Default-mode network streams for coupling to language and control systems
Author(s) -
Evan M. Gordon,
Timothy O. Laumann,
Scott Marek,
Ryan V. Raut,
Caterina Gratton,
Dillan J. Newbold,
Deanna J. Greene,
Rebecca S. Coalson,
Abraham Z. Snyder,
Bradley L. Schlaggar,
Steven E. Petersen,
Nico U.F. Dosenbach,
Scott M. Nelson
Publication year - 2020
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2005238117
Subject(s) - default mode network , cognition , computer science , unitary state , working memory , set (abstract data type) , control (management) , cognitive psychology , psychology , neuroscience , artificial intelligence , political science , law , programming language
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.
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