Assessment of functional decline in stroke patients using 3D deep learning and dynamic functional connectivity based on resting-state fMRI
These findings demonstrate that a 3D deep learning framework based on dFC features from rs-fMRI enables highly accurate and objective classification of UL motor impairment in stroke patients. This approach may provide a valuable alternative to manual UL-FMA scoring, particularly in clinical settings with limited access to experienced evaluators.
Authors
- Chengcheng Han (ORCID: https://orcid.org/0000-0001-5362-7702)
- Jie Peng
- Yingying Gao (ORCID: https://orcid.org/0000-0002-6869-3013)
- Minmin Wang (ORCID: https://orcid.org/0000-0003-0016-2985)
- Guojun Xu (ORCID: https://orcid.org/0000-0002-8526-9281)
- Zhiyong Zhao
- Shuang Wu
Institutions
- Xinjiang Medical University (CN)
- Fudan University (CN)
- Xinjiang Production and Construction Corps (CN)
- First Affiliated Hospital of Xinjiang Medical University (CN)
- Children's Hospital of Zhejiang University (CN)
- Karamay Central Hospital of Xinjiang (CN)
- Huashan Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-11-13
- DOI
- https://doi.org/10.3389/fneur.2025.1666991
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Tarim University