EZcalcium: Open Source Toolbox for Analysis of Calcium Imaging Data

Abstract

Fluorescence calcium imaging using a range of microscopy approaches, such as 2-photon excitation or head-mounted 'miniscopes', is one of the preferred methods to record neuronal activity and glial signals in various experimental settings, including acute brain slices, brain organoids, and behaving animals. Because changes in the fluorescence intensity of genetically encoded or chemical calcium indicators correlate with action potential firing in neurons, data analysis is based on inferring such spiking from changes in pixel intensity values across time within different regions of interest. However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. For decades, the only way to perform these analyses was for individual laboratories to write their own custom code. These routines were typically not well annotated and lacked intuitive graphical user interfaces (GUIs), which made it difficult for scientists in other laboratories to adopt them. Although the panorama is changing with recent tools like CaImAn, Suite2P and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As 2-photon microscopes are becoming increasingly affordable, 2 the bottleneck is no longer the hardware, but the software used to analyze the calcium data in an optimal manner and consistently across different groups. We addressed this unmet need by incorporating recent software solutions for motion correction, segmentation, signal extraction and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software, which we named EZcalcium.

Plasticity after cortical stroke involves potentiating responses of pre-existing circuits but not functional remapping to new circuits

“…Fluorescence traces ( F/F) of neuronal calcium transients were extracted using custom-written semi-automated MATLAB routines as previously described (He et al, 2017). The vast majority of movies (>90%) did not require motion correction, but for those that did, X-Y drift was corrected using either a frame-by-frame, hidden Markov model-based registration routine (Dombeck et al, 2007) or the motion correction module from EZcalcium (Cantu et al, 2020) based on NoRMCorre nonrigid template matching (Pnevmatikakis and Giovannucci, 2017). To determine whether individual neurons showed time-locked responses to whisker stimulations, we first calculated a modified Z score vector for each neuron and then used a probabilistic bootstrapping method to correlate calcium transients with epochs of stimulation, as described (He et al, 2017).…”

Systems Neuroscience of Natural Behaviors in Rodents

“…The popularity of open source tracking (Ebbesen & Froemke, 2020;Graving et al, 2019;Karashchuk et al, 2020;Machado et al, 2015;A. Mathis et al, 2018;Pereira et al, 2019) , automated behavioral annotation (Eyjolfsdottir et al, 2016;Graving & Couzin, 2020;Hsu & Yttri, 2019;Kabra et al, 2013;Wiltschko et al, 2015) , closed-loop behaviorally-driven experimentation (Forys et al, 2020;Nourizonoz et al, 2020;Schweihoff et al, 2019) , electrophysiology and calcium imaging analysis software (Buccino et al, 2018;Cantu et al, 2020;Chaure et al, 2018;Chung et al, 2017;Giovannucci et al, 2019;Pachitariu, Steinmetz, et al, 2016;Pachitariu, Stringer, et al, 2016) , open source hardware (Aharoni & Hoogland, 2019;J. Brown et al, 2018;Siegle et al, 2017; , and open data (Kranstauber et al, 2011;Oh et al, 2014;Ruebel et al, 2019;Yatsenko et al, 2018;Zheng et al, 2018) are paving the way.…”

Systems Neuroscience of Natural Behaviors in Rodents

“…The popularity of open source tracking (Machado et al, 2015;A. Mathis et al, 2018;Graving et al, 2019;Pereira et al, 2019;Ebbesen and Froemke, 2020;Karashchuk et al, 2020), automated behavioral annotation (Kabra et al, 2013;Wiltschko et al, 2015;Eyjolfsdottir et al, 2016;Hsu and Yttri, 2019;Graving and Couzin, 2020), closed-loop behaviorally driven experimentation (Schweihoff et al, 2019;Forys et al, 2020;Nourizonoz et al, 2020), electrophysiology and calcium imaging analysis software (Pachitariu et al, 2016a,b;Chung et al, 2017;Buccino et al, 2018;Chaure et al, 2018;Giovannucci et al, 2019;Cantu et al, 2020), open source hardware (Siegle et al, 2017;J. Brown et al, 2018;Aharoni and Hoogland, 2019;Voigts et al, 2019), and open data (Kranstauber et al, 2011;Oh et al, 2014;Yatsenko et al, 2018;Zheng et al, 2018;Ruebel et al, 2019) are paving the way.…”