Wang Rong, Zhang Zhen-Zhen, Ma Jun, Yang Yong, Lin Pan, Wu Ying
State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China.
College of Electrical and Information Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
Chaos. 2015 Dec;25(12):123112. doi: 10.1063/1.4937451.
The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.
脑网络的时间演化特性对于复杂的脑过程至关重要。在本文中，我们研究了在任务积极子网、任务消极子网和全脑网络中，静息状态和视觉刺激状态下动态脑网络的差异。首先基于滑动窗口方法从人类功能磁共振成像数据构建动态脑网络，然后计算网络对应的特征值。我们使用特征值分析来分析特征值的全局特性，并使用随机矩阵理论（RMT）方法来测量局部特性。对于全局特性，特征值分布的移动和最大特征值的减小与所有网络中的视觉刺激有关。对于局部特性，通过最近邻间距分布测量的特征值中的短程相关性并不总是对视觉刺激敏感。然而，通过谱刚度和数方差评估的特征值中的长程相关性不仅预测了动态脑网络的普遍行为，还表明了不同网络中的不一致变化。这些结果表明，在视觉刺激下，任务积极子网和全脑网络的动态脑网络更随机，而任务消极子网的动态脑网络更规则。我们的研究结果为功能脑网络中谱特性的重要性提供了更深入的见解，特别是RMT在揭示复杂系统内在特性方面的不可比拟的作用。
