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Related Experiment Video

Updated: Jun 1, 2026

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

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

Published on: July 1, 2014

A method for the estimation of functional brain connectivity from time-series data.

A Wilmer¹, M H E de Lussanet, M Lappe

¹Deptartment of Psychology, Westf. Wilhelms-University, Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience (OCC), Münster, Germany.

Cognitive Neurodynamics

|June 2, 2011

Summary

This study introduces a novel method to analyze temporal synchronization for understanding brain information flow. The technique effectively reveals time-dependent functional connectivity and delays between cortical regions.

Keywords:

Functional connectivity MEG magnetencephalography Network analysis Time-delayed phase synchronization

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Published on: March 21, 2019

Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Understanding temporal interactions between cortical areas is crucial in cognitive neuroscience.
Neuronal information processing relies on electrical activity transfer and synchronization between regions.

Purpose of the Study:

To develop and evaluate a method for revealing time-dependent functional connectivity and information flow between cortical regions.
To assess the stability and utility of phase-based statistical analyses for high temporal resolution data.

Main Methods:

Applied statistical phase analyses to model networks (Rössler attractors, Ornstein-Uhlenbeck systems) with time-dependent coupling.
Utilized mean phase coherence and the general synchronization index to analyze synchronization dynamics.
Investigated uni-directional, bi-directional, and time-delayed feedback connectivity patterns.

Main Results:

The developed methods are robust and stable across stochastic and periodic systems.
Successfully identified brief periods of phase coupling and time delays.
Demonstrated the effectiveness of phase measures in modeling functional connectivity patterns.

Conclusions:

The proposed method provides a reliable basis for generating functional connectivity models in neuroscience.
Phase-based analyses are valuable tools for understanding temporal dynamics in neuronal networks.
The technique aids in uncovering complex information processing and interactions within the brain.