three different datasets. Finally, we fit the optimal generative model to data from a lifespan study (with ages ranging from 7-85 years) and found that the penalty on long-distance connections weakened monotonically with age. Older subjects’ connectomes were fit poorly compared to those of younger individuals, a result driven primarily by an inability to match edge length and clustering coefficient distributions. This suggests that the human connectome undergoes a characteristic reorganization across the lifespan. A total of N = 40 healthy participants underwent an MRI session on a 3-T Siemens Trio scanner with a 32-channel head-coil. The magnetization-prepared rapid graident-echo (MPRAGE) sequence was 1 mm in-plane resolution and 1.2 mm slice thickness. The DSI sequence included 128 diffusion-weighted volumes plus one reference b$_{0}$ volume, maximum b -value of 8000 s · mm − 2 and 2 . 2 × 2 . 2 × 3 . 0 mm voxel size. The echo planar imaging (EPI) sequence was 3.3 mm in-plane resolution and 0.3 mm slice thickness with TR of 1920 ms . DSI and MPRAGE data were processing using the Connectome Mapping Toolkit (Daducci et al., 2012). Segmentation of grey and white matter was based on MPRAGE volumes. The cerebral cortex was parcellated into n = 219 ROIs (Cammoun et al., 2012), of which we retained the 108 comprising the right hemisphere. We enforced an average connectome density of ρ ≈ 10%, resulting in a streamline threshold of 27 streamlines (i.e. a minimum of 27 streamlines must have connected two regions for us to consider the presence of an anatomical connection). These same data have been analyzed elsewhere (Avena-Koenigsberger et al., 2014; Go˜ ni et al., 2014; Betzel et al., 2013). 2. Methods 2.1. Data acquisition and processing 4