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Correlation and Causation

Correlation and Causation

Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...

Criteria for Causality: Bradford Hill Criteria - II

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:

Causality in Epidemiology

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...

Criteria for Causality: Bradford Hill Criteria - I

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:

Cause and Effect

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?

Center of Gravity

Center of Gravity

The center of gravity (COG) of an object is the point where the object's total weight is considered to be concentrated. Knowing the location of the center of gravity is useful when predicting the behavior of a moving object or designing static structures. In a uniform gravitational field, the center of gravity is similar to the center of mass (COM); yet, these two points can be positioned differently. For example, the Moon's center of mass lies very close to its geometric center, but its center...

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Updated: May 28, 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

Fiber-centered Granger causality analysis.

Xiang Li¹, Kaiming Li, Lei Guo

¹Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|October 15, 2011

Summary

This summary is machine-generated.

This study reveals that Granger causality analysis (GCA) on white matter fibers provides structural insights into brain connectivity. Fiber-centered GCA on fMRI data demonstrates reproducible and dynamic causal relationships within the brain.

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

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

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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

Area of Science:

Neuroimaging
Computational Neuroscience
Brain Connectivity

Background:

Granger causality analysis (GCA) is a key tool in brain imaging, but its structural basis and dynamic functional properties are not fully understood.
Existing GCA methods often focus on regional interactions, potentially overlooking the role of direct white matter pathways.

Purpose of the Study:

To investigate the structural underpinnings and functional dynamics of GCA using a novel fiber-centered approach.
To elucidate the relationship between white matter structure and functional causality in resting-state and task-based fMRI data.

Main Methods:

Extracted fMRI BOLD signals from the endpoints of white matter fibers identified via diffusion tensor imaging (DTI).
Performed fiber-centered GCA on both resting-state and natural stimulus fMRI datasets.
Validated reproducibility through cross-session and cross-subject comparisons.

Main Results:

Granger causalities were significantly stronger along white matter fibers compared to non-fiber-connected regions, confirming structural underpinnings of functional causality.
Observations were reproducible within and across subjects and sessions.
Fiber-centered GCA on natural stimulus fMRI data revealed significant temporal changes, offering dynamic insights.

Conclusions:

Fiber-centered GCA provides a robust method to link white matter structural connectivity with functional causality in the brain.
This approach offers novel insights into the dynamic functional organization of the brain, moving beyond static regional analyses.