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Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional

Xinyan Yuan¹, Shaolong Wei², Ying Sun¹

¹School of Electronics and Information, Jiangsu Vocational College of Business, Nantong, China.

Frontiers in Neuroscience

|August 5, 2025

Summary

This study introduces a novel method using multi-task feature selection and counterfactual explanations to accurately identify schizophrenia (SZ) from brain imaging data, enhancing diagnostic tools.

Keywords:

counterfactual explanation feature selection functional connectivity rs-fMRI schizophrenia

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Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Resting-state functional magnetic resonance imaging (rs-fMRI) reveals functional brain networks crucial for understanding schizophrenia (SZ).
High dimensionality and small sample sizes in rs-fMRI data present challenges for accurate SZ classification and model generalization.

Purpose of the Study:

To enhance the accuracy and interpretability of schizophrenia identification using rs-fMRI data.
To develop a robust multi-task feature selection method combined with counterfactual explanations.

Main Methods:

rs-fMRI data were preprocessed to create functional connectivity (FC) matrices.
A multi-task feature selection framework utilizing the Gray Wolf Optimizer (GWO) identified abnormal FC features in SZ patients.
Counterfactual explanations were applied to refine abnormal FC features, improving model prediction and clinical interpretability.

Main Results:

The proposed method demonstrated superior classification accuracy compared to existing approaches across five real-world SZ datasets.
The approach provided novel insights into SZ analysis through enhanced feature selection and explanation.

Conclusions:

Integrating multi-task feature selection and counterfactual explanations significantly improves SZ identification accuracy and interpretability.
This method offers valuable clinical insights by highlighting key FC features associated with SZ, potentially aiding diagnostic tool development.