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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...
Classification of Connective Tissues01:30

Classification of Connective Tissues

The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense.
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
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,...
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...
Fibril-associated Collagen01:11

Fibril-associated Collagen

Fibril-associated collagens are a type of collagens present in the extracellular matrix with interrupted triple helices or FACIT (Fibril-associated collagens interrupted triple-helices). FACIT help connect and attach the collagen fibrils with each other as well as with other proteins of the extracellular matrix.
For example, the type II collagen fibrils in cartilage have covalently bound type IX fibril-associated collagens at regular intervals. Other types of fibril-associated collagens are...

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Related Experiment Video

Updated: May 15, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Group-wise consistent fiber clustering based on multimodal connectional and functional profiles.

Bao Ge1, Lei Guo, Tuo Zhang

  • 1School of Automation, Northwestern Polytechnical University, Xi'an, China. oct.bob@gmail.com

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 5, 2013
PubMed
Summary

This study introduces a novel two-stage method for consistent brain fiber clustering using diffusion tensor imaging (DTI). It successfully identifies group-wise consistent fiber bundles with strong functional coherence across subjects.

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

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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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:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Fiber clustering is crucial for brain connectivity modeling and white matter integrity analysis using diffusion tensor imaging (DTI).
  • Existing methods often focus on single features, limiting group-wise consistency across modalities.
  • Identifying harmonized, group-wise consistent fiber bundles across subjects remains a challenge.

Purpose of the Study:

  • To propose a novel hybrid two-stage approach for identifying group-wise consistent fiber bundles.
  • To incorporate both connectional and functional features for enhanced fiber clustering.
  • To achieve fiber bundle identification that is harmonious across subjects and modalities.

Main Methods:

  • A hybrid two-stage approach was developed, leveraging 358 dense cortical landmarks for backbone bundle identification.
  • The first stage identifies representative backbone bundles based on anatomical landmarks.
  • The second stage classifies remaining fibers using correlations of resting-state fMRI signals at fiber endpoints.

Main Results:

  • The proposed method successfully achieved group-wise consistent fiber bundles across subjects.
  • Identified bundles exhibited similar shapes and anatomical profiles.
  • Strong functional coherences were observed within the identified fiber bundles.

Conclusions:

  • The novel hybrid approach effectively identifies group-wise consistent fiber bundles.
  • Integration of connectional and functional features improves tract-based analysis.
  • This method advances brain connectivity modeling and clinical neuroscience applications.