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

Causality in Epidemiology01:21

Causality in Epidemiology

1.7K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.7K
What are Estimates?01:06

What are Estimates?

8.9K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.9K
Interpreting R Charts01:22

Interpreting R Charts

359
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
359
Interpreting Run Charts01:25

Interpreting Run Charts

4.0K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
4.0K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.3K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.3K
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

1.2K
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
1.2K

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

Sex-dependent grey matter atrophy in Alzheimer's disease progression.

Brain communications·2026
Same author

Frontal-to-Parietal Theta Interactions Mediate Tactile Decision-Making.

Life (Basel, Switzerland)·2026
Same author

Frontal Theta Oscillations in Perceptual Decision-Making Reflect Cognitive Control and Confidence.

Brain sciences·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

Level up the brain! Novel PCA method reveals key neuroplastic refinements in action video gamers.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Decoding neuronal criticality firing patterns for large brain based EEG models.

NeuroImage·2026
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Feb 11, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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

Published on: August 7, 2017

8.5K

Granger-Geweke causality: Estimation and interpretation.

Mukesh Dhamala1, Hualou Liang2, Steven L Bressler3

  • 1Department of Physics and Astronomy, Neuroscience Institute, Georgia State University, Atlanta, GA, USA.

Neuroimage
|April 24, 2018
PubMed
Summary
This summary is machine-generated.

Granger-Geweke causality (GGC) estimation is accurate and applicable to neuroscience. This study refutes claims of bias and high variance, showing GGC provides interpretable results in neurophysiology.

More Related Videos

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
11:33

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

Published on: July 18, 2014

44.1K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

967

Related Experiment Videos

Last Updated: Feb 11, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

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

Published on: August 7, 2017

8.5K
Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
11:33

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

Published on: July 18, 2014

44.1K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

967

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • A recent PNAS article questioned the validity of Granger-Geweke causality (GGC) estimation.
  • The article suggested GGC is biased, has high variance, and is unsuitable for neuroscience due to receiver independence.

Purpose of the Study:

  • To re-evaluate the claims made by Stokes and Purdon regarding Granger-Geweke causality (GGC).
  • To demonstrate the accuracy and applicability of GGC in neuroscience research.

Main Methods:

  • Utilized numerical simulation examples from the original PNAS article.
  • Applied both spectral factorization-enabled nonparametric and VAR-model based parametric approaches for GGC estimation.

Main Results:

  • Correctly estimated GGC measures show no significant bias or high variance.
  • The receiver-independence of GGC is not a limitation for neuroscience applications.

Conclusions:

  • Granger-Geweke causality (GGC) is a valid and reliable method for analyzing neural data.
  • When considering experimental context, GGC yields neurophysiologically interpretable results, supporting its use in neuroscience.