Zhou Ye, Xie Hui, Li Xin, Huang Wenhao, Wu Xiaoying, Zhang Xin, Dou Zulin, Li Zengyong, Hou Wensheng, Chen Lin
Key Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing, 400044, P.R. China.
Chongqing Medical Electronics Engineering Technology Research Center, Chongqing University, Chongqing, 400044, P.R. China.
J Neuroeng Rehabil. 2024 Dec 23;21(1):226. doi: 10.1186/s12984-024-01523-6.
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.
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 Additive Explanations (SHAP) was applied to interpret the prediction process and analyze key predictive factors.
Compared to the resting state, task-related FC is a more robust feature for modeling, with screening frequencies above 90%. The combined models built using artificial neural networks (ANNs) and support vector machines (SVMs) significantly outperformed the other algorithms, with an average AUC of 0.861 (± 0.087) for the ANN and an average correlation coefficient (r) of 0.860 (± 0.069) for the SVM. Furthermore, predictive factor analysis of the models revealed that FC measured during tasks is the most important factor for predicting upper limb motor function.
This study confirmed that UE-RAT-induced FC can serve as an important predictor of rehabilitation, especially when combined with clinical information, further enhancing the accuracy of model predictions. These findings provide new insights for the early prediction of patients' recovery potential, which may contribute to personalized rehabilitation decisions.
上肢机器人辅助训练（UE-RAT）诱导的神经激活有助于表征中风后患者大脑中的适应性变化，揭示患者之间恢复潜力的差异。然而，这些与任务相关的神经活动是否能有效预测康复结果仍不清楚。在本研究中，我们利用功能近红外光谱（fNIRS）测量参与者在静息和UE-RAT任务期间的神经活动特征，并通过机器学习开发模型，以验证与任务相关的功能性脑反应是否能预测上肢运动功能的恢复。
使用fNIRS测量82例亚急性中风患者在静息状态和UE-RAT期间额叶上皮质、运动前皮质和初级运动皮质等脑区的皮质激活和脑网络功能连接（FC）。选择Fugl-Meyer上肢评估量表（FMA-UE）作为评估上肢运动功能的指标，并收集人口统计学和神经生理学数据等临床信息。使用最小绝对收缩和选择算子（LASSO）方法在100个随机划分的训练集中筛选稳健特征。基于选定的稳健特征，使用机器学习算法开发临床模型、fNIRS模型以及整合临床和fNIRS特征的组合模型。最后，应用Shapley加法解释（SHAP）来解释预测过程并分析关键预测因素。
与静息状态相比，与任务相关的FC是建模更稳健的特征，筛选频率超过90%。使用人工神经网络（ANN）和支持向量机（SVM）构建的组合模型明显优于其他算法，ANN的平均AUC为0.861（±0.087），SVM的平均相关系数（r）为0.860（±0.069）。此外，对模型的预测因素分析表明，任务期间测量的FC是预测上肢运动功能的最重要因素。
本研究证实，UE-RAT诱导的FC可作为康复的重要预测指标，特别是与临床信息相结合时，可进一步提高模型预测的准确性。这些发现为早期预测患者的恢复潜力提供了新的见解，可能有助于个性化康复决策。
