Fig 4. Application of multi-scale community detection to subject-level and group-level resting state functional brain networks.

(A) An example rsfMRI connectivity matrix from one subject, in which each element linking a pair of brain regions represents the pairwise wavelet coherence between regional time series. (B) The consensus partition representing the multi-scale community structure for the matrix in panel (A). To enhance the visual detection of the communities, we have represented all singleton communities with the same gray color. (C) The consensus partition representing the multi-scale community structure for the group-level functional matrix, which is defined as the average connectivity matrix across participants. Here, again, to enhance clarity, we color the singleton communities in the same gray color. (D) The average number (out of 600 total nodes) as well as the average size (expressed as the percentage of total nodes) of non-singleton communities calculated across layers, which in our case track γ increments. In these analyses, we used a structural resolution parameter γ ∈ [0.95, 1.7], an inter-layer γ increment of 0.01, and 75 layers.