The human brain changes profoundly as it develops. Classical anatomical studies show pruning of short-range connections throughout childhood, in favor of long range ones [

1]. Diffusion imaging may also be combined with fiber tractography to reveal axonal pathways

*in vivo*. In DTI studies, the fractional anisotropy of diffusion, which is sensitive to myelination, increases in childhood, plateaus in adulthood, and then declines in old age [

2]. Defining the developmental trajectory for various aspects of brain structure is critical in determining how the brain normally develops. Normative statistics on brain connectivity are also useful to help identify anomalies of brain wiring that have been implicated in autism, schizophrenia, and other neurological and psychiatric disorders.

Graph theory, a branch of mathematics created to describe and analyze graphs, has recently been applied to characterize structural and functional networks computed from brain images [

3]. Resting-state fMRI and MEG/EEG studies define connectivity in terms of correlations in time-series, for signals recorded from different parts of the brain. Recent high profile studies have estimated the “developmental ages” or relative maturity of subjects, based on resting-state data in children [

4]. If diffusion images are collected, whole-brain tractography can be used to recover the density and integrity of tracts that interconnect pairs of brain regions. The topology and network properties of the resulting connectivity matrices can be summarized in terms of their characteristic path length (CPL), mean clustering coefficient (MCC), global efficiency (EGLOB), small-worldness (SW), and modularity (MOD) [

5]. CPL measures a network’s average path length - the minimum number of edges needed to travel from one node to another. MCC measures how many neighbors of a given node are also connected to each other, relative to the total possible number of connections in the network. EGLOB is the inverse of CPL; networks with lower CPL are more efficient. SW represents the balance between network differentiation and integration, calculated as a ratio of local clustering and characteristic path length of a node relative to the same ratio in a randomized network. MOD is the degree to which a system may be subdivided into smaller networks [

6].

Network metrics have been fruitfully applied to brain networks [

7] but only one study has investigated their developmental trajectory [

8] – in only 30 subjects. Here we scanned a much larger sample of 467 subjects – aged 12 to 30 – with 4-Tesla 105-gradient diffusion imaging. We set out to determine which measures of structural brain connectivity change the most from late childhood to adulthood. We computed graph theory metrics, from whole-brain HARDI tractography, in young and older adolescents (12 and 16 years old) and adults (aged 20–30).