Related Experiment Video
Updated: May 26, 2026
08:27
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
Model selection and gobbledygook: response to Lohmann et al
Karl Friston1, Jean Daunizeau2, Klaas Enno Stephan3
1The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
Neuroimage
|December 14, 2011
Summary
This response clarifies misconceptions regarding dynamic causal modelling, a Bayesian approach for selecting causal models in dynamical systems. It addresses critiques by Lohmann et al. by explaining the procedure
Area of Science:
- Neuroscience
- Computational Biology
- Systems Biology
Background:
- Dynamic causal modelling (DCM) is a Bayesian inference approach to analyse neural or other dynamical systems.
- Model selection is crucial for identifying the best model of system dynamics from competing hypotheses.
- Critiques by Lohmann et al. have raised questions about DCM's application and interpretation.
Purpose of the Study:
- To address and clarify misconceptions presented by Lohmann et al. regarding dynamic causal modelling (DCM).
- To provide a detailed response to specific critiques concerning model selection within the DCM framework.
- To elucidate the principles and application of DCM as a Bayesian model selection procedure.
Main Methods:
- The study involves a critical analysis of the observations made by Lohmann et al.
- It unpacks specific misconceptions related to Bayesian model selection in dynamical systems.
- The response clarifies the theoretical underpinnings and practical application of dynamic causal modelling.
Main Results:
- Identified and addressed three key misconceptions in Lohmann et al.'s critique of DCM.
- Provided clarifications on the Bayesian model selection principles employed by DCM.
- Demonstrated the validity and utility of DCM for analysing causal models of dynamical systems.
Conclusions:
- The critique by Lohmann et al. is based on a misunderstanding of dynamic causal modelling.
- Dynamic causal modelling remains a robust Bayesian procedure for model selection in dynamical systems.
- Further clarification is provided to guide the accurate application and interpretation of DCM.
Related Concept Videos
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:26Pharmacokinetic 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.
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.
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
01:34Woodward–Hoffmann Selection Rules and Microscopic Reversibility
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
01:02Quantifying and Rejecting Outliers: The Grubbs Test
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...