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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Friston KJ,Litvak V,Oswal A,Razi A,Stephan KE,van Wijk BCM,Ziegler G,Zeidman Pdoi
10.1016/j.neuroimage.2015.11.015subject
Has Abstractpub_date
2016-03-01 00:00:00pages
413-431eissn
1053-8119issn
1095-9572pii
S1053-8119(15)01037-Xjournal_volume
128pub_type
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