Venkadesh Siva, Van Horn John Darrell
Department of Psychology, University of Virginia, Charlottesville, VA, United States.
School of Data Science, University of Virginia, Charlottesville, VA, United States.
Front Neurosci. 2021 Oct 29;15:752332. doi: 10.3389/fnins.2021.752332. eCollection 2021.
The anatomical architecture of the brain constrains the dynamics of interactions between various regions. On a microscopic scale, neural plasticity regulates the connections between individual neurons. This microstructural adaptation facilitates coordinated dynamics of populations of neurons (mesoscopic scale) and brain regions (macroscopic scale). However, the mechanisms acting on multiple timescales that govern the reciprocal relationship between neural network structure and its intrinsic dynamics are not well understood. Studies empirically investigating such relationships on the whole-brain level rely on macroscopic measurements of structural and functional connectivity estimated from various neuroimaging modalities such as Diffusion-weighted Magnetic Resonance Imaging (dMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and functional Magnetic Resonance Imaging (fMRI). dMRI measures the anisotropy of water diffusion along axonal fibers, from which structural connections are estimated. EEG and MEG signals measure electrical activity and magnetic fields induced by the electrical activity, respectively, from various brain regions with a high temporal resolution (but limited spatial coverage), whereas fMRI measures regional activations indirectly via blood oxygen level-dependent (BOLD) signals with a high spatial resolution (but limited temporal resolution). There are several studies in the neuroimaging literature reporting statistical associations between macroscopic structural and functional connectivity. On the other hand, models of large-scale oscillatory dynamics conditioned on network structure (such as the one estimated from dMRI connectivity) provide a platform to probe into the structure-dynamics relationship at the mesoscopic level. Such investigations promise to uncover the theoretical underpinnings of the interplay between network structure and dynamics and could be complementary to the macroscopic level inquiries. In this article, we review theoretical and empirical studies that attempt to elucidate the coupling between brain structure and dynamics. Special attention is given to various clinically relevant dimensions of brain connectivity such as the topological features and neural synchronization, and their applicability for a given modality, spatial or temporal scale of analysis is discussed. Our review provides a summary of the progress made along this line of research and identifies challenges and promising future directions for multi-modal neuroimaging analyses.
大脑的解剖结构限制了各个区域之间相互作用的动态过程。在微观尺度上，神经可塑性调节单个神经元之间的连接。这种微观结构的适应性促进了神经元群体（介观尺度）和脑区（宏观尺度）的协调动态。然而，在多个时间尺度上控制神经网络结构与其内在动态之间相互关系的机制尚未得到充分理解。在全脑水平上对这种关系进行实证研究的研究依赖于从各种神经成像模态估计的结构和功能连接性的宏观测量，如扩散加权磁共振成像（dMRI）、脑电图（EEG）、脑磁图（MEG）和功能磁共振成像（fMRI）。dMRI测量沿轴突纤维的水扩散各向异性，据此估计结构连接。EEG和MEG信号分别测量来自不同脑区的电活动和由电活动诱发的磁场，具有高时间分辨率（但空间覆盖有限），而fMRI通过血氧水平依赖（BOLD）信号间接测量区域激活，具有高空间分辨率（但时间分辨率有限）。神经成像文献中有几项研究报告了宏观结构和功能连接之间的统计关联。另一方面，以网络结构为条件的大规模振荡动力学模型（如从dMRI连接性估计的模型）提供了一个平台，用于在介观水平上探究结构 - 动力学关系。此类研究有望揭示网络结构与动力学之间相互作用的理论基础，并且可能补充宏观水平的探究。在本文中，我们回顾了试图阐明脑结构与动力学之间耦合关系的理论和实证研究。特别关注脑连接性的各种临床相关维度，如拓扑特征和神经同步，并讨论它们在给定模态、空间或时间分析尺度上的适用性。我们的综述总结了这一研究方向上取得的进展，并确定了多模态神经成像分析面临的挑战和有前景的未来方向。
