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Dynamic functional connectivity analysis reveals transiently increased segregation in patients with severe stroke
medRxiv - Neurology Pub Date : 2020-06-03 , DOI: 10.1101/2020.06.01.20119263
Anna K. Bonkhoff , Markus D. Schirmer , Martin Bretzner , Mark Etherton , Kathleen Donahue , Carissa Tuozzo , Marco Nardin , Anne-Katrin Giese , Ona Wu , Vince Calhoun , Christian Grefkes , Natalia S. Rost

Background and Purpose To explore the whole-brain dynamic functional network connectivity patterns in acute ischemic stroke (AIS) patients and their relation to stroke severity in the short and long term. Methods We investigated large-scale dynamic functional network connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we established correlation analyses between dynamic connectivity estimates and AIS severity as well as neurological recovery within the first 90 days after stroke. Finally, we built Bayesian hierarchical models to predict acute ischemic stroke severity and examine the inter-relation of dynamic connectivity and clinical measures, with an emphasis on white matter hyperintensity lesion load. Results We identified three distinct dynamic connectivity configurations in the early post-acute stroke phase. More severely affected patients (NIHSS 10-21) spent significantly more time in a highly segregated dynamic connectivity configuration that was characterized by particularly strong connectivity (three-level ANOVA: p<0.05, post hoc t-tests: p<0.05, FDR-corrected for multiple comparisons). Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the acute dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r=-0.68, p<0.05, FDR-corrected). Increasing dwell times, particularly those in a very segregated connectivity configuration, predicted higher acute stroke severity in our Bayesian modelling framework. Conclusions Our findings demonstrate transiently increased segregation between multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first three months post-stroke.

中文翻译:

动态功能连通性分析显示,重度中风患者的暂时性隔离增加

背景与目的探讨急性缺血性卒中(AIS)患者的全脑动态功能网络连接模式及其与短期和长期卒中严重性的关系。方法我们调查了症状发作后2至5天的41名AIS患者的大规模动态功能网络连通性。使用滑动窗口方法和k-均值聚类获得重复出现的动态连接配置。我们评估了三个NIHSS卒中严重程度定义组(轻度,中度和重度感染患者)之间动态模式的差异。此外,我们在卒中后的前90天内建立​​了动态​​连通性估计值与AIS严重程度以及神经系统恢复之间的相关性分析。最后,我们建立了贝叶斯分层模型,以预测急性缺血性卒中的严重程度,并检查动态连接性和临床措施之间的相互关系,重点是白质高强度病变负荷。结果我们在急性中风后早期发现了三种不同的动态连通性配置。受影响更严重的患者(NIHSS 10-21)在高度隔离的动态连接配置中花费了更多的时间,该配置的特点是具有特别强的连接性(三级ANOVA:p <0.05,事后t检验:p <0.05,FDR-已针对多个比较进行更正)。通过NIHSS随时间的实际变化来指示的恢复与双侧顶叶小叶和左角回之间的急性动态连通性显着相关(Pearson's r = -0.68,p <0.05,FDR校正)。驻留时间的增加,尤其是在连接非常隔离的配置中的驻留时间,在我们的贝叶斯建模框架中预测了更高的急性中风严重性。结论我们的研究结果表明,在严重AIS的情况下,多个功能域之间的分离会暂时增加。涉及默认模式网络组件的动态连接与中风后前三个月的恢复显着相关。
更新日期:2020-06-03
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