title: Spectral properties of the temporal evolution of brain network structure.
abstract: 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.
authors.affiliation: State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China.
authors.forename: Rong
authors.initials: R
authors.lastname: Wang
authors.affiliation: College of Electrical and Information Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
authors.forename: Zhen-Zhen
authors.initials: ZZ
authors.lastname: Zhang
authors.affiliation: Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Ma
authors.affiliation: School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, People's Republic of China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
authors.forename: Pan
authors.initials: P
authors.lastname: Lin
authors.affiliation: State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wu
mesh_terms: D001921:Brain / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000502:physiology*; D013647:Task Performance and Analysis; D013997:Time Factors; D014795:Visual Pathways / Q000502:physiology