期刊
NEUROIMAGE
卷 185, 期 -, 页码 35-57出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2018.10.006
关键词
Brain networks; Brain connectivity; Functional MRI; Resting-state functional connectivity
资金
- McDonnell Center for Systems Neuroscience at Washington University
- NIH [K99/R00-MH096801, DP5-OD012109, R01-MH109520, R01-MH108590, R01-AG055556, R01-MH112189, 1U54MH091657]
- Brain and Behavior Foundation (NARSAD) Independent Investigator grant
- [ARRS J7-6829]
Understanding complex systems such as the human brain requires characterization of the system's architecture across multiple levels of organization - from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brain's large-scale network organization, as it provides an overall framework for the organization of all other levels. We developed a highly principled approach to identify cortical network communities at the level of functional systems, calibrating our community detection algorithm using extremely well-established sensory and motor systems as guides. Building on previous network partitions, we replicated and expanded upon well-known and recently-identified networks, including several higher-order cognitive networks such as a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 358 highly-organized subcortical parcels that take part in forming whole-brain functional networks. Notably, the identified subcortical parcels are similar in number to a recent estimate of the number of cortical parcels (360). This whole-brain network atlas - released as an open resource for the neuroscience community - places all brain structures across both cortex and subcortex into a single large-scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease.
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