The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China.
Hum Brain Mapp. 2019 Oct 15;40(15):4331-4344. doi: 10.1002/hbm.24705. Epub 2019 Jul 5.
A major challenge in neuroscience is understanding how brain function emerges from the connectome. Most current methods have focused on quantifying functional connectomes in gray-matter (GM) signals obtained from functional magnetic resonance imaging (fMRI), while signals from white-matter (WM) have generally been excluded as noise. In this study, we derived a functional connectome from WM resting-state blood-oxygen-level-dependent (BOLD)-fMRI signals from a large cohort (n = 488). The WM functional connectome exhibited weak small-world topology and nonrandom modularity. We also found a long-term (i.e., over 10 months) topological reliability, with topological reproducibility within different brain parcellation strategies, spatial distance effect, global and cerebrospinal fluid signals regression or not. Furthermore, the small-worldness was positively correlated with individuals' intelligence values (r = .17, p = .0009). The current findings offer initial evidence using WM connectome and present additional measures by which to uncover WM functional information in both healthy individuals and in cases of clinical disease.
神经科学的一个主要挑战是理解大脑功能如何从连接组中显现出来。大多数当前的方法都集中于从功能磁共振成像（fMRI）获得的灰质（GM）信号中量化功能连接组，而通常将来自白质（WM）的信号排除为噪声。在这项研究中，我们从一个大队列（n = 488）的 WM 静息状态血氧水平依赖（BOLD）-fMRI 信号中得出了一个功能连接组。WM 功能连接组表现出较弱的小世界拓扑结构和非随机模块性。我们还发现了长期（即超过 10 个月）拓扑可靠性，不同脑区划分策略之间的拓扑可重复性、空间距离效应、全局和脑脊液信号回归或不回归。此外，小世界特性与个体的智力值呈正相关（r =.17，p =.0009）。当前的发现提供了使用 WM 连接组的初步证据，并提出了其他措施，以便在健康个体和临床疾病中揭示 WM 功能信息。
