Bayesian model reduction and empirical Bayes for group (DCM) studies

Neuroimage. 2016 Mar;128:413-431. doi: 10.1016/j.neuroimage.2015.11.015. Epub 2015 Nov 11.

Abstract

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction.

Keywords: Bayesian model reduction; Classification; Dynamic causal modelling; Empirical Bayes; Fixed effects; Hierarchical modelling; Random effects.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem*
  • Humans
  • Models, Neurological*
  • Schizophrenia