Sorbonne Université, Institut du Cerveau, Paris Brain Institute, ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013 Paris, France.
AP-HP, Urgences Cerebro-Vasculaires, Hopital Pitie-Salpetriere, Paris, France.
J R Soc Interface. 2022 Mar;19(188):20210850. doi: 10.1098/rsif.2021.0850. Epub 2022 Mar 2.
Plasticity after stroke is a complex phenomenon. Functional reorganization occurs not only in the perilesional tissue but throughout the brain. However, the local connection mechanisms generating such global network changes remain largely unknown. To address this question, time must be considered as a formal variable of the problem rather than a simple repeated observation. Here, we hypothesized that the presence of temporal connection motifs, such as the formation of temporal triangles () and edges () over time, would explain large-scale brain reorganization after stroke. To test our hypothesis, we adopted a statistical framework based on temporal exponential random graph models (tERGMs), where the aforementioned temporal motifs were implemented as parameters and adapted to capture global network changes after stroke. We first validated the performance on synthetic time-varying networks as compared to standard static approaches. Then, using real functional brain networks, we showed that estimates of tERGM parameters were sufficient to reproduce brain network changes from 2 weeks to 1 year after stroke. These temporal connection signatures, reflecting within-hemisphere segregation () and between hemisphere integration (), were associated with patients' future behaviour. In particular, interhemispheric temporal edges significantly correlated with the chronic language and visual outcome in subcortical and cortical stroke, respectively. Our results indicate the importance of time-varying connection properties when modelling dynamic complex systems and provide fresh insights into modelling of brain network mechanisms after stroke.
中风后的可塑性是一种复杂的现象。功能重组不仅发生在病灶周围组织，而且发生在整个大脑中。然而，产生这种全局网络变化的局部连接机制在很大程度上仍然未知。为了解决这个问题，必须将时间视为问题的正式变量，而不是简单的重复观察。在这里，我们假设时间连接模式的存在，例如随着时间的推移形成的时间三角形()和边()，将解释中风后的大脑大规模重组。为了验证我们的假设，我们采用了基于时间指数随机图模型(tERGMs)的统计框架，其中上述时间模式被实现为参数，并适应于捕获中风后全局网络的变化。我们首先将其性能与标准静态方法在合成时变网络上进行了比较。然后，使用真实的功能大脑网络，我们表明 tERGM 参数的估计足以重现中风后 2 周至 1 年的大脑网络变化。这些反映半球内分隔()和半球间整合()的时间连接特征与患者的未来行为有关。特别是，半球间的时间边与皮质下和皮质中风后的慢性语言和视觉结果显著相关。我们的结果表明，在模拟动态复杂系统时，时变连接特性的重要性，并为中风后大脑网络机制的建模提供了新的见解。
