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Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields.
- Source :
- Human Brain Mapping; Dec2016, Vol. 37 Issue 12, p4597-4614, 18p
- Publication Year :
- 2016
-
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 37:4597-4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10659471
- Volume :
- 37
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Human Brain Mapping
- Publication Type :
- Academic Journal
- Accession number :
- 119335742
- Full Text :
- https://doi.org/10.1002/hbm.23331