We first performed hierarchical clustering analysis using the Jaccard metrics as the distance (28) to obtain the pairwise similarity matrix, which was then normalized using a z score with respect to a null model that consisted of a set of 500 surrogates. The average linkage criterion was applied in the final matrix to establish the arrangement of the grouping of neurons shaped as a hierarchical tree (Statistics and Machine Learning Toolbox, MATLAB). To mathematically identify the communities, a threshold had to be established in the hierarchical tree (28). This threshold was set by taking advantage of the variation of information measure (38) to provide the final set of participating neurons in each community.

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