Fig 1.
Detection of hierarchical modular structure.
(a) Mean fluorescence of pixels, averaged over the full recording session. (b) Co-classification matrix generated using all statistically significant hierarchical levels. The dendrogram to the right depicts module splits. (c) The number of hierarchical levels aggregating data from all mice and all recording sessions. Panels (d), (e), and (f) depict module assignments at different levels of the hierarchy. The network diagrams shown in these panels are identical to one another and represent binarized matrices obtained by thresholding the jitter-adjusted correlation matrix of fluorescence traces between pairs of cells. Panels (g), (h), (i), and (j) depict distributions of z-scored mean intra-module Euclidean distance for each module and for each mouse. Panels (b), (d), (e), and (f) depict representative results from mouse 1. Here, the acronyms “IQR” and “Med.” represent interquartile range and median, respectively. Note also that nodes in panels b and d-f are ordered according to their hierarchical community assignments.
Fig 2.
Reconfiguration of correlation structure over time.
(a) Analysis pipeline for comparing correlation structure. For any two correlation matrices, Wu and Wv, whose elements have been z-scored against those obtained under a “jittered” null model in which random offsets were added to timeseries (see Methods), we vectorize the upper triangular elements and compute their similarity using a Pearson correlation coefficient. We compare the observed correlation coefficient against that which we would expect under a null model in which rows and columns of Wu are permuted uniformly at random. In panels (b), (c), (d), and (e), we show the scatterplots of standardized similarity scores for pairs of correlation matrices with the number of days separating their respective recording sessions. In panels (f), (g), (h), and (i), each point represents the standardized similarity scores of module co-assignment matrices across pairs of recording sessions.
Fig 3.
Estimation of core-periphery structure and network flexibility.
(a) Thresholded correlation matrices are separately treated as: a) layers in a multi-layer network, their communities estimated, and network flexibility estimated as the frequency with which a node changes its community assignment across layers; b) the consistency matrix is submitted to a core-periphery detection algorithm and each node’s “coreness” is estimated. Here, consistency measures the fraction of layers (recording sessions) in which a connection was present. (b) Node’s flexibility scores plotted in anatomical space. (c) Nodes’ “coreness” plotted in anatomical space. The size of nodes in panels b and c is proportional to their average weight across all five recording sessions. (d) Because flexibility is a measure of variability while “coreness” is a measure of stability, we find that the two are inversely correlated with one another (red line represents the identity line). (e) Cross-subject consistency of optimal parameters for fitting the core-periphery model. For each mouse, we calculated the difference between observed core quality and that of a null model, and we retained the top 10% of those points. These points are depicted at the level of individual mice in panel f. In panel e, we aggregate those values across all mice.