Gordon Evan M, Laumann Timothy O, Marek Scott, Raut Ryan V, Gratton Caterina, Newbold Dillan J, Greene Deanna J, Coalson Rebecca S, Snyder Abraham Z, Schlaggar Bradley L, Petersen Steven E, Dosenbach Nico U F, Nelson Steven M
Veterans Integrated Service Network 17 Center of Excellence for Research on Returning War Veterans, US Department of Veterans Affairs, Waco, TX 76711;
Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235.
Proc Natl Acad Sci U S A. 2020 Jul 21;117(29):17308-17319. doi: 10.1073/pnas.2005238117. Epub 2020 Jul 6.
The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdivisions were more valid-more internally homogeneous and better matching spatial patterns of task activation-than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.
人类大脑被组织成可通过静息态功能连接（RSFC）识别的大规模网络。这些功能网络与广泛的认知领域相对应；例如，默认模式网络（DMN）在内部导向的认知过程中被激活。然而，功能网络可能包含与更特定认知功能相对应的层次子结构。在这里，我们使用个体特异性精确RSFC来测试是否能在10个健康人类大脑中识别出网络子结构。在所有受试者和网络中，个体化的网络细分比标准网络更有效——内部更均匀且与任务激活的空间模式匹配得更好。这些有效性指标在一个包含全脑约83个子网络的层次尺度上达到最大值。在这个尺度上，九个DMN子网在受试者之间表现出地形相似性，这表明这种方法能够识别个体间同源的神经生物学回路。一些DMN子网与对应认知功能的已知脑组织结构特征相匹配。其他子网代表了DMN与其他标准大规模网络（包括语言和控制网络）耦合的不同信息流。总之，这项工作为研究个体人类的DMN提供了一个详细的组织框架。
