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Measuring the Coupling Direction between Neural Oscillations with Weighted Symbolic Transfer Entropy.

Zhaohui Li^1,2, Shuaifei Li¹, Tao Yu³

¹School of Information Science and Engineering (School of Software), Yanshan University, Qinhuangdao 066004, China.

Entropy (Basel, Switzerland)

|December 29, 2020

Summary

This summary is machine-generated.

We developed weighted symbolic transfer entropy (WSTE) to measure directional brain communication. This novel method accurately identifies neural coupling direction, outperforming traditional methods in simulations and epilepsy data analysis.

Keywords:

coupling direction epileptic seizure neural oscillation symbolic transfer entropy weighted probability distribution

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Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Neural oscillations are crucial for brain processing and communication.
Understanding directional interactions between brain regions requires accurate estimation of neural coupling direction.

Purpose of the Study:

To develop a novel method, weighted symbolic transfer entropy (WSTE), for measuring directionality between neuronal populations.
To improve upon traditional symbolic transfer entropy (STE) by incorporating amplitude information.

Main Methods:

Developed WSTE by combining symbolic transfer entropy (STE) with a weighted probability distribution.
Validated WSTE using simulation analysis to assess its ability to estimate coupling direction.
Applied WSTE to epileptic electrocorticography (ECoG) data.

Main Results:

WSTE effectively estimates coupling direction between neural oscillations in simulations.
WSTE demonstrates higher sensitivity to coupling strength and improved robustness against noise compared to STE.
A significant coupling direction from the anterior nucleus of thalamus (ANT) to the seizure onset zone (SOZ) was identified in epileptic ECoG data.

Conclusions:

WSTE is a superior method for measuring directional coupling between neural oscillations.
The WSTE method offers advantages for characterizing information flow between brain regions.
This technique has potential applications in understanding neurological disorders like epilepsy.