Growing Trees in Child Brains: Graph Theoretical Analysis of Electroencephalography-Derived Minimum Spanning Tree in 5- and 7-Year-Old Children Reflects Brain Maturation

Abstract

The child brain is a small-world network, which is hypothesized to change toward more ordered configurations with development. In graph theoretical studies, comparing network topologies under different conditions remains a critical point. Constructing a minimum spanning tree (MST) might present a solution, since it does not require setting a threshold and uses a fixed number of nodes and edges. In this study, the MST method is introduced to examine developmental changes in functional brain network topology in young children. Resting-state electroencephalography was recorded from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) weighted matrices were calculated in three different frequency bands from which MSTs were constructed, which represent constructs of the most important routes for information flow in a network. From these trees, several parameters were calculated to characterize developmental change in network organization. The MST diameter and eccentricity significantly increased, while the leaf number and hierarchy significantly decreased in the alpha band with development. Boys showed significant higher leaf number, betweenness, degree and hierarchy and significant lower SL, diameter, and eccentricity than girls in the theta band. The developmental changes indicate a shift toward more decentralized line-like trees, which supports the previously hypothesized increase toward regularity of brain networks with development. Additionally, girls showed more line-like decentralized configurations, which is consistent with the view that girls are ahead of boys in brain development. MST provides an elegant method sensitive to capture subtle developmental changes in network organization without the bias of network comparison.

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

“…As far as we know this was the first time for this method to be used for the depression disease. In a previous study, it suggested that more random networks showed low clustering and a short path length, corresponding to MSTs' shorter diameters and higher leaf numbers [40]. Our finding that leaf fraction of the MDD group was higher than the healthy group indicated a shift toward randomization in the brain networks of the MDD group.…”

“…MST appeared in a variety of studies. MST was used as an elegant and sensitive method to capture subtle developmental organization changes in the brain networks of children [40]. In a study of Multiple Sclerosis, findings indicate that MST network analyses were able to detect network changes in the Multiple Sclerosis (MS) patients [41].…”

The trees and the forest: Characterization of complex brain networks with minimum spanning trees

“…In the study of Boersma et al, eyes-closed resting state 14 channel EEGs recorded in 227 children at age 5, with a second recording two years later, were analysed with the synchronization likelihood, a measure of generalized synchronization (Boersma et al, 2013). In the alpha band a number of significant changes in MST measures was found.…”

“…In this particular study both the raw functional connectivity as well as the beta band MST measures were more sensitive than the weighted normalised clustering coefficient and path length. Compared to the study of Boersma et al (Boersma et al, 2013), a strength was the use of the PLI, which is less sensitive to confounding effects of volume conduction and active reference electrodes compared to the synchronization likelihood. This study suggests that network analysis based upon the MST might be a candidate for the development of brain computer interfaces.…”

Analysis of infant cortical synchrony is constrained by the number of recording electrodes and the recording montage

“…Recent studies have provided very useful tools to measure graph based network properties where spatial features may be extracted with post hoc, data-driven analysis of connectivity clusters. The latter approach has recently gained wide interest in neurophysiological studies of all age groups and in various neurocognitive disorders (Boersma et al, 2013;de Haan et al, 2009;Kim et al, 2013;Omidvarnia et al, 2014;Reijneveld et al, 2007).…”