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Classification of First-Episode Schizophrenia Using Multimodal Brain Features: A Combined Structural and Diffusion

Sugai Liang1,2, Yinfei Li1,2, Zhong Zhang3

  • 1Mental Health Centre and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

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Multimodal neuroimaging reliably identifies schizophrenia by analyzing brain structure and white matter integrity. Fusing these features improves classification accuracy, offering a promising objective diagnostic tool for schizophrenia.

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Area of Science:

  • Neuroimaging
  • Psychiatry
  • Machine Learning

Background:

  • Schizophrenia diagnosis relies on clinical symptoms, lacking objective biomarkers.
  • Neuroimaging studies show potential for objective classification of schizophrenia.
  • Previous research explored single neuroimaging metrics with varying success.

Purpose of the Study:

  • To assess the feasibility of identifying schizophrenia using single and multimodal neuroimaging features.
  • To develop and validate a classification model for schizophrenia detection.
  • To investigate the discriminative power of fused structural and diffusion tensor imaging metrics.

Main Methods:

  • Extracted regional gray matter volume, cortical thickness, gyrification, fractional anisotropy (FA), and mean diffusivity (MD) using automated procedures.
  • Employed Gradient Boosting Decision Tree for feature selection and multimodal data fusion.
  • Trained and validated the model on a cohort of first-episode schizophrenia (FES) patients and healthy controls (HCs), tested on an independent dataset.

Main Results:

  • Achieved 75.05% classification accuracy using fused structural and diffusion tensor imaging metrics.
  • Identified key discriminative features including cortical thickness and FA in specific brain regions.
  • Attained 76.54% average accuracy in an independent cohort using combined features.

Conclusions:

  • Multimodal data fusion enhances the accuracy of schizophrenia classification compared to single imaging metrics.
  • Neuroimaging features reflecting gray matter abnormalities and white matter disruptions are powerful discriminators.
  • This approach holds significant potential for developing objective, neuroimaging-based diagnostic tools for schizophrenia.