Brain-Wide Projections and Differential Encoding of Prefrontal Neuronal Classes Underlying Learned and Innate Threat Avoidance

Abstract

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.

Distinct prelimbic cortex neuronal responses to emotional states of others drive emotion recognition in adult male mice

“…However, a limitation of our study is that we did not distinguish between the various sub-populations of PrL pyramidal neurons. Still, it should be noted that a recent study which used fiber photometry to explore the responses of PrL neurons projecting to either the contralateral PrL, nucleus accumbens, or ventral tegmental area using various paradigms of avoidance behavior observed qualitatively similar responses in all sub-populations 34 . Our fiber photometry recordings clearly demonstrated a significant difference in calcium signals recorded at the beginning of long investigation bouts towards preferred or non-preferred stimuli in the SP, ESPi and ESPs tasks.…”

A developmental brain-wide screen identifies retrosplenial cortex as a key player in the emergence of persistent memory

“…Whole-brain TRAP counts were obtained using DeepCOUNT as previously described 30 . Briefly, whole-brain image stacks from the 640nm channel from each brain were segmented in TrailMap 87 using the TRAP2-Ai14 trained model.…”

“…Images of the Fos channel were processed using a custom deep learning pipeline. This pipeline has largely the same architecture as our existing 3D pipeline, DeepCOUNT 30 , but uses a 2D U-…”

“…Images of the Fos channel were processed using a custom deep learning pipeline. This pipeline has largely the same architecture as our existing 3D pipeline, DeepCOUNT 30 , but uses a 2D U-Net instead of a 3D U-Net. A network for segmenting Fos + cells was trained and validated using manually annotated 100x100 pixel images.…”

“…We designed a developmental screen to distinguish between these possibilities. By performing contextual fear conditioning, whole-brain activitydependent neuronal tagging [27][28][29] at different postnatal ages, tissue clearing, light sheet fluorescence microscopy, and high throughput computational analyses 30 , we identified key brain regions whose activity and functional connectivity change significantly during memory retrieval across development. Our analyses indicated that while similar regions are involved in memory retrieval over development, functional connectivity amongst those regions changes significantly between infancy and adulthood.…”