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Updated: Sep 12, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
Published on: July 14, 2023
Neuroimaging and kinematic biomarkers of post-stroke upper limb motor impairment
Joyce L Chen1, Timothy K Lam2, Melanie C Baniña3
1Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, ON M5S 2W6, Canada; Canadian Partnership for Stroke Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON M4N 3M5, Canada.
Abstract:
Structural and functional biomarkers derived from magnetic resonance imaging explain some variance in post-stroke motor impairment. The understanding of the nature of impairment and the discrimination between true behavioural motor recovery/restitution and motor compensation may be improved by the addition of kinematic information. The aim of the study was to determine the influence of neuroimaging combined with kinematic biomarkers in explaining the variance in motor impairment of the upper limb. People living with late sub-acute to chronic stroke (n = 25) underwent the Fugl Meyer Assessment - Upper Limb (FMA-UL), magnetic resonance imaging, and completed a reaching task where upper limb and trunk kinematics were recorded. Regression analyses were performed to determine the amount of variability in FMA-UL explained by the following biomarkers: the amount of corticospinal tract impacted by the stroke lesion (CST involvement), interhemispheric and ipsilesional resting state connectivity, and the Trunk-based Index of Performance (IPt) that measures skilled reaching ability while accounting for trunk compensation. CST involvement, interhemispheric connectivity, and the IPt, together explained ∼49 % of the variance in the FMA-UL (F(3,21) = 8.694, p = 0.001, R2adj = 0.49). The IPt explained an additional 14 % of the variance in the FMA-UL compared to CST involvement alone (p = 0.02). The IPt is a relevant kinematic biomarker of post-stroke upper limb motor impairment. Our findings suggest the importance of using multiple categories of biomarkers to better understand the level of post-stroke motor impairment.
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