Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes (INS) UMR_S 1106, 13005, Marseille, France.
Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes (INS) UMR_S 1106, 13005, Marseille, France; Service de Pharmacologie Clinique et Pharmacovigilance, AP-HM, France.
Neuroimage. 2020 Nov 15;222:117155. doi: 10.1016/j.neuroimage.2020.117155. Epub 2020 Jul 29.
Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks. In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks -including Rapid Visual Processing (RVP, assessing sustained visual attention)- and dFC speed quantified at the level of functional sub-networks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module.
静息态下的动态功能连接（dFC）被认为是认知处理的一个相关物。我们将 dFC 描述为一种在不断变化的连接配置之间流动的方式，dFC 速度的概念则量化了 FC 网络随时间演变的速度。在这里，我们探究了一个假设，即 rs dFC 速度和认知表现的变化在特定的功能子网络中是选择性相关的。特别是，我们关注睡眠剥夺（SD）作为认知功能障碍的一种可逆模型。我们发现，在 24 小时的 SD 后，整个大脑水平（全局）的 dFC 速度显著减慢。然而，全局 dFC 速度的降低与个体任务中认知表现的变化不相关，这些变化是微妙且高度异质的。相反，我们发现个体任务中表现变化之间存在强烈的相关性，包括快速视觉处理（RVP，评估持续的视觉注意力），以及在感兴趣的功能子网水平上量化的 dFC 速度。模块 dFC 速度分析在经典的静态 FC（无时间）和全局 dFC（无空间）之间提供了一个折衷，允许独立量化不同任务的子网之间不同的 dFC 重新配置速度。重要的是，我们发现 RVP 表现与一个特征性的额顶叶模块的模块 dFC 速度之间存在稳健的相关性。
