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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

Charting Cervical Spinal Cord Morphometry Across the Lifespan.

bioRxiv : the preprint server for biology·2026
Same author

Fully Differentiable dMRI Streamline Propagation in PyTorch.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Personalized White Matter Bundle Segmentation for Early Childhood.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

HARMONIZATION MITIGATES DIFFUSION MRI SCANNER EFFECTS IN INFANCY: INSIGHTS FROM THE HEALTHY BRAIN AND CHILD DEVELOPMENT (HBCD) STUDY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same author

Trait-Relevant Tasks Improve Personality Prediction From Structural-Functional Brain Network Coupling.

Human brain mapping·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

Related Experiment Video

Updated: Mar 2, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K

Continuous representations of brain connectivity using spatial point processes.

Daniel Moyer1, Boris A Gutman2, Joshua Faskowitz3

  • 1Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, United States; Information Sciences Institute, University of Southern California, United States; Department of Computer Science, University of Southern California, United States.

Medical Image Analysis
|May 11, 2017
PubMed
Summary
This summary is machine-generated.

We developed a continuous brain connectivity model using a Poisson point process, improving connectome analysis. This novel approach enhances test-retest reliability for structural brain networks.

Keywords:
Connectivity analysisDiffusion MRINon-parametric estimationPoint process

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K

Related Experiment Videos

Last Updated: Mar 2, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.7K

Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • Structural brain connectivity is crucial for understanding brain function.
  • Current methods for analyzing connectomes often rely on discrete parcellations, which can introduce biases.
  • High test-retest reliability is essential for robust neuroimaging studies.

Purpose of the Study:

  • To introduce a novel continuous model for structural brain connectivity.
  • To improve the reliability and analytical flexibility of connectome analysis.
  • To explore novel methods for assessing brain connectivity and sex effects.

Main Methods:

  • A continuous model based on the Poisson point process is proposed, treating streamlines as events in connectome space.
  • Kernel density estimation is used for parameter approximation, with a fast estimation method leveraging Legendre products and spherical heat kernel properties.
  • The model is applied to assess cortical parcellation quality and analyze sex effects on brain connectivity.

Main Results:

  • The continuous model demonstrates potential for assessing cortical parcellation quality.
  • Discrete connectomes derived from the model show substantially higher test-retest reliability than standard methods.
  • Parcellation-free analysis techniques are explored, highlighting the utility of the continuous representation for analyzing sex effects.

Conclusions:

  • The proposed continuous model offers a more reliable and flexible approach to structural brain connectivity analysis.
  • This method advances connectomics by reducing reliance on discrete parcellations and improving data reliability.
  • The continuous representation provides valuable insights into brain structure, function, and sex-related differences.