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Exploring the white matter disruptions for Schizophrenia based on convolutional ensemble kernel randomized network.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Computational Neuroscience

Background:

  • Schizophrenia (SZ) presents with cognitive deficits and structural brain abnormalities.
  • Convolutional Neural Networks (CNNs) offer potential for identifying complex brain alterations.
  • Structural Magnetic Resonance Imaging (sMRI) detects disruptions in white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF).

Purpose of the Study:

  • To develop and evaluate a CNN ensemble KRR-RVFL model for detecting WM disruptions in SZ.
  • To compare the model's performance across different brain tissue types (WM, GM, CSF).
  • To investigate the relationship between tissue volume and symptom severity in SZ.

Main Methods:

  • An eight-layer CNN was integrated with five Kernel Ridge Regression-based Random Vector Functional Link (KRR-RVFL) classifiers.
  • Ensemble averaging of classifier outputs was used for final classification.
  • Correlation analysis was performed between tissue volumes and symptom scales.

Main Results:

  • The proposed CNN ensemble KRR-RVFL achieved 97.33% accuracy in identifying WM disruptions.
  • WM tissue volume showed greater reduction than GM in individuals with SZ.
  • Significant correlations were found between tissue volumes and symptom severity.

Conclusions:

  • The developed deep learning model effectively identifies WM disruptions associated with SZ.
  • WM integrity is significantly compromised in SZ, more so than GM.
  • This approach aids clinicians in the diagnosis of SZ by highlighting the role of WM alterations.