ArticleJournal of neuroengineering and rehabilitation2026
Resting-state and task-state fNIRS fusion assessment method for upper limb motor function in stroke patients.
Article in Journal of neuroengineering and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionPost-stroke rehabilitation strategies rely heavily on accurate motor function assessment. Scale-based assessments face inherent limitations, such as inter-rater variability and long assessment time. Neurophysiological biomarkers are emerging as more objective, complementary assessment tools. However, few studies have integrated multi-states functional near-infrared spectroscopy (fNIRS) data to fit the Fugl-Meyer Assessment (FMA) scores of stroke patients with machine learning.
methodsThis study enrolled 57 stroke patients and acquired fNIRS signals covering the sensorimotor cortex during both resting-state and grip-task conditions. A modified sequential forward selection algorithm was employed to select optimal features from three domains: functional connectivities (FCs) and graph theory metrics of resting-state fNIRS and task-state fNIRS, and clinical characteristics. Support vector regression was then applied on the multi-domain feature set to predict FMA scores.
resultsBoth for cortical and subcortical lesion-type stroke patients, the resting-state + task-state fNIRS fused model demonstrated significantly reduced prediction errors compared to single-state models. Optimal performance was achieved at 45% network sparsity [R2 = 0.8434 (0.6889–0.9311), RMSE = 6.75 (4.26–8.78), MAE = 5.11 (3.17–7.00)], with R2 values exceeding 0.73 across all network sparsity levels for cortical stroke. In subcortical stroke patients, the fused model demonstrated superior performance at 35% network sparsity [R2 = 0.7546 (0.5255–0.8728), RMSE = 10.55 (7.51–13.83), MAE = 7.39 (5.35–10.17)]. And the fusion model demonstrated a better fit than models incorporating either clinical or fNIRS datasets alone.
conclusionOur findings demonstrate that the fusion model integrating resting-state and task-state fNIRS significantly improves motor function assessment accuracy in stroke patients compared to single-state or single-dataset models.
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