Faculty of Veterinary Medicine, University of Teramo, Teramo, Italy.
Department of Neuroscience, University of Padua, Padua 35128, Italy; Departments of Neurology, Radiology, Neuroscience, and Biomedical Engineering, Washington University, St. Louis, MO 63101 USA.
Neuroimage. 2018 Oct 15;180(Pt B):370-382. doi: 10.1016/j.neuroimage.2017.09.063. Epub 2017 Sep 30.
Spontaneous brain activity at rest is spatially and temporally organized in networks of cortical and subcortical regions specialized for different functional domains. Even though brain networks were first studied individually through functional Magnetic Resonance Imaging, more recent studies focused on their dynamic 'integration'. Integration depends on two fundamental properties: the structural topology of brain networks and the dynamics of functional connectivity. In this scenario, cortical hub regions, that are central regions highly connected with other areas of the brain, play a fundamental role in serving as way stations for network traffic. In this review, we focus on the functional organization of a set of hub areas that we define as the 'dynamic core'. In the resting state, these regions dynamically interact with other regions of the brain linking multiple networks. First, we introduce and compare the statistical measures used for detecting hubs. Second, we discuss their identification based on different methods (functional Magnetic Resonance Imaging, Diffusion Weighted Imaging, Electro/Magneto Encephalography). Third, we show that the degree of interaction between these core regions and the rest of the brain varies over time, indicating that their centrality is not stationary. Moreover, alternating periods of strong and weak centrality of the core relate to periods of strong and weak global efficiency in the brain. These results indicate that information processing in the brain is not stable, but fluctuates and its temporal and spectral properties are discussed. In particular, the hypothesis of 'pulsed' information processing, discovered in the slow temporal scale, is explored for signals at higher temporal resolution.
静息状态下的大脑活动在空间和时间上组织在专门用于不同功能域的皮质和皮质下区域的网络中。尽管大脑网络最初是通过功能磁共振成像单独研究的，但最近的研究集中在它们的动态“整合”上。整合取决于两个基本属性：大脑网络的结构拓扑和功能连接的动力学。在这种情况下，皮质中枢区域是与大脑其他区域高度连接的中央区域，在充当网络流量的中转站方面发挥着重要作用。在这篇综述中，我们专注于一组中枢区域的功能组织，我们将其定义为“动态核心”。在静息状态下，这些区域与大脑的其他区域动态相互作用，连接多个网络。首先，我们介绍并比较了用于检测中枢的统计度量。其次，我们讨论了基于不同方法（功能磁共振成像、弥散加权成像、脑电/磁图）的识别。第三，我们表明这些核心区域与大脑其他区域之间的相互作用程度随时间变化，表明它们的中心性不是静态的。此外，核心区域与大脑其他区域之间的强和弱中心性交替期与大脑的强和弱全局效率期相关。这些结果表明，大脑中的信息处理不是稳定的，而是波动的，并且讨论了其时间和频谱特性。特别是，在较慢的时间尺度上发现的“脉冲”信息处理假设，针对更高时间分辨率的信号进行了探索。
