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Investigating White Matter Abnormalities Associated with Schizophrenia Using Deep Learning Model and Voxel-Based

Tripti Goel1, Sirigineedi A Varaprasad1, M Tanveer2

  • 1Biomedical Imaging Lab, Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar 788010, Assam, India.

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Summary

This study uses deep learning on MRI scans to accurately diagnose schizophrenia (SCZ). The ensemble deep random vector functional link (edRVFL) model achieved 96.5% accuracy, identifying white matter abnormalities as key indicators in SCZ patients.

Keywords:
Schizophreniamagnetic resonance imagingrandom vector functional linkvoxel-based morphometry

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Psychiatry

Background:

  • Schizophrenia (SCZ) presents significant diagnostic challenges.
  • Accurate and timely diagnosis is critical for patient outcomes.
  • Deep learning (DL) offers potential for improved SCZ prediction and diagnosis.

Purpose of the Study:

  • To classify individuals with SCZ from healthy controls using structural MRI.
  • To identify specific brain regions associated with SCZ abnormalities.
  • To evaluate the efficacy of DL models in SCZ diagnosis.

Main Methods:

  • Extracted Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) from 99 MRI scans.
  • Utilized a pretrained ResNet-50 network for feature extraction.
  • Employed an ensemble deep random vector functional link (edRVFL) network for classification.
  • Performed voxel-based morphometry (VBM) analysis to examine tissue volumes.

Main Results:

  • The edRVFL model achieved a classification accuracy of 96.5% using WM features.
  • edRVFL outperformed traditional algorithms in SCZ classification.
  • VBM analysis revealed 1363 significant voxels in the WM region of SCZ patients (T-value=6.90, Z-value=6.21).

Conclusions:

  • Deep learning, particularly the edRVFL model, demonstrates high accuracy in diagnosing SCZ from MRI data.
  • White Matter (WM) alterations are strongly associated with structural changes in SCZ.
  • DL models combined with VBM analysis can effectively identify SCZ-related brain abnormalities.