High-throughput mapping of a whole rhesus monkey brain at micrometer resolution

Gapr for large-scale collaborative single-neuron reconstruction

Whole-brain analysis of single-neuron morphology is crucial for unraveling the complex structure of the brain. However, large-scale neuron reconstruction from terabyte and even petabyte data of mammalian brains generated by state-of-the-art light microscopy is a daunting task. Here, we developed 'Gapr' (Gapr accelerates projectome reconstruction) that streamlines deep learning-based automatic reconstruction, 'automatic proofreading' that reduces human workloads at high-confidence sites, and high-throughput collaborative proofreading by crowd users through the Internet. Furthermore, Gapr offers a seamless user interface that ensures high proofreading speed per annotator, on-demand conversion for handling large datasets, flexible workflows tailored to diverse datasets and rigorous error tracking for quality control. Finally, we demonstrated Gapr's efficacy by reconstructing over 4,000 neurons in mouse brains, revealing the morphological diversity in cortical interneurons and hypothalamic neurons. Here, we present Gapr as a solution for large-scale single-neuron reconstruction projects.

Rapid context inference in a thalamocortical model using recurrent neural networks

Cognitive flexibility is a fundamental ability that enables humans and animals to exhibit appropriate behaviors in various contexts. The thalamocortical interactions between the prefrontal cortex (PFC) and the mediodorsal thalamus (MD) have been identified as crucial for inferring temporal context, a critical component of cognitive flexibility. However, the neural mechanism responsible for context inference remains unknown. To address this issue, we propose a PFC-MD neural circuit model that utilizes a Hebbian plasticity rule to support rapid, online context inference. Specifically, the model MD thalamus can infer temporal contexts from prefrontal inputs within a few trials. This is achieved through the use of PFC-to-MD synaptic plasticity with pre-synaptic traces and adaptive thresholding, along with winner-take-all normalization in the MD. Furthermore, our model thalamus gates context-irrelevant neurons in the PFC, thus facilitating continual learning. We evaluate our model performance by having it sequentially learn various cognitive tasks. Incorporating an MD-like component alleviates catastrophic forgetting of previously learned contexts and demonstrates the transfer of knowledge to future contexts. Our work provides insight into how biological properties of thalamocortical circuits can be leveraged to achieve rapid context inference and continual learning.

Increased GluK1 Subunit Receptors in Corticostriatal Projection from the Anterior Cingulate Cortex Contributed to Seizure-Like Activities

The corticostriatal connection plays a crucial role in cognitive, emotional, and motor control. However, the specific roles and synaptic transmissions of corticostriatal connection are less studied, especially the corticostriatal transmission from the anterior cingulate cortex (ACC). Here, a direct glutamatergic excitatory synaptic transmission in the corticostriatal projection from the ACC is found. Kainate receptors (KAR)-mediated synaptic transmission is increased in this corticostriatal connection both in vitro and in vivo seizure-like activities. GluK1 containing KARs and downstream calcium-stimulated adenylyl cyclase subtype 1 (AC1) are involved in the upregulation of KARs following seizure-like activities. Inhibiting the activities of ACC or its corticostriatal connection significantly attenuated pentylenetetrazole (PTZ)-induced seizure. Additionally, injection of GluK1 receptor antagonist UBP310 or the AC1 inhibitor NB001 both show antiepileptic effects. The studies provide direct evidence that KARs are involved in seizure activity in the corticostriatal connection and the KAR-AC1 signaling pathway is a potential novel antiepileptic strategy.

Cellular and circuit architecture of the lateral septum for reward processing

The lateral septum (LS) is composed of heterogeneous cell types that are important for various motivated behaviors. However, the transcriptional profiles, spatial arrangement, function, and connectivity of these cell types have not been systematically studied. Using single-nucleus RNA sequencing, we delineated diverse genetically defined cell types in the LS that play distinct roles in reward processing. Notably, we found that estrogen receptor 1 (Esr1)-expressing neurons in the ventral LS (LSEsr1) are key drivers of reward seeking via projections to the ventral tegmental area, and these neurons play an essential role in methamphetamine (METH) reward and METH-seeking behavior. Extended exposure to METH increases the excitability of LSEsr1 neurons by upregulating hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, thereby contributing to METH-induced locomotor sensitization. These insights not only elucidate the intricate molecular, circuit, and functional architecture of the septal region in reward processing but also reveal a neural pathway critical for METH reward and behavioral sensitization.

Nanoscale volumetric fluorescence imaging via photochemical sectioning

Optical nanoscopy of intact biological specimens has been transformed by recent advancements in hydrogel-based tissue clearing and expansion, enabling the imaging of cellular and subcellular structures with molecular contrast. However, existing high-resolution fluorescence microscopes have limited imaging depth, which prevents the study of whole-mount specimens without physical sectioning. To address this challenge, we developed "photochemical sectioning," a spatially precise, light-based sample sectioning process. By combining photochemical sectioning with volumetric lattice light-sheet imaging and petabyte-scale computation, we imaged and reconstructed axons and myelination sheaths across entire mouse olfactory bulbs at nanoscale resolution. An olfactory-bulb-wide analysis of myelinated and unmyelinated axons revealed distinctive patterns of axon degeneration and de-/dysmyelination in the neurodegenerative mouse, highlighting the potential for peta- to exabyte-scale super-resolution studies using this approach.

Expansion-assisted selective plane illumination microscopy for nanoscale imaging of centimeter-scale tissues

Recent advances in tissue processing, labeling, and fluorescence microscopy are providing unprecedented views of the structure of cells and tissues at sub-diffraction resolutions and near single molecule sensitivity, driving discoveries in diverse fields of biology, including neuroscience. Biological tissue is organized over scales of nanometers to centimeters. Harnessing molecular imaging across intact, three-dimensional samples on this scale requires new types of microscopes with larger fields of view and working distance, as well as higher throughput. We present a new expansion-assisted selective plane illumination microscope (ExA-SPIM) with aberration-free 1×1×3 μm optical resolution over a large field of view (10.6×8.0 mm 2 ) and working distance (35 mm) at speeds up to 946 megavoxels/sec. Combined with new tissue clearing and expansion methods, the microscope allows imaging centimeter-scale samples with 250×250×750 nm optical resolution (4× expansion), including entire mouse brains, with high contrast and without sectioning. We illustrate ExA-SPIM by reconstructing individual neurons across the mouse brain, imaging cortico-spinal neurons in the macaque motor cortex, and visualizing axons in human white matter.

Automated neuronal reconstruction with super-multicolour Tetbow labelling and threshold-based clustering of colour hues

Fluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here, we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we establish stochastic super-multicolour labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron is labelled with a unique combination of fluorescent proteins, which are then imaged and separated by linear unmixing. We also establish an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder), which identifies neurite fragments with similar colour combinations. To classify colour combinations, we develop unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space are clustered based on a given threshold distance. Our strategy allows the reconstruction of neurites for up to hundreds of neurons at the millimetre scale without using their physical continuity.

Integrated platform for multiscale molecular imaging and phenotyping of the human brain

Understanding cellular architectures and their connectivity is essential for interrogating system function and dysfunction. However, we lack technologies for mapping the multiscale details of individual cells and their connectivity in the human organ-scale system. We developed a platform that simultaneously extracts spatial, molecular, morphological, and connectivity information of individual cells from the same human brain. The platform includes three core elements: a vibrating microtome for ultraprecision slicing of large-scale tissues without losing cellular connectivity (MEGAtome), a polymer hydrogel-based tissue processing technology for multiplexed multiscale imaging of human organ-scale tissues (mELAST), and a computational pipeline for reconstructing three-dimensional connectivity across multiple brain slabs (UNSLICE). We applied this platform for analyzing human Alzheimer's disease pathology at multiple scales and demonstrating scalable neural connectivity mapping in the human brain.

A scalable and modular computational pipeline for axonal connectomics: automated tracing and assembly of axons across serial sections

Progress in histological methods and in microscope technology has enabled dense staining and imaging of axons over large brain volumes, but tracing axons over such volumes requires new computational tools for 3D reconstruction of data acquired from serial sections. We have developed a computational pipeline for automated tracing and volume assembly of densely stained axons imaged over serial sections, which leverages machine learning-based segmentation to enable stitching and alignment with the axon traces themselves. We validated this segmentation-driven approach to volume assembly and alignment of individual axons over centimeter-scale serial sections and show the application of the output traces for analysis of local orientation and for proofreading over aligned volumes. The pipeline is scalable, and combined with recent advances in experimental approaches, should enable new studies of mesoscale connectivity and function over the whole human brain.

Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity

Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.

The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex

Neocortex is a complex structure with different cortical sublayers and regions. However, the precise positioning of cortical regions can be challenging due to the absence of distinct landmarks without special preparation. To address this challenge, we developed a cytoarchitectonic landmark identification pipeline. The fluorescence micro-optical sectioning tomography method was employed to image the whole mouse brain stained by general fluorescent nucleotide dye. A fast 3D convolution network was subsequently utilized to segment neuronal somas in entire neocortex. By approach, the cortical cytoarchitectonic profile and the neuronal morphology were analyzed in 3D, eliminating the influence of section angle. And the distribution maps were generated that visualized the number of neurons across diverse morphological types, revealing the cytoarchitectonic landscape which characterizes the landmarks of cortical regions, especially the typical signal pattern of barrel cortex. Furthermore, the cortical regions of various ages were aligned using the generated cytoarchitectonic landmarks suggesting the structural changes of barrel cortex during the aging process. Moreover, we observed the spatiotemporally gradient distributions of spindly neurons, concentrated in the deep layer of primary visual area, with their proportion decreased over time. These findings could improve structural understanding of neocortex, paving the way for further exploration with this method.

A complementary approach for neocortical cytoarchitecture inspection with cellular resolution imaging at whole brain scale

Cytoarchitecture, the organization of cells within organs and tissues, serves as a crucial anatomical foundation for the delineation of various regions. It enables the segmentation of the cortex into distinct areas with unique structural and functional characteristics. While traditional 2D atlases have focused on cytoarchitectonic mapping of cortical regions through individual sections, the intricate cortical gyri and sulci demands a 3D perspective for unambiguous interpretation. In this study, we employed fluorescent micro-optical sectioning tomography to acquire architectural datasets of the entire macaque brain at a resolution of 0.65 μm × 0.65 μm × 3 μm. With these volumetric data, the cortical laminar textures were remarkably presented in appropriate view planes. Additionally, we established a stereo coordinate system to represent the cytoarchitectonic information as surface-based tomograms. Utilizing these cytoarchitectonic features, we were able to three-dimensionally parcel the macaque cortex into multiple regions exhibiting contrasting architectural patterns. The whole-brain analysis was also conducted on mice that clearly revealed the presence of barrel cortex and reflected biological reasonability of this method. Leveraging these high-resolution continuous datasets, our method offers a robust tool for exploring the organizational logic and pathological mechanisms of the brain's 3D anatomical structure.

A robust vessel-labeling pipeline with high tissue clearing compatibility for 3D mapping of vascular networks

The combination of vessel-labeling, tissue-clearing, and light-sheet imaging techniques provides a potent tool for accurately mapping vascular networks, enabling the assessment of vascular remodeling in vascular-related disorders. However, most vascular labeling methods face challenges such as inadequate labeling efficiency or poor compatibility with current tissue clearing technology, which significantly undermines the image quality. To address this limitation, we introduce a vessel-labeling pipeline, termed Ultralabel, which relies on a specially designed dye hydrogel containing lysine-fixable dextran and gelatins for double enhancement. Ultralabel demonstrates not only excellent vessel-labeling capability but also strong compatibility with all tissue clearing methods tested, which outperforms other vessel-labeling methods. Consequently, Ultralabel enables fine mapping of vascular networks in diverse organs, as well as multi-color labeling alongside other labeling techniques. Ultralabel should provide a robust and user-friendly method for obtaining 3D vascular networks in different biomedical applications.

NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

Quantitative analysis of activated neurons in mouse brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it is challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of a large cohort of mice from many terabytes of volumetric imaging data. Here, we introduce NEATmap, a deep learning-based high-efficiency, high-precision and user-friendly software for whole-brain neuronal activity trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+ neurons. We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.

Diverse and asymmetric patterns of single-neuron projectome in regulating interhemispheric connectivity

The corpus callosum, historically considered primarily for homotopic connections, supports many heterotopic connections, indicating complex interhemispheric connectivity. Understanding this complexity is crucial yet challenging due to diverse cell-specific wiring patterns. Here, we utilized public AAV bulk tracing and single-neuron tracing data to delineate the anatomical connection patterns of mouse brains and conducted wide-field calcium imaging to assess functional connectivity across various brain states in male mice. The single-neuron data uncovered complex and dense interconnected patterns, particularly for interhemispheric-heterotopic connections. We proposed a metric "heterogeneity" to quantify the complexity of the connection patterns. Computational modeling of these patterns suggested that the heterogeneity of upstream projections impacted downstream homotopic functional connectivity. Furthermore, higher heterogeneity observed in interhemispheric-heterotopic projections would cause lower strength but higher stability in functional connectivity than their intrahemispheric counterparts. These findings were corroborated by our wide-field functional imaging data, underscoring the important role of heterotopic-projection heterogeneity in interhemispheric communication.

Frontiers and future perspectives of neuroimmunology

Neuroimmunology is an interdisciplinary branch of biomedical science that emerges from the intersection of studies on the nervous system and the immune system. The complex interplay between the two systems has long been recognized. Research efforts directed at the underlying functional interface and associated pathophysiology, however, have garnered attention only in recent decades. In this narrative review, we highlight significant advances in research on neuroimmune interplay and modulation. A particular focus is on early- and middle-career neuroimmunologists in China and their achievements in frontier areas of "neuroimmune interface", "neuro-endocrine-immune network and modulation", "neuroimmune interactions in diseases", "meningeal lymphatic and glymphatic systems in health and disease", and "tools and methodologies in neuroimmunology research". Key scientific questions and future directions for potential breakthroughs in neuroimmunology research are proposed.

Mapping of individual sensory nerve axons from digits to spinal cord with the transparent embedding solvent system

Achieving uniform optical resolution for a large tissue sample is a major challenge for deep imaging. For conventional tissue clearing methods, loss of resolution and quality in deep regions is inevitable due to limited transparency. Here we describe the Transparent Embedding Solvent System (TESOS) method, which combines tissue clearing, transparent embedding, sectioning and block-face imaging. We used TESOS to acquire volumetric images of uniform resolution for an adult mouse whole-body sample. The TESOS method is highly versatile and can be combined with different microscopy systems to achieve uniformly high resolution. With a light sheet microscope, we imaged the whole body of an adult mouse, including skin, at a uniform 0.8 × 0.8 × 3.5 μm3 voxel resolution within 120 h. With a confocal microscope and a 40×/1.3 numerical aperture objective, we achieved a uniform sub-micron resolution in the whole sample to reveal a complete projection of individual nerve axons within the central or peripheral nervous system. Furthermore, TESOS allowed the first mesoscale connectome mapping of individual sensory neuron axons spanning 5 cm from adult mouse digits to the spinal cord at a uniform sub-micron resolution.

Multi-region calcium imaging in freely behaving mice with ultra-compact head-mounted fluorescence microscopes

To investigate the circuit-level neural mechanisms of behavior, simultaneous imaging of neuronal activity in multiple cortical and subcortical regions is highly desired. Miniature head-mounted microscopes offer the capability of calcium imaging in freely behaving animals. However, implanting multiple microscopes on a mouse brain remains challenging due to space constraints and the cumbersome weight of the equipment. Here, we present TINIscope, a Tightly Integrated Neuronal Imaging microscope optimized for electronic and opto-mechanical design. With its compact and lightweight design of 0.43 g, TINIscope enables unprecedented simultaneous imaging of behavior-relevant activity in up to four brain regions in mice. Proof-of-concept experiments with TINIscope recorded over 1000 neurons in four hippocampal subregions and revealed concurrent activity patterns spanning across these regions. Moreover, we explored potential multi-modal experimental designs by integrating additional modules for optogenetics, electrical stimulation or local field potential recordings. Overall, TINIscope represents a timely and indispensable tool for studying the brain-wide interregional coordination that underlies unrestrained behaviors.

Choroid plexus mast cells drive tumor-associated hydrocephalus

Tumor-associated hydrocephalus (TAH) is a common and lethal complication of brain metastases. Although other factors beyond mechanical obstructions have been suggested, the exact mechanisms are unknown. Using single-nucleus RNA sequencing and spatial transcriptomics, we find that a distinct population of mast cells locate in the choroid plexus and dramatically increase during TAH. Genetic fate tracing and intracranial mast-cell-specific tryptase knockout showed that choroid plexus mast cells (CPMCs) disrupt cilia of choroid plexus epithelia via the tryptase-PAR2-FoxJ1 pathway and consequently increase cerebrospinal fluid production. Mast cells are also found in the human choroid plexus. Levels of tryptase in cerebrospinal fluid are closely associated with clinical severity of TAH. BMS-262084, an inhibitor of tryptase, can cross the blood-brain barrier, inhibit TAH in vivo, and alleviate mast-cell-induced damage of epithelial cilia in a human pluripotent stem-cell-derived choroid plexus organoid model. Collectively, we uncover the function of CPMCs and provide an attractive therapy for TAH.

Activation of the glutamatergic cingulate cortical-cortical connection facilitates pain in adult mice

The brain consists of the left and right cerebral hemispheres and both are connected by callosal projections. Less is known about the basic mechanism of this cortical-cortical connection and its functional importance. Here we investigate the cortical-cortical connection between the bilateral anterior cingulate cortex (ACC) by using the classic electrophysiological and optogenetic approach. We find that there is a direct synaptic projection from one side ACC to the contralateral ACC. Glutamate is the major excitatory transmitter for bilateral ACC connection, including projections to pyramidal cells in superficial (II/III) and deep (V/VI) layers of the ACC. Both AMPA and kainate receptors contribute to synaptic transmission. Repetitive stimulation of the projection also evoked postsynaptic Ca2+ influx in contralateral ACC pyramidal neurons. Behaviorally, light activation of the ACC-ACC connection facilitated behavioral withdrawal responses to mechanical stimuli and noxious heat. In an animal model of neuropathic pain, light inhibitory of ACC-ACC connection reduces both primary and secondary hyperalgesia. Our findings provide strong direct evidence for the excitatory or facilitatory contribution of ACC-ACC connection to pain perception, and this mechanism may provide therapeutic targets for future treatment of chronic pain and related emotional disorders.

Translating single-neuron axonal reconstructions into meso-scale connectivity statistics in the mouse somatosensory thalamus

Characterizing the connectomic and morphological diversity of thalamic neurons is key for better understanding how the thalamus relays sensory inputs to the cortex. The recent public release of complete single-neuron morphological reconstructions enables the analysis of previously inaccessible connectivity patterns from individual neurons. Here we focus on the Ventral Posteromedial (VPM) nucleus and characterize the full diversity of 257 VPM neurons, obtained by combining data from the MouseLight and Braintell projects. Neurons were clustered according to their most dominantly targeted cortical area and further subdivided by their jointly targeted areas. We obtained a 2D embedding of morphological diversity using the dissimilarity between all pairs of axonal trees. The curved shape of the embedding allowed us to characterize neurons by a 1-dimensional coordinate. The coordinate values were aligned both with the progression of soma position along the dorsal-ventral and lateral-medial axes and with that of axonal terminals along the posterior-anterior and medial-lateral axes, as well as with an increase in the number of branching points, distance from soma and branching width. Taken together, we have developed a novel workflow for linking three challenging aspects of connectomics, namely the topography, higher order connectivity patterns and morphological diversity, with VPM as a test-case. The workflow is linked to a unified access portal that contains the morphologies and integrated with 2D cortical flatmap and subcortical visualization tools. The workflow and resulting processed data have been made available in Python, and can thus be used for modeling and experimentally validating new hypotheses on thalamocortical connectivity.

Whole-brain Optical Imaging: A Powerful Tool for Precise Brain Mapping at the Mesoscopic Level

The mammalian brain is a highly complex network that consists of millions to billions of densely-interconnected neurons. Precise dissection of neural circuits at the mesoscopic level can provide important structural information for understanding the brain. Optical approaches can achieve submicron lateral resolution and achieve "optical sectioning" by a variety of means, which has the natural advantage of allowing the observation of neural circuits at the mesoscopic level. Automated whole-brain optical imaging methods based on tissue clearing or histological sectioning surpass the limitation of optical imaging depth in biological tissues and can provide delicate structural information in a large volume of tissues. Combined with various fluorescent labeling techniques, whole-brain optical imaging methods have shown great potential in the brain-wide quantitative profiling of cells, circuits, and blood vessels. In this review, we summarize the principles and implementations of various whole-brain optical imaging methods and provide some concepts regarding their future development.

In vivo labeling and quantitative imaging of neuronal populations using MRI

The study of neural circuits, which underlies perception, cognition, emotion, and behavior, is essential for understanding the mammalian brain, a complex organ consisting of billions of neurons. To study the structure and function of the brain, in vivo neuronal labeling and imaging techniques are crucial as they provide true physiological information that ex vivo methods cannot offer. In this paper, we present a new strategy for in vivo neuronal labeling and quantification using MRI. We demonstrate the efficacy of this method by delivering the oatp1a1 gene to the target neurons using rAAV2-retro virus. OATP1A1 protein expression on the neuronal membrane increased the uptake of a specific MRI contrast agent (Gd-EOB-DTPA), leading to hyperintense signals on T1W images of labeled neuronal populations. We also used dynamic contrast enhancement-based methods to obtain quantitative information on labeled neuronal populations in vivo.

High precision vibration sectioning for 3D imaging of the whole central nervous system

BACKGROUND

Imaging and reconstruction of the morphology of neurons within the entire central nervous system (CNS) is important for deciphering the neural circuitry and related brain functions. With combination of tissue clearing and light sheet microscopy, previous studies have imaged the mouse CNS at cellular resolution, while remaining single axons unresolvable due to the tradeoff between sample size and imaging resolution. This could be improved by sectioning the sample into thick slices and imaged with high resolution light sheet microscopy as described in our previous study. However, the achievable quality for 3D imaging of serial thick slices is often hindered by surface undulation and other artifacts introduced by sectioning and handling limitations.

NEW METHODS

In order to improve the imaging quality for mouse CNS, we develop a high-performance vibratome system for sample sectioning and handling automation. The sectioning mechanism of the system was modeled theoretically and verified experimentally. The effects of process parameters and sample properties on sectioning accuracy were studied to optimize the sectioning outcome. The resultant imaging outcome was demonstrated on mouse samples.

RESULTS

Our theoretical model of vibratome effectively depicts the relationship between the sample surface undulation errors and the sectioning parameters. With the guidance of the theoretical model, the vibratome is able to achieve a local surface undulation error of ±0.5 µm and a surface arithmetic mean deviation (Sa) of 220 nm for 300-μm-thick tissue slices. Imaging results of mouse CNS show the continuous sectioning capability of the vibratome.

COMPARISON WITH EXISTING METHOD

Our automatic sectioning and handling system is able to process serial thick slices for 3D imaging of the whole CNS at a single-axon resolution, superior to the commercially available vibratome devices.

CONCLUSION

Our automatic sectioning and handling system can be optimized to prepare thick sample slices with minimal surface undulation and manual manipulation in support of 3D brain mapping with high-throughput and high-accuracy.

Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy

Quantifying neuron morphology and distribution at the whole-brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single-cell labeling and whole-brain imaging to study human brains. We propose adaptive cell tomography (ACTomography), a low-cost, high-throughput, and high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and one donated fresh postmortem brain. We collected three-dimensional images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor field of neuron morphology to characterize anatomical modularity of a human brain.

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry

Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering

Myelinated axons (nerve fibers) efficiently transmit signals throughout the brain via action potentials. Multiple methods that are sensitive to axon orientations, from microscopy to magnetic resonance imaging, aim to reconstruct the brain's structural connectome. As billions of nerve fibers traverse the brain with various possible geometries at each point, resolving fiber crossings is necessary to generate accurate structural connectivity maps. However, doing so with specificity is a challenging task because signals originating from oriented fibers can be influenced by brain (micro)structures unrelated to myelinated axons. X-ray scattering can specifically probe myelinated axons due to the periodicity of the myelin sheath, which yields distinct peaks in the scattering pattern. Here, we show that small-angle X-ray scattering (SAXS) can be used to detect myelinated, axon-specific fiber crossings. We first demonstrate the capability using strips of human corpus callosum to create artificial double- and triple-crossing fiber geometries, and we then apply the method in mouse, pig, vervet monkey, and human brains. We compare results to polarized light imaging (3D-PLI), tracer experiments, and to outputs from diffusion MRI that sometimes fails to detect crossings. Given its specificity, capability of 3-dimensional sampling and high resolution, SAXS could serve as a ground truth for validating fiber orientations derived using diffusion MRI as well as microscopy-based methods. STATEMENT OF SIGNIFICANCE: To study how the nerve fibers in our brain are interconnected, scientists need to visualize their trajectories, which often cross one another. Here, we show the unique capacity of small-angle X-ray scattering (SAXS) to study these fiber crossings without use of labeling, taking advantage of SAXS's specificity to myelin - the insulating sheath that is wrapped around nerve fibers. We use SAXS to detect double and triple crossing fibers and unveil intricate crossings in mouse, pig, vervet monkey, and human brains. This non-destructive method can uncover complex fiber trajectories and validate other less specific imaging methods (e.g., MRI or microscopy), towards accurate mapping of neuronal connectivity in the animal and human brain.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

An anatomical and connectivity atlas of the marmoset cerebellum

The cerebellum is essential for motor control and cognitive functioning, engaging in bidirectional communication with the cerebral cortex. The common marmoset, a small non-human primate, offers unique advantages for studying cerebello-cerebral circuits. However, the marmoset cerebellum is not well described in published resources. In this study, we present a comprehensive atlas of the marmoset cerebellum comprising (1) fine-detailed anatomical atlases and surface-analysis tools of the cerebellar cortex based on ultra-high-resolution ex vivo MRI, (2) functional connectivity and gradient patterns of the cerebellar cortex revealed by awake resting-state fMRI, and (3) structural-connectivity mapping of cerebellar nuclei using high-resolution diffusion MRI tractography. The atlas elucidates the anatomical details of the marmoset cerebellum, reveals distinct gradient patterns of intra-cerebellar and cerebello-cerebral functional connectivity, and maps the topological relationship of cerebellar nuclei in cerebello-cerebral circuits. As version 5 of the Marmoset Brain Mapping project, this atlas is publicly available at https://marmosetbrainmapping.org/MBMv5.html.

Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI

Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.

Distinct reward processing by subregions of the nucleus accumbens

The nucleus accumbens (NAc) plays an important role in motivation and reward processing. Recent studies suggest that different NAc subnuclei differentially contribute to reward-related behaviors. However, how reward is encoded in individual NAc neurons remains unclear. Using in vivo single-cell resolution calcium imaging, we find diverse patterns of reward encoding in the medial and lateral shell subdivision of the NAc (NAcMed and NAcLat, respectively). Reward consumption increases NAcLat activity but decreases NAcMed activity, albeit with high variability among neurons. The heterogeneity in reward encoding could be attributed to differences in their synaptic inputs and transcriptional profiles. Specific optogenetic activation of Nts-positive neurons in the NAcLat promotes positive reinforcement, while activation of Cartpt-positive neurons in the NAcMed induces behavior aversion. Collectively, our study shows the organizational and transcriptional differences in NAc subregions and provides a framework for future dissection of NAc subregions in physiological and pathological conditions.

Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.

Weakly supervised learning analysis of Aβ plaque distribution in the whole rat brain

Alzheimer's disease (AD) is a great challenge for the world and hardly to be cured, partly because of the lack of animal models that fully mimic pathological progress. Recently, a rat model exhibiting the most pathological symptoms of AD has been reported. However, high-resolution imaging and accurate quantification of beta-amyloid (Aβ) plaques in the whole rat brain have not been fulfilled due to substantial technical challenges. In this paper, a high-efficiency data analysis pipeline is proposed to quantify Aβ plaques in whole rat brain through several terabytes of image data acquired by a high-speed volumetric imaging approach we have developed previously. A novel segmentation framework applying a high-performance weakly supervised learning method which can dramatically reduce the human labeling consumption is described in this study. The effectiveness of our segmentation framework is validated with different metrics. The segmented Aβ plaques were mapped to a standard rat brain atlas for quantitative analysis of the Aβ distribution in each brain area. This pipeline may also be applied to the segmentation and accurate quantification of other non-specific morphology objects.

Cortical pain induced by optogenetic cortical spreading depression: from whole brain activity mapping

BACKGROUND

Cortical spreading depression (CSD) is an electrophysiological event underlying migraine aura. Traditional CSD models are invasive and often cause injuries. The aim of the study was to establish a minimally invasive optogenetic CSD model and identify the active networks after CSD using whole-brain activity mapping.

METHODS

CSD was induced in mice by light illumination, and their periorbital thresholds and behaviours in the open field, elevated plus-maze and light-aversion were recorded. Using c-fos, we mapped the brain activity after CSD. The whole brain was imaged, reconstructed and analyzed using the Volumetric Imaging with Synchronized on-the-fly-scan and Readout technique. To ensure the accuracy of the results, the immunofluorescence staining method was used to verify the imaging results.

RESULTS

The optogenetic CSD model showed significantly decreased periorbital thresholds, increased facial grooming and freezing behaviours and prominent light-aversion behaviours. Brain activity mapping revealed that the somatosensory, primary sensory, olfactory, basal ganglia and default mode networks were activated. However, the thalamus and trigeminal nucleus caudalis were not activated.

CONCLUSIONS

Optogenetic CSD model could mimic the behaviours of headache and photophobia. Moreover, the optogenetic CSD could activate multiple sensory cortical regions without the thalamus or trigeminal nucleus caudalis to induce cortical pain.

Viral Tools for Neural Circuit Tracing

Neural circuits provide an anatomical basis for functional networks. Therefore, dissecting the structure of neural circuits is essential to understanding how the brain works. Recombinant neurotropic viruses are important tools for neural circuit tracing with many advantages over non-viral tracers: they allow for anterograde, retrograde, and trans-synaptic delivery of tracers in a cell type-specific, circuit-selective manner. In this review, we summarize the recent developments in the viral tools for neural circuit tracing, discuss the key principles of using viral tools in neuroscience research, and highlight innovations for developing and optimizing viral tools for neural circuit tracing across diverse animal species, including nonhuman primates.

Review of Generative Adversarial Networks in mono- and cross-modal biomedical image registration

Biomedical image registration refers to aligning corresponding anatomical structures among different images, which is critical to many tasks, such as brain atlas building, tumor growth monitoring, and image fusion-based medical diagnosis. However, high-throughput biomedical image registration remains challenging due to inherent variations in the intensity, texture, and anatomy resulting from different imaging modalities, different sample preparation methods, or different developmental stages of the imaged subject. Recently, Generative Adversarial Networks (GAN) have attracted increasing interest in both mono- and cross-modal biomedical image registrations due to their special ability to eliminate the modal variance and their adversarial training strategy. This paper provides a comprehensive survey of the GAN-based mono- and cross-modal biomedical image registration methods. According to the different implementation strategies, we organize the GAN-based mono- and cross-modal biomedical image registration methods into four categories: modality translation, symmetric learning, adversarial strategies, and joint training. The key concepts, the main contributions, and the advantages and disadvantages of the different strategies are summarized and discussed. Finally, we analyze the statistics of all the cited works from different points of view and reveal future trends for GAN-based biomedical image registration studies.

Decoding psychosis: from national genome project to national brain project

The mind has puzzled humans for centuries, and its disorders, such as psychoses, have caused tremendous difficulties. However, relatively recent biotechnological breakthroughs, such as DNA technology and neuroimaging, have empowered scientists to explore the more fundamental aspects of psychosis. From searching for psychosis-causing genes to imaging the depths of the brain, scientists worldwide seek novel methods to understand the mind and the causes of its disorders. This article will briefly review the history of understanding and managing psychosis and the main findings of modern genetic research and then attempt to stimulate thought for decoding the biological mechanisms of psychosis in the present era of brain science.

Transcriptome, connectome and neuromodulation of the primate brain

Single-cell transcriptomic analysis has facilitated cell type identification in the brain and mapping of cell type-specific connectomes, helping to elucidate neural circuits underlying brain functions and to treat brain disorders by neuromodulation. Yet, we lack a consensual definition of neuronal types/subtypes and clear distinction between cause and effect within interconnected networks.

Theory of the Multiregional Neocortex: Large-Scale Neural Dynamics and Distributed Cognition

The neocortex is a complex neurobiological system with many interacting regions. How these regions work together to subserve flexible behavior and cognition has become increasingly amenable to rigorous research. Here, I review recent experimental and theoretical work on the modus operandi of a multiregional cortex. These studies revealed several general principles for the neocortical interareal connectivity, low-dimensional macroscopic gradients of biological properties across cortical areas, and a hierarchy of timescales for information processing. Theoretical work suggests testable predictions regarding differential excitation and inhibition along feedforward and feedback pathways in the cortical hierarchy. Furthermore, modeling of distributed working memory and simple decision-making has given rise to a novel mathematical concept, dubbed bifurcation in space, that potentially explains how different cortical areas, with a canonical circuit organization but gradients of biological heterogeneities, are able to subserve their respective (e.g., sensory coding versus executive control) functions in a modularly organized brain.

Imaging peripheral nerve micro-anatomy with MUSE, 2D and 3D approaches

Understanding peripheral nerve micro-anatomy can assist in the development of safe and effective neuromodulation devices. However, current approaches for imaging nerve morphology at the fiber level are either cumbersome, require substantial instrumentation, have a limited volume of view, or are limited in resolution/contrast. We present alternative methods based on MUSE (Microscopy with Ultraviolet Surface Excitation) imaging to investigate peripheral nerve morphology, both in 2D and 3D. For 2D imaging, fixed samples are imaged on a conventional MUSE system either label free (via auto-fluorescence) or after staining with fluorescent dyes. This method provides a simple and rapid technique to visualize myelinated nerve fibers at specific locations along the length of the nerve and perform measurements of fiber morphology (e.g., axon diameter and g-ratio). For 3D imaging, a whole-mount staining and MUSE block-face imaging method is developed that can be used to characterize peripheral nerve micro-anatomy and improve the accuracy of computational models in neuromodulation. Images of rat sciatic and human cadaver tibial nerves are presented, illustrating the applicability of the method in different preclinical models.

Smart imaging to empower brain-wide neuroscience at single-cell levels

A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.

Whole-brain mapping of efferent projections of the anterior cingulate cortex in adult male mice

The anterior cingulate cortex (ACC) is a key cortical region that plays an important role in pain perception and emotional functions. Previous studies of the ACC projections have been collected primarily from monkeys, rabbits and rats. Due to technological advances, such as gene manipulation, recent progress has been made in our understanding of the molecular and cellular mechanisms of the ACC-related chronic pain and emotion is mainly obtained from adult mice. Few anatomic studies have examined the whole-brain projections of the ACC in adult mice. In the present study, we examined the continuous axonal outputs of the ACC in the whole brain of adult male mice. We used the virus anterograde tracing technique and an ultrahigh-speed imaging method of Volumetric Imaging with Synchronized on-the-fly-scan and Readout (VISoR). We created a three-dimensional (3D) reconstruction of mouse brains. We found that the ACC projected ipsilaterally primarily to the caudate putamen (CPu), ventral thalamic nucleus, zona incerta (ZI), periaqueductal gray (PAG), superior colliculus (SC), interpolar spinal trigeminal nucleus (Sp5I), and dorsal medullary reticular nucleus (MdD). The ACC also projected to contralateral brain regions, including the ACC, reuniens thalamic nucleus (Re), PAG, Sp5I, and MdD. Our results provide a whole-brain mapping of efferent projections from the ACC in adult male mice, and these findings are critical for future studies of the molecular and synaptic mechanisms of the ACC and its related network in mouse models of brain diseases.

Mapping thalamic-anterior cingulate monosynaptic inputs in adult mice

The anterior cingulate cortex (ACC) is located in the frontal part of the cingulate cortex, and plays important roles in pain perception and emotion. The thalamocortical pathway is the major sensory input to the ACC. Previous studies have show that several different thalamic nuclei receive projection fibers from spinothalamic tract, that in turn send efferents to the ACC by using neural tracers and optical imaging methods. Most of these studies were performed in monkeys, cats, and rats, few studies were reported systematically in adult mice. Adult mice, especially genetically modified mice, have provided molecular and synaptic mechanisms for cortical plasticity and modulation in the ACC. In the present study, we utilized rabies virus-based retrograde tracing system to map thalamic-anterior cingulate monosynaptic inputs in adult mice. We also combined with a new high-throughput VISoR imaging technique to generate a three-dimensional whole-brain reconstruction, especially the thalamus. We found that cortical neurons in the ACC received direct projections from different sub-nuclei in the thalamus, including the anterior, ventral, medial, lateral, midline, and intralaminar thalamic nuclei. These findings provide key anatomic evidences for the connection between the thalamus and ACC.

The cortical connectome of primate lateral prefrontal cortex

The lateral prefrontal cortex (LPFC) of primates plays an important role in executive control, but how it interacts with the rest of the cortex remains unclear. To address this, we densely mapped the cortical connectome of LPFC, using electrical microstimulation combined with functional MRI (EM-fMRI). We found isomorphic mappings between LPFC and five major processing domains composing most of the cerebral cortex except early sensory and motor areas. An LPFC grid of ∼200 stimulation sites topographically mapped to separate grids of activation sites in the five domains, coarsely resembling how the visual cortex maps the retina. The temporal and parietal maps largely overlapped in LPFC, suggesting topographically organized convergence of the ventral and dorsal streams, and the other maps overlapped at least partially. Thus, the LPFC contains overlapping, millimeter-scale maps that mirror the organization of major cortical processing domains, supporting LPFC's role in coordinating activity within and across these domains.

Seeing the Forest and Its Trees Together: Implementing 3D Light Microscopy Pipelines for Cell Type Mapping in the Mouse Brain

The brain is composed of diverse neuronal and non-neuronal cell types with complex regional connectivity patterns that create the anatomical infrastructure underlying cognition. Remarkable advances in neuroscience techniques enable labeling and imaging of these individual cell types and their interactions throughout intact mammalian brains at a cellular resolution allowing neuroscientists to examine microscopic details in macroscopic brain circuits. Nevertheless, implementing these tools is fraught with many technical and analytical challenges with a need for high-level data analysis. Here we review key technical considerations for implementing a brain mapping pipeline using the mouse brain as a primary model system. Specifically, we provide practical details for choosing methods including cell type specific labeling, sample preparation (e.g., tissue clearing), microscopy modalities, image processing, and data analysis (e.g., image registration to standard atlases). We also highlight the need to develop better 3D atlases with standardized anatomical labels and nomenclature across species and developmental time points to extend the mapping to other species including humans and to facilitate data sharing, confederation, and integrative analysis. In summary, this review provides key elements and currently available resources to consider while developing and implementing high-resolution mapping methods.

Cross-modal coherent registration of whole mouse brains

Recent whole-brain mapping projects are collecting large-scale three-dimensional images using modalities such as serial two-photon tomography, fluorescence micro-optical sectioning tomography, light-sheet fluorescence microscopy, volumetric imaging with synchronous on-the-fly scan and readout or magnetic resonance imaging. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modal image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulting from different sample preparation methods and imaging modalities. We introduce a cross-modal registration method, mBrainAligner, which uses coherent landmark mapping and deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We build a brain atlas for the fluorescence micro-optical sectioning tomography modality to facilitate single-cell mapping, and used our method to generate a whole-brain map of three-dimensional single-neuron morphology and neuron cell types.