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

Introduction to MATLAB01:24

Introduction to MATLAB

MATLAB stands for Matrix Laboratory. MathWorks developed MATLAB as a multi-paradigm numerical computing environment and proprietary programming language. It has evolved significantly over the years to become a tool utilized by engineers, scientists, and mathematicians for various tasks, including matrix calculations, developing algorithms, data analysis, and visualization. MATLAB's applications span various industries and disciplines. It's used in image and signal processing, communications,...
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
Correlation and Causation01:27

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...
Mesh Analysis01:20

Mesh Analysis

Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
Mesh Analysis for AC Circuits01:12

Mesh Analysis for AC Circuits

In the domain of radio communication, the significance of impedance matching must be considered. It is crucial to ensure the efficient transmission of signals between radio transmitters and receivers. Achieving this balance involves using impedance-matching circuits, with one fundamental configuration comprising a resistor, capacitor, and inductor.
The process of harmonizing these impedances begins with a clear understanding of the input and output signals. Once these signals are known, the...
Causality in Epidemiology01:21

Causality in Epidemiology

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...

You might also read

Related Articles

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

Sort by
Same author

Timescapes of non-human experience.

Trends in cognitive sciences·2026
Same author

Evolving reservoir computers reveal bidirectional coupling between predictive power and emergent dynamics.

Patterns (New York, N.Y.)·2026
Same author

Mapping of Subjective Accounts into Interpreted Clusters (MOSAIC): Topic Modelling and LLM applied to Stroboscopic Phenomenology.

Neuroscience of consciousness·2026
Same author

The role of active inference in conscious awareness.

PloS one·2025
Same author

On the minimal theory of consciousness implicit in active inference.

Physics of life reviews·2025
Same author

Hemispherotomy leads to persistent sleep-like slow waves in the isolated cortex of awake humans.

PLoS biology·2025
Same journal

Detection of cochlear microphonic for differential diagnosis between auditory neuropathy mice and noise-induced sensorineural hearing loss mice.

Journal of neuroscience methods·2026
Same journal

Assessment metrics for pain control in rats: A methodological commentary.

Journal of neuroscience methods·2026
Same journal

Infant EEG preprocessing pipelines: A capability framework and current gaps in practice.

Journal of neuroscience methods·2026
Same journal

Methods for Measuring Neural Activity During Voluntary Wheel Running.

Journal of neuroscience methods·2026
Same journal

Serotype-dependent differences in AAV cellular transduction rates in the hypothalamus of Arctic ground squirrels.

Journal of neuroscience methods·2026
Same journal

Rapid generation of human sensory neurons from iPSC for modeling of peripheral neuropathies.

Journal of neuroscience methods·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 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

A MATLAB toolbox for Granger causal connectivity analysis.

Anil K Seth1

  • 1Sackler Centre for Consciousness Science and School of Informatics, University of Sussex, Brighton, BN1 9QJ, UK. a.k.seth@sussex.ac.uk

Journal of Neuroscience Methods
|December 8, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces the Granger Causal Connectivity Analysis (GCCA) MATLAB toolbox for assessing directed functional connectivity in neuroscience. It offers tools for analyzing various neural signals and computing network indices, enhancing causal connectivity research.

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

Related Experiment Videos

Last Updated: Jun 18, 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

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

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Assessing directed functional connectivity in neural time series data is a significant challenge.
  • Granger causality analysis combined with network theory offers a promising approach.
  • Existing methods may lack comprehensive, accessible tools for diverse neuroscience data.

Purpose of the Study:

  • To introduce the freely available MATLAB toolbox named 'Granger Causal Connectivity Analysis' (GCCA).
  • To provide researchers with a core set of methods for directed functional connectivity analysis.
  • To facilitate the application of Granger causality and network theory to various neuroscience data types.

Main Methods:

  • The GCCA toolbox implements Granger causality analysis for multivariate steady-state and event-related data.
  • It includes functions for data preprocessing, statistical significance assessment, and result validation.
  • Network-level indices such as 'causal density' and 'causal flow' are computed and displayed.

Main Results:

  • The toolbox offers a streamlined approach to analyzing directed functional connectivity across neuroelectric, neuromagnetic, and fMRI data.
  • It enables the computation and visualization of network properties related to causal flow.
  • The GCCA toolbox is designed to be small, extensible, and easily integrated into research workflows.

Conclusions:

  • The GCCA toolbox provides a valuable, accessible resource for neuroscience researchers.
  • It simplifies the assessment of directed functional connectivity using Granger causality and network theory.
  • The toolbox supports the analysis of complex neural systems and enhances understanding of causal relationships within brain networks.