Fluorescence calcium imaging using a range of microscopy approaches, such as two-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 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 two-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data optimally and consistently across different groups. We addressed this unmet need by incorporating recent software solutions, namely NoRMCorre and CaImAn, 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 package, which we named EZcalcium.

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.

To understand how the brain produces behavior, we must elucidate the relationships between neuronal connectivity and function. The medial prefrontal cortex (mPFC) is critical for complex functions including decision-making and mood. mPFC projection neurons collateralize extensively, but the relationships between mPFC neuronal activity and brain-wide connectivity are poorly understood. We performed whole-brain connectivity mapping and fiber photometry to better understand the mPFC circuits that control threat avoidance in male and female mice. Using tissue clearing and light sheet fluorescence microscopy, we mapped the brain-wide axon collaterals of populations of mPFC neurons that project to nucleus accumbens (NAc), ventral tegmental area (VTA), or contralateral mPFC (cmPFC). We present DeepTraCE, for quantifying bulk-labeled axonal projections in images of cleared tissue, and DeepCOUNT, for quantifying cell bodies. Anatomical maps produced with DeepTraCE aligned with known axonal projection patterns and revealed class-specific topographic projections within regions. Using TRAP2 mice and DeepCOUNT, we analyzed whole-brain functional connectivity underlying threat avoidance. PL was the most highly connected node with functional connections to subsets of PL-cPL, PL-NAc and PL-VTA target sites. Using fiber photometry, we found that during threat avoidance, cmPFC and NAc-projectors encoded conditioned stimuli, but only when action was required to avoid threats. mPFC-VTA neurons encoded learned but not innate avoidance behaviors. Together our results present new and optimized approaches for quantitative whole-brain analysis and indicate that anatomically-defined classes of mPFC neurons have specialized roles in threat avoidance.SIGNIFICANCE STATEMENT:Understanding how the brain produces complex behaviors requires detailed knowledge of the relationships between neuronal connectivity and function. The medial prefrontal cortex (mPFC) plays a key role in learning, mood, and decision-making, including evaluating and responding to threats. mPFC dysfunction is strongly linked to fear, anxiety and mood disorders. Although mPFC circuits are clear therapeutic targets, gaps in our understanding of how they produce cognitive and emotional behaviors prevent us from designing effective interventions. To address this, we developed a high-throughput analysis pipeline for quantifying bulk-labeled fluorescent axons (DeepTraCE) or cell bodies (DeepCOUNT) in intact cleared brains. Using DeepTraCE, DeepCOUNT, and fiber photometry, we performed detailed anatomical and functional mapping of mPFC neuronal classes, identifying specialized roles in threat avoidance.