Literature DB >> 19190637

Complex brain networks: graph theoretical analysis of structural and functional systems.

Ed Bullmore1, Olaf Sporns.   

Abstract

Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

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Year:  2009        PMID: 19190637     DOI: 10.1038/nrn2575

Source DB:  PubMed          Journal:  Nat Rev Neurosci        ISSN: 1471-003X            Impact factor:   34.870


  2000 in total

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4.  Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks.

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5.  Learning-based structurally-guided construction of resting-state functional correlation tensors.

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6.  A procedure to increase the power of Granger-causal analysis through temporal smoothing.

Authors:  E Spencer; L-E Martinet; E N Eskandar; C J Chu; E D Kolaczyk; S S Cash; U T Eden; M A Kramer
Journal:  J Neurosci Methods       Date:  2018-07-19       Impact factor: 2.390

7.  Changes in the topological organization of the default mode network in autism spectrum disorder.

Authors:  Liting Chen; Yunmi Chen; Huang Zheng; Bin Zhang; Fei Wang; Jin Fang; Yueyue Li; Qiuyin Chen; Shuixing Zhang
Journal:  Brain Imaging Behav       Date:  2021-04       Impact factor: 3.978

8.  A multivariate distance-based analytic framework for connectome-wide association studies.

Authors:  Zarrar Shehzad; Clare Kelly; Philip T Reiss; R Cameron Craddock; John W Emerson; Katie McMahon; David A Copland; F Xavier Castellanos; Michael P Milham
Journal:  Neuroimage       Date:  2014-02-28       Impact factor: 6.556

9.  Connectivity trajectory across lifespan differentiates the precuneus from the default network.

Authors:  Zhi Yang; Catie Chang; Ting Xu; Lili Jiang; Daniel A Handwerker; F Xavier Castellanos; Michael P Milham; Peter A Bandettini; Xi-Nian Zuo
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10.  Children's intellectual ability is associated with structural network integrity.

Authors:  Dae-Jin Kim; Elysia Poggi Davis; Curt A Sandman; Olaf Sporns; Brian F O'Donnell; Claudia Buss; William P Hetrick
Journal:  Neuroimage       Date:  2015-09-15       Impact factor: 6.556

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