Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers
Abstract Depressed mood and anhedonia, the core symptoms of major depressive disorder (MDD), are linked to dysfunction in the brain’s reward and emotion regulation circuits. To develop a predictive model for treatment remission in MDD based on pre-treatment neurocircuitry and clinical features. A to...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01912-8 |
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| author | Rui Liu Ximan Hou Shuyu Liu Yuan Zhou Jingjing Zhou Kaini Qiao Han Qi Ruinan Li Zhi Yang Ling Zhang Jian Cui Cheng Jin Aihong Yu Gang Wang |
| author_facet | Rui Liu Ximan Hou Shuyu Liu Yuan Zhou Jingjing Zhou Kaini Qiao Han Qi Ruinan Li Zhi Yang Ling Zhang Jian Cui Cheng Jin Aihong Yu Gang Wang |
| author_sort | Rui Liu |
| collection | DOAJ |
| description | Abstract Depressed mood and anhedonia, the core symptoms of major depressive disorder (MDD), are linked to dysfunction in the brain’s reward and emotion regulation circuits. To develop a predictive model for treatment remission in MDD based on pre-treatment neurocircuitry and clinical features. A total of 279 untreated MDD patients were analyzed, treated with selective serotonin reuptake inhibitors for 8–12 weeks, and assigned to training, internal validation, and external validation datasets. A hierarchical local-global imaging and clinical feature fusion graph neural network model was constructed. The model achieved 76.21% accuracy (AUC = 0.78) in predicting remission. Validation on the internal and external independent datasets yielded similar performance (accuracy = 72.73%, AUC = 0.74; accuracy = 71.43%, AUC = 0.72). Key contributing brain regions included the right globus pallidus, bilateral putamen, left hippocampus, bilateral thalamus, and bilateral anterior cingulate gyrus. These findings highlight the role of specific circuits in guiding antidepressant treatment. |
| format | Article |
| id | doaj-art-63aa6374cb144bc4bd64b73669a8e077 |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-63aa6374cb144bc4bd64b73669a8e0772025-08-20T03:43:30ZengNature Portfolionpj Digital Medicine2398-63522025-08-018111410.1038/s41746-025-01912-8Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkersRui Liu0Ximan Hou1Shuyu Liu2Yuan Zhou3Jingjing Zhou4Kaini Qiao5Han Qi6Ruinan Li7Zhi Yang8Ling Zhang9Jian Cui10Cheng Jin11Aihong Yu12Gang Wang13Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityMedical Robot Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityDepartment of Psychiatry, Shandong Daizhuang HospitalBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityBeijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical UniversityAbstract Depressed mood and anhedonia, the core symptoms of major depressive disorder (MDD), are linked to dysfunction in the brain’s reward and emotion regulation circuits. To develop a predictive model for treatment remission in MDD based on pre-treatment neurocircuitry and clinical features. A total of 279 untreated MDD patients were analyzed, treated with selective serotonin reuptake inhibitors for 8–12 weeks, and assigned to training, internal validation, and external validation datasets. A hierarchical local-global imaging and clinical feature fusion graph neural network model was constructed. The model achieved 76.21% accuracy (AUC = 0.78) in predicting remission. Validation on the internal and external independent datasets yielded similar performance (accuracy = 72.73%, AUC = 0.74; accuracy = 71.43%, AUC = 0.72). Key contributing brain regions included the right globus pallidus, bilateral putamen, left hippocampus, bilateral thalamus, and bilateral anterior cingulate gyrus. These findings highlight the role of specific circuits in guiding antidepressant treatment.https://doi.org/10.1038/s41746-025-01912-8 |
| spellingShingle | Rui Liu Ximan Hou Shuyu Liu Yuan Zhou Jingjing Zhou Kaini Qiao Han Qi Ruinan Li Zhi Yang Ling Zhang Jian Cui Cheng Jin Aihong Yu Gang Wang Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers npj Digital Medicine |
| title | Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers |
| title_full | Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers |
| title_fullStr | Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers |
| title_full_unstemmed | Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers |
| title_short | Predicting antidepressant response via local-global graph neural network and neuroimaging biomarkers |
| title_sort | predicting antidepressant response via local global graph neural network and neuroimaging biomarkers |
| url | https://doi.org/10.1038/s41746-025-01912-8 |
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