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

Pinotsis, DA; Perry, G; Litvak, V; Singh, KD; Friston, KJ; (2016) Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields. Human Brain Mapping 10.1002/hbm.23331. Green open access

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Abstract

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, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Type: Article
Title: Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/hbm.23331
Publisher version: http://dx.doi.org/10.1002/hbm.23331
Language: English
Additional information: © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Bayesian model reduction, classification, dynamic causal modeling, empirical Bayes, gamma oscillations, neural fields, random effects
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1514832
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