The Gamma Rhythm

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! THE GAMMA RHYTHM: AN ELECTROPHYSIOLOGICAL MEASURE OF NEURONAL SYNCHRONY BY JULIAN SAMUEL COUNTESS A Thesis Submitted to the Division of Natural Sciences New College of Florida in partial fulfillment of the requirements for the degree Bachelor of Arts in Biological Psychology Under the sponsorship of Dr. Alfred Beulig Sarasota, Florida May, 2011

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! "" Acknowledgements T hanks to Linda Eneix for providing necessary experimental stimuli. Also to Dr. George Rozelle for his time, cooperation and technical assistance at the Mind Spa Mental Fitness Center I'd like to thank my thesis committee for their support. T his undergraduate thesis is dedicated to my family, friends and subjects.

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! """ Table of Contents Acknowledgements ii List of Figures i v Abstract.. v Literature Review .. 1 Methods 2 5 Results 3 0 Discussion .. 3 5 Appendi ces 3 9 References .. 4 1

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! "# List of Figures Figure 1 3 2 Figure 2 3 2 Figure 3 3 3 Figure 4 3 3 Figure 5 3 4 Figure 6. 3 4

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! # The Gamma Rhythm: An Electrophysiological Measure Of Neuronal Synchrony Julian Countess New College of Florida, 2011 ABSTRACT Recent brain research suggests w idespread gamma wavelength neural activity (~40 Hz) has been strongly correlated with cognitive tasks pertaining to perceptual segm entation, mediating attention, sensory integration and object representation Further evidence suggests that gamma activity serves as a neurological substrate for facilitating awareness of objects in one's surr oundings. Musical experience demonstrably changes the brain connectivity and morphology in select regions related to auditory processes and sensorimotor representation, thus enhancing the comprehension and performance of music The current study sought to test the impact of musical experience on evoked gamma band oscillations by comparing the relative strength between groups of musicians and non musicians, during verbal and musical listening tasks. Statistical results indicated a non significant relationshi p of gamma activity between during both verbal and musical tasks within the non musicians (p > 1). The degree of increased gamma activation betwe en tasks for musicians did not meet the criteria for statistical significance (p > 0.06 25 ) Even so, this trend compliments prior findings in the literature. These data suggest the gamma rhythm is relatively more accentuated when utilized to mediate perception of complex auditory representations, such as music, in individuals with musical experience. Dr. Alfred Be ulig Division of Natural Sciences, Biology

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! Literature Review Part 0 Preface Nearly a century ago, electrophysiology and behavioral neuroscience underwent considerable methodological changes, resulting in a paradigm shift within brain science Since the discovery of the electroencephalogram (EEG) by Hans Berger in 1929, neuroscientists have been able to record patterns of output created by electrical brain activity. Subsequently, these oscillations at various frequencies were correlated with certain behav ioral states. As a result, they received considerable attention from the neuroscientific community ( Herrmann FrŸnd, Lenz, 2009). Berger initially identified multiple frequency bands, ranging from 8 30 Hz. The first two patterns were alpha (8 1 2 Hz) and b eta (1 2 30 Hz) bands ( Herrmann FrŸnd, Lenz, 2009). The alpha rhythm occurs during periods of mental inactivity, especially when the subject's eyes are closed (Fisch, 1991). Alpha activity is predominately recorded over the posterior region of the occipita l lobe The EEG activity is attenuated by visual attention or mental activity (Fisch, 1991). The beta rhythm is also present during wakefulness in the fro nto central regions of the head. Additonally, t actile stimulation or contralateral movement blocks be ta activity (Fisch, 1991). Other frequencies, including the gamma frequency range (30 80 Hz) were not identified until later ( Herrmann FrŸnd, Lenz, 2009). See appendix 1 for a list of established frequency bands. The EEG output is created by measuring the changes of electrical activity across the membrane of cortical nerve cells ; this ac tivity is compris ed of mostly inhibitory and excitatory postsynaptic potentials (Fisch, 199 1). The fluctuations of

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! # postsynaptic potentials summate within the cortex to c reate locally sustained current, which can last from 15 to greater than 200 milliseconds. These local field potential s extend from the brain to the scalp, where the electrode measures the activity. The EEG amalgamates the electrophysiological signals creat ed by the brain and neural processes by recording simultaneous activity at multiple sites. Individual neuronal action potentials have smaller potential fields that last approximately 1 millisecond or less and therefore do not significantly impact EEG cortical measurements (Fisch, 199 1). The topic of interest for the current study is exclusively related to the rhythmic gamma activity generated by large populations of cortical cells This rhythm is believed to arise from the complex interaction between the organizing impulses from specific neural structures, such as the thalamus, and cortical neurons (Fisch, 199 1). The attributes of any given waveform include repetition, frequency, amplitude, distri bution, phase, timing, persistence and reactivity. The scope of this thesis pertains to evoked gamma band oscillations and concomitant cognitive processes, with respect to musical experience or a lack thereof. Herrmann et al. (1999) elucidate three types of oscillatory activity, each of which are distinguished by the degree of phase locking to the stimulus. Within the framework, there are spontaneous, induced and evoked oscillations. Spontaneous oscillations are not correlated with the onset of an experime ntal stimulus. Induced oscillations are correlated with a stimulus and are slightly phase locked (Herrmann et al., 1999). Evoked oscillations are completely phase locked across every trial, such

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! $ that the phase remains the same during each stimulus presenta tion (Herrmann et al., 1999). The gamma frequency has remained a topic of interest although its precise function still is not known The distributed synchronous nature of the gamma band presents an interesting opportunity to study the brain by utilizing va rious neuroimaging methodologies. As a result, EEG studies have been employed to measure the temporal dynamics of electrical [gamma band] activity generated during various cognitive (Ruiz et al., 2010, Hameroff, 2010, Kieffaber and Cho, 2010) and physiolog ical processes (Cantero et al., 2003, Zaehle et al., 2010, Tiemann et al., 2010). The current study seeks to test the strength of the correlation between the presence of gamma band activity during musical processes and musical experience. Many recent stud ies have sought to identify the types of interaction that occur between diffuse neuronal populations during various behaviors, such as those implicated in musical processing The EEG is sensitive to voltage changes on the order of millisecon d s For this re ason, EEG methodology has been commonly employed to resolve the temporal activities that take place across the brain Therefore, EEG studies could yield information about the timing of episodically synchronized brain activity in response to given cognitiv e tasks (Ruiz et al., 2010). Many interesting correlations have been established between increased gamma synchrony and cognitive activit ies e.g., handling integrated information within short and long term memory (Keizer et al., 2010). Since EEG measurements are exquisitely sensitive and register most any muscular contraction or superfluous eye movement, analyses on raw data may be

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! % complicated by large amounts of noise (i.e. artifact). The current study utilized au to mated procedures detailed in t he methods section to reject artifact, effectively improving the signal to noise ratio. Other studies have employed the EEG measurements in conjunction with functional magnetic resonance imaging (fMRI) The intent of such st ud ies is to spatially identify t he locus of certain global rhythmic responses (Mulert et al., 2010, Freyer et al., 2009). fMRI affords researchers the ability to spatially resolve anatomical activity by monitorin g blood oxygen level dynamics rather than electrical dynamics. Studies that employ both methods concurrently are especially useful since EEG measurements allow for the fine temporal resolution of electrical activity, while the fMRI measurements facilitate fine spatial or anatomical resolution of active structures. The catalyst fo r the gamma band is believed to emanate from the interior of the corte x, based on findings pertaining to the complex feed forward and feedback loops within the limbic system (Tiesinga and Sejnowski, 2009). Part I The Neural Mechanism The human brain can b e grossly simplified when deconstructed to a series of multiple, converging and diverging parallel loops of circuitry that are effectively capable of globally integrating functionally segregated information on various spatial and temporal scales (Shepher d, 1994). This complex interconnectivity affords humans the ability to rapidly filter or compile various aspects of sensory information into perceptual representations. Information that gives rise to such cognitive processes is routed through the hippocampus a structure which is critically implicated in storing new declarative perceptual representations (Kossyln and

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! & Koenig, 1992 ). Afferent connections from the parahippocampal gyrus and perirhinal cortex project to the entorhinal cortex, then both continue to the hippocampus (Kosslyn and Koenig, 1992). The hippocampus also processes input from other anatomical structures Pathways for such a c tivity include the amygdala; septum and hypothalamus via the fornix; anterior thalamic nucleus and subcallosal area, via the ci ngulum (Kossylyn and Koenig, 1992). Arguably, the projections of hippocampal efferents a re equally complex. The circuit that is created by the afferent and efferent projectsions helps to mediate several important cognitive functions. B alancing dynamic excitatory inputs within the cortex is accomplished by specialized inhibitory interneurons The inhibitory connections between interneurons effect ively ensures the diversity of cortical functions (Buzs‡ki, 2006). The inhibit ory effects of interneurons are catalyzed in several ways within a given cortical circuit. Each has a different result on the input activation and the firing patterns of neighbor ing neurons, which in turn dramatically affects all involved components within the circuit in a nonlinear fashion (Buzs‡ki, 2006). Through these specialized inhibitory cellular functions, the structures within the aforementioned circuit afford the central nervous system a great deal of interconnectivity and processing power. Accordingly, various modes of inhibitory action allow for the stabilization of cortical dynamics, dampening of afferent excitation and segregation of neuronal assemblies. This is accom plished through either feedforward or negative feedback, or by lateral or tonic inhibition (Buzs‡ki, 2006). These inhibitory mechanisms impact the likelihood that a neuronal population will discharge within a temporal window of milliseconds Within this t ime frame, the fast coupling of excitatory and inhibitory

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! populations through interneurons allows for the precision of network wide spike timing, on the order of sub milliseconds (Buzs‡ki, 2006). The precision and uniformity of spike timing (40 100 Hz) is specifically mediated by phasic GABA A receptor mediated GABA inhibition (Buzs‡ki, 2006). Inhibitory postsynaptic potentials are created by an increase in ionic conductance that induces increased permeability of the postsynaptic cell membrane to K + or Cl (Kandel, 1976). The excess synaptically released GABA can also activate other extrasynaptic GABA A receptors within the cortical circuit (Mann and Mody, 2010) which is known as tonic inhibition The widespread inhibitory action then synchronizes the activity of excitatory neurons, which allows for the rhythmic generation of the gamma band. Therefore, t he synchron ous oscillations are created by balancing the effects of tonic inhibition and excitati on of cortical interneurons (Mann and Mody, 2010). The excitatory inhibitory feedback loop between the reticular nucleus of the thalamus and the cortex is believed to be the locus of the gamma rhythm (Cantero et al., 2003). The loop is formed through inter actions between inhibitory connections from the reticular nucleus to the cortex and excitatory inputs from thalamocortical and corticothalamic axons (Mannion and Taylor, 1992). It is the activity of this oscillatory mechanism that sustains neuronal synchro ny throughout the cortex. When considering the nature of these integrative interactions, it is important to account for the way in which the degree of neuronal diversity impacts the segregation and transfer of information within the cortex. Although the co rtical output primarily comprise s pyramidal neurons (Shepherd, 1994) each cortical level contains several

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! ( types of cell types (Buzs‡ki, 2006). Typically, pyramidal cell s bave an apical dendrite with distal branches and a basal dendritic tree Distributed across these dendri tic trees and branches are spiny projections at several levels (Sheperd, 1994). The spines propagate excitatory synaptic inputs, as well as the less occasional inhibitory synaptic input to neighboring cells. Essentially there are two c omponents that influence the dynamical activities of neural networks. Primarily, the intrinsic properties of individual cells within a given cortical region dictate the frequency at which the system resonates Secondarily, the architecture, or interconne ctivity, of the internal network determines how the activity of one neuron affects the rest of the system. Consider the role of an inhibitory interneuron at cortical level V5 which intrinsically oscillates at 40 Hz and has a sub threshold membrane potenti al at roughly 40 mV (Mannion and Taylor, 1992). Such a cell plays a crucial role in coordinating the spike timing of a large population of cells that synchronize at 40 Hz in a context dependent fashion (Singer, 1999). The heterogeneous linkage s generated by specializ ed neuronal morpholog ies allow s for a cellular architecture that facilitates functionally relevant long range corticocortical pathways, which leads to the formation of cortical columns (Buzs‡ki, 2006). The complex cellular foundation of neural networks leads to the high degree of dynamic processes due to the unique features of cortical pyramidal neurons. While the basic achitectonic appearance lacks variety between different cortical regions, cell size and density will still vary base d on systematic connectivity differences and ultimately contribute to the local specialization of neural processes (Buzs‡ki, 2006).

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! ) By extension, the local computation that takes place within a given cortical module is virtually defined by the cellular st ructure (Buzs‡ki, 2006). This is especially relevant when considering the organization of receptive fields and how stimulation results in either excitatory or inhibitory input. To effectively process large amounts of excitatory information, cortical pyrami dal cells must laterally converge. The resulting interconnectivity causes synchronous activity to occur, effectively sync hronizing large amounts of information from distribute d sources (Mannion and Taylor, 1992). Thus, cortical function is defined by the e xtent of excitation and inhibition provided by the input and output connections. The complexity of the computation is directly related to the degree of connectivity to other local and global modules and correlates with anatomical markers of regional cortic al organization (Buzs‡ki, 2006). The gamma rhythm is initiated when a majority of cortical pyramidal cells discharge, then silence at roughly the same time. After the inhibition wears off, a large population of interneurons will fire synchronously; simult aneously, another area within the network is inhibited and silent. The alternation occurs synchronously between silence and discharge, due to the duration of inhibition provided by the interneurons, ultimately resulting in a system wide oscillator (Buzs‡k i, 2006). Thus, the frequency at which the system oscillates is causally determined by the average duration of inhibition provided by interneurons and functions as a neuronal clocking mechanism that is critical to synchronization. Gamma band activity is be lieved to play a critical role in coordinating the system wide transfer and integration of information, which is necessary for a number of complex functions.

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! The gamma band has been correlated with several cognitive processes, resulting in several task spe cific studies ( Herrmann FrŸnd, Lenz, 2009). Some researchers postulat ed that gamma band activity plays a role in developing cortical circuitry (Banerjee and Ellender, 2009); binding of feature information across time, modalities, perceptions or ideas (Mea dor et al., 2002, Senkowski et al., 2006, Widmann et al., 2007); dynamically and functionally linking different stages of conscious awareness (Cantero et al., 2003) and learning (Popescu et al., 2009). Moreover, the gamma band has been implicated in severa l aural processes related to sound localization (Ross, 2008), perception of coherent auditory percepts (Park et al., 2011), and listening to music (Bhattacharya and Petsche, 2001, Bhattacharya et al., 2001). The remainder of this literature review identifi es the postulated roles of the gamma band and their implications. Part II The Gamma Rhythm An interesting problem within behavioral and cognitive neuroscience that is beginning to be addressed is how we experience objects as whole entities, even though t he properties of these objects are encoded and processed in a distributed fashion throughout the brain. In other words, how does the brain segregate and integrate information that is represented by specific neural patterns into one or several distinct obje cts? When one observes a painted landscape t he nervous system facilitates the detection of two distinct objects, figure and ground. This complex activity requires careful selective segregation and integration of sensory information in order to form cohere nt perceptual representations. This is accomplished via the modular and hierarchical organization of receptive fields within the visual system

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! "+ Such processing gives rise to detection of specific attributes like orientation, texture, color, common motion, etc. (Shadlen and Movshon, 19 99). The urgent question is: how does the nervous system mediate this process? This is known as the binding problem (Robertson, 2003). Engel and Singer (2001) reframed the binding problem in terms of feature integration and per ceptual segmentation, both components of this problem also encompass other cognitive computations, including: object recognition, attention, memory formation and recall, motor control, sensorimotor integration, language processing, and logical inference. The assumption is that binding creates a salient internal re presentation by engaging a mechanism that integrates large numbers of specialized inputs. The proposed mechanism for binding information within the nervous system is the rhythmic synchronization of neural discharges within the gamma band (approximately 40 Hz) (Tallon Baudry and Bertrand, 1999). Engel and Singer (2001) posit that the precise neural oscillation represented by gamma band activity provides a temporally sensitive mechanism, which allows for the formation of dynamic cell assemblies. These transie nt formations are based on the selective synchronization that unifies the distributed neural activities. Through this intricate temporal binding, Engel and Singer (2001) contend the human brain is capable of generating and maintaining different states of a wareness. It follows that mediating states of awareness could dramatically impact the rate of complex cognitive computation, e.g. perceptual organization and segmentation. Singer's "temporal correlation hypothesis" (1999) contextualized the binding problem in terms of selective attention with regard to the dynamic coupling and

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! "" integrated responses of neural assemblies. Selective attention is the ability to focus attention on specific objects while ignoring others (Goldstein, 2010). Singer hypothesized that the internal synchronization of cortical discharges present during selective attention may serve as a summated response for joint cognitive processes. The implication is that the basic bottom up process, by which a given system groups together related feat ures during sensory integration, is internally coordinated. Bottom up processes refer to the construction of a perception by analyzing the information that stimulates sensory receptors (Goldstein, 2010) Singer's proposed mechanism is based on the physiol ogical mechanism that invokes the synchronization of discharge times between distant regions within the cortex, or gamma band activity. Experimental studies confirmed that tasks that include visually discerning figure ground, feature binding, or the percep tual switching of bistable patterns and selective attention are associated with transient increases of gamma oscillations (Singer, 1999). Further studies seem to corroborate Singer's hypothesis, demonstrating that gamma band activity is requisite for unifi ed integration of functionally distributed neural processes. Palva et al. (2005) compared phase synchrony within various bandwidths of activity during continuous mental arithmetic tasks that required retention and summation of numerous items in the workin g memory. The data confirmed that an increased task load evoked phase synchrony in the gamma and alpha band, which suggests that cognitive tasks involving working memory are mediated in part by gamma band activity. Haig et al. (1999) found increases in glo bal synchronicity during an auditory recognition task, where gamma activity increased as

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! "# a response to task relevant stimulus presentation. Evidently, gamma band activity becomes augmented while attending to stimuli throughout various sensory modalities. A nother study also linked feature integration to enhanced gamma band activity during multisensory processing (Senkowski et al., 2006). The results of the study indicate that the precision of temporal synchrony between auditory and visual stimuli impact s ear ly cross modal interactions within the cortex, the process which precedes multisensory integration. The implication is that lower temporal precision is indicative of fewer cross modal interactions. The significance of these findings, in addition to the wor k of others (Fell et al., 2003), is underscored by increased activation of local and widespread gamma band activity during tasks that elicit selective attention. This is accomplished through transient functional coupling between distant cortical regions du ring the performance of certain cognitive tasks, including associative learning, mental rotation, conscious recollection and forming coherent perceptual representations (Doesburg et al., 2007a). Doesburg et al. (2007a) experimentally obtained EEG recording s that indicate the coupling and uncoupling synchronous gamma activity in cortical columns encodes figure ground segregation during selective attention in the visual system. These findings seem to confirm that gamma synchronization is present during select ive activation and the functional coupl ing of neural assemblies during a given task. Womelsdorf et al. (2006) elucidated the variable rate at which processing and responding to incoming stimuli occurs specifically during visual processes. The study confir ms that responses to an attended stimulus are enhanced during induced gamma band activity The enhanced responses during gamma band activity are likely

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! "$ to be a result of the improve d signaling of the stimulus within the visual cortex. The change detection paradigm employed in the Womelsdorf et al. (2006) study measured the behavioral reaction time for a subject to indicate a stimulus change, or presentation of an anomalous stimulus. This was demonstrated by change detection tasks involving monkeys where the faster reaction times were correlated with stronger gamma band phase synchronization before and after the change in stimulus, when compared to the response of an irrelevant distractor stimulus. These results indicate that the degree of gamma band synchronization in the visual cortex is an effective predictor of the rate at which visual change detection take s place. Another monkey study indicated that the covariation of firing rates within the primary visual cortex is determined by fluctuations of visual attention and depends on perceptual grouping such that f aster firing ra tes are strongest between neurons that respond to similar features (e.g. contour) of the same object (Roelfsema et al. 2004). It is clear that firing rates of specific cellular assemblies play a central role in the encoding and processing of information within the brain, namely within the hippocampus and thalamus The variable rate at which specific information is processed seems to correlate with fluctuations of gamma activity and seems to corroborate Singer's (1999) hypothesis. The attenuation and accel eration of information processing is evidently impacted by the degree of gamma synchrony, as displayed by the variable reaction times in the Womelsdorf et al. (2006) study. This is especially informative since rate independent modulations in synchrony have been linked to expectation, attention, and response latency; all are processes that adjust the flow of information (Salinas et al. 2001).

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! "% Salinas et al. (2001) propose that the temporal correlations of excitation between pairs of neurons (inhib inhib., excit. excit., or inhib. excit.) present during gamma activation may serve to mediate the strength of a signal, thereby impacting the number of downstream circuits the information reaches. Andino et al. (2005) tested the correlation between response time, expectancy and the power of gamma activity during a simple visuomotor reaction time task. The intent of the study was to test if expectancy, a top down process, could facilitate the rate at which a subject would attend to a stimulus. Top down processing re fers to the knowledge based analysis of incoming sensory information, such as personal knowledge pertaining to prior experiences (Golstein, 2010). The results of the study indicated that neural oscillations present during states of expectancy preceding the stimulus onset play a significant role in mediating the speed of motor actions. Furthermore, it is suggested that these correlations may play a role in mediating spike timing dependent mechanisms responsible for synaptic plasticity, hence enhancing memory of attended stimuli (Salinas et al. 2001). Colgin et al. (2009) also found results that indicate variations of gamma frequency play a role in routing information in the hippocampus. According to Colgin et al. (2009), t he variation between gamma frequenc ies are readily grouped into two components of lower (~25 50 Hz) and higher (~65 140 Hz) gamma band activity respectively The authors propose that the interplay of gamma band activity functionally routes information within the hippocampus, such that var iation s within the gamma band serve as a mechanism for temporally segregating interfering information from distant regions of the brain This would functionally "gate" certain

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! "& information, or allow for the attenuation of irrelevant stimuli within a complex environment. Such a mechanism would be required for a system to selectively attend to a given stimulus in a top down fashion. Other studies have found that similar modulatory gamma activity is present during odor discrimination in the neocortex of rabbit s, monkeys, gerbils, cats and humans (Ruiz et al., 2010). Ruiz et al. (2010) concluded that within a given time frame of synchronized activity amplitude modulation could serve to inform the individual about the content of a given percept as an encoded spa tial pattern during higher cognitive tasks. Kaiser and Lutzenberg (2003) contend that gamma band activity is crucial to bottom up driven perceptions of meaningful information as well as top down guided functions including selective attention, learning and memory. Coupling in the gamma range between the hippocampus and entorhinal cortex was confirmed through invasive recording techniques during the formation of declarative memories ( Herrmann Munk and Engel, 2004). It follows that gamma oscillations are ind icative of the network synchronization necessarily involved in the generation of cognitive representations, not exclusively related to stimulus integration. The significance lies in the fact that gamma activity is consistently present during basic a s well as complex sensory analyses, in addition to cognitive functions, throughout numerous modalities. While the gamma band has been demonstrated to play a role in information processing and binding within the visual system, it has also been implicated in a numb er of other complex sensory processes. When a ball is dropped, sensorimotor integration, expectation and anticipation are required in order to execute a successful

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! "' catch. Visual feedback must be processed in a timely manner to allow for fine motor adjustme nt prior to catching a ball. This activity was monitored during a quantitative EEG (qEEG) coherence study, with the goal of finding some interesting gamma activity during the aforementioned sensorimotor integration task. Teixeira et al. (2010) found a sign ificant difference in gamma coherence between the moment prior to the ball drop and after the ball was dropped. The study indicated increased gamma activity plays an important role in complex motor tasks related to the selection of movements, motor prepara tion, perception and execution of movement, and integration of somatosensory and visual information. Similar conclusions have been drawn from studies related to auditory processes and expectation. Schadow et al. (2009) found phase locked gamma band activit y within the auditory cortex as a response to sequences of six tones. Interestingly, gamma activity was increased during regular tonal intervals but resulted in a negative deflection during irregular intervals. The authors confirm that the gamma band respo nse actually amplifies the neural activity prior to stimulus onset in the auditory modality; gamma band responses seemed to predict tone onsets, even when the tones were omitted. The conclusion was that induced gamma oscillations play a role in developing mental representations of temporally structured tone patterns, whereas evoked activity is sensitive to and modulated by stimulus driven spectral changes. Thus, gamma oscillations potentially reflect the grouping of concurrent sounds, the first stage in aud itory information processing. Zaehle et al. (2010) found that spectral characteristics of aural stimuli are directly reflected in the frequency responses within the auditory cortex and are

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! "( significantly correlated with gamma band activity Fukuda et al. (2010) confirmed that gamma oscillations were augmented in the superior temporal gyrus during the neural processing of acoustic and/or phonetic auditory information. Also, gamma activity was augmented following the articulation of the phone tic syllables and returned to baseline prior to the onset of articulation. Therefore, the magnitude of gamma activity may be related to the volume of auditory processing which varies according to the breadth of auditory stimuli. Perceptual segregation of the auditory scene is an important sensory task, which is mediated by gamma band activity, even in the presence of incoherent stimuli (Doesburg et al. 2007b). Doesburg et al. (2007b) elicited phase synchronous gamma activity by presenting subjects with a c ross modal stimulus involving audiovisual temporal mismatch. The results demonstrated an increase in gamma synchrony during the temporarily incongruent audiovisual presentation, when compared to congruent speech stimuli. The authors interpret this as a mea sure of connectivity between multiple task relevant regions, since both instances of speech perception rely upon a common network of distributed brain areas. The gamma band has been previously studied with the intent of interpreting functional significanc e, with some success. The aforementioned studies have drawn reasonable conclusions about the functional significance and putative mechanisms involving the gamma band during stimuli processing and integration. Some studies have been conducted with the objec tive of comparing fluctuations of gamma activity as individuals process increasingly complex auditory stimuli, including music. In such a study (Ross, 2008), a novel sine wave stimulus induced an auditory steady

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! ") state response (ASSR) in the gamma band. Tha t is to say, adjusting the interaural phase difference of the tone induced a perceptual change from focal to spacious sound (Ross, 2008) The dynamic stimulus elicited resonating gamma band activity that would fluctuate with changes in stimulus frequency. The ASSRs were interpreted with respect to the oscillatory gamma band activity as signals that indicate object representation within the auditory scene The cognitive faculties that are engaged when the visual system processes the painting of a landscape are functionally analogous to those that govern auditory grouping. That is to say, the nervous system dynamically segments the information within the visual scene in the same way that auditory information is parsed within the auditory scene. Then, those bi ts of sensory information are integrated, which results in a single object representation, or perception of a portrait or melody. The physiology and anatomy of the auditory system allows for the associative processing of auditory information, which allows for the perception of harmony (Peretz and Zatorre, 2007). This is readily apparent when considering the integrative auditory processes that become engaged while listening to a musical chord let alone full musical pieces Consonant chords are perceived by detecting pitch relationships of individual tones that overlap in terms of harmonics and fundamental frequencies (Park et al. 2011). When there is not a coherent relationship between these properties within the stimulus, auditory dissociation ensues, resul ting in the perception of a dissonant chord. The underlying assumption is that there are separate perceptual mechanisms for consonant and dissonant chords. Park et al. (2011) found that consonant chords

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! "* stimulate higher amounts of EEG gamma activity than d issonant chords, which was tested in individuals with no musical expertise. These findings suggest that gamma activity represents an acoustically coherent percept that is generated from physical relationships of sounds. Therefore, gamma band activity is in dicative of general perceptual binding of objects into coherent percepts in several modalities. Functionally, the gamma band plays an important physiological role in the central nervous system. By facilitating the self organization of neuronal assemblies into temporal packages of activity ranging from fifteen to thirty milliseconds, the gamma oscillations provide a temporal window that allows pyramidal neurons to integrate excitatory inputs from disparate regions of the brain (Buzs‡ki, 2006). Besides coord inating important processes that mediate neuronal communication, the gamma band establishes an optimal time frame of approximately 200 milliseconds During this period strengthening or weakening synaptic connectivity occurs, via spike timing dependent plas ticity (Buzs‡ki, 2006). Additionally, the gamma band is believed to mediate the construction of complex representations through organizing information from di ffuse neural networks obtained (Buzs‡ki, 2006). As a result, the reviewed literature suggests tha t if this phenomenon does n o t resolve the problem of binding, it at least creates cross modality representations in a versatile temporal code. Part III Music and the brain Music has been defined in a multitude of ways. For the purposes of this study, mus ic is defined as an auditory phenomenon of subjective human experience. That is to say, a phenomenon that is based on a complex set of temporally sensitive cognitive and perceptive operations represented within the central nervous system (AltenmŸller,

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! #+ 2001 ). Each stage of musical processing includes perceptual feature analysis of the numerous implicit characteristics of music, whereby the resulting cognitive activity allows for further associations to be made as the music continues. These are generally segr egated into tonal and temporal relations (Peretz and Zatorre, 2005). As an individual processes music, the activation and interaction of specific regions continuously changes and responds to the music (Birbaumer et al., 1994). Thus, musical processing cannot be precisely localized to individual cellular assemblies within the brain, especially since the processing or production of music is highly variable between individuals (Birbaumer et al., 19 94). Birbaumer et al. (1994) define d a hierarchical model of musical perception that is initiated by the detection of specific auditory characteristics, including key signature, tempo, meter, etc. The association (i.e. binding) of these initial individual percepts allow for the perception of duration, pitch, and the memory or anticipation of rhythm. This model has been confirmed by a neurocognitive study that investigated the components of musical processing. Koelsch (2005) found that preliminary syntactic processes begin 150 to 400 milliseconds after the onset of an aural stimulus. After this a semantic process follows at about 300 to 500 milliseconds. These findings establish a hierarchical, temporally dependent framework for a cognitive mechanism that i nvolves several levels of processing common to both language and music (Peretz and Zatorre, 2007). Thus, the integrative and dynamic nature of musical processing requires a neuronal substrate and accompanying mechanism that can "keep time" with a dynamic s timulus. The gamma band could help the brain manage such a process

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! #" Through experience, musicians generate an extensive musical vocabulary that is employed during musical processing. Conceivably, musicians actively listening to music engage their personal musical repertoire to facilitate the detection and analysis of complex musical structures, such as antecedent and consequent musical phrases (AltenmŸller, 2001). On a more simple level, it is possible that musicians have a greater understanding of the musi cal scale and the tonal relations between given musical structures, which would allow for more complex pitch associations. Further associations can be made within the temporal domain of music, specifically related to the detection of rhythm and implementat ion of metrical organization. Peretz and Zatorre (2005) define rhythm as the segmentation or grouping of an ongoing musical sequence into temporal groups of events based on their duration, and the extraction of an underlying temporal regularity or beat. Be at perception results from the detection of regularity within a given metrical organization, based on the placement of strong and weak beats (Peretz and Zatorre, 2005). Processes relating to temporal grouping and regularity are functionally distinct, as sh own through lesion studies (Peretz and Zatorre, 2005). O ne EEG study confirmed that musical experts are more "deeply" engaged when processing music when compared to less musically trained listeners. This was demonstrated in musicians by which localized "po ckets" of cortical activation occurred in a highly synchronous fashion during music as observed when compared to EEG measurements of non musicians (Birbaumer et al. 1994). The authors believe that the relative increase in widespread cortical activation is indicative of the neurophysiological measure of complex cognitive associations. One study found

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! ## neurophysiological correlates to musical skill acquisition and demonstrated that learning a new instrument results in plastic changes within the brain (Pascual Leone, 2001). Morphological changes have also been shown to occur in individuals after learning to read and play music (Stewart et al., 2003). The results establish that learning an instrument requires procedural and motor learning, facilitated by plastic reorganization of the brain. Such changes likely impact functional and structural components within the sensorimotor cortex and lead to skillful task performance (Stewart et al., 2003) Pantev et al. (2001) specifically found plastic alterations in the somatosensory and auditory cortices of musicians. The study concluded that cortical representations could increase markedly, depend ing on the age musicians begin playing their instrument and the amount of practice they receive. Therefore, musical training changes cortical organization, resulting in a functionally and structurally adapted means for encoding auditory and audiovisual inf ormation (Musacchia et al., 2007). Several specific structural differences in the distribution of gray matter within the cerebral cortex of musicians have been demonstrated throughout various studies. Gaser and Schlaug (2003) found a strong increase in th e volume of gray matter in musicians in the left Heschl's gyrus, inferior temporal gyrus, in addition to regions functionally involved in the processing of visual and motor information. T he authors note some of these structural differences may be attribute d to genetic predispositions or a proclivity for music Even so, the authors attribute their findin gs of structural adaptation s to long term skill acquisition and repetitive rehearsal. MŸnte et al. (2002) concluded that several brain areas differ in size and structure when directly

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! #$ comparing musicians' and non musicians' brains, including the planum temporale, anterior corpus callosum, cerebellum, and primary hand motor cortex. Functional adaptations of auditory representation allow for the specialization of specific aural tasks, including timbre and pitch processing. Over time, the structure of the musician's brain adapts the auditory, motor, visual, and cerebellar regions (Musacchia et al., 2007) The structural differences, including increased amounts of tract myelination (Bengtsson et al., 2005), in musicians' brains are potentially indicative of a functionally adapted basic sensory mechanism used to encode sensory information (Musacchia et al., 2007). One such instance of neural specialization affords s ome musicians the ability to identify and produce any pitch without any external reference point. This phenomenon is known as absolute pitch (Zatorre, 2003). Individuals with absolute pitch exhibit a stronger leftward asymmetry in the planum temporale when compared to non musicians or musicians without absolute pitch (Schlaug et al., 1995). Zatorre et al. (1998) suggest that absolute pitch may depend on the utilization of a specialized network implicated in the retrieval and manipulation of verbal tonal ass ociations, in lieu of unique patterns of cerebral activity. Thus, the representation of individual tones, or the rate at which pitch is processed, may even differ between musicians. Another study found musicians display a left sided dominance through hemi spheric lateralization while processing music (Andrade and Bhattacharya, 2003), which probably indicates a greater degree of analytical processing while interpreting music. Additionally, Andrade and Bhattacharya (2003) report a higher velocity of cerebral blood flow in the left hemisphere of musicians while listening to music

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! #% although right hemisphere lateralization only occurred in non musicians during harmony perception. Evidently, there is a physiological difference within musicians while listening to m usic when compared to non musicians. The study demonstrated relatively greater activation in the secondary auditory cortex, left planum temporale, and dorsolateral region of the prefrontal hemisphere in musicians (Andrade and Bhattacharya, 2003). Furthermo re, Lindenberger et al. (2009) demonstrated cortical phase synchronization between guitarists playing the same short melody, prior to and during the onset of coordinated play. While there was significant synchronization throughout the frontal and central b rain regions, the oscillations were predominately coordinated between theta and delta bands. Bhattacharya and Petsche (2001) studied the impact of musical experience on the degree of gamma phase synchrony during two separate auditory processing tasks. The study required the participants to listen to an excerpt of a text and a piece of classical music. The authors found a significantly higher degree of widespread global gamma phase synchrony in subjects with musical training, when compared to the untrained counterparts, during the musical task. No differences were found between the two groups while listening to the text, nor were there differences in other EEG frequency bands. The high degree of gamma synchronization in musicians [during the musical task] wa s attributed to the relatively greater ability to retrieve musical patterns from their acoustic memory. Intuitively, this skill is a necessary condition for musicians listening to, anticipating, or creating sounds. In a nearly duplicative study, Bhattacharya et al. (2001) found similar results, indicating increased long range gamma activity during the musical condition,

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! #& independent of spectral power Again, no differences were found between the two groups during the resting conditions or while listening to text. The authors found that the degree of spatial synchrony, a measure of signal complexity, was significantly higher in musicians while listening to music. The interpretation was that the increased spatial synchro ny is indicative of the musicians' complex musical memory dynamically binding several features of the complex music into a coherent auditory percep ts The current study was designed to function as a confirmatory study, merely seeking to replicate the find ings of the Bhattacharaya et al. (2001) study. The current study aims to test if musical experience significantly impacts the degree of gamma activity during a complex musical stimulus. The experimental hypothesis is that musical experience will have a po sitive impact on the degree of gamma activity during a complex musical stimulus To test this, the experimental design required the subjects to specifically attend to two complex aural stimuli. Methods Participants Ten males (mean age 20.1 years) were selected from the New College of Florida, a small liberal arts honors college. As such, t he sampling was neither random nor representative of a population. Individuals were screened for musical experience and handedness S creening procedures did not account for substance use and neurological or psychiatric disorders, due to New College of Florida's Institutional Review Board (IRB) policy. Every participant signed a contract of informed consent

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! #' that was approved by the IR B. All of the subjects volunteered to partake in the study; none of the subjects were compensated for participation. Selection Criteria The selection criteria for musicianship were based on previously publ ished literature, in the field (Bhattacharya and P etsche, 2001). The sample included five subjects with musical training (mean age 19.8 years) and five subjects with no such musical training (mean age 20.4 years), all of which were right handed. Musical experience, or lack thereof, was ascertained through a self report musical experience survey (SRMES) [appendix 2 ]. Handedness was also determined via the SRMES. The five most [musically] experienced applicants were selected as musician subjects. Each musician had a minimum of five years musical education (m ean 6.4 years) and practical experience (mean 9 years) on at least one musical instrument. On the self report survey, each musician affirmed familiarity with music theory (mean 5 years) and reading sheet music (mean 7.8 years) Each reported having experie nce as a performing musician (mean 6.8 years). Despite some probable exposure to music, the control subjects were selected because they had no formal musical education or knowledge of music theory, reading sheet music, musical training, or performance. Tho se right handed volunteers who completed and scored the least amount of points on the SRMES and were available to arrange transportation were selected to participate as non musicians. Experimental Design

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! #( The independent varia ble for the current study was the degree of musical experience of participants ; there were two levels [ either musician or non musician ] The dependent variable s (X 1 and X 2 ) corresponded to the recorded EEG measurements, during each task condition. T he level of measure ment for these data was ordinal The results were obtained from a matched pair within subjects design whereby each participant, regardless of group designation, received both verbal and musical listening stimuli. Therefore, a two tailed Wilcoxon signed rank sum test was employed to individually evaluate the changes of gamma activation between tasks when compared to the respective group, due to the small size of each group. Procedure Subjects were prepared for EEG in the conventional fashion at the Mind Spa Mental Fitness Center, under the supervision of Dr. George Rozelle Alcohol was applied to the ears and forehead to remove particulate matter that would increase the impedance of the referential ear electrodes or scalp electrodes. Each participant had his cranium circumference measured to determine the best fitting EEG cap. NuPrep skin cleanser was then applied to the ears and forehead to increase the conductivity at electrode contact sites. The c onditions were consecutively performed in a sequential order; the duration of each condition lasted approximately 120 epochs, or 120,000 msec. The recording process for each subject lasted about six minutes. The aural stimuli were administered by means of over the ear headphones. Condition 0: Baseline measurements Musician Non musician Verbal Listening X 1 X 1 Musical Listening X 2 X 2

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! #) EEG data are dynamic in nature and are extremely difficult to interpret without baseline measurements. Two minutes of spontaneous EEG activity were recorded for each participant with his eyes closed. This initial condition was used to compare the resting measurements to the evoked activity during the following conditions. Additionally, the baseline measurement facilitated artifact rejection of random eye movement and musculofascial tension. Con dition 1: Verbal Listening Task For the first stimulus condition, participants were instructed to listen attentively to a recording of a passage from an unfamiliar article. A middle aged woman monotonously read a two minute portion of an article from the N ational Geographic Magazine, entitled "Common Mushrooms of the United States" (L.C.C. Krieger, May 1920; Volume XXXVII, Number Five). The absence of inflection was intended to limit the perception of dynamic vocal attributes while listening to the text. Condition 2: Music Listening Task For the second stimulus condition, the subjects were instructed to listen attentively to a dynamic musical excerpt that lasted two minutes. The piece was entitled "Mediterranean Suite" and composed by Charles Camilieri of Malta. The musical selection was symphonic and unknown to the subjects. Data Acquisition and Analysis A Lexicor Neurosearch 24 channel qEEG device continuously recorded the spontaneous analogue EEG signals from 19 electrodes at sampling rate of 256 Hz. Th e electrodes were affixed to an elastic cap in the traditional 10 20 International Electrode Placement system and fitted by a trained clinician. All of the electrode

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! #* impedances were measured below 5k! prior to recording. Linked ear referential montage was employed in conjunction with a 60 Hz notch filter, to remove artifact produced by the hardware. Automated artifact rejection was carried out through a qEEG analysis software package called Neuroguide, which was generously provided by the professional clini cian. The autocorrected data were then analyzed using an open source copy of EEGLAB and an institutional copy of MATLAB with homemade routines EEGLAB is a free toolbox extension module for MATLAB, meant for EEG brainwave extrapolation and analysis. Consid er the public pool, where everyone who jumps in creates a series of independent yet overlapping ripples throughout the closed system The EEG creates measurements in such a way that the net activity of each frequency band or jump, is simultaneously record ed, measuring all of the ripples at once across various locations as an aggregate waveform. It would be difficult, if not impossible to determine which swimmer created which ripples, due to constructive and destructive interference. The same is true for EE G measurements, it would be impossible to tell how much a specific bandwith, e.g. gamma, contributed to the net activity without first deconstructing the pool of data. For this reason, independent component analysis was used to deconstruct the aggregate EEG contributions into maximally independent com ponents which allow ed for the estimation of biologically feasible sources of activity during a given behavioral phenomenon (Delorme and Makeig, 2004). In other words, independent component analysis interpol ates the when and where of each swimmer to determine which dive created which wave Independent component analysis is achieved by

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! $+ determining a coordinate time frame or sampling rate, to compute the minimal temporal overlap between data projections, in ef fect decomposing the EEG output to the respective sites of contribution between electrodes (Delorme and Makeig, 2004). Therefore, independent component analysis was used in the current study to compare the spectral power of gamma band activity between the two groups during the two experimental conditions. These data were then manipulated by a fast Fourier transform within EEGLAB to measure the power of each bandwidth of activity by frequency instead of time. Results The quantitative results from the power a nalyses for each task are averaged within groups and depicted by figures one through four for each group. Spectral distribution gamma band activity was nearly identical between groups during verbal task (Figure 1 ). The spectrum indicates the relative spatial location of gamma activity between component pairs of electrodes. The scale is measured in units of power (10*log 10 ( "V 2 /Hz)), where the lower negative numbers indicate a greater degree of activation, as shown in red. Power is also expressed on the y axis for every graph and varies within each group (compare Figures 2 and 3 ). The x axis corresponds to the different bandwidths, which are expressed in Hz The current study specifically focused on out of the lower end of the gamma bandwidth (30 60 Hz), in accordance with the contemporary literature. In agreement with prior studies there were more pronounced differences in the spectral activity between groups during the musical task (Bhattacharya and Petsche, 2001, Bhattacharya et al., 2001 ). Specifically, musicians exhibited a greater degree of

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! $" localized activation over the left frontal and tem poral lobe, when compared to non musicians diffuse activity, during the musical task (Figure 4 ). The non musicians display ed similar patterns of activation, within the group, between the two tasks (Figures 1 and 4 ). Figures 5 and 6 depict the changes in power of each group for the musical task. These graphs display greater amounts of fluctuations for gamma activity in the musicians as a response to the musical stimulus, whereas the non musicians had a relati vely undifferentiated response. Experimental findings were statistically analyzed by way of a Wilcoxon signed rank sum test to compare the differ ences of neuronal power of gamma activity between the two pairs of data within each group The procedure revealed a statistically in significant variance in gamma band activity between the verbal and musical tasks for non musicians (p > 1). The musicians de monstrated increased gamma band activation during the musical task, relative to the verbal task (p > .06 25 ). Although the data failed to approach st rict statistical significance, musical experience seems to positively influence the degree to which gamma ba nd activity is present in musicians during a musical listening ta s k Despite these findings, or lack thereof, musicians displayed a pronounced increase in gamma activation during the musical task as compared to the non musicians. Thus, the observed trend i s consistent with prior findings within the literature (Bhattacharya and Petsche, 2001, Bhattacharya et al., 2001)

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! $# Figure 1 Spectral representation of the average activity of both groups during the verbal task (musicians on the right, non musicians on the left). Figure 2: Fast Fourier transformations of all five musicians were averaged during the verbal task, relating frequency to spectral power or intensity. 10*log 10 ( "V 2 /Hz))

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! $$ Figure 3: Average of the fast Fourier transforms of all five non musicians during the verbal task. Figure 4: Spectral representation of the average activity of both groups during the musical task (musicians on the right, non musicians on the left). 10*log 10 ( "V 2 /Hz))

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! $% Figure 5: Average of the fast Fourier transformations of all five musicians during the musical task. Figure 6: Average the fast Fourier transforms of non musicians' activity during the musical task.

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! $& Discussion Although musical experience should not be misconstrued as musical expertise, this study compared electrophysiological responses to music between individuals with varying levels of musical experience. For the purposes of the current study, musicians were defined as individuals with at least five years of musical practice or education, i ncluding some degree of proficiency with music theory and reading sheet music. The gamma band theoretically mediates top down functions such as attententional processes related auditory grouping, a complex form of sensory integration. The current study att empted to determine if relatively greater amounts gamma band activity in musicians is indicative of deeper levels of processing or complex analyses of musical representations. Musical experience changes the brain of musicians in profound ways, perhaps altering the way they perceive music. This study tried to find correlations between gamma band activity during different modes of aural processing and mus ical experience. These data are indicative of notable differences of neural activity within the gamma band only within the musician group. The goal of the current study was to detect the recruitment of different neural mechanisms, by having participants ac tively listening to two different types of aural stimuli, via recordings from EEG. These tasks required participants to attend to key aural features related to the processing of verbal and musical cues. Similar tasks were used in other EEG studies to evoke gamma oscillations (Birbaumer et al., 1994, Bhattacharya and Petsche, 2001), demonstrating a correlation between complex integrative sensory processes and gamma band activity. Although the gamma band is believed to coordinate the flow of internal informat ion related to numerous sensory processes, the precise role in musical processing remains unclear.

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! $' In an effort to maximize the effect of attentional processes and minimize artifact, subjects were instructed to attentively listen to the stimuli and encoura ged to "keep time" with the musical stimulus with their eyes closed. These measures were also an attempt to attenuate any transient interference from muscular movement or other cognitive processes and induce task specific attentional processing in each con dition. Interpreting significant synchronies within the brain is not a straightforward task, especially when using scalp recordings; drawing conclusions from such data becomes difficult since spurious synchronies may arise from volume conduction or referen ce effects (Herrmann et al., 1999). Certain difficulties were encountered while attempting to execute the intended experimental design. Subjects were full time students recruited from a small honors college in Florida, decreasing the representativeness of the sample to the population. This study was conducted off campus during the academic year, which restricted the sample size from which participants could be recruited. Additionally, the owner of the EEG machine had time constraints of a full time practice which required the sample size to downsize from sixteen to ten subjects. Most studies of this nature have at least 20 participants. This made collecting comparable data a challenge, considering each group was comprised of five subjects. Due to a stipulat ion of New College of Florida 's Institutional Review Board, knowledge of the participants' prior mental health and/or substance use could not be obtained. Ideally, subjects veritably had no history of mental disorders or illicit drug use. Participants had their EEG measurements take n on weekends during the late morning or early afternoons potentially coinciding with hangovers As a result, the sobriety or alertness of the

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! $( participants could not be guaranteed, especially since they were encouraged to abstai n from caffeine use prior to their session. Potential confounds might have arisen from the influence of psychiatric medication, nicotine, caffeine, partyin g, etc Additionally, those musical participants were not full time musicians, but rather individual s enrolled full time at an academic institution, which may have reduced the variance of gamma band activity within the experimental group. The self report survey was used to assess the individual level of musical aptitude, but it is recommended that furthe r studies recruit musician participants who have an extensive background in musical performance or pedagogy, i.e. musical expertise. Perhaps a rigorous method for selecting expert participants may enhance the chances of collecting significant data. An int eresting route for future investigations would be to consider impact of handedness or hemispheric laterality on gamma band activity between musicians. Additionally, a study may yield interesting results if subjects have absolute pitch and/or expertise, suc h that professional performers and conductors are included within the sample. In summation, the extent to which experience induced cortical reorganization impacts gamma band activity has recently been established ( MŸnte et al., 2002); yet the varying degre e of synchrony between musicians and non musicians during musical processing is still an active topic research. Currently, the literature suggests the gamma rhythm is implicated in the bottom up formation of cognitive representations that arise from sensor y integration (Singer, 1999). Furthermore, the gamma band has been correlated to top down functions related to regulating the rate of information flow (Salinas et al., 2001), routing information (Colgin et al., 2009)

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! $) and mediating selective attention (Sing er, 1999 and Fell et al., 2003). Although the current study did not verify that synchronous gamma band activity is an electrophysiological signature of integrative functions related to complex musical processes, the empirical data confirmed increased gamma rhythms are concomitant with musical listening in experienced musicians. Further studies should employ multiple imaging approaches to explore the impact of musical experience on the neural mechanisms involved auditory processing within the brain.

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! $* Appendix 1 List of well established frequency bands Frequency : Name : 0 4 Hz Delta 4 8 Hz Theta 8 12 Hz Alpha 12 30 Hz Beta 30 80 Hz Gamma Herrmann et al. (1999)

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! %+ Appendix 2 Self Report Musical Experience Survey Please complete this survey to the best of your ability and keep your answers brief. The following information is asked to help decide whether or not you qualify to participate in this research study. Please read this carefully. If you do not understand anything, ask the person in charge of the study 1) What is your gender? M F 2) What is your age? _____ 3) Which is your dominant hand? L R 4) Do you have any siblings or parents who are left handed? Y N 5) How many years do you have of musical education? 0 1 2 3 4 5 6 7 8 9 10+ 6) How many year s have you played music? 0 1 2 3 4 5 6 7 8 9 10+ 7) Are you familiar with music theory? Y N If yes, how many years experience do you have? _____ 8) Are you familiar with reading music? Y N If yes, how many years experience do you have? _____ 9) Do you have experience as a performing musician? Y N If yes, how many years experience do you have? _____ 10) Are you able to provide your own transportation to the research facility? (please refer to the address below) Y N Availability: If you have a day/time preference, please indicate it below Monday Tuesday Wednesday Thursday Friday

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! %" References AltenmŸller, E. O. (2001). How many music centers are in the brain? Annals of the New York Academy of Sciences 930 (0), 273 80. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11458834. Andino, S. G., Michel, C., Thut, G., Landis, T., & de Peralta, R. G. (2005). Prediction of Response Speed by Anticipatory High Frequency (Gamma Band) Oscillations in the Human Br ain. Human Brain Mapping 24 50 58. Andrade, P. E., & Bhattacharya, J. (2003). Brain tuned to music. Journal of the Royal Society of Medicine 96 (6), 284 7. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=539509&tool=pmce ntrez&ren dertype=abstract. Banerjee, A., & Ellender, T. J. (2009). Oscillations in the developing cortex: a mechanism for establishing and synchronizing an early network? The Journal of neuroscience : the official journal of the Society for Neuroscience 29 (48), 15 029 30. doi: 10.1523/JNEUROSCI.4567 09.2009. Bengtsson, S. L., Nagy, Z., Skare, S., Forsman, L., Forssberg, H., & UllŽn, F. (2005). Extensive piano practicing has regionally specific effects on white matter development. Nature neuroscience 8 (9), 1148 50. doi: 10.1038/nn1516. Bhattacharya, J., & Petsche, H. (2001). Musicians and the gamma band: a secret affair? Neuroreport (Vol. 12, pp. 371 374). Bhattacharya, J., Petsche, H., & Pereda, E. (2001). Long Range Synchrony in the gamma Band: Role in Music Percep tion. The Journal of Neuroscience 21 (16), 6329 6337.

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! %# Birbaumer, N., Lutzenberger, W, Rau, H., Choi, I., & Braun, C. (1994). Perception of Music and Dimensional Complexity of Brain Activity. International Journal Of Bifurcation And Chaos Buzs‡ki, G. (2006). Rhythms of the brain Oxford : Oxford University Press. Cantero, J., Atienza, M., Madsen, J., & Stickgold, R. (2003). Gamma EEG dynamics in neocortex and hippocampus during human wakefulness and sleep. NeuroImage 22 1271 1280. Colgin, L. L., Den ninger, T., Fyhn, M., Hafting, T., Bonnevie, T., Jensen, O., et al. (2009). Frequency of gamma oscillations routes flow of information in the hippocampus. Nature 462 (7271), 353 7. doi: 10.1038/nature08573. Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single trial EEG dynamics including independent component analysis. Journal of neuroscience methods 134 (1), 9 21. doi: 10.1016/j.jneumeth.2003.10.009. Doesburg, S., Emberson, L., Rahi, A., Cameron, D., & Ward, L. (2007b). A synchrony from synchrony: long range gamma0band neural synchrony accompanies perception of audiovisual speech asynchrony. Experimental Brain Research 185 11 20. Doesburg, S., Roggeveen, A., Kitajo, K., & Ward, L. (2007a). Large scale gamma band phase synch ronization and selective attention. Cerebral Cortex 18 386 396. Engel, A., Fries, P., & Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top down processing. Nature 2 704 716.

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! %$ Engel, A., & Singer, W. (2001). Temporal binding and the neural correlates of sensory awareness. Trends in Cognitive Sciences 5 (1), 16 25. Fell, J., Fernandez, G., Klaver, P., Elger, C., & Fries, P. (2003). Is synchronized neuronal gamma activity relevant for selective attention?. Brain Research Reviews 42 265 272. Fisch, B. J., & Spehlmann, R. (1991). Spehlmann's EEG primer (2nd rev. and enl. ed.). Amsterdam: Elsevier. Freyer, F., Becker, R., Anami, K., Curio, G., Villringer, A., & Ritter, P. (2009). Ultrahigh frequency EEG during fMRI: pushing the limi ts of imaging artifact correction. NeuroImage 48 (1), 94 108. Elsevier Inc. doi: 10.1016/j.neuroimage.2009.06.022. Fukuda, M., Rothermel, R., Juh‡sz, C., Nishida, M., Sood, S., & Asano, E. (2010). Cortical gamma oscillations modulated by listening and over t repetition of phonemes. NeuroImage 49 (3), 2735 45. Elsevier Inc. doi: 10.1016/j.neuroimage.2009.10.047. Gaser, C., & Schlaug, G. (2003). Brain structures differ between musicians and non musicians. The Journal of neuroscience : the official journal of t he Society for Neuroscience 23 (27), 9240 5. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/14534258. Goldstein, E. Bruce. Sensation and perception 8th ed. Belmont, Calif.: Wadsworth Cengage Learning, 2010. Print.

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! %% Haig, a R., Gordon, E., Wright, J. J ., Meares, R. a, & Bahramali, H. (2000). Synchronous cortical gamma band activity in task relevant cognition. Neuroreport 11 (4), 669 75. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10757498. Hameroff, S. (2010). The "conscious pilot" dendritic synch rony moves through the brain to mediate consciousness. Journal of biological physics 36 (1), 71 93. doi: 10.1007/s10867 009 9148 x. Herrmann, C. S., FrŸnd, I., & Lenz, D. (2010). Human gamma band activity: a review on cognitive and behavioral correlates an d network models. Neuroscience and biobehavioral reviews 34 (7), 981 92. doi: 10.1016/j.neubiorev.2009.09.001. Herrmann, C. S., Grigutsch, M., & Busch, N. A. (1999). EEG oscillations and wavelet analysis, (1929), 1 39. Herrmann, C., Munk, M., & Engel, A. (2004). Cognitive functions of gamma band activity: memory match and utilization. Trends in Cognitive Sciences 8 (8), 347 355. Kaiser, J., & Lutzenberger, Werner. (2003). Induced gamma band activity and human brain function. The Neuroscientist : a review j ournal bringing neurobiology, neurology and psychiatry 9 (6), 475 84. doi: 10.1177/1073858403259137. Kandel, E. R. (1976). Cellular basis of behavior an introduction to behavioral neurobiology San Francisco: W H Freeman.

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! %& Keizer, A. W., Verment, R. S., & Hommel, B. (2010). Enhancing cognitive control through neurofeedback: a role of gamma band activity in managing episodic retrieval. NeuroImage 49 (4), 3404 13. doi: 10.1016/j.neuroimage.2009.11.023. Kieffaber, P., & Cho, R. (2010). Induced cortical gamma b and oscillations reflect cognitive control elicited by implicit probability cues in the preparing to overcome prepotency (POP) task. Cognitive, Affective, & Behavioral Neuroscience 10 (4), 431 440. Koelsch, S. (2005). Neural substrates of processing syntax and semantics in music. Current opinion in neurobiology 15 (2), 207 12. doi: 10.1016/j.conb.2005.03.005. Kosslyn, S. M., & Koenig, O. (1992). Wet mind: the new cognitive neuroscience New York: Fre e Press. Lindenberger, U., Li, S., Gruber, W., & Muller, V. (2009). Brains swinging in concert: cortical phase synchronization while playing guitar. BMC Neuroscience 10 (22), 1 12. Mann, E. O., & Mody, I. (2010). Control of hippocampal gamma oscillation frequency by tonic inhibition and excitation of interneurons. Nature neuroscience 13 (2), 205 12. Nature Publishing Group. doi: 10.1038/nn.2464. Meador, K. J., Ray, P. G., Echauz, J. R., Loring, D. W., & Vachtsevanos, G. J. (2002). Gamma coherence and conscious perception. Neurology 59 (6), 847 54. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/12297565. Mulert, C., Leicht, G., Hepp, P., Kirsch, V., Karch, S., Pogarell, O., et al. (2010). Single trial coupling of the gamma band response and the corresponding BOLD

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! %' signal. NeuroImage 49 (3), 2238 47. Elsevier Inc. doi: 10.1016/j.neuroimage.2009.10.058. Musacchia, G., Sams, M., Skoe, E., & Kraus, N. (2007). Musicians have enhanced subcortical auditor y and audiovisual processing of speech and music. Proceedings of the National Academy of Sciences of the United States of America 104 (40), 15894 8. doi: 10.1073/pnas.0701498104. MŸnte, T. F., AltenmŸller, E., & JŠncke, L. (2002). The musician# s brain as a model of neuroplasticity. Nature reviews. Neuroscience 3 (6), 473 8. doi: 10.1038/nrn843. Palva, J. M., Palva, S., & Kaila, K. (2005). Phase synchrony among neuronal oscillations in the human cortex. The Journal of neuroscience : the official journal of t he Society for Neuroscience 25 (15), 3962 72. doi: 10.1523/JNEUROSCI.4250 04.2005. Pantev, C., Engelien, a, Candia, V., & Elbert, T. (2001). Representational cortex in musicians. Plastic alterations in response to musical practice. Annals of the New York A cademy of Sciences 930 300 14. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11458837. Park, J. Y., Park, H., Kim, J. I., & Park, H. J. (2011). Consonant chords stimulate higher EEG gamma activity than dissonant chords. Neuroscience letters 488 (1), 101 5. Elsevier Ireland Ltd. doi: 10.1016/j.neulet.2010.11.011. Pascual leone, A. (2001). The Brain That Plays Music and Is Changed by it. Annals of the New York Academy of Sciences June (930), 315 329.

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! %( Peretz, I., & Zatorre, R. (2007). The cognitive neuro science of music (Repr. ed.). Oxford [u.a.: Univ. Press. Peretz, I., & Zatorre, R. J. (2005). Brain organization for music processing. Annual review of psychology 56 89 114. doi: 10.1146/annurev.psych.56.091103.070225. Popescu, A. T., Popa, D., & ParŽ, D (2009). Coherent gamma oscillations couple the amygdala and striatum during learning. Nature neuroscience 12 (6), 801 7. doi: 10.1038/nn.2305. Robertson, L. (2003). Vinding, Spatial attention and perceptual awareness. NATURE REVIEWS 4 93 102. Roelfsema, P. R., Lamme, V. a F., & Spekreijse, H. (2004). Synchrony and covariation of firing rates in the primary visual cortex during contour grouping. Nature neuroscience 7 (9), 982 91. doi: 10.1038/nn1304. Ross, B. (2008). A novel type of auditory res ponses: temporal dynamics of 40 Hz steady state responses induced by changes in sound localization. Journal of neurophysiology 100 (3), 1265 77. doi: 10.1152/jn.00048.2008. Ruiz, Y., Pockett, S., Freeman, W. J., Gonzalez, E., & Li, G. (2010). A method to s tudy global spatial patterns related to sensory perception in scalp EEG. Journal of neuroscience methods 191 (1), 110 118. Elsevier B.V. doi: 10.1016/j.jneumeth.2010.05.021. Salinas, E., Sejnowski, T. J., Studies, B., & Jolla, L. (2001). HHMI Author Manusc ript CORRELATED NEURONAL ACTIVITY AND THE FLOW OF NEURAL INFORMATION. Direct 2 (8), 539 550.

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! %) Schadow, J., Dettler, N., Paramei, G. V., Lenz, D., FrŸnd, I., Sabel, B. a, et al. (2009). Impairments of Gestalt perception in the intact hemifield of hemianopic patients are reflected in gamma band EEG activity. Neuropsychologia 47 (2), 556 68. doi: 10.1016/j.neuropsychologia.2008.10.012. Schadow, J., Lenz, D., Dettler, N., FrŸnd, I., & Herrmann, C. S. (2009). Early gamma band responses reflect anticipatory top do wn modulation in the auditory cortex. NeuroImage 47 (2), 651 8. Elsevier Inc. doi: 10.1016/j.neuroimage.2009.04.074. Schlaug, G. (2003). The Brain of Musicians. In I. Peretz & R. J. Zatorre (Eds.), The Cognitive Neuroscience of Music (pp. 281 299). Oxford: Oxford University Press. Schlaug, G., JŠncke, L., Huang, Y., & Steinmetz, H. (1995). In Vivo Evidence of Structural Brain Asymmetry in Musicians. Senkowski, D., Talsma, D., Grigutsch, M., Herrmann, C., & Woldorff, M. (2006). Good ti mes for multisensory integration: Effects of precision of temporal synchrony as revealed by gamma band oscillations. Neuropsychologia 45 561 571. Shadlen, M. N., & Movshon, J. a. (1999). Synchrony unbound: a critical evaluation of the temporal binding hyp othesis. Neuron 24 (1), 67 77, 111 25. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10677027. Shepherd, G. (1994). Neurobiology (3 ed.). Oxford : Oxford University Press. Singer, W. (1999). Neuronal Synchrony : A Versatile Code for the Definition of Re lations ? Most of our knowledge about the functional organization, 24 49 65.

PAGE 54

! %* Stewart, Lauren, Henson, Rik, Kampe, Knut, Walsh, Vincent, Turner, Robert and Frith, U. (2003). Brain changes after learning to read and play music. Brain Tallon Baudry, C., & B ertrand, O. (1999). Oscillatory gamma activity in humans and its role in object representatio. Trends in Cognitive Sciences 3 (4), 151 162. Taylor, J. G., & Mannion, C. L. (1992). Coupled oscillating neurons London: Springer Verlag. Teixeira, S., Velasques, B., Ribeiro, P., Machado, S., Cunha, M., Domingues, C. A., et al. (2010). Gamma band oscillations in fronto central areas during performance of a sensorimotor integration task: A qEEG coherence study. Neuroscience Letters 483 114 117. Tiemann, L., Schulz, E., Gross, J., & Ploner, M. (2010). Gamma oscillations as a neuronal correlate of the attentional effects of pain. Pain 150 (2), 302 8. International Association for the Study of Pain. doi: 10.1016/j.pain.2010.05.014. Tiesinga, P., & Sejnowski, T. J. (2009). Cortical enlightenment: are attentional gamma oscillations driven by ING or PING? Neuron 63 (6), 727 32. Elsevier Inc. doi: 10.1016/j.neuron.2009.09.009. Varela, F., Lachaux, J. philippe, Rodriguez, E., & Martinerie, J. (2001). The brainweb: Phase large scale integration. Neuroscience 2 (April). Widman, A., Gruber, T., Kujala, T., Tervaniemi, M., & Schroger, E. (2007). Binding symbols and sounds: evidence from event related oscillatory gamma band activity. Cerebral Cortex 17 2696 2702.

PAGE 55

! &+ Womelsdorf, T., Fries, P., Pitra, P., & Desimone, R. (2006). Gamma band synchronization in visual cortex predicts speed of change detection. Nature 439 (9), 733 736. Zaehle, T., & Lenz, D. (2010). Resonance phenomena in the human auditory cortex: individual resonance frequencies of the cerebral cortex determine electrophysiological responses. Experimental Brain Research 203 629 635. Zatorre, R. J. (2003). Absolute pitch: a model for understanding the influence of genes and development on neural and cognitiv e function. Nature neuroscience 6 (7), 692 5. doi: 10.1038/nn1085. Zatorre, R. J., Belin, P., & Penhune, V. B. (2002). Structure and function of auditory cortex: music and speech. Trends in cognitive sciences 6 (1), 37 46. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11849614. Zatorre, R., Perry, D., Beckett, C., Westbury, C., & Evans, A. (1998). Functional anatomy of musical processing in listeners with absolute pitch and relative pitch. PNAS 95 3172 3177. !