In this report we generate synthetic networks using a generative model. The algorithm for producing synthetic networks is simple. Starting with a sparse seed network (62 bi-directional edges that were common across all 40 participants), edges were added one at a time over a series of steps until M total connections were placed (where M = 576 ± 57 connections across subjects). At each step we allow for the possibility that any pair of unconnected nodes, u and v , will be connected. Connections are formed probabilistically, where the relative probability of connection formation is given by: In this expression E ( u, v ) denotes the Euclidean distance between brain re- gions u and v . The exponent η controls the characteristic connection length. When η < 0, short-range connections are favored, while η > 0 increases the probability of forming longer connections. The other term, K ( u, v ), represents an arbitrary non-geometric relationship between nodes u and v and the value of γ scales its relative importance. The precise definition of K ( u, v ) is flexi- ble and can be varied to realize different wiring rules. For instance, setting K ( u, v ) = k$_{u}$k$_{v}$ and γ > 0 implements a variant of preferential attachment, wherein higher degree nodes are more likely to become connected. Alterna- tive definitions can be used to implement rules such as degree assortativity (e.g. K ( u, v ) = | k$_{u}$ − k$_{v}$ | , where nodes with similar/dissimilar numbers of connections preferentially connect to one another) or homophily (e.g. K ( u, v ) = $^{∑}$$_{w}$a$_{uw}$ a$_{wv}$ where connections form between nodes with more or fewer common neighbors). In Table 1 we show a complete list of all non-geometric wiring rules. We limit our analysis to generative models whose wiring rules include only two components, though we could accommodate more components, in principle. We impose this 2.2. Generative algorithm P ( u, v ) = E ( u, v ) η × K ( u, v ) γ (1) 5