Open AccessIntersubject variability and induced gamma in the visual cortex: DCM with empirical B ayes and neural fields
Author(s) -
Pinotsis Dimitris A.,
Perry Gavin,
Litvak Vladimir,
Singh Krish D.,
Friston Karl J.
Publication year - 2016
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23331
Subject(s) - magnetoencephalography , neuroscience , interneuron , bayesian probability , visual cortex , hum , psychology , neuroimaging , computer science , artificial intelligence , pattern recognition (psychology) , inhibitory postsynaptic potential , electroencephalography , art , performance art , art history
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.