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

High-Order Resting-State Functional Connectivity is Predictive of Working Memory Decline After Brain Tumor Resection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Resolving and characterizing the incidence of millihertz EEG modulation in critically ill children.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2022
Same author

Macroperiodic Oscillations Are Associated With Seizures Following Acquired Brain Injury in Young Children.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society·2021
Same author

Poly (ε-Caprolactone)/Cellulose Nanofiber Blend Nanocomposites Containing ZrO2 Nanoparticles: A New Biocompatible Wound Dressing Bandage with Antimicrobial Activity.

Advanced pharmaceutical bulletin·2020
Same author

Identifying Disruptions in Intrinsic Brain Dynamics due to Severe Brain Injury.

Conference record. Asilomar Conference on Signals, Systems & Computers·2020
Same author

Intrinsic network reactivity differentiates levels of consciousness in comatose patients.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2018

Related Experiment Video

Updated: Feb 20, 2026

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

3.4K

An improved synchronization likelihood method for quantifying neuronal synchrony.

Sina Khanmohammadi1

  • 1Center for Collective Dynamics of Complex Systems (CoCo), The State University of New York at Binghamton, 4400 Vestal Parkway East, Binghamton, NY, 13902, USA; Department of Systems Science and Industrial Engineering, The State University of New York at Binghamton, 4400 Vestal Parkway East, Binghamton, NY, 13902, USA.

Computers in Biology and Medicine
|October 20, 2017
PubMed
Summary

A new Discrete Synchronization Likelihood (DSL) method uses Manhattan distance for robust synchronization quantification in dynamical systems. It outperforms traditional Synchronization Likelihood (SL), especially with noisy or complex data.

Keywords:
Brain networksFunctional connectivityNetwork dynamicsNonlinear couplingNonlinear synchronization measuresSynchronization likelihood

More Related Videos

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

5.0K
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

7.5K

Related Experiment Videos

Last Updated: Feb 20, 2026

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

3.4K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

5.0K
How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

7.5K

Area of Science:

  • Computational Neuroscience
  • Nonlinear Dynamics
  • Data Analysis

Background:

  • Quantifying synchronization in unknown dynamical systems is crucial, particularly in computational neuroscience.
  • Synchronization Likelihood (SL) is a promising method but sensitive to outliers due to its use of Euclidean distance.
  • Existing methods struggle with the nonlinear and non-stationary nature of biological and physical systems.

Purpose of the Study:

  • To introduce a novel Discrete Synchronization Likelihood (DSL) method for improved synchronization quantification.
  • To address the limitations of Euclidean distance in the conventional SL method.
  • To enhance the analysis of interrelationships between complex dynamical systems.

Main Methods:

  • Proposed Discrete Synchronization Likelihood (DSL) using Manhattan distance (l1 norm) on discretized signals.
  • Tested DSL on coupled Hénon Maps (unidirectional/bidirectional, identical/non-identical).
  • Evaluated DSL on a Watts-Strogatz small-world network (Kuramoto model) and the ADHD-200 fMRI dataset.

Main Results:

  • DSL demonstrated comparable or superior performance to the conventional SL method.
  • The proposed method showed robustness, especially in bivariate cases with subtle changes.
  • DSL effectively handled sudden shifts in multivariate dynamical systems.

Conclusions:

  • Discrete Synchronization Likelihood (DSL) offers a more robust alternative to traditional SL for analyzing synchronization.
  • The method is effective for complex, real-world datasets like fMRI, even with unknown dynamics.
  • DSL advances the quantification of coupling and interrelationships in nonlinear, non-stationary systems.