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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

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

Sort by
Same author

DeepFixel: Crossing white matter fiber identification through spherical convolutional neural networks.

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

Nonlinear Asymmetric Blood Oxygenation Level Dependent Responses in Somatosensory Cortex.

Human brain mapping·2026
Same author

Low-Cost and Detunable Wireless Resonator Glasses for Enhanced Eye MRI With Concurrent High-Quality Whole-Brain MRI.

Magnetic resonance in medicine·2026
Same author

Functional magnetic resonance imaging insights into nociceptive signal processing network in rat lumbar spinal cord.

Pain reports·2026
Same author

White-Matter BOLD Mediates Time-Varying Cortico-Cortical Functional Connectivity.

bioRxiv : the preprint server for biology·2025
Same author

Heat action plans in eight Indian cities: Knowledge gaps & opportunities for intersectoral heat governance.

The Indian journal of medical research·2025
Same journal

Correction to "On the shape of the radiation survival curve in tumor spheroids: The role of oxygen heterogeneity".

Medical physics·2026
Same journal

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

Medical physics·2026
Same journal

Accuracy of quantitative <sup>177</sup>Lu SPECT/CT imaging: A systematic review.

Medical physics·2026
Same journal

Physics-constrained dual-domain network for CBCT reconstruction from orthogonal X-rays in gynecologic radiotherapy.

Medical physics·2026
Same journal

Decomposition-based harmonization for quantitative PET imaging across scanners and radiotracers.

Medical physics·2026
Same journal

Development and evaluation of an in vivo dose-based monitoring system for electron FLASH radiation therapy.

Medical physics·2026
See all related articles

Related Experiment Video

Updated: Jun 8, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

An improved Bayesian tensor regularization and sampling algorithm to track neuronal fiber pathways in the language

Arabinda Mishra1, Adam W Anderson, Xi Wu

  • 1Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232, USA. arabinda.mishra@vanderbilt.edu

Medical Physics
|October 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neuronal fiber tracking algorithm using a Bayesian approach to improve brain connectivity mapping. The method enhances the reconstruction of fibers in functionally important brain regions, outperforming conventional techniques, especially in low signal-to-noise conditions.

More Related Videos

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation
16:23

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

Published on: May 23, 2017

Related Experiment Videos

Last Updated: Jun 8, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation
16:23

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

Published on: May 23, 2017

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Conventional tractography methods struggle with weakly myelinated fibers near gray matter regions.
  • Low fractional anisotropy and noise in these areas hinder accurate fiber tracking.
  • Reconstructing connections to functionally important brain regions is crucial for understanding brain structure-function relationships.

Purpose of the Study:

  • To design a neuronal fiber tracking algorithm for improved reconstruction of fibers linked to functionally important human brain regions.
  • To address the limitations of conventional methods in areas with low signal-to-noise ratio and partial volume averaging.
  • To develop a stochastic approach based on a Bayesian regularization framework for enhanced tractography.

Main Methods:

  • Utilized a Bayesian regularization framework with a stochastic approach for fiber pathway reconstruction.
  • Modeled a priori and conditional probability density functions as multivariate normal distributions.
  • Employed adaptive and multiple sampling of estimated tensor element vectors, incorporating variance to mitigate noise and partial volume averaging (PVA).

Main Results:

  • The algorithm demonstrated superior performance compared to conventional (Euler's) and other stochastic methods, particularly in low signal-to-noise ratio scenarios.
  • Successfully delineated fibers in major language pathways (Broca's to SMA and Broca's to Wernicke's) in 12 healthy subjects.
  • Extracted a significantly higher number of fibers compared to conventional approaches, despite a marginally larger standard deviation.

Conclusions:

  • Adaptive sampling within a Bayesian framework effectively delineates neuronal fibers, enabling better analysis of brain structure-function relationships.
  • The developed algorithm shows promise for in vivo diffusion tensor imaging (DTI) data analysis.
  • Simulated and in vivo results validate the theoretical underpinnings of the proposed tractography method.