Graph Convolutional Network-Based Fusion of Multi-State fNIRS Data for Assessing Post-Stroke Upper Limb Motor Function.

Conventional neuroimaging tools for post-stroke motor function evaluation (e.g., EEG, fMRI) have some constraints. Conversely, functional near-infrared spectroscopy (fNIRS) offers a viable compromise. Nevertheless, few studies have yet quantitatively assessed the current motor function scores based on fNIRS data. This study proposed a Graph Convolutional Network (GCN) and Support Vector Regression (SVR) fusion model to fit Fugl-Meyer Assessment (FMA) scores by leveraging a multi-state integration of fNIRS metrics and clinical indicators. After preprocessing, brain network features and GCN features were extracted from the fNIRS data. Then a modified forward search method was used for SVR model training and feature selection from three feature sets: resting-state/task-state fNIRS feature sets and clinical feature set. Finally, the SVR model was employed to estimate the FMA scores. The coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE)were utilized to evaluate the models. The GCN-SVR fusion model demonstrated good goodness-of-fit, and exhibited limited variability. The multi-state model demonstrated better performance than both single-state models (P<0.001). For cortical cases, Aggregate measures over the nine sparsity levels confirmed both high accuracy and stable fitting (R²=0.8397±0.0487, RMSE=6.75±1.06, MAE=4.91±0.94), with R² consistently above 0.76 across sparsity levels. In subcortical patients, the multi-state model achieved a mean R² of 0.7562±0.0185, with RMSE=10.51±0.40 and MAE=7.74±0.53. The proposed GCN‑SVR fusion algorithm based on fNIRS data achieved high accuracy and stable performance in fitting FMA scores, while subset‑based sequential forward selection enhances multi-dataset feature selection.