Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study
Abstract
Background: Following stroke, motor and cognitive impairments often limit functional independence. Personalized rehabilitation requires comprehensive clinical assessments, which are costly and time-consuming. Imaging scans are routinely collected, but their predictive value in determining post-stroke function remains unclear. Objectives: To determine the ability of clinical assessments, lesion features with medical chart information, and a combined clinical and biomedical model to predict kinematic outcomes from a robot-based tracking task in chronic stroke individuals. Methods: Eighteen participants (70+/-6 years) completed a robot-based tracking task and a battery of motor and cognitive assessments. Three elastic net regression models were developed: 1) clinical assessments only; 2) lesion metrics and chart information only; and 3) a combined model. Models were evaluated using leave-one-subject-out cross-validation, bootstrapping, and permutation testing with false discovery rate correction. Robot-based outcomes included tracking error, normalized distance, velocity error response, and delay. Results: The clinical assessment model significantly predicted tracking error (R^2=0.45), delay (R^2=0.22), velocity error response (R^2=0.18), and normalized distance (R^2=0.12). The combined model significantly predicted tracking error (R^2=0.37), delay (R^2=0.19), and velocity error response (R^2=0.11). The imaging model did not significantly predict any outcome. Box and Blocks Test and Color Trails 1 were stable features. The right dorsal attention network was a stable predictor in several combined models. Conclusion: Clinical assessments outperformed lesion features in predicting tracking performance after chronic stroke. Attention- and motor-related measures were the strongest predictors, highlighting the importance of cognitive-motor interactions in function. A larger sample and more granular lesion features may improve prediction from imaging biomarkers.
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BibTeX
@misc{azar2026predicting,
title = {{Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study}},
author = {Reem M. Azar and Erica L. Waters and Joana Opong-Duah and Rochelle J. Mendonca and Pamela Z. Cacchione and Michelle J. Johnson},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.26366424},
url = {https://www.medrxiv.org/content/10.64898/2026.10.28.26366424v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Predicting Robot-Based Tracking Performance After Stroke Using Lesion Features and Clinical Assessments: A Pilot Study
AU - Reem M. Azar
AU - Erica L. Waters
AU - Joana Opong-Duah
AU - Rochelle J. Mendonca
AU - Pamela Z. Cacchione
AU - Michelle J. Johnson
PY - 2026
DA - 2026/10/28
PB - medRxiv
DO - 10.64898/2026.10.28.26366424
UR - https://www.medrxiv.org/content/10.64898/2026.10.28.26366424v1
AB - Background: Following stroke, motor and cognitive impairments often limit functional independence. Personalized rehabilitation requires comprehensive clinical assessments, which are costly and time-consuming. Imaging scans are routinely collected, but their predictive value in determining post-stroke function remains unclear. Objectives: To determine the ability of clinical assessments, lesion features with medical chart information, and a combined clinical and biomedical model to predict kinematic outcomes from a robot-based tracking task in chronic stroke individuals. Methods: Eighteen participants (70+/-6 years) completed a robot-based tracking task and a battery of motor and cognitive assessments. Three elastic net regression models were developed: 1) clinical assessments only; 2) lesion metrics and chart information only; and 3) a combined model. Models were evaluated using leave-one-subject-out cross-validation, bootstrapping, and permutation testing with false discovery rate correction. Robot-based outcomes included tracking error, normalized distance, velocity error response, and delay. Results: The clinical assessment model significantly predicted tracking error (R^2=0.45), delay (R^2=0.22), velocity error response (R^2=0.18), and normalized distance (R^2=0.12). The combined model significantly predicted tracking error (R^2=0.37), delay (R^2=0.19), and velocity error response (R^2=0.11). The imaging model did not significantly predict any outcome. Box and Blocks Test and Color Trails 1 were stable features. The right dorsal attention network was a stable predictor in several combined models. Conclusion: Clinical assessments outperformed lesion features in predicting tracking performance after chronic stroke. Attention- and motor-related measures were the strongest predictors, highlighting the importance of cognitive-motor interactions in function. A larger sample and more granular lesion features may improve prediction from imaging biomarkers.
ER -