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Comparison of data-driven thresholding methods using directed functional brain networks.

Thilaga Manickam¹, Vijayalakshmi Ramasamy², Nandagopal Doraisamy³

¹Department of Mathematics, Amrita School of Physical Sciences, 77649 Amrita Vishwa Vidyapeetham , Coimbatore, Tamilnadu 641112, India.

Reviews in the Neurosciences

|September 1, 2024

Summary

This study reviews thresholding methods for functional brain networks (FBNs) derived from electroencephalogram (EEG) data. Data-driven methods like MCC and OMST effectively detect cognitive load changes in brain networks.

Keywords:

cognition electroencephalography functional brain network graph theory thresholding

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Area of Science:

Neuroscience
Graph Theory
Signal Processing

Background:

Electroencephalogram (EEG) data offers insights into neuronal transactions.
Functional Brain Networks (FBNs) model EEG data using graph theory.
Thresholding methods refine FBNs by filtering weak connections.

Purpose of the Study:

To review various thresholding methods for FBN analysis.
To evaluate data-driven thresholding methods for characterizing cognitive behavior.
To identify effective methods for detecting cognitive load-induced brain network changes.

Main Methods:

EEG data was modeled as weighted, fully connected graphs (FBNs).
Various thresholding techniques were reviewed, focusing on data-driven approaches.
Four data-driven methods (MST, MCC, USPT, OMST) were analyzed using directed FBNs from cognitive load EEG data.

Main Results:

Data-driven thresholding methods are unbiased as they avoid arbitrary user-defined thresholds.
Minimum Connected Component (MCC) and Orthogonal Minimum Spanning Tree (OMST) methods detected cognitive load-induced changes.
The study analyzed the efficacy of MST, MCC, USPT, and OMST in characterizing cognitive behavior.

Conclusions:

MCC and OMST are effective data-driven thresholding methods for analyzing cognitive load effects on directed FBNs.
The findings highlight the utility of graph theoretical approaches in neuroscience.
Further research can explore these methods for understanding brain function under varying cognitive states.