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

Major Somatic Sensory Pathways01:28

Major Somatic Sensory Pathways

929
Sensory impulses related to touch, pressure, vibration, and proprioception from various body parts, such as the limbs, trunk, neck, and posterior head, travel to the cerebral cortex through the posterior column-medial lemniscus pathway. The pathway’s name derives from the two white-matter tracts that convey the impulses: the spinal cord's posterior column and the brainstem's medial lemniscus. First-order sensory neurons extend their axons into the spinal cord, forming the...
929
Parallel Processing01:20

Parallel Processing

147
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
147

You might also read

Related Articles

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

Sort by
Same author

A lightweight network for segmenting tree-like structures in medical images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

Diagnostic and sex effects on frontostriatal brain wiring structural organization in early psychosis affective subjects compared to healthy controls.

Molecular psychiatry·2026
Same author

Cerebellar pathway diffusion MRI measures are linked to core autism symptoms in early adolescents aged 9 to 11 years.

Brain structure & function·2026
Same author

Connectional neuroanatomy of U-fibers in the rhesus monkey brain.

bioRxiv : the preprint server for biology·2026
Same author

Cross-population white matter atlas creation for concurrent mapping of brain connections in neonates and adults with diffusion MRI tractography.

Fundamental research·2026
Same author

Multimodal animal health monitoring in extensive livestock production systems.

Frontiers in veterinary science·2026

Related Experiment Video

Updated: Jun 15, 2025

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

3.9K

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through

Ari Tchetchenian1, Leo Zekelman2,3, Yuqian Chen2

  • 1Biomedical Image Computing Group, School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, New South Wales, Australia.

Human Brain Mapping
|August 26, 2024
PubMed
Summary

This study introduces Deep Multimodal Saliency Parcellation (DeepMSP), a novel method for mapping human cerebellar pathways. DeepMSP integrates brain structure and function data to reveal how these pathways support motor and cognitive abilities.

Keywords:
cerebellar pathwaysdeep learningdiffusion MRIexplainable AImultitask learningtractographywhite matter parcellation

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

494
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.4K

Related Experiment Videos

Last Updated: Jun 15, 2025

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

3.9K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

494
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.4K

Area of Science:

  • Neuroscience
  • Brain Imaging
  • Computational Neuroscience

Background:

  • Human cerebellar pathway parcellation is crucial for understanding brain function.
  • Current methods rely solely on fiber tract structure, potentially missing functional relevance.
  • Cerebellar pathways are involved in diverse cognitive and motor functions.

Purpose of the Study:

  • To develop a multimodal, data-driven method for parcellating cerebellar pathways.
  • To incorporate both microstructural/connectivity data and individual functional performance measures.
  • To enhance the study of structure-function relationships in the cerebellum.

Main Methods:

  • Proposed Deep Multimodal Saliency Parcellation (DeepMSP) method.
  • Trained a multitask deep network to predict cognitive/motor measures from structural features.
  • Computed structure-function saliency values and clustered them for parcellation.
  • Applied to Human Connectome Project Young Adult dataset (n=1065).

Main Results:

  • Identified stable cerebellar pathway parcels with unique structure-function saliency patterns.
  • 1D CNN and transformer architectures performed comparably for multitask prediction.
  • A low-dimensional saliency representation with motor/cognitive bias yielded best parcellation.
  • Four parcels (k=4) showed optimal cluster quality, with motor and cognitive saliencies distributed across pathways.

Conclusions:

  • DeepMSP enables multimodal, data-driven tractography parcellation of cerebellar pathways.
  • The method successfully integrates structural and functional data to map pathways.
  • This approach has the potential to advance the understanding of cerebellar structure-function relationships.