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dc.contributor.author
Yao, Yu
dc.contributor.author
Stephan, Klaas
dc.date.accessioned
2021-06-18T13:29:52Z
dc.date.available
2021-06-18T03:46:45Z
dc.date.available
2021-06-18T13:27:56Z
dc.date.available
2021-06-18T13:29:52Z
dc.date.issued
2021-07-01
dc.identifier.other
10.1002/hbm.25431
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/490244
dc.identifier.doi
10.3929/ethz-b-000490244
dc.description.abstract
In this article, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of latent parameters of subject-wise generative models. Specifically, we focus on the case where the subject-wise generative model is a dynamic causal model (DCM) for functional magnetic resonance imaging (fMRI) and clusters are defined in terms of effective brain connectivity. While an attractive approach for detecting mechanistically interpretable subgroups in heterogeneous populations, inverting such a hierarchical model represents a particularly challenging case, since DCM is often characterized by high posterior correlations between its parameters. In this context, standard MCMC schemes exhibit poor performance and extremely slow convergence. In this article, we investigate the properties of hierarchical clustering which lead to the observed failure of standard MCMC schemes and propose a solution designed to improve convergence but preserve computational complexity. Specifically, we introduce a class of proposal distributions which aims to capture the interdependencies between the parameters of the clustering and subject-wise generative models and helps to reduce random walk behaviour of the MCMC scheme. Critically, these proposal distributions only introduce a single hyperparameter that needs to be tuned to achieve good performance. For validation, we apply our proposed solution to synthetic and real-world datasets and also compare it, in terms of computational complexity and performance, to Hamiltonian Monte Carlo (HMC), a state-of-the-art Monte Carlo technique. Our results indicate that, for the specific application domain considered here, our proposed solution shows good convergence performance and superior runtime compared to HMC.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley-Blackwell
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Computational psychiatry
en_US
dc.subject
Functional magnetic resonance imaging
en_US
dc.subject
Generative embedding
en_US
dc.subject
Markov Chain Monte Carlo Sampling
en_US
dc.subject
Model inversion
en_US
dc.title
Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2021-06-11
ethz.journal.title
Human Brain Mapping
ethz.journal.volume
42
en_US
ethz.journal.issue
10
en_US
ethz.pages.start
2973
en_US
ethz.pages.end
2989
en_US
ethz.size
17 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Hoboken, NJ
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.
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.relation.isSupplementedBy
10.3929/ethz-b-000475337
ethz.date.deposited
2021-06-18T03:46:49Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-06-18T13:30:05Z
ethz.rosetta.lastUpdated
2021-06-18T13:30:05Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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