of model performance. This leaves open the possibility that wiring rules not explored in this report could produce superior results. For example, geometric models of forms not considered here (e.g.“connect all nodes separated by a dis- tance less than d$_{threshold}$ ”, models with regional inhomogeneities (see Rubinov et al. (2015))), or more accurate modeling of interregional white-matter fiber lengths in place of Euclidean distance could possibly lead to improved fits. In addition, models of altogether different form could be implemented. For exam- ple, in our report we make the assumption that the formation of connections depends equally on both geometric and topological constraints. An alternative approach might be to form one subset of connections on the basis of geometry and another set of connections on the basis of topology. While enumerating of all possible wiring rules or model variations is impractical, a number of meth- ods have been proposed that aim to discover wiring rules by evolving models themselves (Bailey et al., 2012; Menezes and Roth, 2014), as opposed to propos- ing a model and fitting its parameters, as we did here. These approaches, we believe, warrant further attention. In any case, consideration of a broader class (or classes) of models represents an important avenue for future work. 

Another methodological consideration concerns the evaluation of a synthetic network’s fitness. The synthetic networks are mapped into a morphospace (Go˜ ni et al., 2013) according to their geometric and topological properties and com- pared to the observed connectome along the same dimensions. Whether these properties are the most appropriate measures for network comparison is unclear. In principle, one could define alternative energy functions whose minima may not coincide with those reported here, and for which the MI model is not the best performer. Though the exploration of alternative energy functions is beyond the scope of this report, we attempted to mitigate the concern that our choice of energy function biased our results by performing a series of additional tests, the 

