results of which indicated that the MI model consistently outperformed other models. 

Another consideration relates to the combination of diffusion imaging and tractography for inferring the connectome’s organization. Though diffusion imaging/tractography represents the state of the art for in vivo reconstruc- tion of the brain’s anatomical connections, these technologies are nonetheless prone to false positives and negatives (Thomas et al., 2014), which could poten- tially affect our results. While the use of multiple atlases, independent datasets, and alternative processing streams help reduce the bias of any single processing strategy they do not completely address the issue. The shortcomings of diffusion imaging and tractography, while presently limiting, also serve to highlight the need to development new non-invasive methods for mapping the human brain. 

A final consideration is related to the size of networks, the definition of nodes, and the scalability of our models. In general, how one defines a network’s nodes has implications for the network properties of the resulting graph (Zalesky et al., 2010). It is likely that the size and number of nodes factor into the performance of the models studied here. The networks analyzed in this report consisted of either n = 74 or n = 108 nodes, representing two different parcellations of the cortex. However, it is becoming increasingly common to model brain networks with up to thousands of nodes. Because the number of possible positions to place an edge grows as O ( n $^{2}$), the space of all networks that the model could generate becomes much larger as n increases. Models with n >> 10 2 may require stronger parametric constraints (e.g. larger magnitudes for η or γ ) or incorporating additional topological information (and an additional parameter) into a model’s wiring rule. In general, the choice of how to define a network’s nodes and at what scale the human connectome is best described is unclear, though future work on data-driven parcellations will surely help address this 

