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

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 Common Fund Data Ecosystem (CFDE).

bioRxiv : the preprint server for biology·2026
Same author

Exploring Links between Brain Image-Derived Phenotypes and Accelerometer-Measured Physical Activity in the UK Biobank.

bioRxiv : the preprint server for biology·2026
Same author

Common and distinct neural correlates of social interaction processing and theory of mind in narratives.

Nature communications·2026
Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026
Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026
Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026
Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026
Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026
Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Sep 3, 2025

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

2.7K

Mode decomposition-based time-varying phase synchronization for fMRI.

Hamed Honari1, Martin A Lindquist2

  • 1Department of Electrical and Computer Engineering, Johns Hopkins University, USA.

Neuroimage
|July 29, 2022
PubMed
Summary
This summary is machine-generated.

Multivariate variational mode decomposition (MVMD) offers a data-driven method for analyzing time-varying phase synchronization in resting-state fMRI data, overcoming limitations of traditional filtering techniques.

Keywords:
Functional connectivityMode decompositionMultivariate variational mode decompositionPhase synchronizationResting-state fMRITime-varying phase synchronization

More Related Videos

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.8K
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

12.4K

Related Experiment Videos

Last Updated: Sep 3, 2025

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

2.7K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.8K
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

12.4K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Signal Processing

Background:

  • Resting-state functional magnetic resonance imaging (rs-fMRI) is crucial for studying brain connectivity.
  • Measuring time-varying functional connectivity (TVC) and phase synchronization (PS) is of significant interest.
  • Traditional methods require arbitrary bandpass filter choices, limiting data-driven analysis.

Purpose of the Study:

  • To explore data-driven mode decomposition (MD) techniques for estimating time-varying phase synchronization (TVC) in rs-fMRI.
  • To introduce and evaluate multivariate variational mode decomposition (MVMD) as an alternative to conventional filtering.

Main Methods:

  • Comparison of various MD techniques: empirical mode decomposition (EMD), bivariate EMD (BEMD), noise-assisted multivariate EMD (na-MEMD).
  • Introduction and application of multivariate variational mode decomposition (MVMD) for TVC analysis.
  • Validation using simulations and real rs-fMRI data.

Main Results:

  • MVMD effectively decomposes signals into narrow-band components, suitable for PS analysis.
  • MVMD demonstrated superior performance compared to other evaluated MD approaches.
  • The study confirmed MVMD's reliability for investigating time-varying PS in rs-fMRI.

Conclusions:

  • MVMD provides a robust, data-driven solution for analyzing time-varying phase synchronization in rs-fMRI.
  • This method overcomes the limitations associated with a priori filter selection.
  • MVMD is a promising tool for advancing the understanding of dynamic brain connectivity.