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

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

Related Experiment Video

Updated: Apr 22, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

25.9K

Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain*†

Sean L Simpson1, F DuBois Bowman2, Paul J Laurienti3

  • 1Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC.

Statistics Surveys
|October 14, 2014
PubMed
Summary

Complex functional brain network analysis, using network science and statistics, offers new insights into brain function and disorders. Integrating these methods can revolutionize our understanding of the brain as a whole system.

Keywords:
Graph theoryconnectivityfMRInetwork modelneuroimagingsmall-world

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

1.7K
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

654

Related Experiment Videos

Last Updated: Apr 22, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

25.9K
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

1.7K
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

654

Area of Science:

  • Neuroscience
  • Network Science
  • Statistics

Background:

  • Functional brain network analysis has advanced significantly, driven by clinical implications.
  • Network science, derived from graph theory, enables viewing the brain as an integrated system.
  • Statistics has been crucial for neuroimaging but underutilized in complex network analyses.

Purpose of the Study:

  • To survey statistical and network science tools for functional magnetic resonance imaging (fMRI) network data analysis.
  • To discuss methodological gaps in current brain network analyses.
  • To highlight the potential of fusing statistical and network science methods for understanding brain function and disorders.

Main Methods:

  • Review of widely used statistical and network science tools for fMRI data.
  • Discussion of challenges and limitations in current methodologies.
  • Exploration of the integration of statistical and network approaches.

Main Results:

  • Identified key statistical and network science tools applicable to fMRI network data.
  • Highlighted existing methodological gaps in the field.
  • Emphasized the potential impact of integrating novel statistical methods with network analysis.

Conclusions:

  • The fusion of network science and statistical methods offers a powerful approach to brain network analysis.
  • This integration can significantly enhance our understanding of normal brain function and neurological disorders.
  • Correct application and interpretation of these fused methods may revolutionize brain function research.