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.

Long-range axonal projections provide the foundation for functional connectivity between brain regions and are critical in the modulation of behavior. Descending projections from medial prefrontal cortex (mPFC) to various target regions regulate critical behavioral functions including decision making, social behavior and mood. While specific mPFC projections have distinct behavioral roles, individual mPFC projection neurons can also innervate multiple target regions. Yet how mPFC projection neurons divide their axons across the brain is poorly understood. In this study, we mapped the axon collaterals of mPFC neurons that project to nucleus accumbens (NAc), ventral tegmental area (VTA), or contralateral mPFC (cmPFC) in mice. We used tissue clearing and light sheet fluorescence microscopy to visualize the 3-D structure of axonal arbors across the intact brain. While machine learning can automate analysis of axons in images of cleared tissue, it is challenging to train a model that generalizes to all axonal structures because the appearance of axons varies by target region. In this study, we present DeepTraCE (Deep learning-based image Tracing with Combined-model Enhancement), a new strategy for axon segmentation and quantification in images of cleared tissue. DeepTraCE is based on the deep-learning framework TRAILMAP; it achieves highly accurate axon detection by combining multiple machine learning models that are each applied to different brain regions. Using DeepTraCE, we find that cmPFC, NAc, and VTA-projecting mPFC neurons represent largely separable classes with unique axon collaterals in cortical, olfactory, and thalamic regions, respectively.