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Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl’s gyrus and planum temporale

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@techreport{b864dd845f29441eaa16133fd7dc5ac1,
title = "Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl{\textquoteright}s gyrus and planum temporale",
abstract = "Learning of complex auditory sequences such as language and music can be thought of as the continuous optimisation of internal predictive representations of sound-pattern regularities, driven by prediction errors. In predictive coding (PC), this occurs through changes in the intrinsic and extrinsic connectivity of the relevant cortical networks, whereby minimization of precision-weighted prediction error signals improves the accuracy of future predictions. Here, we employed Dynamic Causal Modelling (DCM) on functional magnetic resonance (fMRI) data acquired during the presentation of complex auditory patterns. In an oddball paradigm, we presented 52 volunteers (non-musicians) with isochronous 5-tone melodic patterns (standards), randomly interleaved with rare novel patterns comprising contour or pitch interval changes (deviants). Here, listeners must update their standard melodic models whenever they encounter unexpected deviant stimuli. Contour deviants induced an increased BOLD response, as compared to standards, in primary (Heschl{\textquoteright}s gyrus, HG) and secondary auditory cortices (planum temporale, PT). Within this network, we found a left-lateralized increase in feedforward connectivity from HG to PT for deviant responses and a concomitant disinhibition within left HG. Consistent with PC, our results suggest that model updating in auditory pattern perception and learning is associated with specific changes in the excitatory feedforward connections encoding prediction errors and in the intrinsic connections that encode the precision of these errors and modulate their gain.",
keywords = "brain connectivity, auditory system, DCM, fMRI",
author = "Massimo Lumaca and Martin Dietz and Hansen, {Niels Chr.} and {Quiroga Martinez}, {David Ricardo} and Peter Vuust",
year = "2019",
month = nov,
day = "20",
doi = "10.1101/848416",
language = "English",
publisher = "bioRxiv",
type = "WorkingPaper",
institution = "bioRxiv",

}

RIS

TY - UNPB

T1 - Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl’s gyrus and planum temporale

AU - Lumaca, Massimo

AU - Dietz, Martin

AU - Hansen, Niels Chr.

AU - Quiroga Martinez, David Ricardo

AU - Vuust, Peter

PY - 2019/11/20

Y1 - 2019/11/20

N2 - Learning of complex auditory sequences such as language and music can be thought of as the continuous optimisation of internal predictive representations of sound-pattern regularities, driven by prediction errors. In predictive coding (PC), this occurs through changes in the intrinsic and extrinsic connectivity of the relevant cortical networks, whereby minimization of precision-weighted prediction error signals improves the accuracy of future predictions. Here, we employed Dynamic Causal Modelling (DCM) on functional magnetic resonance (fMRI) data acquired during the presentation of complex auditory patterns. In an oddball paradigm, we presented 52 volunteers (non-musicians) with isochronous 5-tone melodic patterns (standards), randomly interleaved with rare novel patterns comprising contour or pitch interval changes (deviants). Here, listeners must update their standard melodic models whenever they encounter unexpected deviant stimuli. Contour deviants induced an increased BOLD response, as compared to standards, in primary (Heschl’s gyrus, HG) and secondary auditory cortices (planum temporale, PT). Within this network, we found a left-lateralized increase in feedforward connectivity from HG to PT for deviant responses and a concomitant disinhibition within left HG. Consistent with PC, our results suggest that model updating in auditory pattern perception and learning is associated with specific changes in the excitatory feedforward connections encoding prediction errors and in the intrinsic connections that encode the precision of these errors and modulate their gain.

AB - Learning of complex auditory sequences such as language and music can be thought of as the continuous optimisation of internal predictive representations of sound-pattern regularities, driven by prediction errors. In predictive coding (PC), this occurs through changes in the intrinsic and extrinsic connectivity of the relevant cortical networks, whereby minimization of precision-weighted prediction error signals improves the accuracy of future predictions. Here, we employed Dynamic Causal Modelling (DCM) on functional magnetic resonance (fMRI) data acquired during the presentation of complex auditory patterns. In an oddball paradigm, we presented 52 volunteers (non-musicians) with isochronous 5-tone melodic patterns (standards), randomly interleaved with rare novel patterns comprising contour or pitch interval changes (deviants). Here, listeners must update their standard melodic models whenever they encounter unexpected deviant stimuli. Contour deviants induced an increased BOLD response, as compared to standards, in primary (Heschl’s gyrus, HG) and secondary auditory cortices (planum temporale, PT). Within this network, we found a left-lateralized increase in feedforward connectivity from HG to PT for deviant responses and a concomitant disinhibition within left HG. Consistent with PC, our results suggest that model updating in auditory pattern perception and learning is associated with specific changes in the excitatory feedforward connections encoding prediction errors and in the intrinsic connections that encode the precision of these errors and modulate their gain.

KW - brain connectivity

KW - auditory system

KW - DCM

KW - fMRI

U2 - 10.1101/848416

DO - 10.1101/848416

M3 - Working paper

BT - Learning of complex auditory patterns changes intrinsic and feedforward effective connectivity between Heschl’s gyrus and planum temporale

PB - bioRxiv

ER -