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Deconvolution pipeline ====================== Figure S5 presents the details of the pipeline. -First step: align_PSF_DataStack.m Empirical 3D system PSF is aligned (along the z-axis) with the raw data stack. This is achieved by performing deconvolution (Richardson-Lucy) of a small number of z-slices (typically separated by 100 µm) of data with a set of 2D PSFs sampled at different depths (typically separated by 10 µm). The resulting deconvolved images are analyzed (manually or automatically) for sharpness to determine the global z-axis alignment of the system PSF and the raw data stack. -Second step: deconv_Time_Series_Data.m All the z-slices of the image stack are deconvolved using 2D PSFs sampled from system PSF at correspondingly aligned z-positions. For time lapse datasets, PSF and stack alignment is calculated using the first time point data, which is then used to deconvolve all the time points Segmentation ============ nuclear_Segmentation.m script used for cellular segmentation Delta F over F ============== gen_Baseline_For_DFOF.m : generates baseline F as an average over entire recording duration gen_DFOF.m : generate delta F over F PCA and ICA analyses for identifying synchrony (Figure 7) ================================================= Synchrony_ICA_PCA_analysisCode.m : All the code used to generate Figure 7 is calculate_iterative_noise.m : function to calculate the noise level cutoff for dF/F traces Utility scripts =============== proj_In_Time.m : generate maximum intensity, average intensity and standard deviation over a specified recording duration gen_XZ_YZ_MIProj.m : generate XZ and YZ maximum intensity projections empirical_psf_analysis.py Python scripts used for FWHM calculations of the empirical PSF (Figure 1C-top plot)