ORIGINAL RESEARCH article
Front. Neurol.
Sec. Artificial Intelligence in Neurology
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1666991
This article is part of the Research TopicTechnology Developments and Clinical Applications of Artificial Intelligence in Neurodegenerative DiseasesView all 16 articles
Assessment of Functional Decline in Stroke Patients Using 3D Deep Learning and Dynamic Functional Connectivity Based on Resting-State fMRI
Provisionally accepted
- 1Zhejiang University, Hangzhou, China
- 2Zhejiang University School of Medicine Children's Hospital National Clinical Research Center for Child Health, Hangzhou, China
- 3The First Hospital of Xinjiang Production and Construction Group, Akesu, China
- 4Department of Rehabilitation, Huashan Hospital, Fudan University, Shanghai, China
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Abstract: This study aimed to develop an automated approach for assessing upper limb (UL) motor impairment severity in stroke patients using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI). Dynamic functional connectivity (dFC) was computed with the ipsilesional primary motor cortex (M1) as a seed and extracted from rs-fMRI data of 69 stroke patients. These dFC features were used to train a three-dimensional convolutional neural network (3D-CNN) for automatic classification of UL motor impairment severity. Patients were divided into two groups according to UL Fugl-Meyer Assessment (UL-FMA) scores: mild-to-moderate impairment (UL-FMA > 20; n = 29, maximum = 66) and severe impairment (0 ≤UL-FMA ≤ 20; n = 40). UL-FMA scores served as labels for supervised learning. The model achieved a balanced accuracy of 99.8% ± 0.2%, with a specificity of 99.9% ± 0.2% and a sensitivity of 99.7% ± 0.3%. Several brain regions—including the angular gyrus, medial orbitofrontal cortex, dorsolateral superior frontal gyrus, superior parietal lobule, supplementary motor area, thalamus, cerebellum, and middle temporal gyrus— were linked to UL motor impairment severity. 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.
Keywords: Stroke, resting-state functional magnetic resonance imaging, Dynamic Functional Connectivity, ipsilesional primary motor cortex, Three-dimensional convolutional neural network
Received: 16 Jul 2025; Accepted: 20 Oct 2025.
Copyright: © 2025 Zhao, Gao, Xu, Peng, Han, Wu, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhiyong Zhao, zhaozhiyong_zju@zju.edu.cn
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