Increased Cost Efficiency of Economical Brain Structural Networks in the Early Developing Brain

Introduction: Brain networks are the graph representation of brain regional connectivity which is typically estimated as the correlation between regional measures of brain structure and function using imaging analysis techniques [1]. Recent studies have shown that brain anatomical and functional networks as studied with MRI, EEG, MEG, and functional MRI often exhibit so called small-world network properties, i.e., these brain networks have dense, clustered local connectivity and relatively few long-range connections, which are topologically in between of regular lattices and random graphs [1, 2]. The small world structure is an optimal and economical model for brain networks to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information. The small world characteristics of brain networks might be the evolutionary result of brain development and have been competitively selected to solve the economic problem of optimizing the brain information processing [3]. To test the hypothesis that the economical brain networks have been competitively selected, as well as to confirm previous findings that brain networks have economical properties, we studied the efficiency and cost of brain structural networks derived from longitudinal MRI data of healthy pediatric subjects between 2 weeks and 2 years of age.