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

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

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

Learning partially directed functional networks from meta-analysis imaging data.

Jane Neumann¹, Peter T Fox, Robert Turner

¹Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, D-04103, Leipzig, Germany. neumann@cbs.mpg.de

|October 10, 2009

Summary

This summary is machine-generated.

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

Symptom Overlap and Neurobiological Similarities Between Posttraumatic Stress Disorder and Tinnitus.

Human brain mapping·2026

Same author

Advancing fair and explainable machine learning for neuroimaging dementia pattern classification in multi-racial and multi-ethnic populations.

Nature communications·2026

Same author

Residential Therapy With Navigated Transcranial Magnetic Stimulation for Combat-Related PTSD: A Randomized Clinical Trial.

JAMA network open·2026

Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

Alzheimer's Imaging Consortium.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Same author

Corrigendum to "ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations" [NeuroImage 281 (2023): 120383].

NeuroImage·2025

Same journal

Category-selective neural decreases in the human ventral occipito-temporal cortex as defined with intracranial recordings.

NeuroImage·2026

Same journal

EEG-Based Brain Fingerprints Elicited by Focal Transcranial Magnetic Stimulation of the Primary Motor Cortex.

NeuroImage·2026

Same journal

The Association between Brain Oscillatory Activity and Immediate Memory under Different Magnetoencephalography Paradigms: A population-based Study.

NeuroImage·2026

Same journal

Brain response to awe experiences in virtual reality: an integrated linear and nonlinear EEG analysis.

NeuroImage·2026

Same journal

Convergent imaging and genetic signatures of gray matter atrophy in Parkinson's disease.

NeuroImage·2026

Same journal

What actually matters in multi-compartment EEG head models: A controlled FEM study of parcellation granularity, skull layering, mesh quality, noise, and inverse solver.

NeuroImage·2026

See all related articles

This study introduces a novel method for discovering brain functional networks using fMRI meta-analysis. The approach reliably infers directed dependencies between brain regions from co-activation data.

Area of Science:

Neuroimaging and Computational Neuroscience
Brain network analysis
Functional Magnetic Resonance Imaging (fMRI) meta-analysis

Background:

Understanding functional brain networks is crucial for neuroscience.
Existing methods often struggle with inferring directed relationships from complex neuroimaging data.
fMRI meta-analysis aggregates findings across studies but requires robust analytical tools.

Purpose of the Study:

To develop a novel exploratory method for discovering partially directed functional networks.
To leverage fMRI meta-analysis data for inferring probabilistic dependencies between brain regions.
To validate the method's reliability through simulations and real-world data application.

Main Methods:

Employs structure learning of Bayesian networks to identify directed probabilistic dependencies.

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

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

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

Published on: November 8, 2012

Related Experiment Videos

Last Updated: Jun 19, 2026

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

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

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

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

Published on: November 8, 2012

Utilizes co-activation patterns across multiple independent neuroimaging experiments.

Applies the method to an extensive meta-analysis of fMRI data (>500 studies, thousands of coordinates).

Main Results:

Demonstrated the reliability of the proposed method through simulation studies.
Successfully inferred Bayesian networks capturing both directed and undirected dependencies.
Identified probabilistic relationships between brain regions involved in motor and cognitive control tasks.

Conclusions:

The developed method offers a reliable approach for discovering directed functional brain networks from fMRI meta-analysis.
It effectively captures complex probabilistic dependencies between brain regions.
The findings contribute to a deeper understanding of brain organization in cognitive and motor functions.