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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

An interpretable approach for schizophrenia classification using fMRI and sMRI features.

Archita Chakraborty¹, Linkon Chowdhury², Selvarajah Thuseethan²

¹School of Science, Engineering and Technology, East Delta University, Chittagong, 4209 Bangladesh.

Health Information Science and Systems

|December 19, 2025

Summary

This study introduces a novel machine learning framework using multimodal magnetic resonance imaging (MRI) for improved schizophrenia diagnosis. The approach enhances classification accuracy and provides interpretable visualizations of brain abnormalities.

Keywords:

Machine learning Model interpretability Multimodal MRI Neuroimaging Schizophrenia classification

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Last Updated: Jul 1, 2026

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Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

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Published on: September 8, 2021

Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Schizophrenia diagnosis relies on neuroimaging, but multimodal data integration and interpretability remain challenging.
Existing machine learning models face limitations in visualizing complex neuroimaging features for schizophrenia classification.

Purpose of the Study:

To develop a novel framework integrating structural MRI (sMRI) and functional MRI (fMRI) for enhanced schizophrenia classification.
To improve the interpretability of machine learning models in neuroimaging for clinical decision-making.

Main Methods:

Utilized independent component analysis (ICA) for feature extraction from sMRI and fMRI data.
Developed a multi-scale recurrent neural network (MsRNN) for classification.
Employed layer-wise relevance propagation and gradient-weighted class activation mapping for explainable AI (XAI).

Main Results:

Achieved 83.33% accuracy on the MLSP dataset and 89.8% on the COBRE dataset.
Generated clinically meaningful visualizations highlighting discriminative brain regions.
Demonstrated superior performance compared to conventional models.

Conclusions:

The proposed multimodal, XAI-integrated framework offers improved accuracy and transparency for schizophrenia diagnosis.
This approach aids in identifying disorder-specific abnormalities and supports clinical decision-making.