期刊
HUMAN BRAIN MAPPING
卷 37, 期 12, 页码 4597-4614出版社
WILEY
DOI: 10.1002/hbm.23331
关键词
empirical Bayes; random effects; dynamic causal modeling; neural fields; classification; Bayesian model reduction; gamma oscillations
资金
- Wellcome Trust [088130/Z/09/Z]
- UK MEG Partnership Grant (MRC/EPSRC) [MR/K005464/1]
- Epilepsy Research UK [A0940]
- MRC [MR/K005464/1] Funding Source: UKRI
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 modelsand 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. (c) The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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