Comparison of fluctuations in global network topology of modeled and empirical brain functional connectivity.
Comparison of fluctuations in global network topology of modeled and empirical brain functional connectivity.
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DOI：
10.1371/journal.pcbi.1006497
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发表时间：
2018-09
影响因子：
4.3
通讯作者：
Sporns O
中科院分区：
文献类型：
--
作者：
Fukushima M;Sporns O
大规模大脑活动的动态模型已被用于重现关于人脑功能连接的许多经验发现。通过将建模数据与经验数据进行比较，已证明可重现的特征包括在几分钟的静息态功能磁共振成像中测量的功能连接性，以及其在数十秒的时间尺度上的时间分辨波动。然而，对于功能连接的全局网络拓扑的波动，例如隔离和集成拓扑之间或高模块化拓扑和低模块化拓扑之间的波动，尚未对建模数据和经验数据进行比较。由于这些全球网络水平的波动已被证明与人类认知和行为有关，因此越来越需要通过计算模型来阐明它们的再现性。为了解决这个问题，我们直接比较了建模数据和经验数据之间功能连接的全局网络拓扑的波动，并阐明了自发脑动力学的稳态模型可以在多大程度上重现经验观察到的波动。使用根据大脑结构连接连接的耦合相位振荡器系统来模拟建模波动。通过执行模型参数搜索，我们发现量化网络集成和模块化的全局指标的建模波动幅度超过经验数据中观察到的波动幅度的 80%。根据这些指标的波动确定的网络状态的时间属性也被发现是可重复的，尽管它们在功能连接方面的空间模式并不完全匹配。这些结果表明，模拟静息态活动的稳态模型可以再现分离和整合中经验波动的幅度，而其他因素，例如控制非稳态动力学的主动机制和/或更准确地绘制大脑结构连接性，对于完全再现与这些波动相关的空间模式是必要的。在人类神经科学中，人们对静息态大脑活动的共激活模式的时间波动越来越感兴趣。为了阐明这些波动的生成机制，理论研究试图通过使用大规模自发大脑活动的动态模型进行模拟来重现其经验特性。然而，迄今为止，对可重复性的评估尚未扩展到共激活模式的全局网络拓扑的波动，最近表明与人类认知和行为有关。在这里，我们研究了通常用于模拟静息态活动的静态模型可以在多大程度上重现根据经验观察到的全局网络拓扑波动的空间和时间模式。我们发现这样的模型成功地再现了经验波动的幅度及其时间动态，而模拟并没有完全解释它们的空间模式。我们的结果表明，平稳模型可以解释全局网络拓扑波动的许多经验特性，而完整复制需要非平稳动力学建模和/或模拟基础解剖连接的更高估计精度。这一发现为了解人脑中共激活模式的全局网络拓扑波动如何出现提供了新的见解。
Dynamic models of large-scale brain activity have been used for reproducing many empirical findings on human brain functional connectivity. Features that have been shown to be reproducible by comparing modeled to empirical data include functional connectivity measured over several minutes of resting-state functional magnetic resonance imaging, as well as its time-resolved fluctuations on a time scale of tens of seconds. However, comparison of modeled and empirical data has not been conducted yet for fluctuations in global network topology of functional connectivity, such as fluctuations between segregated and integrated topology or between high and low modularity topology. Since these global network-level fluctuations have been shown to be related to human cognition and behavior, there is an emerging need for clarifying their reproducibility with computational models. To address this problem, we directly compared fluctuations in global network topology of functional connectivity between modeled and empirical data, and clarified the degree to which a stationary model of spontaneous brain dynamics can reproduce the empirically observed fluctuations. Modeled fluctuations were simulated using a system of coupled phase oscillators wired according to brain structural connectivity. By performing model parameter search, we found that modeled fluctuations in global metrics quantifying network integration and modularity had more than 80% of magnitudes of those observed in the empirical data. Temporal properties of network states determined based on fluctuations in these metrics were also found to be reproducible, although their spatial patterns in functional connectivity did not perfectly matched. These results suggest that stationary models simulating resting-state activity can reproduce the magnitude of empirical fluctuations in segregation and integration, whereas additional factors, such as active mechanisms controlling non-stationary dynamics and/or greater accuracy of mapping brain structural connectivity, would be necessary for fully reproducing the spatial patterning associated with these fluctuations. In human neuroscience, there is growing interest in temporal fluctuations in coactivation patterns of resting-state brain activity. To elucidate generative mechanisms of these fluctuations, theoretical studies try to reproduce their empirical properties by simulations using dynamic models of large-scale spontaneous brain activity. However, evaluations of the reproducibility have not been extended so far to the fluctuations in global network topology of coactivation patterns, recently shown to be related to human cognition and behavior. Here we examine the extent to which a stationary model typically used for simulating resting-state activity can reproduce spatial and temporal patterns of the empirically observed fluctuations in global network topology. We found that such a model successfully reproduced the magnitude of empirical fluctuations as well as their temporal dynamics, whereas their spatial patterning was not fully accounted for by the simulation. Our results suggest that stationary models can explain many empirical properties in the fluctuations in global network topology, while modeling of non-stationary dynamics and/or greater estimation accuracy of anatomical connections underlying the simulation would be required for complete replication. This finding provides new insights into how fluctuations in global network topology of coactivation patterns emerge in the human brain.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
5.3
作者：
Deco, Gustavo;Ponce-Alvarez, Adrian;Corbetta, Maurizio
通讯作者：
Corbetta, Maurizio
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
5.7
作者：
Cabral, Joana;Hugues, Etienne;Deco, Gustavo
通讯作者：
Deco, Gustavo
影响因子：
4.6
作者：
Deco, Gustavo;Kringelbach, Morten L.;Ritter, Petra
通讯作者：
Ritter, Petra