.6, p < .01). On average,

during this time, motor cortical areas became more desynchronized over time (See Figure

3).


There was no main effect of handedness in upper beta band between 300-600 ms.

FC5 vs. FC6

There was no main effect of electrode site in the upper beta band for the 300-600 ms time

interval.

Interactions

There were no interactions found in the upper beta band for the 300-600 ms time interval.


Alpha frequency band (8-12 Hz)


In the alpha frequency range, the right speaker location became significantly

synchronized between 1000 to 1100 ms (p <.01) while the left speaker location showed

no significant difference from baseline.


32

There was a significant effect of time for the alpha frequency bands (F(3,45) = 7.6, p <

.001). For alpha, synchronization progressively increased from 800 ms-1200 ms (See

Figure 3).


There was no main effect of handedness for the alpha frequency band between 800-1200

ms.

FC5 vs. FC6

There was no main effect of electrode site for alpha between 800-1200 ms.

Interactions

There were no interactions in the alpha frequency band between 800-1200 ms.



In the lower beta range, the right speaker location became significantly synchronized at

1000-1100 ms (p <.01) while the left speaker showed trends of becoming synchronized at

1100-1200 ms.


There was a significant effect of time for lower beta (F(3,45) = 10.0, p < .001). For lower

beta, synchronization progressively increased from 800 ms-1200 ms (See Figure 3). To

determine when significant changes in synchronization occurred, a post-hoc paired

33

sample t-test was performed. Between 800-900 ms and 900-1000 ms, the left speaker

location had a significant change in power (p < .01). However, the right speaker location

had significant changes in power between 900-1000 ms and 1000-1100 ms, at a later time

interval (p <. 01) (See Figure 4).


There was no main effect of handedness for lower beta between 800-1200.

FC5 vs. FC6

There was no main effect of electrode site between 800-1200 ms.

Interactions

There were no interactions found in the lower beta frequency band between 800-1200 ms.

Central beta band (18-24 Hz)


The right speaker location in the central beta range became significantly synchronized

between 1100-1200 (p <.01) while the left speaker showed trends of being significant

between 900-1000 ms.


There was no main effect in the central beta range between 800-1200.


There was no main effect of handedness for the central beta range between 800-1200 ms.

34

FC5 vs. FC6

There was no main effect of central beta in examining the 800-1200 time interval.

Interactions

There was an interaction between speaker location and site (F(1,15) = 5.9, p < .03) in the

central beta frequency range. Post-hoc paired sample t-tests were performed to better

understand the relationship between speaker location and site. There was a significant

difference between FC5 and FC6 when sounds were presented from the right speaker

location (p < .03). FC5 was greater than FC6 (See Figure 8). There was no significant

difference in FC5 and FC6 when sounds were presented from the left speaker. In

addition, there was a difference between the right and left speaker locations for FC5 (p <

.001). For FC5, sounds presented from the right speaker elicited a greater

synchronization than sounds presented from the left speaker (See Figure 6).



In the upper beta range, the right speaker location showed trends of becoming significant

between 1100-1200 ms (p <.01) while the left speaker location was not significantly

different from baseline at any time interval.


There was no main effect of speaker location in the upper beta band in examining 800-

1200 time interval.

35


There was no main effect of handedness in examining the upper beta range between 800-

1200 ms.

FC5 vs. FC6

There was no main effect of electrode site in assessing the upper beta band between 800-

1200 ms.

Interactions

There were no found interactions in the upper beta range between 800-1200 ms.


Alpha (8-12 Hz)


The synchronization seen in the right speaker continued until the end of data acquisition.


There was a main effect of speaker location. There was a significant difference between

the right and left speaker locations between 8-12 Hz (F(1,15) = 8.2, p < .01) (See Figure 5).


There was a main effect of group. During this time interval in the alpha frequency band,

left-handers showed greater synchronization than right- handers (F(1,15) = 5.1, p < .04)

(See Figure 7). Alpha showed significant differences between right and left-handers

36

between 1300-1400 ms (F(1,15) = 11.3, p < .01) and 1400-1500 (F(1,15) = 6.6, p < .03).

Between 1200-1700, a Pearson R Correlation was employed to determine whether there

was a relationship between degree of handedness and power perturbations. Right

Speaker location FC5 was correlated with handedness (p< .04) (See Figure 8).

FC5 vs. FC6

There was no main effect of electrode site in the alpha frequency range between 1200-

1700 ms.

Interactions

There were no interactions in the data for the alpha frequency band between 1200-1700

ms.



The synchronization seen in the right speaker continued until the end of data acquisition.


There was a main effect of speaker location. There was a significant difference between

the right and left speaker locations between 12-18 Hz (F(1,15) = 10.5, p < .01). EEG in

response to sound from the right speaker location was more synchronized than the left

speaker location in the lower beta frequency band (See Figure 3 & 5)).


37

There was no main effect of handedness in the lower beta frequency band between 1200-

1700 ms.

FC5 vs. FC6

There was no main effect of electrode site in the lower beta frequency range between

1200-1700 ms.

Interactions

There were no significant interactions in the lower beta frequency range in the 1200-1700

time interval.

Central beta band (18-24 Hz)


The significant synchronization continued until the end of data acquisition.


There was no main effect of speaker location in the central beta range between 1200-

1700 ms.


There was no main effect of handedness in the central beta frequency band between

1200-1700 ms.

FC5 vs. FC6

38

There was no main effect of electrode site between 1200-1700 in the central beta

frequency band.

Interactions

There were no interactions present in the data in the central beta band between 1200-1700

ms.



The significant synchronization present in upper beta at 1100-1200 ms subsided and did

not continue.


There was no main effect of speaker location observed in the upper beta band between

1200-1700 ms.


There was no main effect of handedness in the upper beta band between 1200-1700 ms.

FC5 vs. FC6

There was no main effect of electrode site in the upper beta band in the 1200-1700 ms

time interval.

Interactions

There were no interactions found in the upper beta band between 1200-1700 ms.

39

Discussion

Main Findings

For EEG measures there was an initial increase in power (ERS) that was related to

the ERPs. This was followed by a transient ERD during the 300-600 ms time window

and then a long lasting synchronization that commenced around 800 ms and continued

through the end of data acquisition (1700 ms) (See Table 1). Between 300-600 ms in the

central beta band we found that sound from the right speaker caused the electrodes over

motor cortical areas to become desynchronized at an earlier time than the left speaker

location (See Table 1). Between 800-1200 ms in the lower beta band, the right speaker

location induced synchronized earlier than the left speaker location (See Table 1).

Between 800-1200 ms in the central beta band, when sounds were presented from the

right speaker, the contralateral hemisphere showed greater synchronization than the

ipsilateral hemisphere; however, when sounds were presented from the left speaker, there

was no difference between the contralateral or ipsilateral hemispheres (See Table1).

From 1200-1700 ms in the alpha frequency band, individuals who where left-handed

showed greater synchronization than right-handed (See Table 1). These responses were

correlated negatively with degree of handedness.

General profile of ERS/ERD

In this section I will consider why the ERD elicited by our sounds likely

represents motor cortex processing. First, the ERD were measured from scalp electrodes

overlying frontal motor areas. Secondly, ERD is commonly observed premovement.

Event-related desynchronization is characteristic of an awake brain involved in motor

output and motor preparation (Van Winsum, Sergeant & Geuze, 1984; Pfurtscheller &

40

Berghold, 1989). A desynchronized state can also indicate a state of maximum motor

readiness (Thatcher et al., 1983). Increased ERD amplitude is seen in conjunction with

increased motor corticospinal excitability as assessed using MEPs (Takemi, Masakado,

Liu & Ushiba, 2013). However, although we saw a transient beta ERD the beta ERD did

not continue through the 800-1200 and 1200-1700 time windows as hypothesized. At 1

sec after sound presentation our participants showed beta ERS instead of beta ERD. The

current study still supports the hypothesis that sound is priming the motor cortex (Mock

et al., 2011) because beta ERD was present. However, beta ERD was present from 300-

600 but not at 800-1200. Several researchers findings suggest a negative correlation

between duration of desynchronization and manual reaction time (e.g. Van Winsum et al.,

1984). Thus, the sounds in our study may be priming the motor cortex for faster reaction

time. The differences in results between the present study and Mock et al., (2011) might

be due to differences in the intrinsic properties of the sounds. Mock et al., (2011) used

environmental sounds and sounds with emotional valence, whereas the present study

utilized frequency modulated tones.

However, several experimental parameters should be taken into account when

considering this hypothesis. One should take into consideration that ERD measured here

might be generated in non-motor areas. Current source may be localized over motor

areas; however, due to volume conduction the current is spread to many different regions

(Van den Broek, Reinders, Donderwinkel & Peters, 1998). Thus, although the electrodes

were preferentially placed over motor areas, it cannot be assumed that the electrodes are

measuring voltage changes due to neurons in the corresponding brain region directly

under the electrode. One should also consider that ERD is not specific to motor

41

functions. For example, alpha ERD may also reflect attentional and semantic processing

(Klimesch, Schimke & Schwaiger, 1994).

300-‐600 ms time window

We will next explain the possible consequences of desynchronizations occurring

earlier when sound is presented from the right hemispace. The second hypothesis that

neither speaker location nor sound location will influence corticospinal excitability was

not supported. In addition, hypothesis one was not supported because beta ERD was seen

over both the contralateral and ipsilateral hemispheres, not just over the dominant

hemisphere. Previous studies has correlated the duration of beta ERD with mean reaction

time; the longer the ERD duration, the more prolonged the reaction time, indicating an

increase in motor organization time (Williams et al., 2003; Kühn et al., 2004). The basal

ganglia and subthalamic nucleus (STN) modulate ERD/ERS (Brown and Marsden 1999;

Cassim et al., 2002). Beta band ERD is reduced and finally reversed as movement is

inhibited in the subthalamic nucleus (STN) (Kühn et al., 2004). Thus, a delayed ERD

seen in response to sound coming from the left hemispace may be an automatic bottom

up motor response that is occurring earlier in the right hemispace. However, sounds

presented from the right hemispace correspond to a faster reaction time and shorter motor

preparatory time. Future behavioral studies should be done to test this hypothesis.

The implications of a delayed ERD have been studied in patients with Parkinson’s

disease (PD). Current Parkinson’s Disease (PD) models posit impaired modulation of

alpha and beta band oscillations in both subcortical and motor cortical areas in the

pathophysiology of bradykinesia and akinesia (Brown and Marsden 1999; Cassim et al.

2002). In patients with PD, a delayed preparatory or pre-movement cortical alpha and

42

beta ERD is observed (Cassidy et al., 2002, Levy et al., 2002). Viable therapeutic

options such as treatment with levodopa (Devos et al., 2003; Devos and Defebvre 2006)

or deep-brain stimulation of the subthalamic nucleus (STN) (Devos et al. 2004; Devos

and Defebvre 2006) results in earlier ERD and reversal of akinesia and bradykinesia.

Thus, the earlier ERD, as seen in PD patients after successful treatment, suggests that the

central processing of movement commences earlier. Therefore, sounds presented from

the right hemispace results in an earlier ‘priming’ of the motor cortex to action. This

deduction is further supported by the fact that 300-600 ms is within the typical time range

for subjects to respond to simple reaction time experiments. Several studies have

examined this phenomenon (e.g. Beurrier et al., 2001) but none have examined it with

regards to sound. To my knowledge, there are no studies examining PD and spatial

hearing, particularly studies having patients responding to sounds from the left vs. right

hemispace. Future studies should address this issue.

a. 800-‐1200 ms time window

Within the 800-1200 ms time window in the lower beta frequency band sound

presented from the right hemispace showed earlier cortical responses than sounds

presented from the left hemispace. Results suggest some preference to the right

hemispace. Neurological disorders provide great insight into normal functioning of the

human brain. The next paragraph attempts to explain why sound presented from the right

hemispace showed an earlier cortical response using a prominent neurological disorder as

a model.

Hemineglect (or hemispatial neglect syndrome) is a disorder characterized by an

abnormal bias towards one side of space. Most hemineglect patients give more

43

attentional salience to the right hemispace, effectively neglecting the left hemispace

(Corbetta and Shulman, 2011). The line-bisection task is commonly used to assess the

severity of visual hemineglect. Patients typically will dissect horizontal lines

significantly to the right of the true center, implying they are giving more attentional

salience to the right hemispace or are, alternatively, ignoring the majority of the left

hemispace (McCourt & Jewell, 1999). Based on human functional neuroimaging studies,

the posterior parietal cortex is thought to be integral for attending to spatial locations

(Fink et al., 1997).

More recent research has examined auditory hemineglect, which normally

presents with a bias to the right hemispace (Corbetta & Shulman, 2011). Normally,

patients with auditory hemineglect have unilateral omissions when perceiving two stimuli

presented simultaneously from both the left and right hemispace (Hugdahl et al., 1991).

They normally exhibit neglect of auditory stimuli in the left hemispace due to impairment

in the contralateral hemisphere. For example, when addressed verbally from the left, a

person with auditory hemineglect may not respond or more commonly, may behave as if

they heard the voice originating from the right. The most widely employed paradigm to

study auditory hemineglect is the dichotic listening test, in which two different auditory

stimuli are presented simultaneously. Ear related asymmetry in auditory hemineglect has

been accounted for by several theories. One model posits that completely neglecting one

hemispace reflects an over-attention to the ipsilateral hemispace, due to inter-hemispheric

imbalance created by unilateral brain damage (Kinsbourne, 1977). The basal ganglia has

been documented as being involved in spatial attention in primates (Boussaoud and

Kermadi, 1997) and in humans (Mesulam, 1990). The insula has been documented as

44

being involved in selection of relevant auditory information (Habib et al., 1995).

Damage to either of these areas can induce auditory hemineglect (Bellmann, Meuli &

Clark, 2000). Therefore, we believe sounds presented to the right side of space

automatically receive more attention than sounds presented from the left side of space

due to preferential processing in the basal ganglia and insula.

In addition to the dramatic impairments in attending to the left side of space in

hemineglect, studies in healthy subjects show differences in attending and processing

information from the left vs. right. Neurologically normal subjects also systematically

err in visual tasks such as the visual line-bisection. However, they err to the left of the

true center, suggesting a right-sided neglect or increased attentional salience to the left

side in a phenomenon called ‘pseudoneglect’ (McCourt and Jewell, 1999). A similar

phenomenon can be seen in the tactile and kinesthetic modalities (Bowers & Heilman,

1980). Pseudoneglect and hemineglect represent a set of twin phenomena that

demonstrate hemispheric attentional asymmetries (McCourt & Jewell, 1999). While

pseudoneglect has been studied extensively in the visual modality, it has not been studied

thoroughly in the auditory modality. One study found a right hemispace bias when two

sounds were presented simultaneously, with one from each side (Dufour, Touzalin &

Candas, 2007). To our knowledge for the first time, the present study supports the

existence of a right bias in audiospatial attention even when sounds are presented

intermittently.

b. 800-‐1200 ms time window

We will now discuss longer-latency ERS results, and interpret results that indicate

sounds presented from the right speaker induced greater ERS in the contralateral

45

hemisphere than in the ipsilateral hemisphere in central beta. A general observation is

that ERD over a brain region corresponds to cortical activation, with the brain region

prepared to process information and having an increased excitability of cortical neurons

(Pfurtscheller and Lopes da Silva, 1999). Whereas brain regions that are not central to

performing a task or may have potentially confounding information, exhibit ERS

(Pfurtscheller & Lopes da Silva, 1999b). One study assessing corticospinal excitability

during this time window suggests that the ERS might be due to either late attentional

orienting effects or a reorienting of the participant back to the nature video (Mock et al.,

2011). One event-related potential studies on reorienting using task relevant stimuli

found a long latency ‘reorienting negativity’ (Schroger & Wolff, 1998). Dipole analysis

of this component suggests it has a source in the primary motor cortex (Horvath, Diano &

Van Den Pol, 1999). This is further supported by the fact that after attention is captured,

an action usually follows (Sokolov, 1963).

We will now discuss an alternative interpretation of the results. After an ERD,

ERS possibility indicates subsequent ‘idling’ of underlying neuronal networks

(Mulholland, 1995). The greater the ERS, the greater the processing that took place

before the ERS commenced (Lutzenberger, Preissi & Pulvermüller, 1994). According to

the aforementioned hypotheses, the greater ERD could correspond to greater motor

priming. Hence, since greater ERS intensity corresponds to greater motor priming, the

contralateral hemisphere had a greater motor priming than the ipsilateral hemisphere.

The question now becomes why is the contralateral hemisphere more primed than

the ipsilateral hemisphere. Studies show that when action is needed in the contralateral

hemispace, the preferred hand use decreases strongly, since most subjects shift to using

46

their ipsilateral hand (Helbig and Gabbard, 2004). From these data, the researchers

concluded that while hand dominance is the key factor in determining hand selection in

the ipsilateral space, attention is a mediating factor in determining hand selection in the

contralateral hemispace, a phenomena termed kinesthetic efficiency and/or hemispheric

bias (Gabbard et al., 1997). Thus, we can conclude that since the sound was presented

from the right speaker, we would expect the left motor cortex to be primed because the

subject would want to respond with the ipsilateral hand (right hand) because attention is a

mediating factor.

1200-‐1700 ms time window

Left-handed subject (or sinistrals) showing greater synchronization than those

who were right–handed (or dextrals) in alpha supports the claim that cortical spatial

processing differs between sinistrals and dextrals. We did not see a difference between

the dominant and non-dominant hand as hypothesized and seen in previous studies (Mock

et al., 2011). There are few studies on ERSP changes in response to sounds and even

fewer that have looked at ERSP changes this late after sound onset with respect to

handedness. Handedness is a clear example of anatomical asymmetry; in fact, this

lateralization defines handedness. Left-handers, as a group, are more variable than right-

handers on many behavioral and psychological measures, suggesting a greater within-

group heterogeneity (Arroyo & Uematsu, 1992). One study found a correlation between

the lateralization seen in handedness and functional activation of the motor cortex

(Dassonville et al., 1997). Left-handed subjects have been shown to be less strongly

lateralized in terms of hemispheric spatial function (McGlone & Davidson, 1973; Vogel,

Bowers, & Vogel, 2003). Gesturing has been found to be more bilaterally organized in

47

left-handers than in right-handers (King & Kimura, 1972). Even when sounds are

presented from the right hemispace in a dichotic listening task, left-handers still showed

bilateral organization. Thus, we believe the handedness affects seen in the present study

might be due to interaction between asymmetrical hemispheric activations in either group

and an asymmetrical allocation of attention between the left and right hemispheres.

Future Research

Considering the large ERS and ERD observed in this study in the alpha and beta

bands, it might be interesting to examine frequency bands higher than gamma (Arroyo &

Uematsu, 1992). In addition, it might be of interest to examine older subjects. Older

subjects would be expected a desynchronization longer in duration and a resultant shorter

synchronization indicating inefficient motor preparation (Salthouse, 1996). Moreover, it

would be of interest to examine patients with neurological disorders, specifically patients

with problems initiating or stopping movements such as Parkinson’s patients or patients

with attentional problems such as hemineglect. We could examine the duration and

latencies of ERD and ERS in response to sounds and compare the finding to normal

subjects in hopes of gaining more insight into the disorders at a neuronal level in order to

develop better therapeutic options.

48

Table 1: Inclusion and exclusion criteria

Inclusion criteria: Exclusion criteria:

Age 18-35 Major neurological or psychiatric disorder Head injury Substance abuse Education >12

49

Figure 1: Depiction of experimental setup

50

Figure 2. Average plot of ERP (top) and ERSP (bottom) data collapsed across

speaker location, electrode site, and handedness.

51

Figure 3. Plot or ERSP amplitude as a function of speaker location and time.

Starting from the upper left and moving clockwise frequencies shown are a. alpha

(8-12 Hz), b. lower beta (12-18 Hz), c. upper beta (24-30 Hz) and d. central beta

(18-24 Hz).

52

Figure 4: Plot of ERSP amplitude as a function of time (between 800-1200m ms)

and speaker location. *=p <.05, **=p <.01.

53

Figure 5: Plot of ERSP data for frontal electrode site (FC5, FC6) and speaker

location.

Time (ms)

Left Speaker, FC5

Time (ms)

Left Speaker, FC6

-500 0 500 1000 1500

10

20

30

40

50

Time (ms)

Right Speaker, FC5

Right Speaker, FC6

10

20

30

40

50

-500 0 500 1000 1500

Time (ms)

Figure 7: Plot of ERSP data for frontal electrode sites (FC5, FC6) and speaker location.

54

Figure 6: Plot of ERSP amplitude between 800-1200 ms for frontal electrode sites

(FC5, FC6) and speaker location. *=p<.05, **=p<.01

FC50.0

0.6

0.2

0.4

FC6

Left speakerRight speaker

Electrode site

Figure 6: Plot of ERSP amplitude between 800-1200 ms for frontal electrode sites(FC5, FC6) and speaker location. *=p<.05, **=p<.01.

18-24 Hertz

55

Figure 7: Plot of ERSP data for frontal electrode sites (FC5, FC6), speaker location and handedness.

Time (ms)

Figure 9: Plot of ERSP data for frontal electrodes sites (FC5, FC6), speaker location, andhandedness.

Left Speaker, FC5, Right HandersLeft Speaker, FC5, Left Handers

56

Figure 8: Pearson’s R correlation for degree of handedness and power perturbations.

57

Table 2:

Time of interest Frequency Band Significant Findings General ERD/ERS profile

8-30 Hz Synchronization corresponding to ERP up to 500 ms, followed by transient desynchronization between 8-30 Hz, which lasted up to 800 ms, followed by longstanding synchronization that lasted until the end of data acquisition.


18-24 Hz The left speaker became desynchronized at a later time than the right speaker

a. 800-1200 ms time window

12-18 Hz The right speaker location had ERS earlier than the left speaker location.

b. 800-1200 ms time window

18-24 Hz When sounds were presented from the right speaker, FC5 was greater than FC6; however, when sounds were presented from the left speaker, there was no difference.


8-12 Hz Left-handers showed greater synchronization than right-handers.

Overview of main results.

58

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