Graph-Theory-Based Multilevel Cortical Functional Connectivity Developmental Analysis

Functional connectivity (FC) is an efficient measurement to describe brain’s traits in information processing. But FCs at developmental stages from infancy to adolescence usually have severe individual variance, may affect the FC characterization analysis. Yet few past studies try to address this problem. In this article, we select quiet sleep (QS) and nonrapid eye movement (NREM) period electroencephalogram (EEG) of 42 healthy subjects from 0 to 17 years old for study. Random network combined with stability measurement using Shannon entropy (SE) is used to construct the individual-level FC (ILFC), which can describe the individual brain’s interaction. Majority voting is applied to construct the group-level FC (GLFC), which can describe the core part of ILFCs within the same age. Based on FCs, graph theory and statistical analysis are further applied, where the following conclusions are observed: 1) the
$\beta $
band of EEG is most important in showing the age related variance of functional separation and integration; 2) 3 months–3 years old is the transition period from intrasubnetwork interacting mode to intersubnetwork interacting mode; 3) the phenomenon of complementation and overlay among GLFCs in different bands can be found; and 4) the GLFC’s network centrality becomes not obvious as age increases during 1–14 years old.