Hierarchical modularity in human brain functional networks

The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) d...

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Main Authors: David Meunier, Renaud Lambiotte, Alex Fornito, Karen Ersche, Edward T Bullmore
Format: Article
Language:English
Published: Frontiers Media S.A. 2009-10-01
Series:Frontiers in Neuroinformatics
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/neuro.11.037.2009/full
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spelling doaj-7bb51cf565d242448c93f8928559787e2020-11-24T23:58:10ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962009-10-01310.3389/neuro.11.037.2009571Hierarchical modularity in human brain functional networksDavid Meunier0David Meunier1Renaud Lambiotte2Alex Fornito3Alex Fornito4Alex Fornito5Karen Ersche6Karen Ersche7Edward T Bullmore8Edward T Bullmore9Edward T Bullmore10Brain Mapping Unit,School of Clinical Medicine,University of CambridgeUniversity of CambridgeImperial CollegeBrain Mapping Unit,School of Clinical Medicine,University of CambridgeMelbourne Neuropsychiatry Centre,Centre for Neuroscience,University of MelbourneUniversity of CambridgeBrain Mapping Unit,School of Clinical Medicine,University of CambridgeUniversity of CambridgeAddenbrooke's HospitalBrain Mapping Unit,School of Clinical Medicine,University of CambridgeUniversity of CambridgeThe idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.http://journal.frontiersin.org/Journal/10.3389/neuro.11.037.2009/fullBrainnetworkhierarchyinformationmodularitygraph theory
collection DOAJ
language English
format Article
sources DOAJ
author David Meunier
David Meunier
Renaud Lambiotte
Alex Fornito
Alex Fornito
Alex Fornito
Karen Ersche
Karen Ersche
Edward T Bullmore
Edward T Bullmore
Edward T Bullmore
spellingShingle David Meunier
David Meunier
Renaud Lambiotte
Alex Fornito
Alex Fornito
Alex Fornito
Karen Ersche
Karen Ersche
Edward T Bullmore
Edward T Bullmore
Edward T Bullmore
Hierarchical modularity in human brain functional networks
Frontiers in Neuroinformatics
Brain
network
hierarchy
information
modularity
graph theory
author_facet David Meunier
David Meunier
Renaud Lambiotte
Alex Fornito
Alex Fornito
Alex Fornito
Karen Ersche
Karen Ersche
Edward T Bullmore
Edward T Bullmore
Edward T Bullmore
author_sort David Meunier
title Hierarchical modularity in human brain functional networks
title_short Hierarchical modularity in human brain functional networks
title_full Hierarchical modularity in human brain functional networks
title_fullStr Hierarchical modularity in human brain functional networks
title_full_unstemmed Hierarchical modularity in human brain functional networks
title_sort hierarchical modularity in human brain functional networks
publisher Frontiers Media S.A.
series Frontiers in Neuroinformatics
issn 1662-5196
publishDate 2009-10-01
description The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.
topic Brain
network
hierarchy
information
modularity
graph theory
url http://journal.frontiersin.org/Journal/10.3389/neuro.11.037.2009/full
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