Prediction of motor recovery using indirect connectivity in a lesion network after ischemic stroke

Abstract

Background: Recovery prediction can assist in the planning for impairment-focused rehabilitation after a stroke. This study investigated a new prediction model based on a lesion network analysis. To predict the potential for recovery, we focused on the next link-step connectivity of the direct neighbors of a lesion. Methods: We hypothesized that this connectivity would contribute to recovery after stroke onset. Each lesion in a patient who had suffered a stroke was transferred to a healthy subject. First link-step connectivity was identified by observing voxels functionally connected to each lesion. Next (second) link-step connectivity of the first link-step connectivity was extracted by calculating statistical dependencies between time courses of regions not directly connected to a lesion and regions identified as first link-step connectivity. Lesion impact on second link-step connectivity was quantified by comparing the lesion network and reference network. Results: The lower the impact of a lesion was on second link-step connectivity in the brain network, the better the improvement in motor function during recovery. A prediction model containing a proposed predictor, initial motor function, age, and lesion volume was established. A multivariate analysis revealed that this model accurately predicted recovery at 3 months poststroke ( R 2 = 0.788; cross-validation, R 2 = 0.746, RMSE = 13.15). Conclusion: This model can potentially be used in clinical practice to develop individually tailored rehabilitation programs for patients suffering from motor impairments after stroke.