Fig. 1. Pseudocode explaining the three steps of exemplar-based parcellation.

In the first step (exemplar-search), K = 2, …, 25 exemplars are derived for each individual with a group constraint, i.e. by greedily optimizing a nonnegative monotone submodular function defined as the summation of the utility function over individuals. In the second step (individual-clustering), for each single individual, every node in the cortical area is assigned to its closest exemplar, where closeness is defined using a squared Euclidean distance function. Finally, in the third step (group-clustering), the group-level parcellation is derived by majority voting over all individual-level parcellations (i.e. the node-to-network assignment vectors).