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

Mathematical and machine learning-assisted modelling of Raman spectroscopy for biomedical applications.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Tissue and Serum Concentrations of Time-Dependent Antibiotics in Infected Diabetic Foot Ulcers by Bolus or Continuous Administration: The Randomised DFIATIM Trial.

Diabetes/metabolism research and reviews·2026
Same author

Is Temporal Variability a Standalone Predictor in Medical Data? An Actigraphy Study in Bipolar Disorder.

Studies in health technology and informatics·2026
Same author

The effect of <i>Lactiplantibacillus plantarum</i> probiotic supplement on rainbow trout challenged with <i>Aeromonas salmonicida</i>.

Veterinarni medicina·2026
Same author

Individualized cortical gradient and network topology reveal symptom-linked disruptions and neurobiological subtypes in schizophrenia.

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

Resting-state EEG alpha-BOLD coupling spatially follows cortical cell-type and receptor gradients.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Sep 2, 2025

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

1.2K

Human brain structural connectivity matrices-ready for modelling.

Antonín Škoch1,2,3, Barbora Rehák Bučková1,3,4, Jan Mareš1,3

  • 1National Institute of Mental Health, Klecany, Czech Republic.

Scientific Data
|August 9, 2022
PubMed
Summary

This study provides readily analyzable brain structural connectivity matrices derived from diffusion imaging. This accessible dataset empowers researchers to explore brain structure and function without extensive data processing expertise.

More Related Videos

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

26.4K
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

5.7K

Related Experiment Videos

Last Updated: Sep 2, 2025

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

1.2K
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

26.4K
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

5.7K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Medical Imaging

Background:

  • The human brain is a complex computational system.
  • Neuroimaging techniques measure brain structure and activity.
  • Diffusion-weighted imaging and tractography reveal white matter organization.

Purpose of the Study:

  • To simplify the analysis of brain structural connectivity.
  • To provide a dataset of pre-processed structural connectivity matrices.
  • To make advanced neuroimaging analysis accessible to a wider research community.

Main Methods:

  • Probabilistic diffusion tractography was used to map white matter fibers.
  • Tractography results were segmented into anatomical units.
  • Structural connectivity matrices were generated, estimating connection strength between regions.

Main Results:

  • A dataset of brain structural connectivity matrices was created.
  • The dataset includes underlying diffusion and structural imaging data.
  • Data from 88 healthy subjects is provided.

Conclusions:

  • The provided dataset simplifies structural connectivity analysis.
  • Researchers can now more easily model and analyze brain networks.
  • This facilitates broader research into brain structure-function relationships.