Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Structural Classification of Joints

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...

You might also read

Related Articles

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

Sort by

Same author

Probing Molecular Structural Changes of Buried Interfaces between Polyethylene and Nylon in Polymer Thin Films after Stretching.

Langmuir : the ACS journal of surfaces and colloids·2026

Same author

Using machine learning to identify individuals at elevated risk of diabetes from low-cost complete blood count data for opportunistic screening.

Scientific reports·2026

Same author

The Impact of the Number of Implantation-Window uNK Cells on Pregnancy Outcomes and Decidualization.

Reproductive sciences (Thousand Oaks, Calif.)·2026

Same author

SERS analysis of cancer cell-derived exosome phenotypes using a rare-earth ion coordination-dominated exosome capture strategy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026

Same author

Molecular Behavior of Antibodies IgG-1 and IgG-4 at Buried Polymer/Solution Interfaces Probed by Sum Frequency Generation Vibrational Spectroscopy.

Analytical chemistry·2026

Same author

Prognostic implications of dominant lesion misidentification in multiple primary lung cancer.

Translational lung cancer research·2026

Search research articles

Related Experiment Video

Updated: Nov 16, 2025

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

Characterizing the Structural Pattern of Heavy Smokers Using Multivoxel Pattern Analysis.

Yufeng Ye^1,2, Jian Zhang³, Bingsheng Huang^1,4,5

¹Department of Radiology, Panyu Central Hospital, Guangzhou, China.

Frontiers in Psychiatry

|February 22, 2021

Summary

This summary is machine-generated.

Heavy smokers show distinct gray matter patterns in brain regions like the temporal and prefrontal cortex. Multivoxel pattern analysis (MVPA) identified these differences, offering new insights into smoking addiction mechanisms and cessation strategies.

Keywords:

machine learning multivoxel pattern analysis smoking addiction structural magnetic resonance imaging voxel-based morphometry

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Nov 16, 2025

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

Neuroimaging
Addiction Neuroscience
Public Health

Background:

Smoking addiction is a significant global health concern, linked to numerous chronic diseases and deaths.
Understanding the neurological underpinnings of smoking addiction is crucial for developing effective interventions.

Purpose of the Study:

To identify discriminative gray matter regions in heavy smokers compared to healthy controls using multivoxel pattern analysis (MVPA).
To compare the effectiveness of MVPA with traditional voxel-based morphometry (VBM) in detecting smoking-related brain alterations.

Main Methods:

Structural magnetic resonance imaging (sMRI) data from heavy smokers and controls.
Application of a data-driven MVPA technique employing a searchlight algorithm and support vector machine.
Comparison of MVPA findings with conventional VBM approaches.

Main Results:

MVPA achieved over 81% accuracy in classifying heavy smokers from controls.
Key discriminative regions included the temporal cortex, prefrontal cortex, occipital cortex, thalamus, insula, cingulate gyri, and precuneus.
Several identified regions were not typically reported by VBM analyses.

Conclusions:

MVPA effectively identifies subtle gray matter differences associated with smoking addiction, including regions overlooked by VBM.
These findings enhance our understanding of the neural mechanisms of chronic smoking.
The results may inform the development of novel and more effective smoking cessation treatments.