Department of Psychological and Brain Sciences, USA; Cognitive Science Program, USA; Program in Neuroscience, USA; Network Science Institute, Indiana University, Bloomington, IN, 47405, USA.
Department of Psychological and Brain Sciences, USA.
Neuroimage. 2020 Jun;213:116687. doi: 10.1016/j.neuroimage.2020.116687. Epub 2020 Feb 29.
Brain networks are flexible and reconfigure over time to support ongoing cognitive processes. However, tracking statistically meaningful reconfigurations across time has proven difficult. This has to do largely with issues related to sampling variability, making instantaneous estimation of network organization difficult, along with increased reliance on task-free (cognitively unconstrained) experimental paradigms, limiting the ability to interpret the origin of changes in network structure over time. Here, we address these challenges using time-varying network analysis in conjunction with a naturalistic viewing paradigm. Specifically, we developed a measure of inter-subject network similarity and used this measure as a coincidence filter to identify synchronous fluctuations in network organization across individuals. Applied to movie-watching data, we found that periods of high inter-subject similarity coincided with reductions in network modularity and increased connectivity between cognitive systems. In contrast, low inter-subject similarity was associated with increased system segregation and more rest-like architectures. We then used a data-driven approach to uncover clusters of functional connections that follow similar trajectories over time and are more strongly correlated during movie-watching than at rest. Finally, we show that synchronous fluctuations in network architecture over time can be linked to a subset of features in the movie. Our findings link dynamic fluctuations in network integration and segregation to patterns of inter-subject similarity, and suggest that moment-to-moment fluctuations in functional connectivity reflect shared cognitive processing across individuals.
大脑网络是灵活的，并随着时间的推移重新配置以支持持续的认知过程。然而，跟踪随时间具有统计学意义的重新配置一直很困难。这在很大程度上与与采样变异性相关的问题有关，使得网络组织的即时估计变得困难，同时越来越依赖于无任务（认知不受限制）的实验范式，限制了随时间解释网络结构变化的能力。在这里，我们使用时变网络分析结合自然观看范式来解决这些挑战。具体来说，我们开发了一种测量主体间网络相似性的方法，并将该方法用作 coincidence filter 来识别个体之间网络组织的同步波动。将其应用于观看电影的数据，我们发现主体间相似性高的时期与网络模块性降低以及认知系统之间的连接性增加相吻合。相比之下，主体间相似性低与系统分离增加和更类似于休息的结构有关。然后，我们使用一种数据驱动的方法来揭示随时间遵循相似轨迹并且在观看电影时比在休息时相关性更强的功能连接簇。最后，我们表明，网络架构随时间的同步波动可以与电影中的某些特征相关联。我们的研究结果将网络整合和分离的动态波动与主体间相似性的模式联系起来，并表明功能连接的瞬间波动反映了个体之间的共享认知处理。
