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Cited by 788Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesComplex Network Analysis Techniques

Mapping Human Whole-Brain Structural Networks with Diffusion MRI

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Hagmann, Patric, Kurant, Maciej, Gigandet, Xavier, Thiran, Patrick, Wedeen, Van J., Meuli, Reto, Thiran, Jean-Philippe·2007·PLoS ONE
DOI 10.1371/journal.pone.0000597PMC PMC1895920
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Abstract

Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.

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  • Sparse small-world attributes are robust in that they are reproducible across imaging modalities, disease states, and species.

    “Sparse small-world attributes are robust in that they are reproducible across imaging modalities (diffusion MRI, 45,46 resting fMRI, 2,78,102 structural MRI, 48 EEG, 82 MEG, 90 and PET 30,79 ), disease states, and species (such as the cat, macaque, and nematode Caenorhabditis elegans).20”

    From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [46]

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References 58 · Cited by 788

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Authors
Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V. J., Meuli, R., & Thiran, J.-P.
Affiliations
  • Centre Hospitalier Universitaire Vaudois (CH)
  • École Polytechnique Fédérale de Lausanne (CH)
  • Harvard University (US)
Corresponding author
Hagmann, Patric
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2007-07-04
Volume
2
Issue
7
Pages
e597
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0000597
PMC
PMC1895920
OpenAlex
W1972412818
Cited by
788
References
58
Citations by year
2026: 10 · 2025: 13 · 2024: 32 · 2023: 33 · 2022: 26 · 2021: 32 · 2020: 44 · 2019: 51 · 2018: 53 · 2017: 58 · 2016: 61 · 2015: 58 · 2014: 47 · 2013: 69 · 2012: 57
FWCI
22.75
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IRIS (submittedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • SERVAL (Université de Lausanne) (submittedVersion, other-oa)
Funders
  • National Science Foundation
  • Francis Crick Institute
  • École Polytechnique Fédérale de Lausanne
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
  • Centre d'Imagerie BioMédicale
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
white matter connectivity, diffusion MRI, human brain, degree distribution, small-world networks, brain network topology
MeSH terms
Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Nerve Fibers, Organ Size, Periaqueductal Gray, Diffusion Magnetic Resonance Imaging

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