What Cascade Spreading Models Can Teach Us about the Brain

Abstract

The precise relationship between functional and structural connectivity in the brain is not well understood. Research in this area has, so far, mostly remained descriptive. In this issue of Neuron, Mišić et al. (2015) forge a promising new direction by modeling the propagation of information as it relates to spatially constrained network properties. From these preliminary results a glimmer of hope in uncovering deep principles of brain organization begins to emerge.

Figure 1.

Schematic Depiction of Cascade Spreading Models. (A) Basic inputs to the model. (B) Example of how a global cascade forms in four steps following a single initial perturbation. At t = 0 only one node is active. At successive time points, nodes are updated synchronously according to the status of their neighbors, until the perturbation reaches all nodes in the network (i.e., global cascade). (C) Schematic depiction of a single-seed model at its initial and final stages. (D) Same as (C) for a double-seed collaborative model. (E) Same as (C) for a double-seed competitive model. In this case, nodes can take one of two possible active states (e.g., red or blue). (F) Same as (E), except that this time, the number of perturbing seeds can be greater than 2.