人脑默认模式网络的动力学行为

Dynamics of the default mode network in human brain

大脑具有自适应、自组织、多稳态等重要特征,是典型的复杂系统.人脑在静息态下的关键功能子网络--默认模式网络(DMN)的激活处于多状态间持续跳转的非平衡过程,揭示该过程背后的动力学机制具有重要的科学意义和临床应用前景.本文基于功能磁共振获得的血氧水平依赖(BOLD)信号,建立了DMN吸引子跳转非平衡过程的能量图景、吸引子非联通图、跳转关系网络等;以高级视觉皮层和听觉等皮层活动为例,通过对应激活DMN状态空间的分布,以及XGBoost、深度神经网络等算法验证了DMN状态变化与外部脑区状态的密切依赖关系;通过偏相关、收敛交叉映射等方法分析了DMN内各个脑区之间的相互作用.本文结果有助于理解静息态下大脑内在非平衡过程的动力学机制,以及从动力学的角度探索具有临床意义的脑功能障碍生物标志物.

Brain is a typical complex system with characteristics such as self-adaptation,self-organization,and multistability.The activity of the default mode network(DMN),a crucial functional subnetwork of the human brain in resting state,obeys typical non-equilibrium statistical mechanical processes in which the system continually switches among multiple metastable states.Revealing the underlying dynamical mechanism of these processes has important scientific significance and clinical application prospects.In this paper,according to the blood oxygen level dependent(BOLD)signals obtained from functional magnetic resonance imaging(fMRI),we build an energy landscape,disconnectivity graph and transition network to explore the non-equilibrium processes of DMN switching among different attractors in resting state.Taking the activities of high-level visual and auditory cortices for examples,we verify the intimate relationship between the dynamics of DMN and the activity modes of these external brain regions,through comparing the distributions in state space and the algorithms such as XGBoost and deep neural networks.In addition,we analyze the interaction between various DMN regions in the resting state by using the techniques such as compressive-sensing-based partial correlation and convergence cross mapping.The results in this paper may presnt new insights into revealing the dynamics of the intrinsic non-equilibrium processes of brain in resting state,and putting forward clinically significant biomarkers for brain dysfunction from the viewpoint of dynamics.