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  Diagnostic classification of schizophrenia patients on the basis of regional reward-related fMRI signal patterns

Koch, S. P., Hägele, C., Haynes, J.-D., Heinz, A., Schlagenhauf, F., & Sterzer, P. (2015). Diagnostic classification of schizophrenia patients on the basis of regional reward-related fMRI signal patterns. PLoS One, 10(3): e0119089. doi:10.1371/journal.pone.0119089.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-A784-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-79AE-2
Genre: Journal Article

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 Creators:
Koch, Stefan P.1, Author
Hägele, Claudia1, Author
Haynes, John-Dylan2, Author
Heinz, Andreas1, Author
Schlagenhauf, Florian1, 3, Author              
Sterzer, Philipp1, Author
Affiliations:
1Department of Psychiatry and Psychotherapy, Charité University Medicine Berlin, Germany, ou_persistent22              
2Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
3Max Planck Fellow Group Cognitive and Affective Control of Behavioural Adaptation, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1753350              

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 Abstract: Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way.

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Language(s): eng - English
 Dates: 2014-08-042014-12-072015-03-23
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pone.0119089
PMID: 25799236
PMC: PMC4370557
Other: eCollection 2015
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 10 (3) Sequence Number: e0119089 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: /journals/resource/1000000000277850