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  Dissecting psychiatric spectrum disorders by generative embedding

Brodersen, K. H., Deserno, L., Schlagenhauf, F., Lin, Z., Penny, W. D., Buhmann, J. M., et al. (2013). Dissecting psychiatric spectrum disorders by generative embedding. NeuroImage: Clinical, 4, 98-111. doi:10.1016/j.nicl.2013.11.002.

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 Creators:
Brodersen, Kay H.1, 2, Author
Deserno, Lorenz3, 4, Author              
Schlagenhauf, Florian3, 4, Author              
Lin, Zhihao1, 2, Author
Penny, Will D.5, Author
Buhmann, Joachim M.2, Author
Stephan, Klaas E.1, 5, 6, Author
Affiliations:
1Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich, Switzerland, ou_persistent22              
2Machine Learning Laboratory, Department of Computer Science, ETH Zurich, Switzerland, ou_persistent22              
3Department of Psychiatry and Psychotherapy, Charité University Medicine Berlin, Germany, ou_persistent22              
4Max Planck Fellow Group Cognitive and Affective Control of Behavioural Adaptation, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1753350              
5Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
6Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Switzerland, ou_persistent22              

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Free keywords: Clustering; Clinical validation; Balanced purity; Schizophrenia; Variational Bayes
 Abstract: This proof-of-concept study examines the feasibility of defining subgroups in psychiatric spectrum disorders by generative embedding, using dynamical system models which infer neuronal circuit mechanisms from neuroimaging data. To this end, we re-analysed an fMRI dataset of 41 patients diagnosed with schizophrenia and 42 healthy controls performing a numerical n-back working-memory task. In our generative-embedding approach, we used parameter estimates from a dynamic causal model (DCM) of a visual-parietal-prefrontal network to define a model-based feature space for the subsequent application of supervised and unsupervised learning techniques. First, using a linear support vector machine for classification, we were able to predict individual diagnostic labels significantly more accurately (78%) from DCM-based effective connectivity estimates than from functional connectivity between (62%) or local activity within the same regions (55%). Second, an unsupervised approach based on variational Bayesian Gaussian mixture modelling provided evidence for two clusters which mapped onto patients and controls with nearly the same accuracy (71%) as the supervised approach. Finally, when restricting the analysis only to the patients, Gaussian mixture modelling suggested the existence of three patient subgroups, each of which was characterised by a different architecture of the visual-parietal-prefrontal working-memory network. Critically, even though this analysis did not have access to information about the patients' clinical symptoms, the three neurophysiologically defined subgroups mapped onto three clinically distinct subgroups, distinguished by significant differences in negative symptom severity, as assessed on the Positive and Negative Syndrome Scale (PANSS). In summary, this study provides a concrete example of how psychiatric spectrum diseases may be split into subgroups that are defined in terms of neurophysiological mechanisms specified by a generative model of network dynamics such as DCM. The results corroborate our previous findings in stroke patients that generative embedding, compared to analyses of more conventional measures such as functional connectivity or regional activity, can significantly enhance both the interpretability and performance of computational approaches to clinical classification.

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Language(s): eng - English
 Dates: 2013-10-062013-07-042013-11-072013-11-16
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.nicl.2013.11.002
PMID: 24363992
PMC: PMC3863808
Other: eCollection 2014
 Degree: -

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Title: NeuroImage: Clinical
Source Genre: Journal
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Publ. Info: Elsevier
Pages: - Volume / Issue: 4 Sequence Number: - Start / End Page: 98 - 111 Identifier: ISSN: 2213-1582
CoNE: https://pure.mpg.de/cone/journals/resource/2213-1582