Assessment of functional decline in stroke patients using 3D deep learning and dynamic functional connectivity based on resting-state fMRI - Gao, Yingying; Xu, Guojun; Peng, Jie; Han, Chengbin; Wu, Shifei; Wang, Minmin; Wang, Hewei; Zhao, Zhiyong (Frontiers in Neurology, 2025)
IntroductionThis 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).MethodsDynamic 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.ResultsThe 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.DiscussionThese 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.