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 Experiment Videos

Synchronization likelihood with explicit time-frequency priors.

T Montez1, K Linkenkaer-Hansen, B W van Dijk

  • 1Department of Clinical Neurophysiology and MEG Centre, VU University Medical Center, Amsterdam, The Netherlands. t.montez@vumc.nl

Neuroimage
|October 7, 2006
PubMed
Summary
This summary is machine-generated.

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

Reliability and state-dependency of EEG connectivity, complexity and network characteristics.

Scientific reports·2025
Same author

Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.

Network neuroscience (Cambridge, Mass.)·2023
Same author

The influence of very preterm birth on adolescent EEG connectivity, network organization and long-term outcome.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2023
Same author

Network Hyperexcitability in Early Alzheimer's Disease: Is Functional Connectivity a Potential Biomarker?

Brain topography·2023
Same author

Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls.

Schizophrenia (Heidelberg, Germany)·2023
Same author

Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificity.

Network neuroscience (Cambridge, Mass.)·2022
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
See all related articles

This study refines the Synchronization Likelihood (SL) algorithm for analyzing nonlinear brain signal dependencies. The improved method enhances accuracy by optimizing parameters based on time-frequency characteristics, reducing user input.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Cognitive functions rely on integrated activity from distinct brain regions.
  • Nonlinear interactions between brain areas are crucial but often missed by linear correlation measures.
  • Detecting nonlinear dependencies is vital for understanding neuronal coupling in health and disease.

Purpose of the Study:

  • To introduce a refined parameter selection rationale for the Synchronization Likelihood (SL) algorithm.
  • To enhance the accuracy and reduce user dependency in detecting nonlinear signal interdependencies.
  • To improve the analysis of functional coupling in neural systems using electroencephalography (EEG) and magnetoencephalography (MEG) data.

Main Methods:

  • Developed a new parameter selection strategy for SL based on time-frequency analysis of signal patterns.

Related Experiment Videos

  • Reduced the number of user-adjustable parameters in the SL algorithm from six to two.
  • Validated the refined SL method using simulated data (Hénon systems) and real-world EEG data from an epileptic seizure.

    • Main Results:

    • The proposed parameter selection method effectively captures nonlinear dependencies in neural signals.
    • The refined SL algorithm demonstrated improved performance in simulations and clinical EEG data analysis.
    • Reduced parameterization simplifies the application of SL while maintaining or enhancing analytical power.

    Conclusions:

    • The optimized SL algorithm provides a more robust and user-friendly tool for characterizing nonlinear functional brain connectivity.
    • This advancement is particularly valuable for analyzing complex physiological signals like EEG/MEG in neurological conditions.
    • The findings support the use of advanced signal processing techniques for deeper insights into brain dynamics.