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

Association Areas of the Cortex01:21

Association Areas of the Cortex

8.6K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
8.6K
Survival Tree01:19

Survival Tree

358
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
358

You might also read

Related Articles

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

Sort by
Same author

Clinical course and outcomes of antibody-mediated rejection after heart transplant in the contemporary era.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation·2026
Same author

Altered neurodevelopmental trajectories of brain structure in Tourette syndrome and Chronic Tic Disorders.

medRxiv : the preprint server for health sciences·2026
Same author

Chimeric antigen receptor T-cell therapy and cardiovascular outcomes in US Medicare beneficiaries.

European heart journal·2026
Same author

Gene expression profiling does not predict long-term risk of malignancy in heart transplant recipients.

JHLT open·2026
Same author

Aspirin Prophylaxis for Preeclampsia Prevention in Nigeria: An Explanatory Sequential Mixed Methods Study.

Journal of the American College of Cardiology·2025
Same author

Immune Checkpoint Inhibitor-Associated Cardiovascular Toxic Effects: International Cardio-Oncology Society Position Statement.

JAMA oncology·2025

Related Experiment Video

Updated: Jan 5, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K

Detecting associations between intact connectomes and clinical covariates using recursive partitioning

Dake Yang1, Elena Deych1, Berkley Shands1

  • 1BioRankings, St. Louis, Missouri.

Statistics in Medicine
|October 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Gibbs-RPart, a novel regression method for analyzing brain connectomes. It partitions covariate spaces to reveal how brain connectivity patterns change with different factors.

Keywords:
connectomeobject-oriented data analysisrecursive partitioningregression

More Related Videos

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

6.0K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.6K

Related Experiment Videos

Last Updated: Jan 5, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
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

6.0K
A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.6K

Area of Science:

  • Neuroscience
  • Statistical modeling
  • Graph theory

Background:

  • Neuroscientists study changes in brain connectomes (functional connectivity graphs) relative to covariates.
  • Traditional regression models are unsuitable for complex, graph-valued outcomes like connectomes.
  • Existing methods struggle to analyze how covariates influence the structure of brain networks.

Purpose of the Study:

  • To develop a novel regression framework for analyzing graph-valued data, specifically brain connectomes.
  • To extend the object-oriented data analysis paradigm for graphical outcomes.
  • To enable the analysis of connectome changes in relation to covariates.

Main Methods:

  • Introduced Gibbs-RPart, a method combining recursive partitioning with the Gibbs distribution.
  • The approach partitions the covariate space into distinct regions.
  • Connectomes within each region exhibit higher similarity compared to those in other regions.

Main Results:

  • Gibbs-RPart effectively handles complex, graph-valued outcomes in regression analysis.
  • The method partitions covariate spaces, grouping similar connectome structures.
  • Demonstrates applicability beyond connectomes to general graphical outcomes.

Conclusions:

  • Gibbs-RPart offers a robust statistical approach for regression with graph-valued data.
  • This method advances the analysis of brain connectome variability in relation to covariates.
  • Extends previous work on hypothesis testing for populations of connectomes.