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
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
Correlation and Causation01:27

Correlation and Causation

43.0K
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...
43.0K
Cause and Effect01:53

Cause and Effect

12.5K
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?
12.5K
Cross-Sectional Research01:50

Cross-Sectional Research

12.7K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
12.7K

You might also read

Related Articles

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

Sort by
Same author

Multimodal data synchronization: a high-level software methodology for heterogeneous devices.

BMC biomedical engineering·2026
Same author

Dissecting the integrated information of cardiovascular and cardiorespiratory systems at rest and during physiological stress.

Physiological measurement·2026
Same author

Autonomic impairment in advanced heart-failure patients revealed by nonlinear heart rate variability measured during exercise.

Scientific reports·2026
Same author

Appropriate Null Models for Testing the Effect of the Head Model on MEG Functional Connectivity Fingerprinting.

Brain topography·2026
Same author

EMBC Special Issue: Investigating High-Order Behaviors in Multivariate Cardiovascular Interactions via Nonlinear Prediction and Information-Theoretic Tools.

IEEE transactions on bio-medical engineering·2026
Same author

Resting-state brain dynamics: insights from oscillatory activity in brain networks.

Reviews in the neurosciences·2026
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Feb 15, 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

Multiscale Granger causality.

Luca Faes1, Giandomenico Nollo1, Sebastiano Stramaglia2

  • 1Bruno Kessler Foundation, Trento, Italy and BIOtech, Department of Industrial Engineering, University of Trento, Italy.

Physical Review. E
|January 20, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to analyze information transfer across multiple time scales using multiscale Granger causality (GC). The novel approach enhances accuracy in complex systems, revealing climate dynamics between temperature and CO2.

More Related Videos

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

7.0K
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

Related Experiment Videos

Last Updated: Feb 15, 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
Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

7.0K
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

Area of Science:

  • Complex Systems Analysis
  • Statistical Physics
  • Dynamical Systems Theory

Background:

  • Analyzing complex physical and biological systems requires understanding dynamics across multiple temporal scales.
  • Existing methods for assessing dynamic complexity at different time scales are established, but multiscale analysis of directed interactions lacks theoretical formalization.
  • Empirical evaluations of multiscale directed interactions are challenging due to practical issues like filtering and downsampling.

Purpose of the Study:

  • To extend the Granger causality (GC) measure for quantifying information transfer across multiple time scales.
  • To develop a theoretically sound and computationally reliable method for multiscale directed interaction analysis.

Main Methods:

  • The study extends Granger causality (GC) to quantify information transfer across multiple time scales.
  • Multiscale processing of vector autoregressive (AR) processes is shown to introduce a moving average (MA) component, resulting in an ARMA process.
  • State space (SS) models are used to represent the ARMA process, enabling computation of exact GC values at arbitrarily large time scales.

Main Results:

  • The proposed state space (SS) approach demonstrates significantly higher estimation accuracy compared to traditional AR modeling of filtered and downsampled data.
  • Peculiar features of multiscale Granger causality (GC) in basic AR processes are identified through theoretical formulation.
  • Meaningful multiscale patterns of information transfer between global temperature and carbon dioxide concentration time series were disclosed using the improved method.

Conclusions:

  • The developed state space (SS) model provides a computationally reliable framework for exact multiscale Granger causality (GC) analysis.
  • This novel approach overcomes limitations of traditional methods, offering enhanced accuracy for analyzing complex systems.
  • The method successfully revealed significant multiscale information transfer patterns in climate data, highlighting its applicability to real-world environmental dynamics.