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

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by

Same author

From Attention Control to Stimulus Selection: Neural Mechanisms Revealed by Multivariate Pattern and Functional Connectivity Analyses.

bioRxiv : the preprint server for biology·2026

Same author

Rhythmic sampling and competition of target and distractor representations in visual sensory memory.

Cerebral cortex (New York, N.Y. : 1991)·2026

Same author

The Ventral Attention Network Mediates Attentional Reorienting to Cross-Modal Expectancy Violations: Evidence from EEG and fMRI.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026

Same author

Neural Mechanisms of Willed Attention Control.

bioRxiv : the preprint server for biology·2026

Same author

Neural representation of emotional valence in human amygdala and surrounding regions.

NeuroImage·2026

Same author

Is there a ubiquitous spectrolaminar motif of local field potential power across primate neocortex?

Nature neuroscience·2025

Search research articles

Related Experiment Video

Updated: Jul 6, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Analyzing information flow in brain networks with nonparametric Granger causality.

Mukeshwar Dhamala¹, Govindan Rangarajan, Mingzhou Ding

¹Department of Physics and Astronomy, Brains and Behavior Program, Center for Behavioral Neuroscience, Georgia State University, Atlanta, GA 30303, USA. mdhamala@gsu.edu

|April 9, 2008

Summary

This study introduces a novel nonparametric method to estimate Granger causality, a measure of information flow in neural networks. This approach bypasses complex autoregressive modeling, offering a more accessible way to analyze brain connectivity.

More Related Videos

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Published on: October 6, 2023

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Related Experiment Videos

Last Updated: Jul 6, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

Published on: October 6, 2023

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Understanding neural interactions requires analyzing complex multivariate data from neurophysiological recordings and neuroimaging.
Determining the direction of information flow in brain networks is a critical challenge in neuroscience.
Granger causality is a statistically principled method for assessing directed influence between time series, but its estimation typically requires autoregressive modeling.

Purpose of the Study:

To propose a novel nonparametric approach for estimating Granger causality in neural data.
To eliminate the need for explicit autoregressive modeling in Granger causality estimation.
To provide a method for estimating both pairwise and conditional Granger causality measures.

Main Methods:

Developed a nonparametric method utilizing Fourier and wavelet transforms.
Applied the method to synthetic network data with known connectivity.
Validated the approach using local field potential recordings from monkeys during a sensorimotor task.

Main Results:

The proposed nonparametric method effectively estimates Granger causality.
The technique successfully identified information flow directions in both synthetic and real neural data.
Eliminated the requirement for autoregressive modeling, simplifying the analysis of neural connectivity.

Conclusions:

The nonparametric Fourier and wavelet transform-based approach offers a viable alternative for Granger causality estimation.
This method enhances the accessibility and efficiency of analyzing directed information flow in brain networks.
The findings contribute to a better understanding of neural interactions and brain network dynamics.