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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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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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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
6.8K
Structural Joints: Fibrous Joints01:03

Structural Joints: Fibrous Joints

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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
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Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

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As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
Synchondrosis
A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
4.0K
Joints01:26

Joints

35.7K
Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
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Basics of Multivariate Analysis in Neuroimaging Data
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Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data.

Kan Li1, Sheng Luo2

  • 1Merck Research Lab, Merck & Co, 351 North Sumneytown Pike, North Wales, PA 19454, USA.

Computational Statistics & Data Analysis
|December 19, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to analyze complex health data from neurodegenerative disease research, improving predictions for Alzheimer's disease progression and outcomes.

Keywords:
Alzheimer’s diseaseDynamic predictionJoint modelingLongitudinal functional data

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Area of Science:

  • Biostatistics
  • Medical Statistics
  • Longitudinal Data Analysis

Background:

  • Neurodegenerative diseases like Alzheimer's Disease (AD) involve complex, multi-faceted progression.
  • Analyzing longitudinal functional and scalar outcomes alongside survival data is crucial for understanding disease trajectory.
  • Existing models often struggle to simultaneously account for the intricate associations within such data structures.

Purpose of the Study:

  • To propose a multivariate functional joint model framework for simultaneous analysis of longitudinal functional outcomes, scalar outcomes, and survival data.
  • To model the associations among multiple longitudinal (functional and scalar) and survival processes.
  • To enable dynamic prediction of future health outcomes and conversion risk in diseases like AD.

Main Methods:

  • Developed a functional joint model comprising longitudinal function-on-scalar, regular longitudinal, and survival submodels.
  • Incorporated time-dependent functional and scalar covariates within the survival submodel.
  • Employed a Bayesian approach for parameter estimation and introduced a dynamic prediction framework.

Main Results:

  • The proposed model framework effectively integrates diverse data types (functional, scalar, survival) common in neurodegenerative disease studies.
  • Demonstrated the model's capability to account for complex associations between different disease progression markers.
  • Successfully applied the model to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study data.

Conclusions:

  • The multivariate functional joint model provides a robust framework for analyzing complex longitudinal and survival data in neurodegenerative disease research.
  • The dynamic prediction capability enhances the ability to forecast individual patient trajectories and risks.
  • The model's application to the ADNI study validates its utility in understanding Alzheimer's disease progression.