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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

4.2K
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...
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Structural Classification of Joints01:20

Structural Classification of Joints

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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...
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Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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Coordination Number and Geometry02:57

Coordination Number and Geometry

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For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
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Related Experiment Video

Updated: Jul 15, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Joint cortical registration of geometry and function using semi-supervised learning.

Jian Li1,2, Greta Tuckute3,4, Evelina Fedorenko3,4,5

  • 1A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School.

Arxiv
|September 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces JOSA, a novel framework for brain image registration that aligns both cortical folding patterns and functional maps. JOSA improves anatomical and functional alignment, offering a powerful tool for neuroimaging research.

Keywords:
Cortical registrationSemi-supervised Learning

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Related Experiment Videos

Last Updated: Jul 15, 2025

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Brain surface-based image registration is crucial for spatial correspondence between cortical surfaces in brain image analysis.
  • Current methods often assume geometry predicts function, leading to misalignments due to structural and functional variability across subjects.

Conclusions:

  • JOSA offers a significant advancement in brain surface-based image registration by integrating anatomical and functional alignment.
  • The framework's ability to work without functional data during inference broadens its applicability in neuroscientific research.
  • The joint alignment approach addresses the limitations of methods relying solely on anatomical geometry.