Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training

作者：Ye Zhou, Hui Xie, Xin Li, Wenhao Huang, Xiaoying Wu, Xin Zhang, Zulin Dou, Zengyong Li, Wensheng Hou, Lin Chen · 发表于：Journal of NeuroEngineering and Rehabilitation · 年份：2024 · DOI：10.1186/s12984-024-01523-6 · 被引用次数：12 · 研究领域：Optical Imaging and Spectroscopy Techniques、Stroke Rehabilitation and Recovery、EEG and Brain-Computer Interfaces

BACKGROUND: Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants' neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function. METHODS: Cortical activation and brain network functional connectivity (FC) in brain regions such as the superior frontal cortex, premotor cortex, and primary motor cortex were measured using fNIRS in 82 subacute stroke patients in the resting state and during UE-RAT. The Fugl-Meyer Upper Extremity Assessment Scale (FMA-UE) was chosen as the index for assessing upper extremity motor function, and clinical information such as demographic and neurophysiological data was also collected. Robust features were screened in 100 randomly divided training sets using the least absolute shrinkage and selection operator (LASSO) method. Based on the selected robust features, machine learning algorithms were used to develop clinical models, fNIRS models, and combined models that integrated both clinical and fNIRS features. Finally, Shapley Additiv...