Network analysis of resting state EEG in the developing young brain: Structure comes with maturation

Abstract

During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths. We used graph theoretical concepts to examine changes in functional brain networks during normal development in young children. Resting-state eyes-closed electroencephalography (EEG) was recorded (14 channels) from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) was calculated in three different frequency bands and between each pair of electrodes to obtain SL-weighted graphs. Mean normalized clustering index, average path length and weight dispersion were calculated to characterize network organization. Repeated measures analysis of variance tested for time and gender effects. For all frequency bands mean SL decreased from 5 to 7 years. Clustering coefficient increased in the alpha band. Path length increased in all frequency bands. Mean normalized weight dispersion decreased in beta band. Girls showed higher synchronization for all frequency bands and a higher mean clustering in alpha and beta bands. The overall decrease in functional connectivity (SL) might reflect pruning of unused synapses and preservation of strong connections resulting in more cost-effective networks. Accordingly, we found increases in average clustering and path length and decreased weight dispersion indicating that normal brain maturation is characterized by a shift from random to more organized small-world functional networks. This developmental process is influenced by gender differences early in development.

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

“…In those studies where connectivity data or conventional graph analysis showed an effect, an effect was always present in the MST analysis as well (Boersma et al, 2011;2013;Olde Dubbelink et al, 2013;van Dellen et al, 2013). In other studies MST analyses succeed in demonstrating an experimental effect while other methods were less successful (Demuru et al, 2013;Lee et al, 2006).…”

“…If we compare this pattern to the change observed between age 5 and age 7, one could argue that brain networks in girls show a more mature pattern. The same EEG dataset has been analysed before with more classical graph theoretical techniques (Boersma et al, 2011), showing that the clustering coefficient increased in the alpha band, and the path length in all bands, between age 5 and 7. Clustering was higher in girls compared to girls in the alpha and beta bands.…”

“…During normal development the topology changes from random to a more small-world-like organization, and this process is strongly related to genetic factors (Boersma et al, 2011;Schutte et al, 2013;Smit et al, 2008). Brain network organization is different in males and females, possibly due to the influence of sex hormones on brain development (Douw et al, 2011;Gong et al, 2009b).…”

Graph-based network analysis in schizophrenia

“…In children, the long distant connections (edges) are weak in contrast with stronger short-distance edges. During development, short-distance connections weaken, while the long-distance connections increase in strength [46][47][48][49] . The SWN architecture already exists in the 1st month of life [48] and this organization seems to assume higher values in adults [50] .…”

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

“…Second, increasing numbers of pair-wise synchrony estimates poses a statistical challenge due to multiple comparisons, and needs solutions akin to those used in neuroimaging (McIntosh and Mišic, 2013;Singh et al, 2011;Singh and Phillips, 2010). Recently introduced graph measures, especially those based on weighted and surrogate-normalized matrices (Boersma et al, 2011;Rubinov and Sporns, 2010), may effectively mitigate the challenge associated with massive pairwise comparisons and the underlying assumptions of temporal stationarity and inter-individual similarity (Sporns, 2013).…”