Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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 - 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:

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:

Correlation and Causation

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...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

You might also read

Related Articles

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

Sort by

Same author

Dissecting the contribution of recent reward versus recent performance history on cognitive effort allocation.

Neuropsychologia·2026

Same author

Rapid modulation of choice behavior by ultrasound on the human frontal eye fields.

Nature communications·2026

Same author

Topographic Variation in Human Neurotransmitter Receptor Densities Explains Differences in Intracranial EEG Spectra.

Human brain mapping·2025

Same author

Canalization and plasticity in psychopathology.

Neuropharmacology·2022

Same author

An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data.

Frontiers in artificial intelligence·2021

Same author

REBUS and the Anarchic Brain: Toward a Unified Model of the Brain Action of Psychedelics.

Pharmacological reviews·2019

Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026

Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026

Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026

Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026

Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026

Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026

See all related articles

Search research articles

Related Experiment Videos

Ten simple rules for dynamic causal modeling.

K E Stephan¹, W D Penny, R J Moran

¹Laboratory for Social and Neural Systems Research, Institute for Empirical Research in Economics, University of Zurich, Zurich, Switzerland. k.stephan@iew.uzh.ch

|November 17, 2009

Summary

This summary is machine-generated.

Dynamic causal modeling (DCM) offers a Bayesian framework for understanding brain activity. This tutorial provides ten rules for best practices in applying DCM to neuroimaging and electrophysiology data.

Related Experiment Videos

Area of Science:

Neuroscience
Computational Neuroscience
Bayesian Inference

Background:

Dynamic causal modeling (DCM) is a Bayesian framework for inferring hidden neuronal states from brain activity measurements.
It estimates effective synaptic connection strengths and context-dependent modulation between neuronal populations.
DCM is widely applied to neuroimaging and electrophysiological data analysis.

Purpose of the Study:

To provide a tutorial for the growing community of DCM users.
To offer good practice recommendations for applying DCM.
To help users avoid pitfalls in DCM application and interpretation.

Main Methods:

The study presents ten simple rules for best practices in Dynamic Causal Modeling.
These rules are derived from the theoretical foundations of DCM.
The recommendations aim to guide the application and interpretation of DCM results.

Main Results:

The article outlines ten practical recommendations for using Dynamic Causal Modeling.
These guidelines address the complexity of DCM compared to conventional methods.
The focus is on ensuring accurate inference of neuronal states and connectivity.

Conclusions:

Adherence to these ten rules can improve the application and interpretation of Dynamic Causal Modeling.
This tutorial aims to enhance the usability and reliability of DCM in neuroscience research.
Understanding DCM's theoretical underpinnings is crucial for researchers utilizing this technique.