论文标题
控制理论说明了功能连接的动态重新配置中的能源效率
Control Theory Illustrates the Energy Efficiency in the Dynamic Reconfiguration of Functional Connectivity
论文作者
论文摘要
大脑的功能连通性随着时间的流逝而波动，而不是在静止状态下保持固定模式的稳定。这种波动建立了多种模式之间非随机顺序过渡的动态功能连接性。然而，它仍然没有探索过渡如何促进整个大脑网络作为动力学系统，以及这种动态重新配置机制的实用性可以带来广泛使用的图理论测量。为了解决这些问题，我们建议使用静止状态fMRI和人类Connectome项目的行为测量对功能性脑网络进行充满活力的分析。通过在不同的相邻矩阵下比较状态过渡能量，我们证明，动态功能连接性的能量成本少60％，以支持静止状态动力学，而不是静态连通性在通过默认模式网络推动过渡时的静态连接性。此外，我们证明，将图理论测量和基于能量的控制测量值组合为特征向量可以为行为得分提供互补的预测能力。我们的方法将统计推断和动态系统检查整合到了解大脑网络的方面。
The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores. Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.
