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

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

May 2009 | Volume 5 | Issue 5 | e1000381 | Damien A. Fair, Alexander L. Cohen, Jonathan D. Power, Nico U. F. Dosenbach, Jessica A. Church, Francis M. Miezin, Bradley L. Schlaggar, Steven E. Petersen
The study by Fair et al. (2009) investigates the development of functional brain networks using resting-state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques. The researchers analyzed four separate networks: the default mode, cerebellar, fronto-parietal, and cingulo-opercular networks. They found that as children grow into young adults, the organization of these networks shifts from a local, anatomically based structure to a more distributed, functionally based architecture. Specifically, regions in children are more closely grouped by anatomical proximity, while in adults, they are arranged by functional relationships. This shift is accompanied by a general decrease in correlation strength between anatomically close regions and an increase in correlation strength between distant regions. The study also found that both child and adult networks exhibit "small-world" properties, characterized by high clustering coefficients and short average path lengths, suggesting efficient information processing. Additionally, community detection algorithms revealed stable communities within the graphs that differ between young children and young adults. These findings have implications for understanding the development of neural systems underlying cognition and the maturation of functional brain networks.The study by Fair et al. (2009) investigates the development of functional brain networks using resting-state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques. The researchers analyzed four separate networks: the default mode, cerebellar, fronto-parietal, and cingulo-opercular networks. They found that as children grow into young adults, the organization of these networks shifts from a local, anatomically based structure to a more distributed, functionally based architecture. Specifically, regions in children are more closely grouped by anatomical proximity, while in adults, they are arranged by functional relationships. This shift is accompanied by a general decrease in correlation strength between anatomically close regions and an increase in correlation strength between distant regions. The study also found that both child and adult networks exhibit "small-world" properties, characterized by high clustering coefficients and short average path lengths, suggesting efficient information processing. Additionally, community detection algorithms revealed stable communities within the graphs that differ between young children and young adults. These findings have implications for understanding the development of neural systems underlying cognition and the maturation of functional brain networks.
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