Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application
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.
Authors
- Ning Mao (ORCID: https://orcid.org/0000-0001-7097-5530)
- Mengshi Dong
- Yanqing Tang (ORCID: https://orcid.org/0000-0002-1331-4936)
- Ji Chen (ORCID: https://orcid.org/0000-0001-5208-2050)
- Chao Li (ORCID: https://orcid.org/0000-0001-5723-1414)
- Feng Chen (ORCID: https://orcid.org/0000-0002-9054-943X)
- Fei Wang (ORCID: https://orcid.org/0000-0002-5982-2303)
- Hao Yan (ORCID: https://orcid.org/0000-0003-2392-0793)
- Xiaozhu Liu (ORCID: https://orcid.org/0000-0001-7419-3480)
- Shuai Wang (ORCID: https://orcid.org/0009-0002-7506-5426)
- Jie Qin (ORCID: https://orcid.org/0000-0002-4088-3296)
Institutions
- Weifang Medical University (CN)
- Sun Yat-sen University (CN)
- Forschungszentrum Jülich (DE)
- Shanghai Jiao Tong University (CN)
- Peking University (CN)
- Yuhuangding Hospital (CN)
- Institute of Psychology, Chinese Academy of Sciences (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- Beijing Shijitan Hospital (CN)
- Peking University Sixth Hospital (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- BMC Psychiatry
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1186/s12888-025-06817-0
- Citations
- 4
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 5.72
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province