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

Pinotsis, Dimitris A, Perry, Gavin ORCID:, Litvak, Vladimir, Singh, Krish Devi ORCID: and Firston, Karl 2016. Intersubject variability and induced gamma in the visual cortex: DCM with empirical bayes and neural fields. Human Brain Mapping 37 (12) , pp. 4597-4614. 10.1002/hbm.23331

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Psychology
Uncontrolled Keywords: empirical Bayes; random effects; dynamic causal modeling; neural fields; classification; Bayesian model reduction; gamma oscillations
Publisher: Wiley-Blackwell
ISSN: 1065-9471
Funders: Wellcome Trust, MRC, EPSRC, Epilepsy Research UK
Date of First Compliant Deposit: 9 November 2016
Date of Acceptance: 22 July 2016
Last Modified: 02 Nov 2022 09:39

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