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

You might also read

Related Articles

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

Sort by

Same author

The individuality of single-frame functional brain connectivity.

Imaging neuroscience (Cambridge, Mass.)·2026

Same author

Poorer Physical Function Is Associated With Elevated Spatial Entropy in the Aging Brain Network Landscape.

Aging cell·2026

Same author

Spatial Entropy of Brain Network Landscapes: A Novel Method to Assess Spatial Disorder in Brain Networks.

Human brain mapping·2026

Same author

The Effects of Mindfulness on Brain Network Dynamics Following an Acute Stressor in a Population of Drinking Adults.

Brain sciences·2026

Same author

Introducing Structural Reliance: A New Method to Assess Structure-Function Coupling in the Brain.

Human brain mapping·2026

Same author

Neural compensation in persons with HIV and marijuana use: Insights from a reorganized DMN.

Network neuroscience (Cambridge, Mass.)·2026

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.

Chal E Tomlinson¹, Paul J Laurienti^2,3, Robert G Lyday^2,3

¹Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Network Neuroscience (Cambridge, Mass.)

|June 19, 2023

Summary

This study introduces a new statistical framework, 3M-BANTOR, to link multiple brain network features to traits. This advances understanding of brain function and individual differences.

Keywords:

Connectivity Graph theory Human Connectome Project (HCP)Jaccard Kolmogorov–Smirnov Log-Euclidean Riemannian metric Mixed model Neuroimaging Pearson correlation distance Repeated observations Riemannian manifold distance fMRI

More Related Videos

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Related Experiment Videos

Last Updated: Jul 26, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Area of Science:

Neuroscience
Network Science
Biostatistics

Background:

Brain network analysis is crucial for understanding brain function, but statistical methods linking network architecture to phenotypic traits are underdeveloped.
Previous work established a regression framework for single-task brain networks and phenotypic differences.
Extending this to multitask and multisession contexts is necessary for comprehensive brain analysis.

Purpose of the Study:

To extend a novel analytic framework for assessing relationships between brain network architecture and phenotypic traits.
To develop methods for multitask and multisession brain network analyses, accommodating multiple networks per individual.
To compare the performance of various statistical approaches, including a novel mixed-model (3M-BANTOR), against existing methods.

Main Methods:

Developed an extended regression framework for multitask and multisession brain network data.
Explored similarity metrics for comparing brain connection matrices and adapted standard statistical tests (F-test, F-test with SLE).
Proposed a novel mixed-model approach (3M-BANTOR) and a simulation strategy for symmetric positive-definite matrices on the Riemannian manifold.

Main Results:

Simulation studies evaluated estimation and inference across different methods, including comparisons with multivariate distance matrix regression (MDMR).
The 3M-BANTOR model demonstrated utility in analyzing the relationship between fluid intelligence and brain network distances using Human Connectome Project (HCP) data.

Conclusions:

The developed framework and 3M-BANTOR model provide advanced tools for relating complex brain network structures to phenotypic variations.
This work bridges the gap in statistical methodologies for multitask and multisession brain network research.
The findings highlight the potential for deeper insights into brain organization and its connection to cognitive functions like fluid intelligence.