Machine learning fMRI classifier delineates subgroups of schizophrenia patients

Maya Bleich-Cohen*, Shahar Jamshy, Haggai Sharon, Ronit Weizman, Nathan Intrator, Michael Poyurovsky, Talma Hendler

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Background: The search for a validated neuroimaging-based brain marker in psychiatry has thus far been fraught with both clinical and methodological difficulties. The present study aimed to apply a novel data-driven machine-learning approach to functional Magnetic Resonance Imaging (fMRI) data obtained during a cognitive task in order to delineate the neural mechanisms involved in two schizophrenia subgroups: schizophrenia patients with and without Obsessive-Compulsive Disorder (OCD). Methods: 16 schizophrenia patients with OCD ("schizo-obsessive"), 17 pure schizophrenia patients, and 20 healthy controls underwent fMRI while performing a working memory task. A whole brain search for activation clusters of cognitive load was performed using a recently developed data-driven multi-voxel pattern analysis (MVPA) approach, termed Searchlight Based Feature Extraction (SBFE), and which yields a robust fMRI-based classifier. Results: The SBFE successfully classified the two schizophrenia groups with 91% accuracy based on activations in the right intraparietal sulcus (r-IPS), which further correlated with reduced symptom severity among schizo-obsessive patients. Conclusions: The results indicate that this novel SBFE approach can successfully delineate between symptom dimensions in the context of complex psychiatric morbidity.

Original languageEnglish
Pages (from-to)196-200
Number of pages5
JournalSchizophrenia Research
Issue number1-3
StatePublished - 1 Dec 2014


FundersFunder number
U.S. Department of DefenseW81XWH-11-2-0008


    • MVPA
    • N-back
    • OCD
    • R-IPS
    • Schizo-obsessive
    • Searchlight


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