Modeling Post-Stroke Motor Behavior from Structural Lesion Networks: A Preliminary Study with Robot-Based Assessments.

Abstract

There is an urgent need to improve post-stroke rehabilitation, particularly through pragmatic, affordable solutions such as robotic therapy. Understanding the relationship between structural lesion networks and performance on robot-based assessments could provide insights to personalize robot-assisted rehabilitation. In this study, we used elastic net regression to predict robot-task performance from a dataset that includes both arms of 15 chronic stroke participants (n = 30). Our main finding is that lesion volume in seven key brain networks and cortical/subcortical regions was able to predict a principle component explaining 54% of the variance in behavioral metrics with a training R ² = 0.56 and a cross-validation R ² = 0.41 (bootstrap 95% CI [-0.12 - 0.73]), significantly exceeding the null model generated with permutation testing.