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Placebo effects are notable demonstrations of mind–body interactions1,2. During pain perception, in the absence of any treatment, an expectation of pain relief can reduce the experience of pain—a phenomenon known as placebo analgesia3–6. However, despite the strength of placebo effects and their impact on everyday human experience and the failure of clinical trials for new therapeutics7, the neural circuit basis of placebo effects has remained unclear. Here we show that analgesia from the expectation of pain relief is mediated by rostral anterior cingulate cortex (rACC) neurons that project to the pontine nucleus (rACC→Pn)—a precerebellar nucleus with no established function in pain. We created a behavioural assay that generates placebo-like anticipatory pain relief in mice. In vivo calcium imaging of neural activity and electrophysiological recordings in brain slices showed that expectations of pain relief boost the activity of rACC→Pn neurons and potentiate neurotransmission in this pathway. Transcriptomic studies of Pn neurons revealed an abundance of opioid receptors, further suggesting a role in pain modulation. Inhibition of the rACC→Pn pathway disrupted placebo analgesia and decreased pain thresholds, whereas activation elicited analgesia in the absence of placebo conditioning. Finally, Purkinje cells exhibited activity patterns resembling those of rACC→Pn neurons during pain-relief expectation, providing cellular-level evidence for a role of the cerebellum in cognitive pain modulation. These findings open the possibility of targeting this prefrontal cortico-ponto-cerebellar pathway with drugs or neurostimulation to treat pain.Analgesia from the expectation of pain relief is mediated by rostral anterior cingulate cortex neurons that project to the pontine nucleus. [ABSTRACT FROM AUTHOR]

Neuronal anatomy is central to the organization and function of brain cell types. However, anatomical variability within apparently homogeneous populations of cells can obscure such insights. Here, we report large-scale automation of neuronal morphology reconstruction and analysis on a dataset of 813 inhibitory neurons characterized using the Patch-seq method, which enables measurement of multiple properties from individual neurons, including local morphology and transcriptional signature. We demonstrate that these automated reconstructions can be used in the same manner as manual reconstructions to understand the relationship between some, but not all, cellular properties used to define cell types. We uncover gene expression correlates of laminar innervation on multiple transcriptomically defined neuronal subclasses and types. In particular, our results reveal correlates of the variability in Layer 1 (L1) axonal innervation in a transcriptomically defined subpopulation of Martinotti cells in the adult mouse neocortex. Reconstruction of neuronal morphology imaged by brightfield microscopy remains a challenge. Here, authors show gene expression correlates of laminar innervation on transcriptomically defined cortical populations by developing a method for automation. [ABSTRACT FROM AUTHOR]

This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction. Omics data's diversity and high-dimensionality challenge integration across technologies and with imaging. Here, authors introduce mapping method xIV-LDDMM that estimates geometric and feature transformations to integrate tissue-scale atlases with molecular and cellular-scale data. [ABSTRACT FROM AUTHOR]

In mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1–3, including several brain regions (for example, refs. 1–11). However, a comprehensive cell atlas of the whole brain is still missing. Here we imaged a panel of more than 1,100 genes in approximately 10 million cells across the entire adult mouse brains using multiplexed error-robust fluorescence in situ hybridization12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating multiplexed error-robust fluorescence in situ hybridization and single-cell RNA sequencing data. Using this approach, we generated a comprehensive cell atlas of more than 5,000 transcriptionally distinct cell clusters, belonging to more than 300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of this atlas to the mouse brain common coordinate framework allowed systematic quantifications of the cell-type composition and organization in individual brain regions. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes of cells. Finally, this high-resolution spatial map of cells, each with a transcriptome-wide expression profile, allowed us to infer cell-type-specific interactions between hundreds of cell-type pairs and predict molecular (ligand–receptor) basis and functional implications of these cell–cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a foundation for functional investigations of neural circuits and their dysfunction in health and disease.A comprehensive cell atlas of the whole mouse brain with high molecular and spatial resolution is generated. [ABSTRACT FROM AUTHOR]

Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications. [ABSTRACT FROM AUTHOR]

Genetically encoded voltage indicators (GEVIs) allow optical recordings of membrane potential changes in defined cell populations. Transgenic reporter animals that facilitate precise and repeatable targeting with high expression levels would further the use of GEVIs in the in vivo mammalian brain. However, the literature on developing and applying transgenic mouse lines as vehicles for GEVI expression is limited. Here we report the first in vivo experiments using a transgenic reporter mouse for the GEVI ArcLight, which utilizes a Cre/tTA dependent expression system (TIGRE 1.0). We developed two mouse lines with ArcLight expression restricted to either olfactory receptor neurons, or a subpopulation of interneurons located in the granule and glomerular layers in the olfactory bulb. The ArcLight expression in these lines was sufficient for in vivo imaging of odorant responses in single trials using epifluorescence and 2-photon imaging. The voltage responses were odor-specific and concentration-dependent, which supported earlier studies about perceptual transformations carried out by the bulb that used calcium sensors of neural activity. This study demonstrates that the ArcLight transgenic line is a flexible genetic tool that can be used to record the neuronal electrical activity of different cell types with a signal-to-noise ratio that is comparable to previous reports using viral transduction. [ABSTRACT FROM AUTHOR]

Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.The BRAIN Initiative Cell Census Network has constructed a multimodal cell census and atlas of the mammalian primary motor cortex in a landmark effort towards understanding brain cell-type diversity, neural circuit organization and brain function. [ABSTRACT FROM AUTHOR]

Pathological impulsivity is a debilitating symptom of multiple psychiatric diseases with few effective treatment options. To identify druggable receptors with anti-impulsive action we developed a systematic target discovery approach combining behavioural chemogenetics and gene expression analysis. Spatially restricted inhibition of three subdivisions of the prefrontal cortex of mice revealed that the anterior cingulate cortex (ACC) regulates premature responding, a form of motor impulsivity. Probing three G-protein cascades with designer receptors, we found that the activation of Gi-signalling in layer-5 pyramidal cells (L5-PCs) of the ACC strongly, reproducibly, and selectively decreased challenge-induced impulsivity. Differential gene expression analysis across murine ACC cell-types and 402 GPCRs revealed that - among Gi-coupled receptor-encoding genes - Grm2 is the most selectively expressed in L5-PCs while alternative targets were scarce. Validating our approach, we confirmed that mGluR2 activation reduced premature responding. These results suggest Gi-coupled receptors in ACC L5-PCs as therapeutic targets for impulse control disorders. In order to identify receptors that may serve as therapeutic targets in the treatment of impulse control disorders, van der Veen et al developed a systematic target discovery approach combining behavioural chemogenetics and gene expression analysis. In doing so, they identified Gi-coupled receptors in layer-5 pyramidal cells of the anterior cingulate cortex as potential therapeutic targets, which could pave the way for the development of treatments. [ABSTRACT FROM AUTHOR]

How the brain processes information accurately despite stochastic neural activity is a longstanding question1. For instance, perception is fundamentally limited by the information that the brain can extract from the noisy dynamics of sensory neurons. Seminal experiments2,3 suggest that correlated noise in sensory cortical neural ensembles is what limits their coding accuracy4–6, although how correlated noise affects neural codes remains debated7–11. Recent theoretical work proposes that how a neural ensemble's sensory tuning properties relate statistically to its correlated noise patterns is a greater determinant of coding accuracy than is absolute noise strength12–14. However, without simultaneous recordings from thousands of cortical neurons with shared sensory inputs, it is unknown whether correlated noise limits coding fidelity. Here we present a 16-beam, two-photon microscope to monitor activity across the mouse primary visual cortex, along with analyses to quantify the information conveyed by large neural ensembles. We found that, in the visual cortex, correlated noise constrained signalling for ensembles with 800–1,300 neurons. Several noise components of the ensemble dynamics grew proportionally to the ensemble size and the encoded visual signals, revealing the predicted information-limiting correlations12–14. Notably, visual signals were perpendicular to the largest noise mode, which therefore did not limit coding fidelity. The information-limiting noise modes were approximately ten times smaller and concordant with mouse visual acuity15. Therefore, cortical design principles appear to enhance coding accuracy by restricting around 90% of noise fluctuations to modes that do not limit signalling fidelity, whereas much weaker correlated noise modes inherently bound sensory discrimination. A microscopy system that enables simultaneous recording from hundreds of neurons in the mouse visual cortex reveals that the brain enhances its coding capacity by representing visual inputs in dimensions perpendicular to correlated noise. [ABSTRACT FROM AUTHOR]

Satiation is the process by which eating and drinking reduce appetite. For thirst, oropharyngeal cues have a critical role in driving satiation by reporting to the brain the volume of fluid that has been ingested1–12. By contrast, the mechanisms that relay the osmolarity of ingested fluids remain poorly understood. Here we show that the water and salt content of the gastrointestinal tract are precisely measured and then rapidly communicated to the brain to control drinking behaviour in mice. We demonstrate that this osmosensory signal is necessary and sufficient for satiation during normal drinking, involves the vagus nerve and is transmitted to key forebrain neurons that control thirst and vasopressin secretion. Using microendoscopic imaging, we show that individual neurons compute homeostatic need by integrating this gastrointestinal osmosensory information with oropharyngeal and blood-borne signals. These findings reveal how the fluid homeostasis system monitors the osmolarity of ingested fluids to dynamically control drinking behaviour. Drinking behaviour in mice is regulated by a signal derived from the water and salt content of the gastrointestinal tract that is transmitted to forebrain neurons that control thirst via the vagus nerve. [ABSTRACT FROM AUTHOR]

Activity in the motor cortex predicts movements, seconds before they are initiated. This preparatory activity has been observed across cortical layers, including in descending pyramidal tract neurons in layer 5. A key question is how preparatory activity is maintained without causing movement, and is ultimately converted to a motor command to trigger appropriate movements. Here, using single-cell transcriptional profiling and axonal reconstructions, we identify two types of pyramidal tract neuron. Both types project to several targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex; populations of these neurons produced early preparatory activity that persisted until the movement was initiated. The second type projects to motor centres in the medulla and mainly produced late preparatory activity and motor commands. These results indicate that two types of motor cortex output neurons have specialized roles in motor control. [ABSTRACT FROM AUTHOR]

A combination of functional imaging and gene-expression profiling in brain tissue has been used to unravel the properties of 35 subtypes of neuron in mice, revealing a gene-expression axis that governs each subtype's activity. The activity of transcriptomically defined neurons measured in live mice. [ABSTRACT FROM AUTHOR]

In humans and other mammalian species, lesions in the preoptic area of the hypothalamus cause profound sleep impairment, indicating a crucial role of the preoptic area in sleep generation. However, the underlying circuit mechanism remains poorly understood. Electrophysiological recordings and c-Fos immunohistochemistry have shown the existence of sleep-active neurons in the preoptic area, especially in the ventrolateral preoptic area and median preoptic nucleus. Pharmacogenetic activation of c-Fos-labelled sleep-active neurons has been shown to induce sleep. However, the sleep-active neurons are spatially intermingled with wake-active neurons, making it difficult to target the sleep neurons specifically for circuit analysis. Here we identify a population of preoptic area sleep neurons on the basis of their projection target and discover their molecular markers. Using a lentivirus expressing channelrhodopsin-2 or a light-activated chloride channel for retrograde labelling, bidirectional optogenetic manipulation, and optrode recording, we show that the preoptic area GABAergic neurons projecting to the tuberomammillary nucleus are both sleep active and sleep promoting. Furthermore, translating ribosome affinity purification and single-cell RNA sequencing identify candidate markers for these neurons, and optogenetic and pharmacogenetic manipulations demonstrate that several peptide markers (cholecystokinin, corticotropin-releasing hormone, and tachykinin 1) label sleep-promoting neurons. Together, these findings provide easy genetic access to sleep-promoting preoptic area neurons and a valuable entry point for dissecting the sleep control circuit. [ABSTRACT FROM AUTHOR]

Intracellular Ca2+ signaling is considered to be important for multiple astrocyte functions in neural circuits. However, mice devoid of inositol triphosphate type 2 receptors (IP3R2) reportedly lack all astrocyte Ca2+ signaling, but display no neuronal or neurovascular deficits, implying that astrocyte Ca2+ fluctuations are not involved in these functions. An assumption has been that the loss of somatic Ca2+ fluctuations also reflects a similar loss in astrocyte processes. We tested this assumption and found diverse types of Ca2+ fluctuations in astrocytes, with most occurring in processes rather than in somata. These fluctuations were preserved in Ip3r2−/− (also known as Itpr2−/−) mice in brain slices and in vivo, occurred in end feet, and were increased by G protein-coupled receptor activation and by startle-induced neuromodulatory responses. Our data reveal previously unknown Ca2+ fluctuations in astrocytes and highlight limitations of studies that used Ip3r2−/− mice to evaluate astrocyte contributions to neural circuit function and mouse behavior. [ABSTRACT FROM AUTHOR]

It is important to digitally reconstruct the 3D morphology of neurons and brain vasculatures. A number of previous methods have been proposed to automate the reconstruction process. However, in many cases, noise and low signal contrast with respect to the image background still hamper our ability to use automation methods directly. Here, we propose an adaptive image enhancement method specifically designed to improve the signal-to-noise ratio of several types of individual neurons and brain vasculature images. Our method is based on detecting the salient features of fibrous structures, e.g. the axon and dendrites combined with adaptive estimation of the optimal context windows where such saliency would be detected. We tested this method for a range of brain image datasets and imaging modalities, including bright-field, confocal and multiphoton fluorescent images of neurons, and magnetic resonance angiograms. Applying our adaptive enhancement to these datasets led to improved accuracy and speed in automated tracing of complicated morphology of neurons and vasculatures. [ABSTRACT FROM AUTHOR]

Odor stimulation evokes complex spatiotemporal activity in the olfactory bulb, suggesting that both the identity of activated neurons and the timing of their activity convey information about odors. However, whether and how downstream neurons decipher these temporal patterns remains unknown. We addressed this question by measuring the spiking activity of downstream neurons while optogenetically stimulating two foci in the olfactory bulb with varying relative timing in mice. We found that the overall spike rates of piriform cortex neurons (PCNs) were sensitive to the relative timing of activation. Posterior PCNs showed higher sensitivity to relative input times than neurons in the anterior piriform cortex. In contrast, olfactory bulb neurons rarely showed such sensitivity. Thus, the brain can transform a relative time code in the periphery into a firing rate-based representation in central brain areas, providing evidence for the relevance of a relative time-based code in the olfactory bulb. [ABSTRACT FROM AUTHOR]

The climbing fiber input to the cerebellar cortex is thought to provide instructive signals that drive the induction of motor skill learning. We found that optogenetic activation of Purkinje cells, the sole output neurons of the cerebellar cortex, can also drive motor learning in mice. This dual control over the induction of learning by climbing fibers and Purkinje cells can expand the learning capacity of motor circuits. [ABSTRACT FROM AUTHOR]

Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron's complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection. Whole-brain reconstruction of neuron morphology is even more challenging as it involves processing tens of teravoxels of imaging data. Validating such reconstructions is extremely laborious. We develop TeraVR, an open-source virtual reality annotation system, to address these challenges. TeraVR integrates immersive and collaborative 3-D visualization, interaction, and hierarchical streaming of teravoxel-scale images. Using TeraVR, we have produced precise 3-D full morphology of long-projecting neurons in whole mouse brains and developed a collaborative workflow for highly accurate neuronal reconstruction. Reconstructing the full shape of neurons is a major informatics challenge as it requires handling huge whole-brain imaging datasets. Here the authors present an open-source virtual reality annotation system for precise and efficient data production of neuronal shapes reconstructed from whole brains. [ABSTRACT FROM AUTHOR]

Spatiotemporally synchronised neuronal activity is central to sensation, motion and cognition. Brain circuits consist of dynamically interconnected neuronal cell-types, thus elucidating how neuron types synergise within the network is key to understand the neuronal orchestra. Here we show that in neocortex neuron-network coupling is neuronal cell-subtype specific. Employing in vivo two-photon (2-p) Calcium (Ca) imaging and 2-p targeted whole-cell recordings, we cell-type specifically investigated the coupling profiles of genetically defined neuron populations in superficial layers (L) of mouse primary visual cortex (V1). Our data reveal novel subtlety of neuron-network coupling in inhibitory interneurons (INs). Parvalbumin (PV)- and Vasoactive intestinal peptide (VIP)-expressing INs exhibit skewed distributions towards strong network-coupling; in Somatostatin (SST)-expressing INs, however, two physiological subpopulations are identified with distinct neuron-network coupling profiles, providing direct evidence for subtype specificity. Our results thus add novel functional granularity to neuronal cell-typing, and provided insights critical to simplifying/understanding neural dynamics. Synchronised neuronal activity is essential for cortical function, yet mechanistic insights into this process remain limited. Here, authors use a combination of in vivo imaging and targeted whole-cell recordings to demonstrate that Somatostatin neurons, in the superficial layers of the mouse primary visual cortex, exhibit functional heterogeneity and can be classified into two distinct subtypes characterized as either having type I uncorrelated, or type II highly correlated with network activity. [ABSTRACT FROM AUTHOR]

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Single-cell characterization and perturbation of neurons provides knowledge critical to addressing fundamental neuroscience questions including the structure-function relationship and neuronal cell-type classification. Here we report a robot for efficiently performing in vivo single-cell experiments in deep brain tissues optically difficult to access. This robot automates blind (non-visually guided) single-cell electroporation (SCE) and extracellular electrophysiology, and can be used to characterize neuronal morphological and physiological properties of, and/or manipulate genetic/chemical contents via delivering extraneous materials (for example, genes) into single neurons in vivo. Tested in the mouse brain, our robot successfully reveals the full morphology of single-infragranular neurons recorded in multiple neocortical regions, as well as deep brain structures such as hippocampal CA3, with high efficiency. Our robot thus can greatly facilitate the study of in vivo full morphology and electrophysiology of single neurons in the brain. [ABSTRACT FROM AUTHOR]

Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. Although methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here we present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.