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

Reliable Change of Blood-Based Biomarkers Following Acute Sport-Related Concussion: A CARE Consortium Study.

Sports medicine (Auckland, N.Z.)·2026
Same author

Intensity-dependent topographical expansion of sensory representations.

bioRxiv : the preprint server for biology·2026
Same author

A Beta-Binomial Model for Estimating Zero- or One-inflated Pain Trajectories.

bioRxiv : the preprint server for biology·2026
Same author

Detection of multiple influential observations on model selection.

Biometrics·2026
Same author

The Genetic and Environmental Architecture of the Human Functional Connectome.

ArXiv·2026
Same author

BundleWarp: Enhancing white matter tractometry and morphometry with precise neuronal mapping using streamline-based nonlinear registration.

Medical image analysis·2026
Same journal

Category-selective neural decreases in the human ventral occipito-temporal cortex as defined with intracranial recordings.

NeuroImage·2026
Same journal

EEG-Based Brain Fingerprints Elicited by Focal Transcranial Magnetic Stimulation of the Primary Motor Cortex.

NeuroImage·2026
Same journal

The Association between Brain Oscillatory Activity and Immediate Memory under Different Magnetoencephalography Paradigms: A population-based Study.

NeuroImage·2026
Same journal

Brain response to awe experiences in virtual reality: an integrated linear and nonlinear EEG analysis.

NeuroImage·2026
Same journal

Convergent imaging and genetic signatures of gray matter atrophy in Parkinson's disease.

NeuroImage·2026
Same journal

What actually matters in multi-compartment EEG head models: A controlled FEM study of parcellation granularity, skull layering, mesh quality, noise, and inverse solver.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 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

7.7K

Assessing uncertainty in dynamic functional connectivity.

Maria Kudela1, Jaroslaw Harezlak2, Martin A Lindquist3

  • 1Indiana University RM Fairbanks School of Public Health, Department of Biostatistics, Indianapolis, IN 46202, United States.

Neuroimage
|January 31, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to accurately assess dynamic functional connectivity (FC) in resting-state fMRI data. This technique combines multivariate linear process bootstrap with sliding-window analysis to provide reliable confidence bands for FC estimates.

Keywords:
Dynamic confidence bandsDynamic functional connectivityMultivariate time series bootstrapTime-varying correlation

More Related Videos

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

10.6K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.8K

Related Experiment Videos

Last Updated: Mar 8, 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

7.7K
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

10.6K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.8K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Statistical Analysis

Background:

  • Functional connectivity (FC) analysis using resting-state fMRI (rs-fMRI) is crucial for understanding brain function.
  • FC is increasingly recognized as dynamic, not stationary, necessitating methods to track its temporal changes.
  • Current sliding-window techniques for dynamic FC estimation suffer from inherent variability, leading to potential misinterpretation of noise as true signal.

Purpose of the Study:

  • To develop a robust method for assessing uncertainty in dynamic functional connectivity (FC) estimates.
  • To introduce a novel approach combining the multivariate linear process bootstrap (MLPB) with sliding-window analysis.
  • To provide confidence bands for dynamic FC estimates to distinguish true changes from noise.

Main Methods:

  • Application of the multivariate linear process bootstrap (MLPB) method.
  • Integration of MLPB with the sliding-window technique for time-series analysis.
  • Estimation of confidence bands for dynamic functional connectivity.

Main Results:

  • The proposed method effectively assesses uncertainty in dynamic FC estimates.
  • Numerical simulations demonstrated the efficacy of the MLPB and sliding-window combination.
  • Application to rs-fMRI data confirmed the method's ability to provide reliable confidence bands.

Conclusions:

  • The combined MLPB and sliding-window approach offers a reliable way to quantify uncertainty in dynamic FC.
  • This method helps differentiate genuine brain activity fluctuations from estimation noise.
  • Accurate assessment of dynamic FC is critical for advancing our understanding of brain dynamics.