Objective Thorough assessment of cerebral dysfunction after acute brain lesions is paramount to optimize predicting short- and long-term clinical outcomes. The potential of dynamic resting-state connectivity for prognosticating motor recovery has not been explored so far. Methods We built random forest classifier-based prediction models of acute upper limb motor impairment and recovery after stroke. Predictions were based on structural and resting-state fMRI data from 54 ischemic stroke patients scanned within the first days of symptom onset. Functional connectivity was estimated using both a static and dynamic approach. Individual motor performance was phenotyped in the acute phase and six months later. Results 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 and 95%-confidence interval (AUC±95%-CI): 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 (AUC±95%-CI: 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 (AUC±95%-CI: 0.89±0.01) in combination with the initial motor impairment. Here, better recovery was linked to a shorter time spent in a functionally integrated network configuration in the acute phase post-stroke. Interpretation Dynamic connectivity-derived parameters constitute potent predictors of acute motor impairment and post-stroke recovery, which in the future might inform personalized therapy regimens to promote recovery from acute stroke.

中文翻译：

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动态连通性可预测急性运动障碍和中风后恢复

目的全面评估急性脑损伤后的脑功能障碍对于优化预测短期和长期临床结果至关重要。迄今为止，尚未探讨动态静息状态连通性用于预测运动恢复的潜力。方法我们建立了基于森林分类器的中风急性上肢运动障碍和恢复的预测模型。预测是基于症状发作的头几天内扫描的54例缺血性中风患者的结构和静止状态fMRI数据。使用静态和动态方法估计功能连接性。在急性期和六个月后对个体运动表现进行表型分析。结果基于在特定动态连通性配置上花费的时间的模型在有无运动障碍的患者（曲线下的样本外面积和95％置信区间（AUC±95％-CI））中实现了最佳区分±0.01）。相反，可以使用基于动态连通性变异性的模型（AUC±95％-CI：0.83±0.01）将中度至重度障碍患者与轻度缺陷患者区分开。在这里，同病感觉运动皮层和壳核之间的连通性差异是患者之间最大的区别。最后，通过在特定的连接配置（AUC±95％-CI：0.89±0.01）中花费的时间与初始运动障碍相结合，可以最好地预测运动恢复。这里，卒中后急性期，更好的恢复能力与在功能集成网络配置中花费的时间较短有关。解释动态连接性参数是急性运动障碍和中风后恢复的有效预测指标，将来可能会为个性化治疗方案提供参考，以促进急性中风的恢复。