On Optimizing Miniscope Data Analysis with Simulated Data: A Study of Parameter Optimization in the Minian Analysis Pipeline

In vivo calcium imaging is widely used in neuroscience to assess the activity of neuronal ensembles. The advent of the single-photon miniature fluorescence microscope (miniscope) has made it possible to use intravital calcium imaging in freely moving animals. Various algorithms and analysis packages have been developed to analyze miniscope data. The present work uses model data with different noise levels as an example to examine the relationship between the accuracy of neuron detection and the values of parameters in Minian, a package for analyzing miniscope data. On the basis of the results obtained, recommendations are given for changing the values of the Minian parameters depending on the noise level in the processed data. The results obtained here provide preliminary guidance for selecting appropriate values for Minian parameters for processing experimental data. The results of this study are expected to be relevant to neuroscientists using intravital calcium imaging in freely moving animals.