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 Video

Updated: May 8, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

BundleWarp: Enhancing white matter tractometry and morphometry with precise neuronal mapping using streamline-based

Bramsh Qamar Chandio1, Emanuele Olivetti2, David Romero-Bascones3

  • 1Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA; Department of Intelligent Systems Engineering, Indiana University Bloomington, IN, USA; Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.

Medical Image Analysis
|May 6, 2026
PubMed
Summary

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

Reliable Change of Blood-Based Biomarkers Following Acute Sport-Related Concussion: A CARE Consortium Study.

Sports medicine (Auckland, N.Z.)·2026
Same author

The Hidden Architecture of Brain Structural Variability in 22q11.2 Deletion Syndrome: A Multi-site Study.

medRxiv : the preprint server for health sciences·2026
Same author

Mapping the structural connections between the anterior cingulate cortex and the insula/ventrolateral prefrontal cortex.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Lifespan normative modeling of brain microstructure.

Nature communications·2026
Same author

SPECTRA: Spatial Inference for Tractometry Toward Precision Mapping of White Matter Microstructure.

bioRxiv : the preprint server for biology·2026
Same author

Chronic Anemia Patients Demonstrate Diffuse Demyelination.

American journal of hematology·2026
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026
See all related articles
This summary is machine-generated.

BundleWarp accurately aligns white matter tracts using nonlinear registration, improving disease detection. This method enhances subject reproducibility and tractometry analysis for neurodegenerative diseases.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Tractometry analysis offers detailed micro-level examination of white matter tracts using diffusion MRI and tractography.
  • Accurate alignment of white matter tracts is crucial for reliable tractometry, but nonlinear registration can introduce artifacts.
  • Existing methods struggle to preserve topological and anatomical features during nonlinear registration of complex tractography data.

Purpose of the Study:

  • To introduce BundleWarp, a novel streamline-based nonlinear deformable registration method specifically designed for white matter tracts.
  • To develop a tract morphometry framework using BundleWarp's displacement field for analyzing white matter tract shape differences.
  • To enhance the sensitivity of tractometry analysis for detecting disease-related changes in white matter.
Keywords:
Alzheimer’s diseaseAmyloidBUANBWBundleWarpDeformable streamline-based registrationDiffusion MRIMorphometryParkinson’s diseaseTAUTractometry

More Related Videos

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions
10:05

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

Published on: August 26, 2014

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Related Experiment Videos

Last Updated: May 8, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions
10:05

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

Published on: August 26, 2014

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Main Methods:

  • BundleWarp employs a probability density estimation framework with motion coherence penalties for aligning white matter bundles.
  • Displacement field regularization is used to maintain the anatomical integrity of tracts during registration.
  • A tract morphometry framework quantifies shape differences using the generated displacement fields.

Main Results:

  • BundleWarp effectively quantifies bundle shape differences and enhances structural harmonization in tractometry analysis.
  • Test-retest experiments show BundleWarp significantly improves subject fingerprinting and within-subject reproducibility.
  • The method demonstrates enhanced sensitivity for detecting structural and microstructural changes in white matter tracts associated with neurodegenerative diseases.

Conclusions:

  • BundleWarp provides a robust tractometry framework with improved accuracy and reproducibility for white matter analysis.
  • The method enhances the detection of disease-related changes in white matter tracts, aiding in the diagnosis of conditions like Alzheimer's disease.
  • BundleWarp facilitates precise mapping of neuronal pathways and offers a sensitive tool for neuroimaging research.