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Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields

This article describes the first application of a generic (empirical) Bayesian analysis of between‐subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non‐invasive (MEG) data can be used to characterize subject‐specific differences in cortical m...

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Detalles Bibliográficos
Autores principales: Pinotsis, Dimitris A., Perry, Gavin, Litvak, Vladimir, Singh, Krish D., Friston, Karl J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5111616/
https://www.ncbi.nlm.nih.gov/pubmed/27593199
http://dx.doi.org/10.1002/hbm.23331
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author Pinotsis, Dimitris A.
Perry, Gavin
Litvak, Vladimir
Singh, Krish D.
Friston, Karl J.
author_facet Pinotsis, Dimitris A.
Perry, Gavin
Litvak, Vladimir
Singh, Krish D.
Friston, Karl J.
author_sort Pinotsis, Dimitris A.
collection PubMed
description This article describes the first application of a generic (empirical) Bayesian analysis of between‐subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non‐invasive (MEG) data can be used to characterize subject‐specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation‐inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject‐specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal‐interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models—and optimize the corresponding connectivity estimates at the within and between‐subject level. We also consider Bayesian cross‐validation to obtain predictive estimates for gamma‐response phenotypes, using a leave‐one‐out procedure. Hum Brain Mapp 37:4597–4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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spelling pubmed-51116162016-11-16 Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields Pinotsis, Dimitris A. Perry, Gavin Litvak, Vladimir Singh, Krish D. Friston, Karl J. Hum Brain Mapp Research Articles This article describes the first application of a generic (empirical) Bayesian analysis of between‐subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non‐invasive (MEG) data can be used to characterize subject‐specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation‐inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject‐specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal‐interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models—and optimize the corresponding connectivity estimates at the within and between‐subject level. We also consider Bayesian cross‐validation to obtain predictive estimates for gamma‐response phenotypes, using a leave‐one‐out procedure. Hum Brain Mapp 37:4597–4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. John Wiley and Sons Inc. 2016-09-04 /pmc/articles/PMC5111616/ /pubmed/27593199 http://dx.doi.org/10.1002/hbm.23331 Text en © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Pinotsis, Dimitris A.
Perry, Gavin
Litvak, Vladimir
Singh, Krish D.
Friston, Karl J.
Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields
title Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields
title_full Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields
title_fullStr Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields
title_full_unstemmed Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields
title_short Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields
title_sort intersubject variability and induced gamma in the visual cortex: dcm with empirical bayes and neural fields
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5111616/
https://www.ncbi.nlm.nih.gov/pubmed/27593199
http://dx.doi.org/10.1002/hbm.23331
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