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. 2022 Nov 11;5:1178. doi: 10.1038/s42003-022-04077-2

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 9 — a Example of temporally correlated cellular time courses from one FOV. Spatially separated cells (0,1,2) and (3,4,5) display synchronized hyperpolarisations. Importantly these synchronizations occur in different subsets of the groups and in spatially separated cells, indicating the synchronization is not simply due to cross-talk in the imaging. b Quantification of correlation. Events are put into time bins, generating cellular event rasters. The average pairwise correlation between cells in a recording was calculated. A null hypothesis of zero correlation with the same temporal statistics was generated by temporally shuffling each cell’s event raster to obtain an estimate of the expected level of correlation if there were no cellular synchronization. c The observed pairwise correlations are significantly higher in the observed data than shuffled data for all bin sizes, indicating that cellular activity is temporally correlated. The points plot the mean, and the bars indicate the 95% confidence interval for the measured and shuffled case for each bin size.