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Memories of fearful events can last a lifetime. The prelimbic (PL) cortex, a subregion of prefrontal cortex, plays a critical role in fear memory retrieval over time. Most studies have focused on acquisition, consolidation, and retrieval of recent memories, but much less is known about the neural mechanisms of remote memory. Using a new knock-in mouse for activity-dependent genetic labeling (TRAP2), we demonstrate that neuronal ensembles in the PL cortex are dynamic. PL neurons TRAPed during later memory retrievals are more likely to be reactivated and make larger behavioral contributions to remote memory retrieval compared to those TRAPed during learning or early memory retrieval. PL activity during learning is required to initiate this time-dependent reorganization in PL ensembles underlying memory retrieval. Finally, while neurons TRAPed during earlier and later retrievals have similar broad projections throughout the brain, PL neurons TRAPed later have a stronger functional recruitment of cortical targets.DeNardo et al. characterize TRAP2, which allows genetic access to neurons based on their activity, and use it to show that neuronal ensembles in prelimbic cortex for remote fear memory undergo dynamic changes during the first 14 days after learning.

Serotonin neurons of the dorsal and median raphe nuclei (DR, MR) collectively innervate the entire forebrain and midbrain, modulating diverse physiology and behavior. To gain a fundamental understanding of their molecular heterogeneity, we used plate-based single-cell RNA-sequencing to generate a comprehensive dataset comprising eleven transcriptomically distinct serotonin neuron clusters. Systematic in situ hybridization mapped specific clusters to the principal DR, caudal DR, or MR. These transcriptomic clusters differentially express a rich repertoire of neuropeptides, receptors, ion channels, and transcription factors. We generated novel intersectional viral-genetic tools to access specific subpopulations. Whole-brain axonal projection mapping revealed that DR serotonin neurons co-expressing vesicular glutamate transporter-3 preferentially innervate the cortex, whereas those co-expressing thyrotropin-releasing hormone innervate subcortical regions in particular the hypothalamus. Reconstruction of 50 individual DR serotonin neurons revealed diverse and segregated axonal projection patterns at the single-cell level. Together, these results provide a molecular foundation of the heterogenous serotonin neuronal phenotypes.

The projection targets of a neuronal population are a key feature of its anatomical characteristics. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.

The process of perception is one of the most complicated and compelling biological phenomena, capable of inspiring thousands of years of philosophers, physicians, and scientists. One of these researchers, Walter Freeman of Berkeley once stated, “The brain reaches out into the environment and sees something which is then interpreted according to its own past experiences. First you look, then you see.” While the neural computations involved in “seeing” may not be understood for many years to come, we have made much progress in understanding how the brain “looks” into its environment. And by studying animal behavior, we can hope to infer some understanding of the cells that transform these sensory inputs into motor outputs. Working with simple neural circuits—be they in model organisms or at early stages in development—the problem seems more tractable, yet new findings can shift our understanding of perception in unexpected ways. In this thesis, I present research on motor circuits of the embryonic zebrafish spinal cord. These spinal neurons directly drive the earliest muscle contractions in the fish and are a great model for understanding how activity begins in a nervous system. In the course of these studies, we discovered that the activity within this circuit is strongly inhibited by environmental light at an age before vision and before the spinal cord is connected to brain circuitry. Not all photoreceptors are for sight and there are many examples of deep brain photoreception in invertebrates and basal vertebrates, usually driving circadian and seasonal behaviors. Our finding in zebrafish is surprising due to the direct photodetection by motor neurons in the spinal cord, the developmentally early appearance of this photosensitivity, the possible role for primary cilia in sensing light, and the acute affect on behavior. Additionally, by manipulating spontaneous activity within this circuit, we see effects on the development of neural activity in spinal interneurons. These results change how we think about motor circuits and development. No longer are motor neurons simply passive relay cells, we now can see them as sensory inputs. No longer is development in the spinal cord governed solely by genetic programs, but activity dependent processes can be regulated by the outside world. The existence of this category of nonvisual photoreceptor across taxa indicates a new way for the brain to “reach out into the environment.” Discovering whether and how it alters our perception of the world will hopefully be a focus of future research.

Serotonin neurons from the raphe nuclei project across the entire brain and modulate diverse physiology and behavior by acting on over a dozen receptors. Here, we took a step towards dissecting this complex process by examining the effects of agonists and antagonists of four widely expressed serotonin receptors (2A, 2C, 1A, and 1B) on spontaneous mouse behavior, which we related to time-integrated whole-brain neuronal activity as assessed by the expression of Fos, a canonical immediate-early gene product. Low-dimensional representations of behavioral and Fos map data revealed the dominant factors of variation in each domain, captured predictable differences across drug groups, and enabled predictions of behavioral changes following perturbations in Fos maps and vice versa. Our study provides a rich resource describing the effects of manipulating serotonin receptors on animal behavior and whole-brain integrated neuronal activity. It also establishes an experimental and analysis paradigm for interrogating the relationship between behavior and neuronal activity across different time scales.

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. 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) in mice. 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. 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.

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. Competing Interest Statement The authors have declared no competing interest.

Dopamine neurons project to diverse regions throughout the brain to modulate various brain processes and behaviors. It is increasingly appreciated that dopamine neurons are heterogeneous in their gene expression, circuitry, physiology, and function. Current approaches to target dopamine neurons are largely based on single gene drivers, which either label all dopamine neurons, or mark a sub-set but concurrently label non-dopaminergic neurons. Here we establish a novel mouse line in which Flp recombinase is knocked-in to the endogenous Slc6a3 (dopamine active transporter, DAT) locus. DAT-Flp mice can be used with various Cre-expressing mouse lines to efficiently and selectively label dopaminergic subpopulations using Cre/Flp-dependent intersectional strategies. We demonstrate the utility of this approach by crossing DAT-Flp mice with NEX-Cre mice, to specifically label Neurod6-expressing dopamine neurons that project to the nucleus accumbens medial shell. DAT-Flp mice represent a novel tool, which will help parse the diverse functions mediated by dopaminergic circuits. Competing Interest Statement The authors have declared no competing interest.

The projection targets of a neuronal population are a key feature of its anatomical characterization. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a 3D convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.

How have complex brains evolved from simple circuits? Here we investigated brain region evolution at cell type resolution in the cerebellar nuclei (CN), the output structures of the cerebellum. Using single-nucleus RNA sequencing in mice, chickens, and humans, as well as STARmap spatial transcriptomic analysis and whole-CNS projection tracing in mice, we identified a conserved cell type set containing two classes of region-specific excitatory neurons and three classes of region-invariant inhibitory neurons. This set constitutes an archetypal CN that was repeatedly duplicated to form new regions. Interestingly, the excitatory cell class that preferentially funnels information to lateral frontal cortices in mice becomes predominant in the massively expanded human Lateral CN. Our data provide the first characterization of CN transcriptomic cell types in three species and suggest a model of brain region evolution by duplication and divergence of entire cell type sets. Competing Interest Statement The authors have declared no competing interest.