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Spatial dynamics within and between brain functional domains: A hierarchical approach to study time‐varying brain function

机译:脑功能域内和之间的空间动力学:研究时变脑功能的分层方法

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Abstract The analysis of time‐varying activity and connectivity patterns (i.e., the chronnectome) using resting‐state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high‐order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.
机译:摘要使用静态磁共振成像的时变活动和连接模式(即Chronnectome)的分析已成为持续神经科学讨论的重要组成部分。以前的大多数工作都集中在固定空间节点之间的时间耦合或主要活动/连接模式随时间的转换的变化。在这里,我们介绍一种捕获功能域内(FDS)内的空间动力学的方法,以及FDS内和之间的时间动态。该方法模拟大脑作为具有不同粒度水平的分层功能架构,其中较低的水平具有更高的功能均匀性,并且动态行为较少,更高的水平具有较小的均匀性和更高的行为。首先,使用高阶空间独立分量分析来近似功能单元。功能单元是随着时间的推移具有非常相似的功能活动的区域的图案。接下来,使用功能单元构建FD。最后,从FDS计算功能模块(FMS),提供了脑动力学的整体视图。结果突出了FDS内的空间流动性,包括区域关联的广泛变化,从强烈耦合到完全去耦。此外,FMS捕获FD之间的动态相互作用。精神分裂症患者显示功能活动的瞬态减少,跨越几种FDS,特别是皮质结构域。活动和连接差异在许多情况下传达唯一信息(例如,默认模式)突出显示其互补信息。捕获FD时空变化的提议的分层模型为Macroscale Chronnectome提供了新的洞察力,并识别从现有方法隐藏的更改。

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