Literature DB >> 30291974

Mapping the human brain's cortical-subcortical functional network organization.

Jie Lisa Ji1, Marjolein Spronk2, Kaustubh Kulkarni2, Grega Repovš3, Alan Anticevic1, Michael W Cole4.   

Abstract

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.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Brain connectivity; Brain networks; Functional MRI; Resting-state functional connectivity

Mesh:

Year:  2018        PMID: 30291974      PMCID: PMC6289683          DOI: 10.1016/j.neuroimage.2018.10.006

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


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