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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic 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...

You might also read

Related Articles

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

Sort by
Same author

Moving artificial intelligence from research to real-world clinical use in neurology.

Nature reviews. Neurology·2026
Same author

Global search metaheuristics for neural mass model calibration.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

When seizure counts are not seizures: Measurement error and its implications for epilepsy management and driving policy.

Epilepsia·2026
Same author

A multicenter, video-EEG-based validation of a multimodal wearable device for focal seizure detection in adults: The SeizeIT2 study.

Epilepsia open·2026
Same author

Are seizure forecasts and cycles better than chance? What chance?

Epilepsia·2026
Same author

Out of the lab, into real life: Evaluating at-home EEG self-monitoring.

Epilepsia open·2026
Same journal

Individualized mapping of functional brain networks in older adulthood.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

The language network responds robustly to sentences across tasks.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Neighborhood disadvantage and brain myelination: Insights from infancy to childhood.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Meditation and neurofeedback: A systematic scoping review, synthesis, and future directions.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Interactive shape and color representation in visual working memory for colored objects in the human occipitotemporal cortex.

Imaging neuroscience (Cambridge, Mass.)·2026
See all related articles

Related Experiment Video

Updated: Jun 4, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Dynamics-informed priors (DIP) for neural mass modelling.

Alessia Caccamo1,2, Dominic M Dunstan1,2, Mark P Richardson3

  • 1Department of Mathematics and Statistics, University of Exeter, Exeter, United Kingdom.

Imaging Neuroscience (Cambridge, Mass.)
|June 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces dynamic causal modelling with dynamics-informed priors (DIP-DCM), a new method for neural mass model parameter estimation. DIP-DCM improves inference accuracy by using genetic algorithms to derive data-driven priors, outperforming standard methods.

Keywords:
dynamic causal modellinggenetic algorithmsparameter estimationpharmacodynamics of anti-seizure medicationspectral analysis of M/EEGvariational Bayes

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Related Experiment Videos

Last Updated: Jun 4, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Area of Science:

  • Computational neuroscience
  • Neuroimaging analysis
  • Mathematical modeling

Background:

  • Neural mass models (NMMs) are crucial for understanding brain activity.
  • Parameter estimation in NMMs, often using dynamic causal modelling (DCM), is vital but sensitive to prior assumptions.
  • Limited empirical data and poorly defined priors can bias NMM inference.

Purpose of the Study:

  • To develop a computational extension of DCM for improved parameter estimation.
  • To establish a strategy for mapping NMM parameters to neuroimaging data.
  • To enable data-driven derivation of priors for NMMs in exploratory studies.

Main Methods:

  • Proposed DCM with dynamics-informed priors (DIP-DCM), integrating a genetic algorithm (GA) to map parameter values to model dynamics.
  • Optimized parameter space sub-regions were identified and translated into parameter priors for DCM.
  • DIP-DCM was validated against standard DCM and standalone GA using two neuroimaging datasets.

Main Results:

  • DIP-DCM models demonstrated superior predictive accuracy compared to standard DCM and GA.
  • The method successfully captured mechanistic signatures of psychiatric conditions and drug effects.
  • DIP-DCM effectively navigated local minima and explored parameter spaces guided by model dynamics and data.

Conclusions:

  • DIP-DCM offers an advantageous approach to parameter estimation, especially with limited information.
  • This method facilitates a data-driven derivation of priors, enhancing exploratory research in neuroscience.
  • DIP-DCM shows broad applicability across diverse biological contexts and datasets.