Histological characterization and development of mesial surface sulci in the human brain at 13-15 gestational weeks through high-resolution histology.

Cellular-level anatomical data from early fetal brain are sparse yet critical to the understanding of neurodevelopmental disorders. We characterize the organization of the human cerebral cortex between 13 and 15 gestational weeks using high-resolution whole-brain histological data sets complimented with multimodal imaging. We observed the heretofore underrecognized, reproducible presence of infolds on the mesial surface of the cerebral hemispheres. Of note at this stage, when most of the cerebrum is occupied by lateral ventricles and the corpus callosum is incompletely developed, we postulate that these mesial infolds represent the primordial stage of cingulate, callosal, and calcarine sulci, features of mesial cortical development. Our observations are based on the multimodal approach and further include histological three-dimensional reconstruction that highlights the importance of the plane of sectioning. We describe the laminar organization of the developing cortical mantle, including these infolds from the marginal to ventricular zone, with Nissl, hematoxylin and eosin, and glial fibrillary acidic protein (GFAP) immunohistochemistry. Despite the absence of major sulci on the dorsal surface, the boundaries among the orbital, frontal, parietal, and occipital cortex were very well demarcated, primarily by the cytoarchitecture differences in the organization of the subplate (SP) and intermediate zone (IZ) in these locations. The parietal region has the thickest cortical plate (CP), SP, and IZ, whereas the orbital region shows the thinnest CP and reveals an extra cell-sparse layer above the bilaminar SP. The subcortical structures show intensely GFAP-immunolabeled soma, absent in the cerebral mantle. Our findings establish a normative neurodevelopment baseline at the early stage.

Transcriptomic cytoarchitecture reveals principles of human neocortex organization.

Variation in cytoarchitecture is the basis for the histological definition of cortical areas. We used single cell transcriptomics and performed cellular characterization of the human cortex to better understand cortical areal specialization. Single-nucleus RNA-sequencing of 8 areas spanning cortical structural variation showed a highly consistent cellular makeup for 24 cell subclasses. However, proportions of excitatory neuron subclasses varied substantially, likely reflecting differences in connectivity across primary sensorimotor and association cortices. Laminar organization of astrocytes and oligodendrocytes also differed across areas. Primary visual cortex showed characteristic organization with major changes in the excitatory to inhibitory neuron ratio, expansion of layer 4 excitatory neurons, and specialized inhibitory neurons. These results lay the groundwork for a refined cellular and molecular characterization of human cortical cytoarchitecture and areal specialization.

A Standardized Protocol for the Safe Retrieval of Infectious Postmortem Human Brain for Studying Whole-Brain Pathology.

ABSTRACT: We describe a safe and standardized perfusion protocol for studying brain pathology in high-risk autopsies using a custom-designed low-cost infection containment chamber and high-resolution histology. The output quality was studied using the histological data from the whole cerebellum and brain stem processed using a high-resolution cryohistology pipeline at 0.5 µm per pixel, in-plane resolution with serial sections at 20-µm thickness. To understand the pathophysiology of highly infectious diseases, it is necessary to have a safe and cost-effective method of performing high-risk autopsies and a standardized perfusion protocol for preparing high-quality tissues. Using the low-cost infection containment chamber, we detail the cranial autopsy protocol and ex situ perfusion-fixation of 4 highly infectious adult human brains. The digitized high-resolution histology images of the Nissl-stained series reveal that most of the sections were free of processing artifacts, such as fixation damage, freezing artifacts, and osmotic shock, at the macrocellular and microcellular level. The quality of our protocol was also tested with the highly sensitive immunohistochemistry staining for specific protein markers. Our protocol provides a safe and effective method in high-risk autopsies that allows for the evaluation of pathogen-host interaction, the underlying pathophysiology, and the extent of the infection across the whole brain at microscopic resolutions.

A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Genetic dissection of the glutamatergic neuron system in cerebral cortex.

Diverse types of glutamatergic pyramidal neurons mediate the myriad processing streams and output channels of the cerebral cortex1,2, yet all derive from neural progenitors of the embryonic dorsal telencephalon3,4. Here we establish genetic strategies and tools for dissecting and fate-mapping subpopulations of pyramidal neurons on the basis of their developmental and molecular programs. We leverage key transcription factors and effector genes to systematically target temporal patterning programs in progenitors and differentiation programs in postmitotic neurons. We generated over a dozen temporally inducible mouse Cre and Flp knock-in driver lines to enable the combinatorial targeting of major progenitor types and projection classes. Combinatorial strategies confer viral access to subsets of pyramidal neurons defined by developmental origin, marker expression, anatomical location and projection targets. These strategies establish an experimental framework for understanding the hierarchical organization and developmental trajectory of subpopulations of pyramidal neurons that assemble cortical processing networks and output channels.

Application of Supervised Machine Learning to Extract Brain Connectivity Information from Neuroscience Research Articles.

Understanding the complex connectivity structure of the brain is a major challenge in neuroscience. Vast and ever-expanding literature about neuronal connectivity between brain regions already exists in published research articles and databases. However, with the ever-expanding increase in published articles and repositories, it becomes difficult for a neuroscientist to engage with the breadth and depth of any given field within neuroscience. Natural Language Processing (NLP) techniques can be used to mine 'Brain Region Connectivity' information from published articles to build a centralized connectivity resource helping neuroscience researchers to gain quick access to research findings. Manually curating and continuously updating such a resource involves significant time and effort. This paper presents an application of supervised machine learning algorithms that perform shallow and deep linguistic analysis of text to automatically extract connectivity between brain region mentions. Our proposed algorithms are evaluated using benchmark datasets collated from PubMed and our own dataset of full text articles annotated by a domain expert. We also present a comparison with state-of-the-art methods including BioBERT. Proposed methods achieve best recall and [Formula: see text] scores negating the need for any domain-specific predefined linguistic patterns. Our paper presents a novel effort towards automatically generating interpretable patterns of connectivity for extracting connected brain region mentions from text and can be expanded to include any other domain-specific information.

Multimodal cross-registration and quantification of metric distortions in marmoset whole brain histology using diffeomorphic mappings.

Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified in-vivo to ex-vivo distortions in brain geometry from tissue processing. Further, existing approaches focus on registering unimodal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research and the importance of addressing individual brain architecture variations, new algorithms are necessary to cross-register multimodal data sets including MRIs and multiple histological series. Here we present a computational approach for same-subject multimodal MRI-guided reconstruction of a series of consecutive histological sections, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during different stages of brain histological processing using the Jacobian determinant of the diffeomorphic transformations involved. By mapping the final image stacks to the ex-vivo post-fixation MRI, we show that (a) tape-transfer assisted histological sections can be reassembled accurately into 3D volumes with a local scale change of 2.0 ± 0.4% per axis dimension; in contrast, (b) tissue perfusion/fixation as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs shows a larger median absolute scale change of 6.9 ± 2.1% per axis dimension. This is the first systematic quantification of local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species. These local scale changes will be important for computing local properties to create reference brain maps.

Rapid Emergence of SARS-CoV-2 in the Greater New York Metropolitan Area: Geolocation, Demographics, Positivity Rates, and Hospitalization for 46 793 Persons Tested by Northwell Health.

BACKGROUND: In March 2020, the greater New York metropolitan area became an epicenter for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The initial evolution of case incidence has not been well characterized. METHODS: Northwell Health Laboratories tested 46 793 persons for SARS-CoV-2 from 4 March through 10 April. The primary outcome measure was a positive reverse transcription-polymerase chain reaction test for SARS-CoV-2. The secondary outcomes included patient age, sex, and race, if stated; dates the specimen was obtained and the test result; clinical practice site sources; geolocation of patient residence; and hospitalization. RESULTS: From 8 March through 10 April, a total of 26 735 of 46 793 persons (57.1%) tested positive for SARS-CoV-2. Males of each race were disproportionally more affected than females above age 25, with a progressive male predominance as age increased. Of the positive persons, 7292 were hospitalized directly upon presentation; an additional 882 persons tested positive in an ambulatory setting before subsequent hospitalization, a median of 4.8 days later. Total hospitalization rate was thus 8174 persons (30.6% of positive persons). There was a broad range (>10-fold) in the cumulative number of positive cases across individual zip codes following documented first caseincidence. Test positivity was greater for persons living in zip codes with lower annual household income. CONCLUSIONS: Our data reveal that SARS-CoV-2 incidence emerged rapidly and almost simultaneously across a broad demographic population in the region. These findings support the premise that SARS-CoV-2 infection was widely distributed prior to virus testing availability.

Open access resource for cellular-resolution analyses of corticocortical connectivity in the marmoset monkey.

Understanding the principles of neuronal connectivity requires tools for efficient quantification and visualization of large datasets. The primate cortex is particularly challenging due to its complex mosaic of areas, which in many cases lack clear boundaries. Here, we introduce a resource that allows exploration of results of 143 retrograde tracer injections in the marmoset neocortex. Data obtained in different animals are registered to a common stereotaxic space using an algorithm guided by expert delineation of histological borders, allowing accurate assignment of connections to areas despite interindividual variability. The resource incorporates tools for analyses relative to cytoarchitectural areas, including statistical properties such as the fraction of labeled neurons and the percentage of supragranular neurons. It also provides purely spatial (parcellation-free) data, based on the stereotaxic coordinates of 2 million labeled neurons. This resource helps bridge the gap between high-density cellular connectivity studies in rodents and imaging-based analyses of human brains.

ZEBrA: Zebra finch Expression Brain Atlas-A resource for comparative molecular neuroanatomy and brain evolution studies.

An in-depth understanding of the genetics and evolution of brain function and behavior requires a detailed mapping of gene expression in functional brain circuits across major vertebrate clades. Here we present the Zebra finch Expression Brain Atlas (ZEBrA; www.zebrafinchatlas.org, RRID: SCR_012988), a web-based resource that maps the expression of genes linked to a broad range of functions onto the brain of zebra finches. ZEBrA is a first of its kind gene expression brain atlas for a bird species and a first for any sauropsid. ZEBrA's >3,200 high-resolution digital images of in situ hybridized sections for ~650 genes (as of June 2019) are presented in alignment with an annotated histological atlas and can be browsed down to cellular resolution. An extensive relational database connects expression patterns to information about gene function, mouse expression patterns and phenotypes, and gene involvement in human diseases and communication disorders. By enabling brain-wide gene expression assessments in a bird, ZEBrA provides important substrates for comparative neuroanatomy and molecular brain evolution studies. ZEBrA also provides unique opportunities for linking genetic pathways to vocal learning and motor control circuits, as well as for novel insights into the molecular basis of sex steroids actions, brain dimorphisms, reproductive and social behaviors, sleep function, and adult neurogenesis, among many fundamental themes.

Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks.

Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

ESTIMATING DIFFEOMORPHIC MAPPINGS BETWEEN TEMPLATES AND NOISY DATA: VARIANCE BOUNDS ON THE ESTIMATED CANONICAL VOLUME FORM.

Anatomy is undergoing a renaissance driven by the availability of large digital data sets generated by light microscopy. A central computational task is to map individual data volumes to standardized templates. This is accomplished by regularized estimation of a diffeomorphic transformation between the coordinate systems of the individual data and the template, building the transformation incrementally by integrating a smooth flow field. The canonical volume form of this transformation is used to quantify local growth, atrophy, or cell density. While multiple implementations exist for this estimation, less attention has been paid to the variance of the estimated diffeomorphism for noisy data. Notably, there is an infinite dimensional unobservable space defined by those diffeomorphisms which leave the template invariant. These form the stabilizer subgroup of the diffeomorphic group acting on the template. The corresponding flat directions in the energy landscape are expected to lead to increased estimation variance. Here we show that a least-action principle used to generate geodesics in the space of diffeomor-phisms connecting the subject brain to the template removes the stabilizer. This provides reduced-variance estimates of the volume form. Using simulations we demonstrate that the asymmetric large deformation diffeomorphic mapping methods (LDDMM), which explicitly incorporate the asymmetry between idealized template images and noisy empirical images, provide lower variance estimators than their symmetrized counterparts (cf. ANTs). We derive Cramer-Rao bounds for the variances in the limit of small deformations. Analytical results are shown for the Jacobian in terms of perturbations of the vector fields and divergence of the vector field.

Traumatic microbleeds suggest vascular injury and predict disability in traumatic brain injury.

Traumatic microbleeds are small foci of hypointensity seen on T2*-weighted MRI in patients following head trauma that have previously been considered a marker of axonal injury. The linear appearance and location of some traumatic microbleeds suggests a vascular origin. The aims of this study were to: (i) identify and characterize traumatic microbleeds in patients with acute traumatic brain injury; (ii) determine whether appearance of traumatic microbleeds predict clinical outcome; and (iii) describe the pathology underlying traumatic microbleeds in an index patient. Patients presenting to the emergency department following acute head trauma who received a head CT were enrolled within 48 h of injury and received a research MRI. Disability was defined using Glasgow Outcome Scale-Extended ≤6 at follow-up. All magnetic resonance images were interpreted prospectively and were used for subsequent analysis of traumatic microbleeds. Lesions on T2* MRI were stratified based on 'linear' streak-like or 'punctate' petechial-appearing traumatic microbleeds. The brain of an enrolled subject imaged acutely was procured following death for evaluation of traumatic microbleeds using MRI targeted pathology methods. Of the 439 patients enrolled over 78 months, 31% (134/439) had evidence of punctate and/or linear traumatic microbleeds on MRI. Severity of injury, mechanism of injury, and CT findings were associated with traumatic microbleeds on MRI. The presence of traumatic microbleeds was an independent predictor of disability (P < 0.05; odds ratio = 2.5). No differences were found between patients with punctate versus linear appearing microbleeds. Post-mortem imaging and histology revealed traumatic microbleed co-localization with iron-laden macrophages, predominately seen in perivascular space. Evidence of axonal injury was not observed in co-localized histopathological sections. Traumatic microbleeds were prevalent in the population studied and predictive of worse outcome. The source of traumatic microbleed signal on MRI appeared to be iron-laden macrophages in the perivascular space tracking a network of injured vessels. While axonal injury in association with traumatic microbleeds cannot be excluded, recognizing traumatic microbleeds as a form of traumatic vascular injury may aid in identifying patients who could benefit from new therapies targeting the injured vasculature and secondary injury to parenchyma.

Can One Concurrently Record Electrical Spikes from Every Neuron in a Mammalian Brain?

The classic approach to measure the spiking response of neurons involves the use of metal electrodes to record extracellular potentials. Starting over 60 years ago with a single recording site, this technology now extends to ever larger numbers and densities of sites. We argue, based on the mechanical and electrical properties of existing materials, estimates of signal-to-noise ratios, assumptions regarding extracellular space in the brain, and estimates of heat generation by the electronic interface, that it should be possible to fabricate rigid electrodes to concurrently record from essentially every neuron in the cortical mantle. This will involve fabrication with existing yet nontraditional materials and procedures. We further emphasize the need to advance materials for improved flexible electrodes as an essential advance to record from neurons in brainstem and spinal cord in moving animals.

Relation of koniocellular layers of dorsal lateral geniculate to inferior pulvinar nuclei in common marmosets.

Traditionally, the dorsal lateral geniculate nucleus (LGN) and the inferior pulvinar (IPul) nucleus are considered as anatomically and functionally distinct thalamic nuclei. However, in several primate species it has also been established that the koniocellular (K) layers of LGN and parts of the IPul have a shared pattern of immunoreactivity for the calcium-binding protein calbindin. These calbindin-rich cells constitute a thalamic matrix system which is implicated in thalamocortical synchronisation. Further, the K layers and IPul are both involved in visual processing and have similar connections with retina and superior colliculus. Here, we confirmed the continuity between calbindin-rich cells in LGN K layers and the central lateral division of IPul (IPulCL) in marmoset monkeys. By employing a high-throughput neuronal tracing method, we found that both the K layers and IPulCL form comparable patterns of connections with striate and extrastriate cortices; these connections are largely different to those of the parvocellular and magnocellular laminae of LGN. Retrograde tracer-labelled cells and anterograde tracer-labelled axon terminals merged seamlessly from IPulCL into LGN K layers. These results support continuity between LGN K layers and IPulCL, providing an anatomical basis for functional congruity of this region of the dorsal thalamic matrix and calling into question the traditional segregation between LGN and the inferior pulvinar nucleus.

An active texture-based digital atlas enables automated mapping of structures and markers across brains.

Brain atlases enable the mapping of labeled cells and projections from different brains onto a standard coordinate system. We address two issues in the construction and use of atlases. First, expert neuroanatomists ascertain the fine-scale pattern of brain tissue, the 'texture' formed by cellular organization, to define cytoarchitectural borders. We automate the processes of localizing landmark structures and alignment of brains to a reference atlas using machine learning and training data derived from expert annotations. Second, we construct an atlas that is active; that is, augmented with each use. We show that the alignment of new brains to a reference atlas can continuously refine the coordinate system and associated variance. We apply this approach to the adult murine brainstem and achieve a precise alignment of projections in cytoarchitecturally ill-defined regions across brains from different animals.

Unidirectional monosynaptic connections from auditory areas to the primary visual cortex in the marmoset monkey.

Until the late twentieth century, it was believed that different sensory modalities were processed by largely independent pathways in the primate cortex, with cross-modal integration only occurring in specialized polysensory areas. This model was challenged by the finding that the peripheral representation of the primary visual cortex (V1) receives monosynaptic connections from areas of the auditory cortex in the macaque. However, auditory projections to V1 have not been reported in other primates. We investigated the existence of direct interconnections between V1 and auditory areas in the marmoset, a New World monkey. Labelled neurons in auditory cortex were observed following 4 out of 10 retrograde tracer injections involving V1. These projections to V1 originated in the caudal subdivisions of auditory cortex (primary auditory cortex, caudal belt and parabelt areas), and targeted parts of V1 that represent parafoveal and peripheral vision. Injections near the representation of the vertical meridian of the visual field labelled few or no cells in auditory cortex. We also placed 8 retrograde tracer injections involving core, belt and parabelt auditory areas, none of which revealed direct projections from V1. These results confirm the existence of a direct, nonreciprocal projection from auditory areas to V1 in a different primate species, which has evolved separately from the macaque for over 30 million years. The essential similarity of these observations between marmoset and macaque indicate that early-stage audiovisual integration is a shared characteristic of primate sensory processing.

On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model.

This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 µm meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project.

Towards a comprehensive atlas of cortical connections in a primate brain: Mapping tracer injection studies of the common marmoset into a reference digital template.

The marmoset is an emerging animal model for large-scale attempts to understand primate brain connectivity, but achieving this aim requires the development and validation of procedures for normalization and integration of results from many neuroanatomical experiments. Here we describe a computational pipeline for coregistration of retrograde tracing data on connections of cortical areas into a 3D marmoset brain template, generated from Nissl-stained sections. The procedure results in a series of spatial transformations that are applied to the coordinates of labeled neurons in the different cases, bringing them into common stereotaxic space. We applied this procedure to 17 injections, placed in the frontal lobe of nine marmosets as part of earlier studies. Visualizations of cortical patterns of connections revealed by these injections are supplied as Supplementary Materials. Comparison between the results of the automated and human-based processing of these cases reveals that the centers of injection sites can be reconstructed, on average, to within 0.6 mm of coordinates estimated by an experienced neuroanatomist. Moreover, cell counts obtained in different areas by the automated approach are highly correlated (r = 0.83) with those obtained by an expert, who examined in detail histological sections for each individual. The present procedure enables comparison and visualization of large datasets, which in turn opens the way for integration and analysis of results from many animals. Its versatility, including applicability to archival materials, may reduce the number of additional experiments required to produce the first detailed cortical connectome of a primate brain. J. Comp. Neurol. 524:2161-2181, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.