Literature DB >> 30402926

Neuronal phase consistency tracks dynamic changes in acoustic spectral regularity.

Adam M Gifford1, Michael R Sperling2, Ashwini Sharan2, Richard J Gorniak3, Ryan B Williams4, Kathryn Davis5, Michael J Kahana1,4, Yale E Cohen1,6.   

Abstract

The brain parses the auditory environment into distinct sounds by identifying those acoustic features in the environment that have common relationships (e.g., spectral regularities) with one another and then grouping together the neuronal representations of these features. Although there is a large literature that tests how the brain tracks spectral regularities that are predictable, it is not known how the auditory system tracks spectral regularities that are not predictable and that change dynamically over time. Furthermore, the contribution of brain regions downstream of the auditory cortex to the coding of spectral regularity is unknown. Here, we addressed these two issues by recording electrocorticographic activity, while human patients listened to tone-burst sequences with dynamically varying spectral regularities, and identified potential neuronal mechanisms of the analysis of spectral regularities throughout the brain. We found that the degree of oscillatory stimulus phase consistency (PC) in multiple neuronal-frequency bands tracked spectral regularity. In particular, PC in the delta-frequency band seemed to be the best indicator of spectral regularity. We also found that these regularity representations existed in multiple regions throughout cortex. This widespread reliable modulation in PC - both in neuronal-frequency space and in cortical space - suggests that phase-based modulations may be a general mechanism for tracking regularity in the auditory system specifically and other sensory systems more generally. Our findings also support a general role for the delta-frequency band in processing the regularity of auditory stimuli.
© 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

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Year:  2018        PMID: 30402926      PMCID: PMC6520206          DOI: 10.1111/ejn.14263

Source DB:  PubMed          Journal:  Eur J Neurosci        ISSN: 0953-816X            Impact factor:   3.386


  80 in total

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Authors:  Wilson A Truccolo; Mingzhou Ding; Kevin H Knuth; Richard Nakamura; Steven L Bressler
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Review 2.  Dynamic predictions: oscillations and synchrony in top-down processing.

Authors:  A K Engel; P Fries; W Singer
Journal:  Nat Rev Neurosci       Date:  2001-10       Impact factor: 34.870

3.  Representation of the temporal envelope of sounds in the human brain.

Authors:  A L Giraud; C Lorenzi; J Ashburner; J Wable; I Johnsrude; R Frackowiak; A Kleinschmidt
Journal:  J Neurophysiol       Date:  2000-09       Impact factor: 2.714

4.  Prefrontal cortex involvement in preattentive auditory deviance detection: neuroimaging and electrophysiological evidence.

Authors:  Christian F Doeller; Bertram Opitz; Axel Mecklinger; Christoph Krick; Wolfgang Reith; Erich Schröger
Journal:  Neuroimage       Date:  2003-10       Impact factor: 6.556

5.  Processing of low-probability sounds by cortical neurons.

Authors:  Nachum Ulanovsky; Liora Las; Israel Nelken
Journal:  Nat Neurosci       Date:  2003-04       Impact factor: 24.884

6.  Temporal patterns of human cortical activity reflect tone sequence structure.

Authors:  A D Patel; E Balaban
Journal:  Nature       Date:  2000-03-02       Impact factor: 49.962

7.  Auditory stream segregation in monkey auditory cortex: effects of frequency separation, presentation rate, and tone duration.

Authors:  Yonatan I Fishman; Joseph C Arezzo; Mitchell Steinschneider
Journal:  J Acoust Soc Am       Date:  2004-09       Impact factor: 1.840

8.  Auditory steady-state responses and word recognition scores in normal-hearing and hearing-impaired adults.

Authors:  Andrew Dimitrijevic; M Sasha John; Terence W Picton
Journal:  Ear Hear       Date:  2004-02       Impact factor: 3.570

9.  Consonance and dissonance of musical chords: neural correlates in auditory cortex of monkeys and humans.

Authors:  Y I Fishman; I O Volkov; M D Noh; P C Garell; H Bakken; J C Arezzo; M A Howard; M Steinschneider
Journal:  J Neurophysiol       Date:  2001-12       Impact factor: 2.714

10.  Efficient design of event-related fMRI experiments using M-sequences.

Authors:  Giedrius T Buracas; Geoffrey M Boynton
Journal:  Neuroimage       Date:  2002-07       Impact factor: 6.556

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