CaImAn an open source tool for scalable calcium imaging data analysis

Abstract

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 minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning

“…To detect neurons and extract calcium signals from imaging data, we leveraged an algorithm that 67 simultaneously identifies neurons, de-noises the fluorescence signal and de-mixes signals from 68 spatially overlapping components (Pnevmatikakis et al, 2016;Giovannucci et al, 2018) ( Figure 69 1D middle). The algorithm also estimates spiking activity for each neuron, yielding, for each 70 frame, a number that is related to the spiking activity during that frame ( Figure 1D right).…”

Cocaine Triggers Glial-Mediated Synaptogenesis

“…The fluorescent signals in the videos were first pre-processed with the rolling ball algorithm in ImageJ to remove uneven background illuminations across frames. Each video was then subjected to motion correction, source extraction, and deconvolution in Python using the Calcium Imaging Analysis (CaImAn) software package (Giovannucci et al, 2019). The fluorescent signals then underwent a high-pass filter and motion-correction using the NoRmCorre algorithm (Pnevmatikakis and Giovannucci, 2017).…”

The impact of reporter kinetics on the interpretation of data gathered with fluorescent reporters

“…The latter is built into many modern packages for the analysis of fluorescence transients collected from neurons expressing calcium sensitive fluorophores (Giovannucci et al, 2019;Pnevmatikakis et al, 2016;Zhou et al, 2018). Such approaches allow one to extract the activity pattern that best explains the observed fluorescence transient and thus, to an approximation, recover the impulse-like biological events that generate the fluorescence transient and may be related to behavioral and environmental events.…”