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Related Concept Videos

Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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:
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...
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

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:
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...
Cause and Effect01:53

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

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Related Experiment Video

Updated: Jun 16, 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

Nonlinear connectivity by Granger causality.

Daniele Marinazzo1, Wei Liao, Huafu Chen

  • 1Laboratory of Neurophysics and Physiology, CNRS UMR 8119, Université Paris Descartes, Paris, France. daniele.marinazzo@gmail.com

Neuroimage
|February 6, 2010
PubMed
Summary
This summary is machine-generated.

This study explores nonlinear brain communication using a novel kernel version of Granger causality. This method enhances the analysis of brain signals like EEG and fMRI for better understanding neural information processing.

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

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Related Experiment Videos

Last Updated: Jun 16, 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

New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
05:59

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

Published on: October 6, 2023

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Neuronal population communication underlies brain information processing.
  • Functional connectivity is assumed to be nonlinear, but its extent and roles are unclear.
  • Current methods like Dynamic Causal Modeling (DCM) and linear Granger causality have limitations in capturing nonlinear interactions.

Purpose of the Study:

  • To review existing approaches for analyzing nonlinear interactions in brain systems.
  • To focus on a flexible, recently proposed kernel version of Granger causality.
  • To demonstrate the application of this novel method on real-world neuroimaging data.

Main Methods:

  • Review of methods for nonlinear time series analysis in neuroscience.
  • Application of the kernel version of Granger causality.
  • Analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data.

Main Results:

  • The kernel Granger causality approach is effective for analyzing nonlinear interactions in short, noisy time series.
  • Demonstrated successful application on EEG and fMRI datasets.
  • Provides a flexible tool for investigating effective connectivity beyond linear assumptions.

Conclusions:

  • The kernel version of Granger causality offers a promising approach to quantify nonlinear information transmission in the brain.
  • This method can advance our understanding of brain communication and effective connectivity.
  • Future research can leverage this technique to explore the functional roles of nonlinear interactions in various cognitive processes.