title: Diagnostic classification of schizophrenia patients on the basis of regional reward-related FMRI signal patterns.
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.
authors: Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Germany.
authors: Stefan P
authors: SP
authors: Koch
authors: Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Germany.
authors: Claudia
authors: C
authors: Hägele
authors: Bernstein Center for Computational Neuroscience Berlin, Charité-Universitätsmedizin Berlin, Germany.
authors: John-Dylan
authors: JD
authors: Haynes
authors: Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Germany.
authors: Andreas
authors: A
authors: Heinz
authors: Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
authors: Florian
authors: F
authors: Schlagenhauf
authors: Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité-Universitätsmedizin Berlin, Germany.
authors: Philipp
authors: P
authors: Sterzer
mesh_terms: D000328:Adult; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated; D012201:Reward; D012559:Schizophrenia / Q000145:classification / Q000175:diagnosis*; D012565:Schizophrenic Psychology; D012680:Sensitivity and Specificity; D066328:Ventral Striatum / Q000503:physiopathology; D055815:Young Adult