静息态fMRI多层时间网络的动态和控制特性研究及在老化中的应用

大脑动态特性是当前的研究热点。针对静息态fMRI（resting state fMRI, RS-fMRI）数据，采用滑窗法可以构建多层时间网络，揭示大脑动态特性。但是滑窗法构建的网络节点连接在窗长内不随时间点波动，因此，可以认为这种脑网络反映的是一种准动态特性。在脑动态特性研究中，我们希望时间窗足够小至单时间点，从而建立大脑真正的动态特性。在真正动态特性的基础上，考察脑网络在不同时间点对脑状态的控制特性成为可能。因此，本项目提出单时间点多层时间网络概念，探讨单时间点多层时间网络的构建，考察RS-fMRI脑网络的动态特性及控制特性。具体研究内容将围绕以下四个方面展开：（1）RS-fMRI单时间点多层时间网络构建方法探讨。（2）基于矩阵分解法的单时间点多层时间网络的动态特性分析。（3）RS-fMRI单时间点多层时间网络的控制特性分析。（4）静息态fMRI单时间点多层时间网络在老化中的应用。

Brain dynamics has been a hot topic recently. Based on resting state functional magnetic resonance imaging (RS-fMRI) data, multilayer temporal network using sliding window can be used to investigate brain dynamics. However, the links between nodes in network based on sliding window do not fluctuate with time points within one temporal window. Thus, networks based on sliding window can only represent the quasi dynamic property of brain. When the time window is small enough and shrinks to one time point, the true dynamic properities of brain can be represented. Based on the true dynamics, it is possible to investigate the controllability of brain networks on brain states with time points. In this project, we are going to propose the concept of multiple temporal network of single time point (MTNSTP), explore the construction of MTNSTP and investigate the dynamics and controllability of RS-fMRI brain network. The project will investigate the following four issues. (1) The construction of MTNSTP based on RS-fMRI. (2)The dynamic analysis of MTNSTP based on matrix decomposing methods. (3) The controllability analysis of MTNSTP based on RS-fMRI. (4) The application of RS-fMRI MTNSTP to aging.