Center for Brain and Cognition, Dept. of Technology and Information, Universitat Pompeu Fabra, Carrer Tànger, 122-140, 08018, Barcelona, Spain; Department of Radiology, Centre Hospitalier Universitaire Vaudoise (CHUV), Rue du Bugnon 46, 1011, Lausanne, Switzerland.
Center for Brain and Cognition, Dept. of Technology and Information, Universitat Pompeu Fabra, Carrer Tànger, 122-140, 08018, Barcelona, Spain.
Neuroimage. 2018 May 1;171:40-54. doi: 10.1016/j.neuroimage.2017.12.074. Epub 2017 Dec 30.
Spontaneous activity measured in human subject under the absence of any task exhibits complex patterns of correlation that largely correspond to large-scale functional topographies obtained with a wide variety of cognitive and perceptual tasks. These "resting state networks" (RSNs) fluctuate over time, forming and dissolving on the scale of seconds to minutes. While these fluctuations, most prominently those of the default mode network, have been linked to cognitive function, it remains unclear whether they result from random noise or whether they index a nonstationary process which could be described as state switching. In this study, we use a sliding windows-approach to relate temporal dynamics of RSNs to global modulations in correlation and BOLD variance. We compare empirical data, phase-randomized surrogate data, and data simulated with a stationary model. We find that RSN time courses exhibit a large amount of coactivation in all three cases, and that the modulations in their activity are closely linked to global dynamics of the underlying BOLD signal. We find that many properties of the observed fluctuations in FC and BOLD, including their ranges and their correlations amongst each other, are explained by fluctuations around the average FC structure. However, we also report some interesting characteristics that clearly support nonstationary features in the data. In particular, we find that the brain spends more time in the troughs of modulations than can be expected from stationary dynamics.
在没有任何任务的情况下，对人体进行的自发性活动测量显示出复杂的相关模式，这些模式在很大程度上与使用各种认知和感知任务获得的大规模功能拓扑结构相对应。这些“静息状态网络”（RSN）随时间波动，在秒到分钟的时间尺度上形成和溶解。虽然这些波动，尤其是默认模式网络的波动，与认知功能有关，但尚不清楚它们是源自随机噪声，还是指示可以描述为状态切换的非平稳过程。在这项研究中，我们使用滑动窗口方法将 RSN 的时间动态与相关性和 BOLD 方差的全局调制联系起来。我们比较了经验数据、相位随机化的替代数据以及使用静态模型模拟的数据。我们发现 RSN 时间过程在所有三种情况下都表现出大量的共同激活，并且它们的活动的调制与基础 BOLD 信号的全局动力学密切相关。我们发现观察到的 FC 和 BOLD 波动的许多特性，包括它们的范围及其相互之间的相关性，都可以通过围绕平均 FC 结构的波动来解释。然而，我们也报告了一些有趣的特征，这些特征清楚地支持数据中的非平稳特征。特别是，我们发现大脑在调制的低谷期花费的时间比从静态动力学中预期的要多。
