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Emergence of Metastable Dynamics in Functional Brain Organization via Spontaneous fMRI Signal and Whole-Brain Computational Modeling

机译:通过自发性FMRI信号和全脑计算模拟功能性脑组织中旋转动力学的出现

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Little is known about the mechanisms underlying the resting-state brain organization. This study investigated how metastability, defined as the standard deviation of synchrony described by the Kuramoto order parameter, arises from the structural connectome and relates to empirical measures of metastability in resting-state brain networks. We tested whether spontaneous fMRI brain activity in the functional organization of the human brain operates in a metastable state. We compared between empirical metastability defined in four major resting-state brain networks - auditory network, default mode network, left and right executive control networks - and simulated metastability derived from the Kuramoto model constrained by the empirical anatomical connectivity. Our results show that maximal metastability within resting-state brain networks arises from the model with different coupling strengths. Empirical metastability corresponds to a dynamical region where the simulated metastability is maximized. The emergence of metastable dynamics observed in empirical resting-state functional networks around the region of maximal metastability suggests that such a dynamical regime in the brain may drive the resting state of the brain. Our study may provide a mechanistic explanation of the origin of functional organization of the brain, and may help our understanding of the mechanistic causes of disease.
机译:关于静态脑脑组织底层的机制,众所周知。本研究研究了定义为Kuramoto订单参数所描述的同步偏差的常量性,从结构连接中出现,并涉及休息状态脑网络中的衡量性的实证测量。我们测试了人脑功能组织中的自发性FMRI脑活动是否以亚稳态运行。我们比较了四个主要休息状态脑网络中定义的经验延展性 - 听觉网络,默认模式网络,左右执行控制网络 - 和源自由经验解剖连通性的Kuramoto模型的模拟常规性。我们的结果表明,休息状态大脑网络中的最大亚稳定性来自具有不同耦合强度的模型。经验造型性对应于模拟亚稳态最大化的动态区域。在最大迁移性区域周围观察到在最大迁移性区域周围的亚稳态动力学的出现表明大脑中的这种动态状态可以驱动大脑的静止状态。我们的研究可以提供对大脑功能组织起源的机制解释,并可能有助于我们对疾病的机制原因的理解。

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