Jove
Visualize
Contact Us
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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

MULTIVARIATE DYNAMIC MEDIATION ANALYSIS UNDER A REINFORCEMENT LEARNING FRAMEWORK.

Annals of statistics·2026
Same author

[Analysis of simultaneous heart-lung treatment strategy for chronic obstructive pulmonary disease complicated with coronary heart disease based on blood turbidity theory].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica·2026
Same author

Siderophore-producing bacteria reduce soil cadmium bioavailability and alleviate cadmium stress in alfalfa.

Ecotoxicology and environmental safety·2026
Same author

Monolithic integration of p- and n-type doped 2D WSe<sub>2</sub> for wafer-scale complementary logic circuits.

Nature communications·2026
Same author

Redirection of SARS-CoV-2 to phagocytes by intranasal sACE2-Fc as a universal decoy confers complete prophylactic protection.

eLife·2026
Same author

Anesthetic Management of Acute Right Tension Pneumothorax in a Child With Left Main Bronchial Foreign Body: A Case Report.

Clinical case reports·2026

Related Experiment Video

Updated: Mar 10, 2026

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

6.1K

Hypothesis testing of matrix graph model with application to brain connectivity analysis.

Yin Xia1,2, Lexin Li3

  • 1Department of Statistics, School of Management, Fudan University, Shanghai 200433, China.

Biometrics
|December 14, 2016
PubMed
Summary

This study introduces new statistical methods for brain connectivity analysis using matrix normal distributions. These methods enable direct statistical significance quantification of brain networks derived from neuroimaging data.

Keywords:
Brain connectivity analysisFalse discovery rateGaussian graphical modelMatrix-variate normal distributionMultiple testing

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K
Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

580

Related Experiment Videos

Last Updated: Mar 10, 2026

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

6.1K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K
Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke
05:30

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

580

Area of Science:

  • Neuroscience
  • Statistical modeling
  • Network analysis

Background:

  • Brain connectivity analysis uses graph theory to represent neural interactions from neuroimaging data.
  • Existing methods for sparse precision matrix estimation lack direct statistical significance quantification for network links.

Purpose of the Study:

  • To develop a hypothesis testing framework for brain connectivity analysis within a matrix normal distribution model.
  • To enable statistical significance quantification for estimated brain network links.

Main Methods:

  • Formulated brain connectivity as a precision matrix hypothesis testing problem.
  • Developed oracle and data-driven procedures for hypothesis testing based on separable spatial-temporal dependence.
  • Implemented false discovery rate control for simultaneous tests of conditional dependence.

Main Results:

  • Theoretical results demonstrate asymptotic equivalence between data-driven and oracle procedures.
  • Data-driven procedures exhibit optimality properties.
  • Simulations confirm finite-sample performance, and the methods were applied to electroencephalography data.

Conclusions:

  • The proposed methods offer a robust framework for statistically quantifying brain connectivity from neuroimaging data.
  • The developed data-driven procedures provide reliable and asymptotically optimal link significance assessment.