Topic: motion correction

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minim

The combination of two-photon microscopy recordings and powerful calcium-dependent fluorescent sensors enables simultaneous recording of unprecedentedly large populations of neurons. While these sensors have matured over several generations of development, computational methods to process their fluorescence are often inefficient and the results hard to interpret. Here we introduce Suite2p: a fast, accurate, parameter-free and complete pipeline that registers raw movies, detects active and/or inactive cells (using Cellpose), extracts their calcium traces and infers their spike times. Suite2p ru

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 t