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  A hierarchical model for integrating unsupervised generative embedding and empirical Bayes

Raman, S., Deserno, L., Schlagenhauf, F., & Stephan, K. E. (2016). A hierarchical model for integrating unsupervised generative embedding and empirical Bayes. Journal of Neuroscience Methods, 269, 6-20. doi:10.1016/j.jneumeth.2016.04.022.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-A303-A Version Permalink: http://hdl.handle.net/21.11116/0000-0003-19C3-5
Genre: Journal Article

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 Creators:
Raman, Sudhir1, Author
Deserno, Lorenz2, 3, 4, Author              
Schlagenhauf, Florian2, 3, Author              
Stephan, Klaas E.1, 5, 6, Author
Affiliations:
1Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich, Switzerland, ou_persistent22              
2Department of Psychiatry and Psychotherapy, Charité University Medicine Berlin, Germany, ou_persistent22              
3Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              
4Department of Neurology, Otto von Guericke University Magdeburg, Germany, ou_persistent22              
5Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
6Max Planck Institute for Metabolism Research, Cologne, Germany, ou_persistent22              

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Free keywords: Clustering; Dynamic causal modelling; DCM; Markov chain Monte Carlo sampling; MCMC; Mixture model; Psychiatric spectrum diseases; Schizophrenia
 Abstract: Background Generative models of neuroimaging data, such as dynamic causal models (DCMs), are commonly used for inferring effective connectivity from individual subject data. Recently introduced “generative embedding” approaches have used DCM-based connectivity parameters for supervised classification of individual patients or to find unknown subgroups in heterogeneous groups using unsupervised clustering methods. New method We present a novel framework which combines DCMs with finite mixture models into a single hierarchical model. This approach unifies the inference of connectivity parameters in individual subjects with inference on population structure, i.e. the existence of subgroups defined by model parameters, and allows for empirical Bayesian estimates of a subject’s connectivity based on subgroup-specific prior distributions. We introduce a Markov chain Monte Carlo sampling method for inversion of this hierarchical generative model. Results This paper formally introduces the idea behind our novel concept and demonstrates the face validity of the model in application to both simulated data as well as an empirical fMRI dataset from healthy controls and patients with schizophrenia. Comparison with existing method(s) The analysis of our empirical fMRI data demonstrates that our approach results in superior model evidence than the conventional non-hierarchical inversion of DCMs. Conclusions In this paper, we have presented a novel unified framework to jointly infer the effective connectivity parameters in DCMs for multiple subjects and, at the same time, discover connectivity-defined cluster structure of the whole population, using a mixture model approach.

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Language(s): eng - English
 Dates: 2016-04-232015-11-092016-04-242016-04-302016-08-30
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.jneumeth.2016.04.022
PMID: 27141854
Other: Epub 2016
 Degree: -

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Title: Journal of Neuroscience Methods
  Other : J. Neurosci. Meth.
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 269 Sequence Number: - Start / End Page: 6 - 20 Identifier: ISSN: 0165-0270
CoNE: /journals/resource/954925480594