Jove
Visualize
Contact Us

Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

251
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
251
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

11.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
11.3K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

15.9K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
15.9K
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

50
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
50
Probability Histograms01:17

Probability Histograms

12.7K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
12.7K
Graphs of Polar Equations01:17

Graphs of Polar Equations

74
The polar coordinate system represents points using a distance from a central point (the pole) and an angle from a reference direction (the polar axis). Unlike rectangular coordinates, polar coordinates are ideal for graphing curves with radial symmetry or periodic behavior.Some general forms of graphs in polar coordinates include the following:Equation of a Circle (Centered at the Pole):A graph where the radius remains constant for all angles traces a circle centered at the pole:Equation of a...
74

You might also read

Related Articles

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

Sort by
Same author

Lost in Scheduling? A Reliable Tool for Detecting Subtle Cognitive Decline in Mild Cognitive Impairment and Mild Alzheimer's Disease.

American journal of speech-language pathology·2026
Same author

Decomposing neuroanatomical heterogeneity in depression: insights from an ENIGMA major depressive disorder working group study in 5146 individuals.

Translational psychiatry·2026
Same author

Donanemab treatment effect by baseline tau burden and disease severity: Observations from the TRAILBLAZER-ALZ 2 trial.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Drivers of Rising Prevalence in Major Motor Neurodegenerative Diseases: Temporal Trends in Sweden and France (2003-2022).

Neurology·2026
Same author

Sleep-like slow waves during resting-state: A promising EEG biomarker of amyloid and neurodegeneration in preclinical Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Efficacy and safety of donanemab in the European eligible population: TRAILBLAZER-ALZ 2 post-hoc analyses.

The journal of prevention of Alzheimer's disease·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Nov 7, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.8K

Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold.

Clément Mantoux1,2,3, Baptiste Couvy-Duchesne1,2, Federica Cacciamani1,2

  • 1ARAMIS Project Team, Inria, 75013 Paris, France.

Entropy (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces a statistical framework to model variations in network structures, like brain connectivity. The method accurately captures complex patterns and missing connections in network data.

Keywords:
MCMC-SAEMStiefel manifolddata imputationnetwork modelingnetwork variability

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.8K

Related Experiment Videos

Last Updated: Nov 7, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.8K

Area of Science:

  • Network science
  • Statistical modeling
  • Neuroimaging analysis

Background:

  • Network analysis is crucial for understanding complex systems like brain connectivity.
  • Characterizing variability within groups of networks is essential for robust modeling.
  • Existing methods may not fully capture the intricate similarities and differences across multiple networks.

Purpose of the Study:

  • To develop a statistical framework for modeling variability in network structures.
  • To enable the analysis of heterogeneous populations of adjacency matrices.
  • To infer missing edge weights within network data.

Main Methods:

  • A statistical framework based on manifold-valued latent factors.
  • Decomposition of network adjacency matrices into weighted sums of rank-one patterns.
  • Hierarchical statistical model utilizing mixtures for heterogeneous data.
  • Expectation-Maximization (EM)-based algorithm for parameter estimation.

Main Results:

  • Accurate estimation of latent network structure in both low and high dimensions using synthetic data.
  • Successful application to a large dataset of functional brain connectivity matrices from the UK Biobank.
  • Demonstration that the model describes complex variability with a small number of degrees of freedom.

Conclusions:

  • The proposed statistical framework effectively models network variability.
  • The method accurately captures complex patterns in functional brain connectivity data.
  • The approach offers a parsimonious representation of network structure variations.