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

By Karl J. Friston, Vladimir Litvak, Ashwini Oswal, Adeel Razi, Klaas E. Stephan, B. V. Wijk, Gabriel Ziegler, Peter Zeidman. NeuroImage, 2016

591 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/db3c148536c216d2cf6705882a2d92f73d4350fe

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Citations in the corpus

A guide to group effective connectivity analysis, part 2: Second level analysis with PEB (2019, methodology): “The PEB framework (Friston et al., 2016) introduced hierarchical modelling of the connectivity parameters of DCMs, thereby linking”
Frontal effective connectivity increases with task demands and time on task: a Dynamic Causal Model of electrocorticogram in macaque monkeys (2022, methodology): “In order to identify effective connections between the prefrontal and premotor areas that are consistently modulated by task demands and time on task, we used a (between condition) hierarchical model within the parametric empirical Bayesian (PEB) framework [61, 62, 63].”
Altered Effective Brain Connectivity During Habituation in First Episode Schizophrenia With Auditory Verbal Hallucinations: A Dichotic Listening EEG Study (2022, methodology): “Furthermore, the posterior probability for each model of the log Bayes factor were compared (52).”
Neural Mechanisms of Bidirectional Visuo‐Linguistic Transformation in Interactive Communication (2026, unknown): “PEB considers both the magnitude and uncertainty of each parameter, providing an estimate of the average effect across participants (Friston et al. 2016).”