Mapping Anatomical Connectivity Patterns of Human Cerebral Cortex Using In Vivo Diffusion Tensor Imaging Tractography

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ID: 297037
2008
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Ranked #20 of 107 articles by views in cerebral cortex (new york, ny : 1991)

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Abstract
The characterization of the topological architecture of complex networks underlying the structural and functional organization of the brain is a basic challenge in neuroscience. However, direct evidence for anatomical connectivity networks in the human brain remains scarce. Here, we utilized diffusion tensor imaging deterministic tractography to construct a macroscale anatomical network capturing the underlying common connectivity pattern of human cerebral cortex in a large sample of subjects (80 young adults) and further quantitatively analyzed its topological properties with graph theoretical approaches. The cerebral cortex was divided into 78 cortical regions, each representing a network node, and 2 cortical regions were considered connected if the probability of fiber connections exceeded a statistical criterion. The topological parameters of the established cortical network (binarized) resemble that of a "small-world" architecture characterized by an exponentially truncated power-law distribution. These characteristics imply high resilience to localized damage. Furthermore, this cortical network was characterized by major hub regions in association cortices that were connected by bridge connections following long-range white matter pathways. Our results are compatible with previous structural and functional brain networks studies and provide insight into the organizational principles of human brain anatomical networks that underlie functional states.
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openalex_W2162686834 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gaolang Gong, Yong He, Luis Concha, Catherine Lebel, Donald Gross, Alan C. Evans, Christian Beaulieu
Journal cerebral cortex (new york, ny : 1991)
Year 2008
DOI
10.1093/cercor/bhn102
URL
https://academic.oup.com/cercor/article-pdf/19/3/524/17300772/bhn102.pdf
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