Bayesian model reduction and empirical Bayes for group (DCM) studies.
Neuroimage
; 128: 413-431, 2016 Mar.
Article
em En
| MEDLINE
| ID: mdl-26569570
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.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
/
Modelos Neurológicos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Neuroimage
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
Reino Unido