Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application
Abstract Background Early identification of Schizophrenia Spectrum Disorder (SSD) is crucial for effective intervention and prognosis improvement. Previous neuroimaging-based classifications have primarily focused on chronic, medicated SSD cohorts. However, the question remains whether brain metrics...
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2025-04-01
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| Series: | BMC Psychiatry |
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| Online Access: | https://doi.org/10.1186/s12888-025-06817-0 |
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| author | Chao Li Ji Chen Mengshi Dong Hao Yan Feng Chen Ning Mao Shuai Wang Xiaozhu Liu Yanqing Tang Fei Wang Jie Qin |
| author_facet | Chao Li Ji Chen Mengshi Dong Hao Yan Feng Chen Ning Mao Shuai Wang Xiaozhu Liu Yanqing Tang Fei Wang Jie Qin |
| author_sort | Chao Li |
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| description | Abstract Background Early identification of Schizophrenia Spectrum Disorder (SSD) is crucial for effective intervention and prognosis improvement. Previous neuroimaging-based classifications have primarily focused on chronic, medicated SSD cohorts. However, the question remains whether brain metrics identified in these populations can serve as trait biomarkers for early-stage SSD. This study investigates whether functional connectivity features identified in chronic, medicated SSD patients could be generalized to early-stage SSD. Methods Data were collected from 502 SSD patients and 575 healthy controls (HCs) across four medical institutions. Resting-state functional connectivity (FC) features were used to train a Support Vector Machine (SVM) classifier on individuals with medicated chronic SSD and HCs from three sites. The remaining site, comprising both chronic medicated and first-episode unmedicated SSD patients, was used for independent validation. A univariable analysis examined the association between medication dosage or illness duration and FC. Results The classifier achieved 69% accuracy (p = 0.002), 63% sensitivity, 75% specificity, 0.75 area under the receiver operating characteristic curve, 69% F1-score, 72% positive predictive rate, and 67% negative predictive rate, when tested on an independent dataset. Subgroup analysis showed 71% sensitivity (p = 0.04) for chronic medicated SSD, but poor generalization to first-episode unmedicated SSD (sensitivity = 48%, p = 0.44). Univariable analysis revealed a significant association between FC and medication usage, but not disease duration. Conclusions Classifiers developed on chronic medicated SSD may predominantly capture state features of chronicity and medication, overshadowing potential SSD traits. This partially explains the current classifiers’ non-generalizability across SSD patients with different clinical states, underscoring the need for models that can enhance the early detection of schizophrenia neural pathology. |
| format | Article |
| id | doaj-art-f3115b2f066f4ca1ab7ae98d96bcfe4b |
| institution | OA Journals |
| issn | 1471-244X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj-art-f3115b2f066f4ca1ab7ae98d96bcfe4b2025-08-20T02:17:58ZengBMCBMC Psychiatry1471-244X2025-04-0125111110.1186/s12888-025-06817-0Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical applicationChao Li0Ji Chen1Mengshi Dong2Hao Yan3Feng Chen4Ning Mao5Shuai Wang6Xiaozhu Liu7Yanqing Tang8Fei Wang9Jie Qin10Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen UniversityCenter for Brain Health and Brain Technology, Global Institute of Future Technology, Institute of Psychology and Behavioral Science, Shanghai Jiao Tong UniversityDepartment of Radiology, The Third Affiliated Hospital of Sun Yat-Sen UniversityPeking University Sixth Hospital, Institute of Mental HealthDepartment of Radiology, The Third Affiliated Hospital of Sun Yat-Sen UniversityYantai Yuhuangding Hospital, Qingdao UniversitySchool of Psychology, Shandong Second Medical UniversityEmergency and Critical Care Medical Center, Beijing Shijitan Hospital, Capital Medical UniversityDepartment of Psychiatry, Shengjing Hospital of China Medical UniversityEarly Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical UniversityDepartment of Radiology, The Third Affiliated Hospital of Sun Yat-Sen UniversityAbstract Background Early identification of Schizophrenia Spectrum Disorder (SSD) is crucial for effective intervention and prognosis improvement. Previous neuroimaging-based classifications have primarily focused on chronic, medicated SSD cohorts. However, the question remains whether brain metrics identified in these populations can serve as trait biomarkers for early-stage SSD. This study investigates whether functional connectivity features identified in chronic, medicated SSD patients could be generalized to early-stage SSD. Methods Data were collected from 502 SSD patients and 575 healthy controls (HCs) across four medical institutions. Resting-state functional connectivity (FC) features were used to train a Support Vector Machine (SVM) classifier on individuals with medicated chronic SSD and HCs from three sites. The remaining site, comprising both chronic medicated and first-episode unmedicated SSD patients, was used for independent validation. A univariable analysis examined the association between medication dosage or illness duration and FC. Results The classifier achieved 69% accuracy (p = 0.002), 63% sensitivity, 75% specificity, 0.75 area under the receiver operating characteristic curve, 69% F1-score, 72% positive predictive rate, and 67% negative predictive rate, when tested on an independent dataset. Subgroup analysis showed 71% sensitivity (p = 0.04) for chronic medicated SSD, but poor generalization to first-episode unmedicated SSD (sensitivity = 48%, p = 0.44). Univariable analysis revealed a significant association between FC and medication usage, but not disease duration. Conclusions Classifiers developed on chronic medicated SSD may predominantly capture state features of chronicity and medication, overshadowing potential SSD traits. This partially explains the current classifiers’ non-generalizability across SSD patients with different clinical states, underscoring the need for models that can enhance the early detection of schizophrenia neural pathology.https://doi.org/10.1186/s12888-025-06817-0Schizophrenia spectrum disorderSchizophreniaMachine learningFunctional connectivityClassification |
| spellingShingle | Chao Li Ji Chen Mengshi Dong Hao Yan Feng Chen Ning Mao Shuai Wang Xiaozhu Liu Yanqing Tang Fei Wang Jie Qin Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application BMC Psychiatry Schizophrenia spectrum disorder Schizophrenia Machine learning Functional connectivity Classification |
| title | Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application |
| title_full | Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application |
| title_fullStr | Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application |
| title_full_unstemmed | Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application |
| title_short | Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application |
| title_sort | classification of schizophrenia spectrum disorder using machine learning and functional connectivity reconsidering the clinical application |
| topic | Schizophrenia spectrum disorder Schizophrenia Machine learning Functional connectivity Classification |
| url | https://doi.org/10.1186/s12888-025-06817-0 |
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