EEG-Based Functional Brain Networks: Does the Network Size Matter?

Amir Joudaki,Mahdi Jalili,Maria G Knyazeva,Niloufar Salehi,Pedro Antonio Valdes-Sosa

doi:10.1371/journal.pone.0035673

Amir Joudaki, Mahdi Jalili + Show 3 more

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https://doi.org/10.1371/journal.pone.0035673

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Abstract

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks – whose nodes can vary from tens to hundreds – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Highlights

Human brain is a complex system containing many interconnected regions
To construct large-scale functional or anatomical brain networks, signals recorded via electroencephalography (EEG), magnetocephalography (MEG), or magnetic resonance imaging (MRI) are often used
In this paper we considered high density EEGs recorded from a number of healthy individuals and investigated how graph metrics depend on the network size

Summary

Introduction

Human brain is a complex system containing many interconnected regions. Among various methods for studying the brain, graph theory is a valuable framework for analyzing the anatomical and functional connectome of the brain [1,2,3,4,5]. Within the framework of graph theory, brain regions are considered to be the nodes and connection links (directed/undirected and weighted/ unweighted) are extracted using some statistical measures of association. To construct large-scale functional or anatomical brain networks, signals recorded via electroencephalography (EEG), magnetocephalography (MEG), or magnetic resonance imaging (MRI) are often used. Topological properties of such networks can be analyzed by characterizing the brain as an undirected network, where individual EEG or MEG sensors or else MRI-based regions of interests serve as nodes and a link between any two nodes represents a correlation of the time series associated with these nodes or other statistical measure of their connection [1,6]. Scale-freeness has been shown to be a property of brain networks characterized by power-law degree distribution [14,15]

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