More recently topological properties of the functional connectome have been found to change significantly during child development (Bullmore & Sporns, 2009, 2012 Di Martino et al., 2014), raising the question of how they emerge and change from birth to adolescence. Some of the earliest studies examining the efficiency of functional networks in younger and older adults reported that older adults showed decreased global and Local Efficiency (Achard & Bullmore, 2007) while network Modularity is not significantly changed from younger adults to older adults (Meunier, Achard, Morcom, & Bullmore, 2009). Previous studies using graph theory approaches to analyze fMRI data have demonstrated age‐related changes in the brain functional connectome.

Application of graph theory to functional brain networks has revealed important topological properties that influence human behavior including: efficient network architecture, Small‐Worldness, which reflects an optimal balance between segregation and integration in information processing between regions (Achard, Salvador, Whitcher, Suckling, & Bullmore, 2006 Salvador et al., 2005), modular structure, central communication hubs, and Rich‐Club organization which is formed by the densely interconnected hubs in healthy adults (Cao et al., 2014 Van den Heuvel, Kahn, Goñi, & Sporns, 2012 Van den Heuvel & Sporns, 2011). Graph theory has emerged as a new approach to investigate the complex functional network topology of the human brain connectome (Robinov & Sporns, 2010). Functional connectivity in this data has been defined as inter‐regional temporal correlations among spontaneous BOLD fluctuations in different regions of the brain at rest. Spontaneous fluctuations in the brain can be measured by blood oxygen level dependent (BOLD) signals using functional magnetic resonance imaging (fMRI) (Mateo, Knutsen, Tsai, Shih, & Kleinfeld, 2017). The architecture of human brain functional networks, now known as the human connectome, has recently been studied explosively using resting state functional MRI (r‐fMRI). Our results provide new insight to understand brain maturation of functional brain connectome and its relation to cognitive development from birth through adolescence. We also examine the effects of neurocognitive measures in boys and girls globally and locally. Marginally significant differences were observed in Local Efficiency, Small‐Worldness, and Modularity at a global level between boys and girls throughout development. We observed that Global Efficiency and Rich‐Club coefficient increased with age and Local Efficiency and Small‐Worldness decreased with age, while Modularity at the global level showed an inverted U‐shaped trajectory during development. Here we use graph theory analysis to examine the influence of age, sex, and neurocognitive measures on developmental changes to the global and regional topology of functional brain networks derived from fMRI data recorded in 189 healthy subjects from the age of 0–18 years during rest. Investigation of the brain connectome using functional magnetic resonance imaging (fMRI) and measures derived from graph theory analysis has emerged as a new approach to study brain development, cognitive function, and neurophysiological disorders.

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