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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

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Related Experiment Video

Updated: Jun 8, 2026

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

Probabilistic anatomical connectivity using completion fields.

Parya MomayyezSiahkal1, Kaleem Siddiqi

  • 1Centre for Intelligent Machines, School of Computer Science, McGill University. pmamay@cim.mcgill.ca

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for estimating brain connectivity using diffusion MRI data. The method provides a valid probabilistic measure of connectivity strength between brain regions.

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Last Updated: Jun 8, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Published on: November 8, 2012

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Published on: October 13, 2023

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
13:26

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biophysics

Background:

  • Diffusion magnetic resonance imaging (dMRI) is crucial for mapping brain anatomy.
  • Current methods for estimating fiber tracts and connectivity strength have limitations.
  • Defining robust probabilistic connectivity indices from dMRI remains an open challenge.

Purpose of the Study:

  • To develop a novel method for probabilistic connectivity estimation using dMRI.
  • To address the need for accurate and reliable indices of brain connectivity strength.

Main Methods:

  • Implementation of a stochastic completion field algorithm.
  • Modeling water molecule diffusion incorporating local dMRI data.
  • Utilizing numerical methods for signal analysis.

Main Results:

  • The proposed approach provides a valid probabilistic estimate of connectivity strength.
  • Experimental validation was performed using the MICCAI 2009 Fibre Cup phantom.
  • Demonstrated the algorithm's capability in quantifying connectivity between seed regions.

Conclusions:

  • The novel stochastic completion field algorithm offers a promising tool for dMRI-based brain connectivity analysis.
  • This method advances the estimation of probabilistic connectivity strength.
  • The findings contribute to a better understanding of anatomical brain networks.