Literature DB >> 33768383

Slow Resting State Fluctuations Enhance Neuronal and Behavioral Responses to Looming Sounds.

B Sancristóbal1,2,3, F Ferri4, A Longtin5,6, M G Perrucci4, G L Romani4, G Northoff7,6,8,9.   

Abstract

We investigate both experimentally and using a computational model how the power of the electroencephalogram (EEG) recorded in human subjects tracks the presentation of sounds with acoustic intensities that increase exponentially (looming) or remain constant (flat). We focus on the link between this EEG tracking response, behavioral reaction times and the time scale of fluctuations in the resting state, which show considerable inter-subject variability. Looming sounds are shown to generally elicit a sustained power increase in the alpha and beta frequency bands. In contrast, flat sounds only elicit a transient upsurge at frequencies ranging from 7 to 45 Hz. Likewise, reaction times (RTs) in an audio-tactile task at different latencies from sound onset also present significant differences between sound types. RTs decrease with increasing looming intensities, i.e. as the sense of urgency increases, but remain constant with stationary flat intensities. We define the reaction time variation or "gain" during looming sound presentation, and show that higher RT gains are associated with stronger correlations between EEG power responses and sound intensity. Higher RT gain further entails higher relative power differences between loom and flat in the alpha and beta bands. The full-width-at-half-maximum of the autocorrelation function of the eyes-closed resting state EEG also increases with RT gain. The effects are topographically located over the central and frontal electrodes. A computational model reveals that the increase in stimulus-response correlation in subjects with slower resting state fluctuations is expected when EEG power fluctuations at each electrode and in a given band are viewed as simple coupled low-pass filtered noise processes jointly driven by the sound intensity. The model assumes that the strength of stimulus-power coupling is proportional to RT gain in different coupling scenarios, suggesting a mechanism by which slower resting state fluctuations enhance EEG response and shorten reaction times.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  EEG; Inter-subject variability; Looming and flat sound; Multisensory integration; Ornstein–Uhlenbeck process; Resting state

Mesh:

Year:  2021        PMID: 33768383     DOI: 10.1007/s10548-021-00826-4

Source DB:  PubMed          Journal:  Brain Topogr        ISSN: 0896-0267            Impact factor:   3.020


  41 in total

1.  A method for removing imaging artifact from continuous EEG recorded during functional MRI.

Authors:  P J Allen; O Josephs; R Turner
Journal:  Neuroimage       Date:  2000-08       Impact factor: 6.556

2.  A neural mass model for MEG/EEG: coupling and neuronal dynamics.

Authors:  Olivier David; Karl J Friston
Journal:  Neuroimage       Date:  2003-11       Impact factor: 6.556

3.  The subjective duration of ramped and damped sounds.

Authors:  Massimo Grassi; Christopher J Darwin
Journal:  Percept Psychophys       Date:  2006-11

4.  Neural measures of individual differences in selecting and tracking multiple moving objects.

Authors:  Trafton Drew; Edward K Vogel
Journal:  J Neurosci       Date:  2008-04-16       Impact factor: 6.167

5.  Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses.

Authors:  A Arieli; A Sterkin; A Grinvald; A Aertsen
Journal:  Science       Date:  1996-09-27       Impact factor: 47.728

6.  Frontal cortex selectively overrides auditory processing to bias perception for looming sonic motion.

Authors:  Gavin M Bidelman; Mark H Myers
Journal:  Brain Res       Date:  2019-10-10       Impact factor: 3.252

7.  Chronux: a platform for analyzing neural signals.

Authors:  Hemant Bokil; Peter Andrews; Jayant E Kulkarni; Samar Mehta; Partha P Mitra
Journal:  J Neurosci Methods       Date:  2010-07-15       Impact factor: 2.390

8.  Neural population coding of sound level adapts to stimulus statistics.

Authors:  Isabel Dean; Nicol S Harper; David McAlpine
Journal:  Nat Neurosci       Date:  2005-11-06       Impact factor: 24.884

9.  Dynamic sounds capture the boundaries of peripersonal space representation in humans.

Authors:  Elisa Canzoneri; Elisa Magosso; Andrea Serino
Journal:  PLoS One       Date:  2012-09-28       Impact factor: 3.240

10.  Intertrial Variability in the Premotor Cortex Accounts for Individual Differences in Peripersonal Space.

Authors:  Francesca Ferri; Marcello Costantini; Zirui Huang; Mauro Gianni Perrucci; Antonio Ferretti; Gian Luca Romani; Georg Northoff
Journal:  J Neurosci       Date:  2015-12-16       Impact factor: 6.167

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  2 in total

Review 1.  The brain and its time: intrinsic neural timescales are key for input processing.

Authors:  Mehrshad Golesorkhi; Javier Gomez-Pilar; Federico Zilio; Nareg Berberian; Annemarie Wolff; Mustapha C E Yagoub; Georg Northoff
Journal:  Commun Biol       Date:  2021-08-16

2.  A Roadmap for Computational Modelling of M/EEG.

Authors:  Benedetta Franceschiello; Jérémie Lefebvre; Micah M Murray; Katharina Glomb
Journal:  Brain Topogr       Date:  2022-01-27       Impact factor: 3.020

  2 in total

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