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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Induced Electric Fields01:23

Induced Electric Fields

The fact that emfs are induced in circuits implies that work is being done on the conduction electrons in the wires. What can possibly be the source of this work? We know that it’s neither a battery nor a magnetic field, as a battery does not have to be present in a circuit where current is induced, and magnetic fields never do any work on moving charges. The source of the work is in fact an electric field that is induced in the wires. For example, if a stationary conductor is placed in a...
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...
Induced Electric Fields: Applications01:27

Induced Electric Fields: Applications

An important distinction exists between the electric field induced by a changing magnetic field and the electrostatic field produced by a fixed charge distribution. Specifically, the induced electric field is nonconservative because it does not work in moving a charge over a closed path. In contrast, the electrostatic field is conservative and does no net work over a closed path. Hence, electric potential can be associated with the electrostatic field but not the induced field. The following...
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...

You might also read

Related Articles

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

Sort by
Same author

Ephaptic coupling can explain variability in neural activity.

Cerebral cortex (New York, N.Y. : 1991)·2026
Same author

Scale-invariant evolution: Comment on "homo informatio" by Michael Walker.

Physics of life reviews·2026
Same author

The body does not keep the score: trauma, predictive coding, and the restoration of metastability.

Frontiers in systems neuroscience·2026
Same author

The dysconnection hypothesis of schizophrenia: a 30-year update.

World psychiatry : official journal of the World Psychiatric Association (WPA)·2026
Same author

The methodological foundations of lesion network mapping remain sound.

bioRxiv : the preprint server for biology·2026
Same author

Insula Structure Is Linked to Autonomic Cardiac Dysregulation in Depression.

Biological psychiatry·2026

Related Experiment Video

Updated: May 10, 2026

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

Neural masses and fields in dynamic causal modeling.

Rosalyn Moran1, Dimitris A Pinotsis, Karl Friston

  • 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK ; Virginia Tech Carilion Research Institute, Virginia Tech Roanoke, VA, USA ; Bradley Department of Electrical and Computer Engineering, Virginia Tech Blacksburg, VA, USA.

Frontiers in Computational Neuroscience
|June 12, 2013
PubMed
Summary

Dynamic causal modeling (DCM) analyzes neuronal connectivity using various models like neural masses, conductance-based, and field models. The choice of model depends on empirical data and the specific hypotheses being tested.

Keywords:
dynamic causal modelingelectroencephalographylocal field potential (LFP)magnetoencephalography (MEG)neural mass models

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Related Experiment Videos

Last Updated: May 10, 2026

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent
08:31

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Neuroimaging analysis

Background:

  • Dynamic causal modeling (DCM) is a framework for analyzing effective neuronal connectivity.
  • DCM integrates various electrophysiological responses, including invasive and non-invasive measures.
  • Neuronal population models are crucial for understanding synaptic underpinnings of connectivity.

Purpose of the Study:

  • To review and compare different neuronal population models used in DCM.
  • To elucidate the strengths and applications of neural mass, conductance-based, and field models.
  • To guide the selection of appropriate models based on empirical data and research questions.

Main Methods:

  • Review of differential equation-based neuronal population models (neural mass, conductance-based, field models).
  • Analysis of convolution-based dynamics in neural mass models.
  • Examination of non-linear interactions in conductance-based models.
  • Application of partial differential equations (PDEs) for spatial propagation in field models.

Main Results:

  • Neural mass models can recapitulate empirical evoked and spectral responses.
  • Conductance-based models offer a richer response space due to non-linear dynamics, suitable for multiple resonances.
  • Field models allow inference of connectivity topology and analysis of structure-function relationships, even without spatial data.

Conclusions:

  • The choice of DCM model (neural mass, conductance-based, or field) should be guided by the specific empirical data and hypotheses.
  • Each model class offers distinct advantages for analyzing neuronal connectivity and brain function.
  • DCM provides a flexible framework adaptable to different levels of biological detail and data types.