Functional Brain Networks Develop from a “Local to Distributed” Organization

By Damien A. Fair, Alexander Li Cohen, Jonathan D. Power, Nico U F Dosenbach, Jessica A. Church, Francis M. Miezin, Bradley L. Schlaggar, Steven E. Petersen. PLoS Comput. Biol., 2009

1,570 citations recorded by the source corpus.

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b4ea5ca431d34abe8c5b2b417028ff9d1bfa3536

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In Cognitive Neuroscience

Citations in the corpus

Consensus Paper: The Cerebellum's Role in Movement and Cognition (2013, background): “This progress comes within the context of a larger movement toward understanding the human brain as functioning as an ensemble, rather than a fragmented collection of regions and abilities [258 – 261].”
Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks (2011, background): “Nervous systems also have the complementary topological property of modularity, meaning that the network is nearly decomposable (Simon, 1962) into a set of modules comprising nodes that are densely connected to each other and sparsely connected to nodes in other modules (Newman, 2006; Chen et al., 2”
Schizophrenia, neuroimaging and connectomics (2012, background): “…cite this article as: Fornito, A., et al., Schizophrenia, neur j.neuroimage.2011.12.090 identified in functional imaging studies (Dosenbach et al., 2010; Fair et al., 2009) can more accurately define functionally homogeneous regions, but they are difficult to apply to structural imaging data.”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “These trends pointed to coherent changes that involved a dampening of non-specific local spread of activity and strengthening of long-range links, thereby facilitating cognitive development (Bunge and Wright, 2007; Fair et al., 2009; Uddin, 2011; Uddin et al., 2011).”
Changes in structural and functional connectivity among resting-state networks across the human lifespan (2014, result): “In contrast, aging studies have demonstrated the opposite effect, with RSNs exhibiting decreased FC (Andrews-Hanna et al., 2007; Ferreira and Busatto, 2013; Geerlings et al., 2014).”
Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “Two of these studies investigated the influence of normal aging on the modular structure: Meunier et al. (2009b) focused on the adult period (healthy controls from 25-to 65-years old); whereas Fair et al. (2009) focused on adolescence and early adulthood (healthy controls from 7-to 31-years old).”
Nurturing Nature: How Brain Development Is Inherently Social and Emotional, and What This Means for Education (2019, background): “The basic organization of these networks appears to be present at birth and to develop across the first decades of life (Fair et al., 2009; Hoff, Van den Heuvel, Benders, Kersbergen, & De Vries, 2013; Lin et al., 2008; Liu, Flax, Guise, Sukul, & Benasich, 2008; Supekar, Musen, & Menon, 2009), but…”
Weight-conserving characterization of complex functional brain networks (2011, background): “For instance, recent characterizations partitioned complex resting-state functional MRI networks into modules which correspond to visual, attention, default mode and other “simple” networks (Fair et al., 2009; He et al., 2009; Meunier et al., 2009) and identified prominent central brain regions in h”
Can structure predict function in the human brain? (2010, neutral): “While diffusion-imaging based SC mapping is yet to be systematically assessed across the course of human brain development, several cross-sectional studies have examined the developmental trajectories of FC (Fair et al., 2007; Fair et al., 2009; Supekar et al., 2009).”
Multi‐echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals (2017, background): “…have brought into question many of the compelling findings on brain function based on fMRI, for example, relating to human brain development (Fair et al., 2009). fMRI artifacts also reduce the statistical power of fMRI studies and lead to spurious findings, which has been associated with a…”
Graph-Based Network Analysis of Resting-State Functional MRI (2010, neutral): “…utility of graph-based techniques has been proven by an increasing number of studies to probe potential mechanisms involved in normal development (Fair et al., 2007, 2008, 2009; Supekar et al., 2009), aging (Achard and Bullmore, 2007; Gong et al., 2009b; Meunier et al., 2009a; Micheloyannis et…”