Institute of Theoretical Physics, Lanzhou University, Lanzhou, People's Republic of China.
J Neural Eng. 2019 Jul 23;16(5):056002. doi: 10.1088/1741-2552/ab20bc.
The exploration of time-varying functional connectivity (FC) through human neuroimaging techniques provides important new insights on the spatio-temporal organization of functional communication in the brain's networks and its alterations in diseased brains. However, little is known about the underlying dynamic mechanism with which such a dynamic FC is flexibly organized under the constraint of structural connections. In this work, we explore the relationship between critical dynamics and FC flexibility based on both functional magnetic resonance imaging data and computer models.
First, we proposed the connectivity number entropy (CNE), which was an entropy measure for the flexibility of FC. Through an analysis of resting-state fMRI (rs-fMRI) data from 95 healthy participants, we explored the correlation between CNE and long-range temporal correlations (LRTCs), which can represent the critical dynamics. Then, we employed a whole-brain computer model based on diffusion tensor imaging (DTI) to further demonstrate this relationship.
We found that the most flexible FC is present when the brain is operating close to the critical point of a phase transition. Additionally, around this point, our model can yield the best prediction for the regional distribution of CNE because structural information is reflected the most by the CNE through critical dynamics.
Our results not only reveal the underlying dynamic mechanism for the organization of time-dependent FC but also provide a possible pathway to model the flexible functional organization in the human brain and may have potential application in the analysis of altered dynamic FC in diseased brains.
通过人类神经影像学技术探索时变功能连接（FC），为大脑网络中功能通讯的时空组织及其在病变大脑中的改变提供了重要的新见解。然而，对于这种动态 FC 如何在结构连接的约束下灵活组织的潜在动态机制，我们知之甚少。在这项工作中，我们基于功能磁共振成像（fMRI）数据和计算机模型来探索关键动力学与 FC 灵活性之间的关系。
首先，我们提出了连接数熵（CNE），这是一种用于 FC 灵活性的熵度量。通过对 95 名健康参与者的静息态 fMRI（rs-fMRI）数据进行分析，我们探讨了 CNE 与能够代表关键动力学的长程时间相关性（LRTCs）之间的相关性。然后，我们采用了基于弥散张量成像（DTI）的全脑计算机模型来进一步证明这种关系。
我们发现，当大脑接近相变的临界点时，最灵活的 FC 出现。此外，在这个临界点附近，我们的模型可以对 CNE 的区域分布进行最佳预测，因为结构信息通过关键动力学最能反映 CNE。
我们的结果不仅揭示了时变 FC 组织的潜在动力学机制，还为模型人类大脑中灵活的功能组织提供了一种可能的途径，并可能在分析病变大脑中改变的动态 FC 方面具有潜在的应用价值。
