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Related Experiment Video

Updated: Mar 28, 2026

Exploring the Neural Correlates of Cognitive Reappraisal in Obsessive-Compulsive Disorder Using Task-based Functional Magnetic Resonance Imaging
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Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy.

Xinyu Hu1, Qi Liu1, Bin Li2

  • 1Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.

European Neuropsychopharmacology : the Journal of the European College of Neuropsychopharmacology
|December 29, 2015
PubMed
Summary

Multivariate pattern analysis (MVPA) accurately distinguished obsessive-compulsive disorder (OCD) patients from healthy controls using brain imaging. White matter analysis with support vector machines (SVM) showed the highest diagnostic potential for individual OCD detection.

Keywords:
Gaussian process classifierMultivariate pattern analysisObsessive–compulsive disorderStructural magnetic resonance imagingSupport vector machine

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Area of Science:

  • Neuroimaging
  • Psychiatry
  • Computational Neuroscience

Background:

  • Obsessive-compulsive disorder (OCD) is associated with brain structural abnormalities in gray matter (GM) and white matter (WM).
  • Previous group-average analyses limit clinical application for individual diagnosis.
  • Multivariate pattern analysis (MVPA) offers a promising approach for individual-level discrimination.

Purpose of the Study:

  • To investigate the efficacy of MVPA in discriminating between OCD patients and healthy controls (HCS) using structural MRI data.
  • To compare the performance of Support Vector Machine (SVM) and Gaussian Process Classifier (GPC) for OCD classification.
  • To identify specific brain regions and networks with high discriminative power.

Main Methods:

  • Acquisition of high-resolution T1-weighted MRI scans from 33 OCD patients and 33 HCS.
  • Application of SVM and GPC, established MVPA techniques, to analyze GM and WM volume differences.
  • Evaluation of classifier performance using receiver operating characteristic (ROC) curves.

Main Results:

  • Both GM and WM analyses achieved classification accuracies above 75%.
  • The SVM classifier using WM information yielded the highest accuracy (81.82%, P<0.001).
  • Discriminative brain anomalies were identified in the fronto-striatal circuit, temporo-parieto-occipital junction, and cerebellum.

Conclusions:

  • Structural MRI data, particularly WM features analyzed with SVM, can effectively differentiate OCD patients from HCS.
  • MVPA demonstrates potential for the individual-level diagnosis of OCD.
  • Findings highlight the role of specific distributed brain networks in OCD pathophysiology.