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Bayesian Modelling of Induced Responses and Neuronal Rhythms

Neural rhythms or oscillations are ubiquitous in neuroimaging data. These spectral responses have been linked to several cognitive processes; including working memory, attention, perceptual binding and neuronal coordination. In this paper, we show how Bayesian methods can be used to finesse the ill-...

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Autores principales: Pinotsis, Dimitris A., Loonis, Roman, Bastos, Andre M., Miller, Earl K., Friston, Karl J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592965/
https://www.ncbi.nlm.nih.gov/pubmed/27718099
http://dx.doi.org/10.1007/s10548-016-0526-y
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author Pinotsis, Dimitris A.
Loonis, Roman
Bastos, Andre M.
Miller, Earl K.
Friston, Karl J.
author_facet Pinotsis, Dimitris A.
Loonis, Roman
Bastos, Andre M.
Miller, Earl K.
Friston, Karl J.
author_sort Pinotsis, Dimitris A.
collection PubMed
description Neural rhythms or oscillations are ubiquitous in neuroimaging data. These spectral responses have been linked to several cognitive processes; including working memory, attention, perceptual binding and neuronal coordination. In this paper, we show how Bayesian methods can be used to finesse the ill-posed problem of reconstructing—and explaining—oscillatory responses. We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses—and the identification of important sources of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity. We hope to show that electrophysiological measurements contain much more spatial information than is often thought: on the one hand, the dynamic causal modelling of non-invasive (low spatial resolution) electrophysiology can afford sub-millimetre (hyper-acute) resolution that is limited only by the (spatial) complexity of the underlying (dynamic causal) forward model. On the other hand, invasive microelectrode recordings (that penetrate different cortical layers) can reveal laminar-specific responses and elucidate hierarchical message passing and information processing within and between cortical regions at a macroscopic scale. In short, the careful and biophysically grounded modelling of sparse data enables one to characterise the neuronal architectures generating oscillations in a remarkable detail.
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spelling pubmed-65929652019-07-11 Bayesian Modelling of Induced Responses and Neuronal Rhythms Pinotsis, Dimitris A. Loonis, Roman Bastos, Andre M. Miller, Earl K. Friston, Karl J. Brain Topogr Original Paper Neural rhythms or oscillations are ubiquitous in neuroimaging data. These spectral responses have been linked to several cognitive processes; including working memory, attention, perceptual binding and neuronal coordination. In this paper, we show how Bayesian methods can be used to finesse the ill-posed problem of reconstructing—and explaining—oscillatory responses. We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses—and the identification of important sources of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity. We hope to show that electrophysiological measurements contain much more spatial information than is often thought: on the one hand, the dynamic causal modelling of non-invasive (low spatial resolution) electrophysiology can afford sub-millimetre (hyper-acute) resolution that is limited only by the (spatial) complexity of the underlying (dynamic causal) forward model. On the other hand, invasive microelectrode recordings (that penetrate different cortical layers) can reveal laminar-specific responses and elucidate hierarchical message passing and information processing within and between cortical regions at a macroscopic scale. In short, the careful and biophysically grounded modelling of sparse data enables one to characterise the neuronal architectures generating oscillations in a remarkable detail. Springer US 2016-10-07 2019 /pmc/articles/PMC6592965/ /pubmed/27718099 http://dx.doi.org/10.1007/s10548-016-0526-y Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Paper
Pinotsis, Dimitris A.
Loonis, Roman
Bastos, Andre M.
Miller, Earl K.
Friston, Karl J.
Bayesian Modelling of Induced Responses and Neuronal Rhythms
title Bayesian Modelling of Induced Responses and Neuronal Rhythms
title_full Bayesian Modelling of Induced Responses and Neuronal Rhythms
title_fullStr Bayesian Modelling of Induced Responses and Neuronal Rhythms
title_full_unstemmed Bayesian Modelling of Induced Responses and Neuronal Rhythms
title_short Bayesian Modelling of Induced Responses and Neuronal Rhythms
title_sort bayesian modelling of induced responses and neuronal rhythms
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592965/
https://www.ncbi.nlm.nih.gov/pubmed/27718099
http://dx.doi.org/10.1007/s10548-016-0526-y
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