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

Friston, KJ; Litvak, V; Oswal, A; Razi, A; Stephan, KE; van Wijk, BC; Ziegler, G; (2016) Bayesian model reduction and empirical Bayes for group (DCM) studies. Neuroimage , 128 pp. 413-431. 10.1016/j.neuroimage.2015.11.015. Green open access

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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.

Type: Article
Title: Bayesian model reduction and empirical Bayes for group (DCM) studies
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuroimage.2015.11.015
Publisher version: http://dx.doi.org/10.1016/j.neuroimage.2015.11.015
Language: English
Additional information: © 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Keywords: Bayesian model reduction, Classification, Dynamic causal modelling, Empirical Bayes, Fixed effects, Hierarchical modelling, Random effects
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1478194
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