generative models, we fit the MI model to connectomes of individuals whose ages ranged from 7-85 years, showing that the distance penalty weakened with age while energy increased, an effect driven by growing discrepancies in clustering and edge length distributions. Generative models for brain networks have been investigated before, serving as proofs of concept (Kaiser and Hilgetag, 2004b; Kaiser et al., 2009; Henderson and Robinson, 2013; Lim and Kaiser, 2015; Roberts et al., 2015) or as investiga- tive tools for non-human connectome data (Kaiser and Hilgetag, 2007; Costa et al., 2007a; Nicosia et al., 2013; Langen et al., 2015). One limitation of earlier studies was the use of composite connectivity matrices as empirical benchmarks. For example, Ercsey-Ravasz et al. (2013) and Song et al. (2014) proposed geo- metric models of an incomplete macaque connectome, where connections were based on composite tract-tracing data compiled across multiple subjects and only a subset of cortical areas. Another limitation of earlier work was the lack of model comparison. In many cases proposed generative models were only com- pared against random generative models (Ercsey-Ravasz et al., 2013; Song et al., 2014) where connections were formed with uniform probability, as opposed to models incorporating more plausible generative mechanisms. The first model we examined was the pure geometric model, which was the simplest but also, in accordance with earlier studies, performed the worst. The observation that geometry partly explains the topology of brain networks is in line with in a large literature on wiring minimization (Mitchison, 1991; Laughlin and Sejnowski, 2003; Cherniak et al., 2004; Samu et al., 2014), and has been appreciated in modeling studies of both human brain networks (Henderson and Robinson, 2013; Kaiser and Hilgetag, 2004a; V´ ertes et al., 2012; Klimm et al., 2014) and those of non-human primates (Kaiser and Hilgetag, 2004a; Costa et al., 2007a) and other mammals (Henderson and Robinson, 2011; Rubinov 16