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

Salivary extracellular vesicle-derived microRNAs are related to variances in parameters of obesity, taste and eating behaviour.

Molecular metabolism·2025

Same author

Balancing excitation and inhibition: The role of neural network dynamics in working memory gating.

Imaging neuroscience (Cambridge, Mass.)·2025

Same author

Ultra-processed food consumption affects structural integrity of feeding-related brain regions independent of and via adiposity.

npj metabolic health and disease·2025

Same author

Decrease in decision noise from adolescence into adulthood mediates an increase in more sophisticated choice behaviors and performance gain.

PLoS biology·2024

Same author

Working memory gating in obesity is moderated by striatal dopaminergic gene variants.

eLife·2024

Same author

Neurocomputational Mechanisms Underlying Differential Reinforcement Learning From Wins and Losses in Obesity With and Without Binge Eating.

Biological psychiatry. Cognitive neuroscience and neuroimaging·2024

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain.

Gabriele Lohmann¹, Daniel S Margulies, Annette Horstmann

¹Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. lohmann@cbs.mpg.de

|May 4, 2010

Summary

We introduce eigenvector centrality, a new computational method for analyzing brain networks from resting-state functional magnetic resonance imaging (fMRI) data. This efficient technique reveals how brain activity patterns change with physiological states like hunger or satiety.

More Related Videos

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Jun 13, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Neuroscience
Computational Biology
Network Science

Background:

Resting-state functional magnetic resonance imaging (fMRI) requires novel analysis methods independent of activation models.
Existing centrality measures like betweenness centrality are computationally intensive and limited in application scope for fMRI data.

Purpose of the Study:

To introduce and validate eigenvector centrality as an assumption- and parameter-free method for analyzing resting-state fMRI data.
To demonstrate the computational efficiency and broad applicability of eigenvector centrality across the entire brain.
To investigate the modulation of intrinsic neural architecture by physiological states using eigenvector centrality.

Main Methods:

Developed and applied eigenvector centrality, a network analysis technique inspired by Google's PageRank algorithm.
Utilized linear correlations and spectral coherences as similarity metrics for fMRI time series.
Applied the method to resting-state fMRI data from subjects in hunger and satiety states.

Main Results:

Eigenvector centrality is computationally efficient, enabling voxel-wise analysis across large brain regions.
The method successfully captured intrinsic neural architecture using different similarity metrics, including spectral coherences.
Observed significant modulation of eigenvector centrality by the subjects' physiological state (hunger vs. satiety).

Conclusions:

Eigenvector centrality offers a computationally efficient and flexible alternative for analyzing resting-state fMRI data.
This method provides a powerful tool for understanding intrinsic brain connectivity and its modulation by physiological factors.
The findings highlight the potential of eigenvector centrality for uncovering complex neural architectures at a voxel-wise level.