Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning
Abstract Individuals with schizophrenia experience significant cognitive impairments and alterations in brain function. However, the shared and unique brain functional patterns underlying cognition deficits and symptom severity in schizophrenia remain poorly understood. We design an interpretable gr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-08637-0 |
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| author | Jing Xia Yi Hao Chan Deepank Girish Qian Hui Chew Kang Sim Jagath C. Rajapakse |
| author_facet | Jing Xia Yi Hao Chan Deepank Girish Qian Hui Chew Kang Sim Jagath C. Rajapakse |
| author_sort | Jing Xia |
| collection | DOAJ |
| description | Abstract Individuals with schizophrenia experience significant cognitive impairments and alterations in brain function. However, the shared and unique brain functional patterns underlying cognition deficits and symptom severity in schizophrenia remain poorly understood. We design an interpretable graph-based multi-task deep learning framework to enhance the simultaneous prediction of schizophrenia illness severity and cognitive functioning measurements by using functional connectivity, and identify both shared and unique brain patterns associated with these phenotypes on 378 subjects from three datasets. Our framework outperforms both single-task and state-of-the-art multi-task learning methods in predicting four Positive and Negative Syndrome Scale (PANSS) subscales and four cognitive domain scores. The performance is replicable across three datasets, and the shared and unique functional changes are confirmed by meta-analysis at both regional and modular levels. Our study provides insights into the neural correlates of illness severity and cognitive implications, offering potential targets for further evaluations of treatment effects and longitudinal follow-up. |
| format | Article |
| id | doaj-art-e5f8c2b25d1b4fccbf8c0042696d543b |
| institution | Kabale University |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-e5f8c2b25d1b4fccbf8c0042696d543b2025-08-20T03:46:13ZengNature PortfolioCommunications Biology2399-36422025-08-018111710.1038/s42003-025-08637-0Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learningJing Xia0Yi Hao Chan1Deepank Girish2Qian Hui Chew3Kang Sim4Jagath C. Rajapakse5College of Computing and Data Science, Nanyang Technological UniversityCollege of Computing and Data Science, Nanyang Technological UniversityCollege of Computing and Data Science, Nanyang Technological UniversityResearch Division, Institute of Mental Health (IMH)Research Division, Institute of Mental Health (IMH)College of Computing and Data Science, Nanyang Technological UniversityAbstract Individuals with schizophrenia experience significant cognitive impairments and alterations in brain function. However, the shared and unique brain functional patterns underlying cognition deficits and symptom severity in schizophrenia remain poorly understood. We design an interpretable graph-based multi-task deep learning framework to enhance the simultaneous prediction of schizophrenia illness severity and cognitive functioning measurements by using functional connectivity, and identify both shared and unique brain patterns associated with these phenotypes on 378 subjects from three datasets. Our framework outperforms both single-task and state-of-the-art multi-task learning methods in predicting four Positive and Negative Syndrome Scale (PANSS) subscales and four cognitive domain scores. The performance is replicable across three datasets, and the shared and unique functional changes are confirmed by meta-analysis at both regional and modular levels. Our study provides insights into the neural correlates of illness severity and cognitive implications, offering potential targets for further evaluations of treatment effects and longitudinal follow-up.https://doi.org/10.1038/s42003-025-08637-0 |
| spellingShingle | Jing Xia Yi Hao Chan Deepank Girish Qian Hui Chew Kang Sim Jagath C. Rajapakse Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning Communications Biology |
| title | Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning |
| title_full | Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning |
| title_fullStr | Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning |
| title_full_unstemmed | Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning |
| title_short | Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning |
| title_sort | disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning |
| url | https://doi.org/10.1038/s42003-025-08637-0 |
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