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Cited by 326Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesMedical and Biological Sciences

DTI mapping of human brain connectivity: Statistical fibre tracking and virtual dissection

Open access
Hagmann, P., Thiran, J.-P., Jonasson, L., Vandergheynst, P., Clarke, S., Maeder, P., Meuli, R.·2003·NeuroImage
DOI 10.1016/s1053-8119(03)00142-3
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Abstract

Several approaches have been used to trace axonal trajectories from diffusion MRI data. If such techniques were first developed in a deterministic framework reducing the diffusion information to one single main direction, more recent approaches emerged that were statistical in nature and that took into account the whole diffusion information. Based on diffusion tensor MRI data coming from normal brains, this paper presents how brain connectivity could be modelled globally by means of a random walk algorithm. The mass of connections thus generated was then virtually dissected to uncover different tracts. Corticospinal, corticobulbar, and corticothalamic tracts, the corpus callosum, the limbic system, several cortical association bundles, the cerebellar peduncles, and the medial lemniscus were all investigated. The results were then displayed in the form of an in vivo brain connectivity atlas. The connectivity pattern and the individual fibre tracts were then compared to known anatomical data; a good matching was found.

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  • Diffusion MRI became a foundational tool for the Human Connectome era because it offered a scalable route to individual structural connectivity.

    “Diffusion MRI became a foundational tool for the Human Connectome era because it offered a scalable route to individual structural connectivity (Assaf et al., 2019; Hagmann et al., 2003).”

    From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Hagmann et al., 2003

    The Abstract and Results (page numbers unavailable) describe using diffusion MRI to map brain connectivity in vivo in two healthy volunteers, supporting a route to individual structural connectivity. The paper does not establish that the approach was scalable or that it became foundational to the later Human Connectome era. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Probabilistic approaches have been used to create the first atlases of large-scale brain connections.

    “Probabilistic approaches have also been used to create the first atlases of large-scale brain connections (Behrens et al., 2003; Hagmann et al., 2003; Parker et al., 2003).”

    From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Hagmann et al., 2003

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Research works printed but not linked: 4

  1. Beaulieu, C., 2002. Relating micro-structural properties of neural fibres with anisotropic diffusion, in: Proc. Workshop on Diffusion MRI: Biophysical Issues. ISMRM, pp. 79 -82.
  2. Field, A., Alexander, A., Hasan, K., Arfanakis, K., Witwer, B., Moftakhar, R., Deshmukh, P., Haughton, V., Rowley, H., Noyes, J., B., H., Moritz, C., Meyerand, M., Badie, B., 2002. Diffusion-tensor MR imaging patterns in white matter tracts altered by neoplasm, in: Workshop on Diffusion MRI: Biophysical Issues. ISMRM, pp. 137-140.
  3. Mamata, H., Westin, C., Zou, K., De Girolami, U., F.A., J., S.E., M., 2002. Combined mapping of functional domains and axonal connectivity in cat visual cortex using fmri and dti, in: Workshop on Diffusion MRI: Biophysical Issues, ISMRM, pp. 121-124.
  4. Pajevic, S., Pierpaoli, C., 1999. Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn. Reson. Med. 42 (3), 526 -540.

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Authors
Hagmann, P., Thiran, J.-P., Jonasson, L., Vandergheynst, P., Clarke, S., Maeder, P., & Meuli, R.
Affiliations
  • Centre Hospitalier Universitaire Vaudois (CH)
  • École Polytechnique Fédérale de Lausanne (CH)
Corresponding author
Hagmann, P.
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2003-07
Volume
19
Issue
3
Pages
545-554
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/s1053-8119(03)00142-3
OpenAlex
W2131478329
Cited by
326
References
39
Citations by year
2026: 3 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 4 · 2021: 5 · 2020: 12 · 2019: 9 · 2018: 10 · 2017: 8 · 2016: 9 · 2015: 8 · 2014: 16 · 2013: 30 · 2012: 20
FWCI
16.56
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
  • IRIS (submittedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Anatomy › Medical and Biological Sciences
Keywords
diffusion tensor imaging, brain connectivity, random walk algorithm, virtual dissection, corticospinal tract, corticobulbar tract, corpus callosum, limbic system, cerebellar peduncles, medial lemniscus, axonal trajectories
MeSH terms
Algorithms, Axons, Brain, Brain Mapping, Cerebellum, Cerebral Cortex, Computer Graphics, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Nerve Fibers, Neural Pathways, Pyramidal Tracts, Thalamus

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