Predicting activities of daily living at discharge in stroke patients using rehabilitation robot training-induced...

Predicting activities of daily living (ADL) in stroke patients optimizes discharge planning, which relies on accurate functional assessment. Recent studies have shown that functional connectivity (FC) of brain networks induced by upper extremity rehabilitation robotic training (UE-RAT) effectively reflects functional status, but its prognostic value for ADL remains unclear.

Utilize functional near-infrared spectroscopy (fNIRS) to measure FC during UE-RAT and develop machine learning models to evaluate the predictive value of task-FC for ADL.

This study recruited 86 patients with subacute stroke. Activation and FC features of key brain regions, such as the superior frontal cortex (SFC) and primary motor cortex (M1), were measured in the resting state and during UE-RAT using fNIRS. Concurrently, 38 clinical features were collected. With modified Barthel Index (mBI) ≥75 at discharge as the prediction target, machine learning algorithms such as artificial neural network (ANN) were used to construct resting-state fNIRS model, task-state fNIRS model, clinical model, and combined model, and analyze the importance of the predictor variables based on the Shapley additive interpretation (SHAP).

The combined model constructed by combining clinical and task-state fNIRS features had the best predictive performance (AUC_mean: 0.955, 95% CI: 0.948-0.962). Higher connectivity between the ipsilateral premotor cortex (iPMC) and primary motor cortex (iM1) during the task state, along with higher mBI scores and lower mRS scores, predict significant improvement in functional independence for patients.

UE-RAT induced FC can be a valid biomarker for mBI prediction and can improve the accuracy of rehabilitation prediction.