Machine learning fMRI classifier delineates subgroups of schizophrenia patients

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

Research output: Contribution to journalArticlepeer-review


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


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

All Science Journal Classification (ASJC) codes

  • Psychiatry and Mental health
  • Biological Psychiatry


Dive into the research topics of 'Machine learning fMRI classifier delineates subgroups of schizophrenia patients'. Together they form a unique fingerprint.

Cite this