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Miniscope calcium imaging is increasingly being used to monitor largepopulations of neuronal activities in freely behaving animals. However, due tothe high background and low signal-to-noise ratio of the single-photon basedimaging used in this technique, extraction of neural signals from the largenumbers of imaged cells automatically has remained challenging. Here wedescribe a highly accurate framework for automatically identifying activatedneurons and extracting calcium signals from the miniscope imaging data, seedscleansing Constrained Nonnegative Matrix Factorization (sc-CNMF). This sc-CNMFextends the conventional CNMF with two new modules: i) a neural enhancingmodule to overcome miniscope-specific limitations, and ii) a seeds cleansingmodule combining LSTM to rigorously select and cleanse the set of seeds fordetecting regions-of-interest. Our sc-CNMF yields highly stable and superiorperformance in analyzing miniscope calcium imaging data compared to existingmethods.

Seeds Cleansing CNMF for Spatiotemporal Neural Signals Extraction of Miniscope Imaging Data