Dynamic connectivity predicts acute motor impairment and recovery post-stroke. Issue 4 (30th September 2021)

• Record Type: Journal Article
• Authors: Bonkhoff, Anna K; Rehme, Anne K; Hensel, Lukas; Tscherpel, Caroline; Volz, Lukas J; Espinoza, Flor A; Gazula, Harshvardhan; Vergara, Victor M; Fink, Gereon R; Calhoun, Vince D; Rost, Natalia S; Grefkes, Christian
• Is Part Of: Brain communications. Volume 3:Issue 4(2021)
• Publication Date: 2021-09-30
• Subjects: ischemic stroke -- upper limb motor impairment -- recovery post-stroke -- dynamic functional network connectivity -- precision medicine; 616
• DOI: 10.1093/braincomms/fcab227
• Languages: English
• ISSNs: 2632-1297

Abstract

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