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dc.contributor.author
Friston, Karl J.
dc.contributor.author
Litvak, Vladimir
dc.contributor.author
Oswal, Ashwini
dc.contributor.author
Razi, Adeel
dc.contributor.author
Stephan, Klaas
dc.contributor.author
van Wijk, Bernadette C. M.
dc.contributor.author
Ziegler, Gabriel
dc.contributor.author
Zeidman, Peter
dc.date.accessioned
2019-12-06T14:31:06Z
dc.date.available
2017-06-12T02:13:50Z
dc.date.available
2019-12-06T14:31:06Z
dc.date.issued
2016-03
dc.identifier.issn
1053-8119
dc.identifier.issn
1095-9572
dc.identifier.other
10.1016/j.neuroimage.2015.11.015
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/113873
dc.identifier.doi
10.3929/ethz-b-000113873
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Empirical Bayes
en_US
dc.subject
Random effects
en_US
dc.subject
Fixed effects
en_US
dc.subject
Dynamic causal modelling
en_US
dc.subject
Classification
en_US
dc.subject
Bayesian model reduction
en_US
dc.subject
Hierarchical modelling
en_US
dc.title
Bayesian model reduction and empirical Bayes for group (DCM) studies
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2015-11-11
ethz.journal.title
NeuroImage
ethz.journal.volume
128
en_US
ethz.journal.abbreviated
NeuroImage
ethz.pages.start
413
en_US
ethz.pages.end
431
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
001638029
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03955 - Stephan, Klaas E. / Stephan, Klaas E.
ethz.date.deposited
2017-06-12T02:18:03Z
ethz.source
ECIT
ethz.identifier.importid
imp59365430dc33220908
ethz.ecitpid
pub:175618
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-13T00:01:14Z
ethz.rosetta.lastUpdated
2021-02-15T06:59:14Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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