Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks
IntroductionFunctional 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 significa...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1609547/full |
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| author | Xinyan Yuan Shaolong Wei Ying Sun Lingling Gu Yanyan He Tiantian Chen Hongcheng Yao Haonan Rao |
| author_facet | Xinyan Yuan Shaolong Wei Ying Sun Lingling Gu Yanyan He Tiantian Chen Hongcheng Yao Haonan Rao |
| author_sort | Xinyan Yuan |
| collection | DOAJ |
| description | IntroductionFunctional 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.MethodsWe 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.ResultsOur 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.DiscussionThe 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. |
| format | Article |
| id | doaj-art-4ccd0d7dd35447519c7cbe2b6710f430 |
| institution | DOAJ |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroscience |
| spelling | doaj-art-4ccd0d7dd35447519c7cbe2b6710f4302025-08-20T02:41:11ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-07-011910.3389/fnins.2025.16095471609547Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networksXinyan Yuan0Shaolong Wei1Ying Sun2Lingling Gu3Yanyan He4Tiantian Chen5Hongcheng Yao6Haonan Rao7School of Electronics and Information, Jiangsu Vocational College of Business, Nantong, ChinaSchool of Artificial Intelligence and Computer Science, Nantong University, Nantong, ChinaSchool of Electronics and Information, Jiangsu Vocational College of Business, Nantong, ChinaSchool of Electronics and Information, Jiangsu Vocational College of Business, Nantong, ChinaSchool of Electronics and Information, Jiangsu Vocational College of Business, Nantong, ChinaSchool of Electronics and Information, Jiangsu Vocational College of Business, Nantong, ChinaSchool of Information Science and Technology, Nantong University, Nantong, ChinaSchool of Information Science and Technology, Nantong University, Nantong, ChinaIntroductionFunctional 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.MethodsWe 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.ResultsOur 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.DiscussionThe 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.https://www.frontiersin.org/articles/10.3389/fnins.2025.1609547/fullschizophreniafunctional connectivityrs-fMRIfeature selectioncounterfactual explanation |
| spellingShingle | Xinyan Yuan Shaolong Wei Ying Sun Lingling Gu Yanyan He Tiantian Chen Hongcheng Yao Haonan Rao Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks Frontiers in Neuroscience schizophrenia functional connectivity rs-fMRI feature selection counterfactual explanation |
| title | Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks |
| title_full | Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks |
| title_fullStr | Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks |
| title_full_unstemmed | Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks |
| title_short | Robust multi-task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks |
| title_sort | robust multi task feature selection with counterfactual explanation for schizophrenia identification using functional brain networks |
| topic | schizophrenia functional connectivity rs-fMRI feature selection counterfactual explanation |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1609547/full |
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