Abstract
INTRODUCTION: Functional brain networks measured by resting-state functional magnetic resonance imaging (rs-fMRI) have become a promising tool for understanding the neural mechanisms underlying schizophrenia (SZ). However, the high dimensionality of these networks and small sample sizes pose significant challenges for effective classification and model generalization. METHODS: We propose a robust multi-task feature selection method combined with counterfactual explanations to improve the accuracy and interpretability of SZ identification. rs-fMRI data are preprocessed to construct a functional connectivity matrix, and features are extracted by sorting the upper triangular elements. A multi-task feature selection framework based on the Gray Wolf Optimizer (GWO) is developed to identify abnormal functional connectivity (FC) features in SZ patients. A counterfactual explanation model is applied to reduce perturbations in abnormal FC features, returning the model prediction to normal and enhancing clinical interpretability. RESULTS: Our method was tested on five real-world SZ datasets. The results demonstrate that the proposed method significantly outperforms existing methods in terms of classification accuracy while offering new insights into the analysis of SZ through improved feature selection and explanation. DISCUSSION: The integration of multi-task feature selection and counterfactual explanation improves both the accuracy and interpretability of SZ identification. This approach provides valuable clinical insights by revealing the key functional connectivity features associated with SZ, which could assist in the development of more effective diagnostic tools.