Literature DB >> 23007172

Power and phase properties of oscillatory neural responses in the presence of background activity.

Nai Ding1, Jonathan Z Simon.   

Abstract

Natural sensory inputs, such as speech and music, are often rhythmic. Recent studies have consistently demonstrated that these rhythmic stimuli cause the phase of oscillatory, i.e. rhythmic, neural activity, recorded as local field potential (LFP), electroencephalography (EEG) or magnetoencephalography (MEG), to synchronize with the stimulus. This phase synchronization, when not accompanied by any increase of response power, has been hypothesized to be the result of phase resetting of ongoing, spontaneous, neural oscillations measurable by LFP, EEG, or MEG. In this article, however, we argue that this same phenomenon can be easily explained without any phase resetting, and where the stimulus-synchronized activity is generated independently of background neural oscillations. It is demonstrated with a simple (but general) stochastic model that, purely due to statistical properties, phase synchronization, as measured by 'inter-trial phase coherence', is much more sensitive to stimulus-synchronized neural activity than is power. These results question the usefulness of analyzing the power and phase of stimulus-synchronized activity as separate and complementary measures; particularly in the case of attempting to demonstrate whether stimulus-synchronized neural activity is generated by phase resetting of ongoing neural oscillations.

Entities:  

Mesh:

Year:  2012        PMID: 23007172      PMCID: PMC3543520          DOI: 10.1007/s10827-012-0424-6

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  8 in total

1.  Neural dynamics and the fundamental mechanisms of event-related brain potentials.

Authors:  Ankoor S Shah; Steven L Bressler; Kevin H Knuth; Mingzhou Ding; Ashesh D Mehta; Istvan Ulbert; Charles E Schroeder
Journal:  Cereb Cortex       Date:  2004-03-28       Impact factor: 5.357

2.  Discrimination of speech stimuli based on neuronal response phase patterns depends on acoustics but not comprehension.

Authors:  Mary F Howard; David Poeppel
Journal:  J Neurophysiol       Date:  2010-05-19       Impact factor: 2.714

3.  Detection of synchronized oscillations in the electroencephalogram: an evaluation of methods.

Authors:  Nick Yeung; Rafal Bogacz; Clay B Holroyd; Jonathan D Cohen
Journal:  Psychophysiology       Date:  2004-11       Impact factor: 4.016

Review 4.  Are event-related potential components generated by phase resetting of brain oscillations? A critical discussion.

Authors:  P Sauseng; W Klimesch; W R Gruber; S Hanslmayr; R Freunberger; M Doppelmayr
Journal:  Neuroscience       Date:  2007-04-24       Impact factor: 3.590

5.  Phase patterns of neuronal responses reliably discriminate speech in human auditory cortex.

Authors:  Huan Luo; David Poeppel
Journal:  Neuron       Date:  2007-06-21       Impact factor: 17.173

6.  Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns.

Authors:  Christoph Kayser; Marcelo A Montemurro; Nikos K Logothetis; Stefano Panzeri
Journal:  Neuron       Date:  2009-02-26       Impact factor: 17.173

7.  Entrainment of neuronal oscillations as a mechanism of attentional selection.

Authors:  Peter Lakatos; George Karmos; Ashesh D Mehta; Istvan Ulbert; Charles E Schroeder
Journal:  Science       Date:  2008-04-04       Impact factor: 47.728

8.  Low-frequency neuronal oscillations as instruments of sensory selection.

Authors:  Charles E Schroeder; Peter Lakatos
Journal:  Trends Neurosci       Date:  2008-11-13       Impact factor: 13.837

  8 in total
  12 in total

1.  Encoding of natural sounds by variance of the cortical local field potential.

Authors:  Nai Ding; Jonathan Z Simon; Shihab A Shamma; Stephen V David
Journal:  J Neurophysiol       Date:  2016-02-24       Impact factor: 2.714

Review 2.  Phase-resetting as a tool of information transmission.

Authors:  Carmen C Canavier
Journal:  Curr Opin Neurobiol       Date:  2014-12-17       Impact factor: 6.627

3.  40 Hz Auditory Steady-State Response Is a Pharmacodynamic Biomarker for Cortical NMDA Receptors.

Authors:  Digavalli V Sivarao; Ping Chen; Arun Senapati; Yili Yang; Alda Fernandes; Yulia Benitex; Valerie Whiterock; Yu-Wen Li; Michael K Ahlijanian
Journal:  Neuropsychopharmacology       Date:  2016-02-03       Impact factor: 7.853

4.  Neural oscillations are a start toward understanding brain activity rather than the end.

Authors:  Keith B Doelling; M Florencia Assaneo
Journal:  PLoS Biol       Date:  2021-05-04       Impact factor: 8.029

Review 5.  Cortical entrainment to continuous speech: functional roles and interpretations.

Authors:  Nai Ding; Jonathan Z Simon
Journal:  Front Hum Neurosci       Date:  2014-05-28       Impact factor: 3.169

Review 6.  A Role of Phase-Resetting in Coordinating Large Scale Neural Networks During Attention and Goal-Directed Behavior.

Authors:  Benjamin Voloh; Thilo Womelsdorf
Journal:  Front Syst Neurosci       Date:  2016-03-08

Review 7.  The Involvement of Endogenous Neural Oscillations in the Processing of Rhythmic Input: More Than a Regular Repetition of Evoked Neural Responses.

Authors:  Benedikt Zoefel; Sanne Ten Oever; Alexander T Sack
Journal:  Front Neurosci       Date:  2018-03-07       Impact factor: 4.677

8.  No changes in parieto-occipital alpha during neural phase locking to visual quasi-periodic theta-, alpha-, and beta-band stimulation.

Authors:  Christian Keitel; Christopher S Y Benwell; Gregor Thut; Joachim Gross
Journal:  Eur J Neurosci       Date:  2018-08-03       Impact factor: 3.386

9.  Learning hierarchical sequence representations across human cortex and hippocampus.

Authors:  Simon Henin; Nicholas B Turk-Browne; Daniel Friedman; Anli Liu; Patricia Dugan; Adeen Flinker; Werner Doyle; Orrin Devinsky; Lucia Melloni
Journal:  Sci Adv       Date:  2021-02-19       Impact factor: 14.136

10.  Phase of firing coding of learning variables across the fronto-striatal network during feature-based learning.

Authors:  Benjamin Voloh; Mariann Oemisch; Thilo Womelsdorf
Journal:  Nat Commun       Date:  2020-09-16       Impact factor: 14.919

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