Abstract
In vivo calcium imaging is widely used technique in neuroscience to evaluate the activity of neuronal networks. The miniscope, a single-photon miniature fluorescent microscope, has made it possible to conduct in vivo calcium imaging in freely moving animals. Various algorithms and software packages have been developed for the analysis of miniscope data. This study investigates the relationship between the sensitivity of neuron detection and the processing parameters utilized in the Minian analysis pipeline at different noise levels. To achieve this objective, we generated simulated data possessing certain attributes of an experimentally derived dataset. Simulated data was generated with various noise levels and processed through to the Minian analysis pipeline. Based on our findings, we provide recommendations for optimal values of Minian pipeline parameters depending on different noise levels. The results obtained in this study may serve as a preliminary guide for selecting appropriate parameter values during the processing of experimental data using the Minian analysis pipeline. The findings of this study are expected to be relevant to neuroscientists involved in the acquisition and processing of miniscope data.
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