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

You might also read

Related Articles

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

Sort by
Same author

Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval.

Nature communications·2023
Same author

A Scalable Open-Set ECG Identification System Based on Compressed CNNs.

IEEE transactions on neural networks and learning systems·2021
Same author

Drosophila Brain Functional Data Analysis: A Unified Framework.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Sample Entropy of High Frequency Oscillations for Epileptogenic Zone Localization.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2018
Same author

A novel framework for feature extraction in multi-sensor action potential sorting.

Journal of neuroscience methods·2015
Same author

Matching pursuit and source deflation for sparse EEG/MEG dipole moment estimation.

IEEE transactions on bio-medical engineering·2013

Related Experiment Video

Updated: May 14, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Performance bounds for dynamic causal modeling of brain connectivity.

Shun Chi Wu1, A Lee Swindlehurst

  • 1Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA. scwu@uci.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

This study derives Cramér-Rao bounds for nonlinear dynamic causal models (DCM) used in brain imaging. These bounds assess the accuracy of estimating causal interactions between brain regions from EEG/MEG data under various conditions.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

Related Experiment Videos

Last Updated: May 14, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Complex dynamical models are used to understand brain connectivity and causal interactions.
  • Dynamic Causal Models (DCM) aim to mimic event-related potentials from EEG/MEG for brain functionality analysis.
  • Accurate estimation of DCM parameters is crucial for analyzing effective connectivity.

Purpose of the Study:

  • Derive Cramér-Rao performance bounds for nonlinear dynamic causal model (DCM) parameter estimates.
  • Examine how operating conditions influence the accuracy of DCM parameter estimation.
  • Provide a theoretical framework for assessing the reliability of brain connectivity analyses.

Main Methods:

  • Focus on a class of nonlinear dynamic causal models (DCM) characterized by connectivity parameters.
  • Inference of DCM parameters using simulated or empirical EEG/MEG data.
  • Derivation and analysis of Cramér-Rao lower bounds (CRB) for parameter estimation accuracy.

Main Results:

  • Established theoretical Cramér-Rao bounds for nonlinear DCM parameter estimation.
  • Quantified the impact of factors like noise and sampling rate on estimation precision.
  • Demonstrated how source localization accuracy affects the performance bounds.

Conclusions:

  • The derived Cramér-Rao bounds provide a benchmark for the achievable accuracy in DCM parameter estimation.
  • Understanding these bounds is essential for optimizing experimental designs and data acquisition parameters.
  • This work contributes to more reliable inferences of causal brain network dynamics.