Image, data, and statistical analysis

All 2PEF images were analyzed using custom written code on Matlab (R2018a, R2021b, R2022a, Mathworks) unless otherwise noted (R2014b for CalciumDX, R2018a and above for EZCalcium). Immunohistological images are maximum z projections and representative 2PEF images are average projections in time. Representative correlation matrix on Figure 2(f) was plotted after sorting neuronal calcium transients in ascending order based on the values in the time when highest neuronal ensemble was observed to cluster/highlight neurons with functional connectivity. Data clustering (Figure 2(h) and (i)) was performed using an unsupervised clustering approach based on maximum coordinate difference (Matlab function: spectralcluster). Statistical analyses were done using Matlab (R2021b, MathWorks) or Prism (v.9.4.0, GraphPad Software). Graphs in all plots represents the mean and error bars represent the SD. Data were tested for normal distribution by the D’Agostino-Person test before running any comparisons. When comparing different animals, we used nested one-way ANOVA. To evaluate statistical differences between the linear correlations of two data sets, we generated one-way analysis of covariance models (Matlab function: aoctool) and ran multiple comparison tests (Tukey-Kramer) of the group means (Matlab function: multcompare). To estimate the linear relationship between distance and correlation coefficient we used simple linear regression on Prism. The data that support the findings of this study and codes to plot and analyze data are available from the author on reasonable request.