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      Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence

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          Abstract

          Objective: The objectives of this study were: (1) to determine if musicians have a better ability to detect frequency changes under quiet and noisy conditions; (2) to use the acoustic change complex (ACC), a type of electroencephalographic (EEG) response, to understand the neural substrates of musician vs. non-musician difference in frequency change detection abilities.

          Methods: Twenty-four young normal hearing listeners (12 musicians and 12 non-musicians) participated. All participants underwent psychoacoustic frequency detection tests with three types of stimuli: tones (base frequency at 160 Hz) containing frequency changes (Stim 1), tones containing frequency changes masked by low-level noise (Stim 2), and tones containing frequency changes masked by high-level noise (Stim 3). The EEG data were recorded using tones (base frequency at 160 and 1200 Hz, respectively) containing different magnitudes of frequency changes (0, 5, and 50% changes, respectively). The late-latency evoked potential evoked by the onset of the tones (onset LAEP or N1-P2 complex) and that evoked by the frequency change contained in the tone (the acoustic change complex or ACC or N1′-P2′ complex) were analyzed.

          Results: Musicians significantly outperformed non-musicians in all stimulus conditions. The ACC and onset LAEP showed similarities and differences. Increasing the magnitude of frequency change resulted in increased ACC amplitudes. ACC measures were found to be significantly different between musicians (larger P2′ amplitude) and non-musicians for the base frequency of 160 Hz but not 1200 Hz. Although the peak amplitude in the onset LAEP appeared to be larger and latency shorter in musicians than in non-musicians, the difference did not reach statistical significance. The amplitude of the onset LAEP is significantly correlated with that of the ACC for the base frequency of 160 Hz.

          Conclusion: The present study demonstrated that musicians do perform better than non-musicians in detecting frequency changes in quiet and noisy conditions. The ACC and onset LAEP may involve different but overlapping neural mechanisms.

          Significance: This is the first study using the ACC to examine music-training effects. The ACC measures provide an objective tool for documenting musical training effects on frequency detection.

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          Most cited references66

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          Brain structures differ between musicians and non-musicians.

          From an early age, musicians learn complex motor and auditory skills (e.g., the translation of visually perceived musical symbols into motor commands with simultaneous auditory monitoring of output), which they practice extensively from childhood throughout their entire careers. Using a voxel-by-voxel morphometric technique, we found gray matter volume differences in motor, auditory, and visual-spatial brain regions when comparing professional musicians (keyboard players) with a matched group of amateur musicians and non-musicians. Although some of these multiregional differences could be attributable to innate predisposition, we believe they may represent structural adaptations in response to long-term skill acquisition and the repetitive rehearsal of those skills. This hypothesis is supported by the strong association we found between structural differences, musician status, and practice intensity, as well as the wealth of supporting animal data showing structural changes in response to long-term motor training. However, only future experiments can determine the relative contribution of predisposition and practice.
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            A review of the evidence for P2 being an independent component process: age, sleep and modality.

            This article reviews the event-related potential (ERP) literature in relation to the P2 waveform of the human auditory evoked potential. Within the auditory evoked potential, a positive deflection at approximately 150-250 ms is a ubiquitous feature. Unlike other cognitive components such as N1 or the P300, remarkably little has been done to investigate the underlying neurological correlates or significance of this waveform. Indeed until recently, many researchers considered it to be an intrinsic part of the 'vertex potential' complex, involving it and the earlier N1. This review seeks to describe the evidence supportive of P2 being the result of independent processes and highlights several features, such as its persistence from wakefulness into sleep, the general consensus that unlike most other EEG phenomena it increases with age, and the fact that it can be generated using respiratory stimuli.
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              Morphology of Heschl's gyrus reflects enhanced activation in the auditory cortex of musicians.

              Using magnetoencephalography (MEG), we compared the processing of sinusoidal tones in the auditory cortex of 12 non-musicians, 12 professional musicians and 13 amateur musicians. We found neurophysiological and anatomical differences between groups. In professional musicians as compared to non-musicians, the activity evoked in primary auditory cortex 19-30 ms after stimulus onset was 102% larger, and the gray matter volume of the anteromedial portion of Heschl's gyrus was 130% larger. Both quantities were highly correlated with musical aptitude, as measured by psychometric evaluation. These results indicate that both the morphology and neurophysiology of Heschl's gyrus have an essential impact on musical aptitude.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                25 October 2016
                2016
                : 10
                : 464
                Affiliations
                [1] 1Department of Communication Sciences and Disorders, University of Cincinnati Cincinnati, OH, USA
                [2] 2Department of Composition, Musicology, and Theory, College-Conservatory of Music, University of Cincinnati Cincinnati, OH, USA
                [3] 3Department of Pediatrics and Neurology, Cincinnati Children's Hospital Medical Center Cincinnati, OH, USA
                [4] 4Department of Head and Neck Surgery, University of California, Los Angeles Los Angeles, CA, USA
                Author notes

                Edited by: Tuomas Eerola, University of Durham, UK

                Reviewed by: Jed A. Meltzer, Baycrest Hospital, Canada; Lan Shuai, Haskins Laboratories, USA

                *Correspondence: Fawen Zhang fawen.zhang@ 123456uc.edu

                This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2016.00464
                5078501
                27826221
                dc9fed80-db37-4e4b-aaca-344b913886d2
                Copyright © 2016 Liang, Earl, Thompson, Whitaker, Cahn, Xiang, Fu and Zhang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 April 2016
                : 27 September 2016
                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 77, Pages: 14, Words: 10817
                Funding
                Funded by: University of Cincinnati 10.13039/100008102
                Categories
                Neuroscience
                Original Research

                Neurosciences
                frequency change detection,auditory evoked potentials,acoustic change complex,electrophysiology,cortex

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