Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations
Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations
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DOI：
10.1523/jneurosci.1091-13.2013
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发表时间：
2013-07-03
影响因子：
5.3
通讯作者：
Corbetta, Maurizio
中科院分区：
文献类型：
--
作者：
Deco, Gustavo;Ponce-Alvarez, Adrian;Corbetta, Maurizio
休息时的大脑波动不是随机的，而是以不同大脑区域相关活动的空间模式结构化的。静息态功能连接（FC）如何从大脑的解剖学连接中出现的问题已经激发了一些实验和计算研究来理解结构-功能关系。然而，静息态的机制起源被大规模模型的复杂性所掩盖，并且紧密的结构-功能关系仍然是一个悬而未决的问题。因此，一个现实的，但足够简单的描述相关的大脑动力学是必要的。在这里，我们推导出一个动态平均场模型，该模型始终总结了一个详细的尖峰和基于电导的突触大规模网络的现实动态，其中连接受到来自人类受试者的扩散成像数据的约束。动态平均场近似于系综动力学，其时间演化由系统的最长时间尺度决定。通过这种减少，我们证明了FC出现在稳定的低点火活动状态附近的结构化线性波动接近不稳定。此外，该模型可以进一步简化为一组统计矩的运动方程，提供解剖结构，神经网络动力学和FC之间的直接分析联系。我们的研究表明，FC产生于噪声传播和动力学减慢的波动在一个解剖约束的动力系统。总而言之，减少尖峰模型的统计时刻，这里提供了一个新的框架，明确了解FC的建立通过神经元动力学解剖连接的基础上，并推动任务诱发的研究和临床应用的假设。
Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.