Fig. 1: Updating wiring probabilities within the generative network model iteratively, based on dynamically changing graphical structures.

a The brain’s structural connectivity is modeled as a generative network which grows over time according to parametrized connection costs, (D_i,j)^η and values, (K_i,j)^γ. In this illustration, we use subject one’s optimal model. b Early in network development, the absence of a topology leads to proximal nodes being much more likely to form connections. The displayed distances and probabilities are from the right caudal anterior cingulate (n²), which corresponds to row (D₂,:)^η and (P₂,:). We display it’s six nearest cortical regions. c Later, the relative values (K_i,j) between nodes influence connection probabilities, such that nodes which are more distant (e.g., left rostral anterior cingulate, n⁵⁹ in red) may be preferred to nodes which are closer (e.g., right superior frontal cortex, n²⁷ in cyan). d As costs and values are decoupled, the wiring probability can be rapidly recomputed when dynamic changes in graphical structure occur over developmental time.