ИСТИНА

Processing of large amounts of miniscopic data often poses a time-keeping procedure, requiring user’s efforts for step-by-step launching of various procedures, including manual intervention and inspection of putative neural units. The aim of this NoRMCorre- and CaImAn-based full-Python pipeline is to optimize users' efforts and time for batch miniscopic data processing. Instead of one-by-one analysis of each imaging session, here user should spend some time in the beginning, examining raw videos, specifying field of view and CaImAn parameters with a special interactive module, then launching the batch routines for motion correction and cnmf for all videos, which may take a while in case of significant data amount, and finally inspect all obtained results in batch with a user-friendly and convenient interactive module, and correct them, if necessary. Correction may include deleting and merging existing cell units as well as manual seeding of new ones. Also, the pipeline contains a specially designed handy module for the detection of significant calcium events based on scalable thresholding and trace approximation by the typical waveform of a calcium event with a tunable range of rise and decay parameters. Overall, this user-friendly and intuitive pipeline makes calcium data processing fast and convenient. The pipeline was tested and successfully applied for the processing of a wide range of data, including recordings longer than 70k frames and with over 900 neurons accepted. The study was supported by the Non-profit Foundation for the Development of Science and Education "Intellect", and by the Russian Scientific Foundation, project №20-15-00283.