IEEE transactions on bio-medical engineering · 2026
Structural Path Lengths Predict Post-Stroke Upper Limb Functional Outcome: Activation Likelihood vs. Whole Brain Parcellations.
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Karnadipa Triana, Chong Benjamin, Malik Mishaim, Shim Vickie, Fernandez Justin, Byblow Winston, Stinear Cathy, Wang Alan
Аннотация
OBJECTIVE: This retrospective study aimed to compare the predictive utility of shortest structural path length (SSPL) features derived from a whole-brain atlas and a task-specific atlas for forecasting post-stroke upper-limb motor outcomes.
METHODS: Data from 142 individuals with stroke were analysed. Baseline assessments included clinical measures (National Institutes of Health Stroke Scale [NIHSS], Shoulder Abduction and Finger Extension [SAFE] score, and Fugl-Meyer Assessment of the Upper Extremity [FMUE]), neurophysiological measures (motor evoked potential [MEP] status), and demographic variables. SSPL features were quantified using the Lesion Quantification Toolkit with connectivity defined by the HCP-842 atlas. Features were extracted from two primary parcellation frameworks: a whole-brain Schaefer 100 atlas and a task-specific, sensorimotor-activation likelihood estimation-based atlas (SMAA). Six regression models with nested resampling were trained to predict 12-week upper-limb outcomes, and feature contributions were assessed using Shapley Additive Explanations (SHAP).
RESULTS: Baseline clinical models consistently outperformed SSPL-based models. SMAA-derived SSPL features achieved predictive performance comparable to that of the whole brain atlas but did not provide substantial additional prognostic value when combined with baseline clinical measures.
CONCLUSION: Baseline clinical and neurophysiological measures, particularly MEP and motor impairment scales, remain the strongest predictors of post-stroke upper-limb recovery. Although task-specific parcellations may provide a more compact and interpretable representation of motor-network disruption than whole-brain approaches, their incremental predictive value beyond established clinical predictors was limited.
METHODS: Data from 142 individuals with stroke were analysed. Baseline assessments included clinical measures (National Institutes of Health Stroke Scale [NIHSS], Shoulder Abduction and Finger Extension [SAFE] score, and Fugl-Meyer Assessment of the Upper Extremity [FMUE]), neurophysiological measures (motor evoked potential [MEP] status), and demographic variables. SSPL features were quantified using the Lesion Quantification Toolkit with connectivity defined by the HCP-842 atlas. Features were extracted from two primary parcellation frameworks: a whole-brain Schaefer 100 atlas and a task-specific, sensorimotor-activation likelihood estimation-based atlas (SMAA). Six regression models with nested resampling were trained to predict 12-week upper-limb outcomes, and feature contributions were assessed using Shapley Additive Explanations (SHAP).
RESULTS: Baseline clinical models consistently outperformed SSPL-based models. SMAA-derived SSPL features achieved predictive performance comparable to that of the whole brain atlas but did not provide substantial additional prognostic value when combined with baseline clinical measures.
CONCLUSION: Baseline clinical and neurophysiological measures, particularly MEP and motor impairment scales, remain the strongest predictors of post-stroke upper-limb recovery. Although task-specific parcellations may provide a more compact and interpretable representation of motor-network disruption than whole-brain approaches, their incremental predictive value beyond established clinical predictors was limited.