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

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

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

Sort by
Same author

Management of rehabilitation timing in the care of patients with COPD: a challenge for public health.

Igiene e sanita pubblica·2025
Same journal

Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.

Neuroinformatics·2026
Same journal

CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.

Neuroinformatics·2026
Same journal

Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.

Neuroinformatics·2026
Same journal

HESREN: A Derivative-Informed Reservoir Framework for Detecting Transient Neural Events and Windowless Estimation of Dynamic Functional Connectivity.

Neuroinformatics·2026
Same journal

Computational Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization.

Neuroinformatics·2026
Same journal

Model Validation Pipeline Against Longitudinal Alzheimer's Biomarker Data.

Neuroinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.

Cristian Mendico1

  • 1Institut de Mathématique de Bourgogne, UMR 5584 CNRS, Université Bourgogne Europe, Dijon, France. cristian.mendico@u-bourgogne.fr.

Neuroinformatics
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

Researchers mapped brain signal flow using multimodal data, revealing a branched transport system that aggregates signals onto neural highways. This new model improves understanding of how brain stimulation leads to distributed reactions.

Keywords:
Brain networksBranched optimal transportConnectomicsMultimodal neuroimagingStochastic dynamicsStructure–function coupling

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

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Related Experiment Videos

Last Updated: Jun 12, 2026

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

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

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Area of Science:

  • Neuroscience
  • Computational Biology
  • Systems Neuroscience

Background:

  • Understanding how external stimuli generate brain-wide responses is crucial but challenging.
  • Current models often rely on predefined network structures and do not infer the underlying propagation architecture.
  • The transformation of localized stimulation into distributed neural activity patterns remains a key question.

Purpose of the Study:

  • To infer the brain's propagation architecture from activity data.
  • To develop a model that estimates stimulation and reaction measures and anatomical transport costs.
  • To investigate the trade-off between geometric efficiency and dynamical controllability in neural signal routing.

Main Methods:

  • Combined task-related blood-oxygen-level-dependent (BOLD) responses, source-reconstructed electrophysiology, and tractography-derived anisotropy.
  • Estimated stimulation and reaction measures and defined an anatomical transport cost.
  • Employed variational optimization to infer a branched propagation architecture and a stochastic graph-induced dynamical model.

Main Results:

  • Multimodal data successfully generated anatomically aligned brain reaction maps.
  • Anisotropic costs significantly altered routing backbones compared to isotropic models.
  • Hybrid geometric-dynamical optimization identified non-trivial rank reversals in branching regimes.

Conclusions:

  • The study infers a novel branched transport architecture for neural signal propagation.
  • Anatomical constraints and multimodal data integration are vital for accurate brain-wide modeling.
  • The findings offer new insights into brain connectivity and information flow dynamics.