Life-span development of functional brain networks as assessed with minimum spanning tree analysis of resting state EEG

The brain matures with large quantitative changes in anatomy and function. Graph analysis of EEG has previously revealed increased connectivity between distant brain areas and a decrease in randomness and increased integration in the brain network with concurrent increased modularity. Comparisons of graph parameters across age groups, however, may be confounded with network degree distributions. Here, we analyzed graph parameters from minimum spanning tree (MST) graphs. MST graphs are constructed by selecting only the strongest available connections avoiding loops resulting in a backbone graph that is thought reflect the major qualitative properties of connectivity while allowing a better comparison across age groups by avoiding the degree distribution confound. EEG was recorded in a large (N=1500) population-based sample aged 5 to 71 years. Connectivity was assessed using Phase Lag Index to reduce effects of volume conduction. As previously reported, connectivity increased from childhood to adolescence, continuing to grow nonsignificantly into adulthood decreasing only after ~30 years of age. Leaf number, degree, degree correlation, maximum centrality from the MST graph indicated a pattern of increased integration and decreased randomness from childhood into early adulthood. The observed development in network topology suggested that maturation at the neuronal level is aimed to increase connectivity as well as increase integration of the brain network. We confirm that brain network connectivity shows quantitative changes across the life span, and additionally demonstrate parallel qualitative changes in the connectivity pattern.