Dynamic connectivity predicts acute motor impairment and recovery post-stroke

• DOI: 10.1093/braincomms/fcab227
• Publication Date: 2021-09-24T13:41:25Z
• Authors: Anna K Bonkhoff; Anne K Rehme; Lukas Hensel; Caroline Tscherpel; Lukas J Volz; Flor A Espinoza; Harshvardhan Gazula; Victor M Vergara; Gereon R Fink; Vince D Calhoun; Natalia S Rost; Christian Grefkes
• Keywords: Stroke; Motor impairment; Acute stroke

Abstract Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models motor impairment and recovery post-stroke. Predictions relied on structural resting-state fMRI data from 54 stroke patients scanned within the first days symptom onset. Functional connectivity was estimated via static dynamic approaches. Motor performance phenotyped in phase 6 months later. A model based time spent specific configurations achieved best discrimination between with without impairments (out-of-sample area under curve, 95% confidence interval: 0.67 ± 0.01). In contrast, moderate-to-severe could be differentiated mild deficits using a variability (0.83 Here, ipsilesional sensorimotor cortex putamen discriminated most patients. Finally, predicted by (0.89 0.01) combination initial impairment. better linked shorter functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors recovery, which, future, might inform personalized therapy regimens promote recovery.