Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Measures for pathway analysis in brain white matter using diffusion tensor images.

Laura Astola1, Luc Florack, Bart ter Haar Romeny

  • 1Eindhoven University of Technology, PO Box 513, NL-5600 MB Eindhoven, The Netherlands.

Information Processing in Medical Imaging : Proceedings of the ... Conference
|July 19, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Presurgical structural connectivity predicts postsurgical cognitive impairment in glioma patients.

Brain communications·2025
Same author

Can structure predict function at individual level in the human connectome?

Brain structure & function·2024
Same author

Working memory performance in glioma patients is associated with functional connectivity between the right dorsolateral prefrontal cortex and default mode network.

Journal of neuroscience research·2023
Same author

Subject-Specific Automatic Reconstruction of White Matter Tracts.

Journal of digital imaging·2023
Same author

Modeling of intracerebral interictal epileptic discharges: Evidence for network interactions.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2018
Same author

Multi-modal and multi-vendor retina image registration.

Biomedical optics express·2018
Same journal

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes.

Information processing in medical imaging : proceedings of the ... conference·2026
Same journal

Cycle-Consistent Zero-Shot Through-Plane Super-Resolution for Anisotropic Head MRI.

Information processing in medical imaging : proceedings of the ... conference·2026
Same journal

Brightness-Invariant Tracking Estimation in Tagged MRI.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

Using Multiple Instance Learning to Build Multimodal Representations.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds.

Information processing in medical imaging : proceedings of the ... conference·2024
See all related articles

This study introduces novel local measures for brain white matter connectivity analysis using diffusion tensor imaging (DTI). These measures, based on Riemannian geometry, efficiently preselect seed points for geodesic analysis, enhancing brain connectivity research.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Differential Geometry

Background:

  • Brain white matter connectivity analysis is crucial for understanding neurological function and disease.
  • Diffusion Tensor Imaging (DTI) is a key technique for mapping white matter architecture.
  • Existing methods for connectivity analysis can be computationally intensive.

Purpose of the Study:

  • To develop novel local measures for brain white matter connectivity analysis.
  • To improve the efficiency of extracting white matter tract information using DTI.
  • To introduce measures for geodesic saliency and stability within a Riemannian geometry framework.

Main Methods:

  • Utilizing Riemannian geometry to model white matter tracts as geodesics.
  • Developing local, directly computable measures from DTI data.

Related Experiment Videos

  • Proposing two types of geodesic measures: connectivity saliency and stability.

    • Main Results:

    • Introduced local measures that bypass the need for pre-computed geodesics.
    • Measures allow for efficient preselection of seed points for geodesic extraction.
    • Developed differential and integral measures for geodesic saliency, and a local measure for stability.

    Conclusions:

    • The proposed local measures offer an efficient approach to brain white matter connectivity analysis.
    • These methods enhance the preselection of seed points, reducing computational load.
    • The Riemannian geometry framework provides a robust foundation for these novel connectivity measures.