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Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods.

Fang-Fang Huang1,2, Xiang-Yun Yang1, Jia Luo1

  • 1Department of Clinical Psychology, The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.

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|October 31, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models using functional MRI (fMRI) indices effectively distinguish obsessive-compulsive disorder (OCD) patients from healthy individuals. Combined fMRI data achieved 85% accuracy, highlighting the potential of neuroimaging for OCD diagnosis.

Keywords:
Diagnosis modelFunctional magnetic resonance imagingObsessive-compulsive disorderStructural magnetic resonance imagingSupport vector machine

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Psychiatry

Background:

  • Neuroimaging, particularly magnetic resonance imaging (MRI), has identified neural correlates of obsessive-compulsive disorder (OCD).
  • This has generated interest in using MRI indices for discriminating OCD patients from healthy individuals.

Purpose of the Study:

  • To explore the potential of MRI-based indices for diagnosing OCD using machine learning methods.
  • To evaluate the diagnostic performance of various functional MRI (fMRI) and structural MRI (sMRI) indices.

Main Methods:

  • Fifty OCD patients and fifty healthy subjects were divided into training (80%) and testing (20%) sets.
  • Extracted fMRI indices (ALFF, fALFF, ReHo, DC) and sMRI indices (gray matter volume, cortical thickness, sulcal depth) as features.
  • Employed least absolute shrinkage and selection operator (LASSO) regression for feature reduction, and support vector machine (SVM), logistic regression, and random forest for model development.

Main Results:

  • The SVM model using combined fMRI indices (ALFF, fALFF, ReHo, DC) achieved the highest performance: 94% cross-validation accuracy and 0.90 area under the ROC curve on the testing set (85% accuracy).
  • Selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit.
  • Models based on single MRI indices or combined fMRI and sMRI indices showed lower diagnostic accuracy, with a maximum of 75% validation accuracy for the SVM model of ALFF.

Conclusions:

  • Combined fMRI indices demonstrate significant potential for discriminating OCD patients from healthy individuals.
  • The complementary effect of various fMRI indices enhances classification accuracy.
  • The involvement of brain regions both within and outside the CSTC circuit underscores the importance of a comprehensive feature selection approach in OCD diagnosis.