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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:
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...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
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:
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

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Related Experiment Video

Updated: May 22, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Post-hoc selection of dynamic causal models.

M J Rosa1, K Friston, W Penny

  • 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, WC1N 3BG, UK. m.rosa@ucl.ac.uk

Journal of Neuroscience Methods
|May 8, 2012
PubMed
Summary
This summary is machine-generated.

Dynamic Causal Modelling (DCM) offers a faster way to compare many brain models using a post-hoc approximation. This method reduces computational time for analysing functional magnetic resonance imaging data.

Related Experiment Videos

Last Updated: May 22, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Dynamic Causal Modelling (DCM) traditionally involves comparing a few neurobiologically informed models.
  • Model evidence is approximated using a free energy bound after individual model fitting.
  • A recent shift towards exploring larger model spaces necessitates more efficient methods.

Purpose of the Study:

  • To explore the relationship between free energy bounds and post-hoc approximations of model evidence.
  • To investigate these approximations within deterministic (bilinear) DCMs for fMRI data.
  • To evaluate the computational benefits of post-hoc methods for large-scale model comparison.

Main Methods:

  • Utilized a post-hoc approximation for model evidence calculation, optimizing only the full model.
  • Employed a generalization of the Savage-Dickey density ratio for submodel evidence.
  • Focused on deterministic (bilinear) dynamic causal models (DCMs).
  • Applied methods to functional magnetic resonance imaging (fMRI) data.

Main Results:

  • Demonstrated the feasibility of using post-hoc approximations for model evidence in DCM.
  • Showed significant reduction in computational time for model fitting compared to traditional methods.
  • Established the relationship between free energy bounds and post-hoc approximations in this context.

Conclusions:

  • Post-hoc approximations offer a computationally efficient alternative for model comparison in DCM.
  • This approach facilitates the exploration of larger model spaces in fMRI analysis.
  • The findings support the use of these methods for hypothesis-driven and exploratory neuroscience research.