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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-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...

You might also read

Related Articles

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

Sort by
Same author

Machine learning methods on BioVid heat pain database for pain intensity estimation.

Scientific reports·2025
Same author

Factorized binary search: Change point detection in the network structure of multivariate high-dimensional time series.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

The impact of government actions and risk perception on the promotion of self-protective behaviors during the COVID-19 pandemic.

PloS one·2023
Same author

A Multiple change-point detection framework on linguistic characteristics of real versus fake news articles.

Scientific reports·2023
Same author

Diffusion tensor imaging of superficial prefrontal white matter in healthy aging.

Brain research·2022
Same author

Mental Health Symptoms Unexpectedly Increased in Students Aged 11-19 Years During the 3.5 Years After the 2016 Fort McMurray Wildfire: Findings From 9,376 Survey Responses.

Frontiers in psychiatry·2021

Related Experiment Video

Updated: Jul 13, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.3K

Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity.

Andreas Anastasiou1, Ivor Cribben2, Piotr Fryzlewicz3

  • 1Department of Mathematics and Statistics, University of Cyprus, Canada.

Medical Image Analysis
|October 26, 2021
PubMed
Summary

This study introduces a new statistical method, cross-covariance isolate detect (CCID), to identify changes in brain functional connectivity (FC) networks over time without relying on sliding windows. CCID offers a faster and more precise way to analyze dynamic brain activity in fMRI data.

Keywords:
Change-point analysisDynamic functional connectivityNetworksTime varying connectivityfMRI

More Related Videos

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

7.4K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.8K

Related Experiment Videos

Last Updated: Jul 13, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.3K
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

7.4K
Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis
05:59

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

2.8K

Area of Science:

  • Neuroimaging
  • Statistical analysis
  • Computational neuroscience

Background:

  • Functional connectivity (FC) networks exhibit non-stationary behavior in fMRI data.
  • Existing methods often use sliding windows, which have limitations for analyzing dynamic FC.
  • Accurate detection of changes in brain network structure is crucial for understanding brain function.

Purpose of the Study:

  • To introduce a novel statistical method, cross-covariance isolate detect (CCID), for detecting change-points in FC networks.
  • To overcome the limitations of the sliding window approach in time-varying connectivity analysis.
  • To provide a computationally efficient and accurate tool for analyzing dynamic brain networks.

Main Methods:

  • Developed the cross-covariance isolate detect (CCID) method to detect multiple change-points in the second-order structure of multivariate time series.
  • CCID identifies unknown numbers and locations of change-points, suitable for frequent, small-magnitude changes.
  • Proposed a new information criterion for CCID to identify change-points and applied it to simulated and real fMRI data.

Main Results:

  • CCID effectively detects multiple change-points in functional connectivity networks.
  • The method is computationally fast and can identify changes in specific brain regions.
  • CCID demonstrated superior performance compared to existing change-point methods on simulated and fMRI datasets.

Conclusions:

  • CCID provides a robust and efficient alternative to sliding window methods for analyzing time-varying functional connectivity.
  • The method is particularly well-suited for task-based fMRI data and has potential applications in EEG, MEG, and ECoG.
  • Understanding dynamic brain network organization has significant clinical implications.