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Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types 1 , 2 , yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits. Sparse labelling and whole-brain imaging are used to reconstruct and classify brain-wide complete morphologies of 1,741 individual neurons in the mouse brain, revealing a dependence on both brain region and transcriptomic profile.

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex. Single-cell transcriptomics of more than 20,000 cells from two functionally distinct areas of the mouse neocortex identifies 133 transcriptomic types, and provides a foundation for understanding the diversity of cortical cell types.

The neocortex is disproportionately expanded in human compared with mouse 1 , 2 , both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth 3 . Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer’s disease 4 , 5 . Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease. Combined patch clamp recording, biocytin staining and single-cell RNA-sequencing of human neurocortical neurons shows an expansion of glutamatergic neuron types relative to mouse that characterizes the greater complexity of the human neocortex.

The intrinsic dynamics of neuronal populations are shaped by both microscale attributes and macroscale connectome architecture. Here we comprehensively characterize the rich temporal patterns of neural activity throughout the human brain. Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6000 statistical properties of individual brain regions’ time-series across the neocortex. We identify two robust spatial gradients of intrinsic dynamics, one spanning a ventromedial-dorsolateral axis and dominated by measures of signal autocorrelation, and the other spanning a unimodal-transmodal axis and dominated by measures of dynamic range. These gradients reflect spatial patterns of gene expression, intracortical myelin and cortical thickness, as well as structural and functional network embedding. Importantly, these gradients are correlated with patterns of meta-analytic functional activation, differentiating cognitive versus affective processing and sensory versus higher-order cognitive processing. Altogether, these findings demonstrate a link between microscale and macroscale architecture, intrinsic dynamics, and cognition.

Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ∼900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography—the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function. Laser microdissection and microarrays are used to assess 900 precise subdivisions of the brains from three healthy men with 60,000 gene expression probes; the resulting atlas allows comparisons between humans and other animals, and will facilitate studies of human neurological and psychiatric diseases. Atlas of the brain High-resolution maps of genome-wide gene expression have been available for mice for a few years, but only relatively coarse equivalents have been published for the human brain because of the challenges presented by the 1,000-fold increase in size and the limited availability and quality of postmortem tissue. Now Michael Hawrylycz and colleagues at the Allen Institute for Brain Science in Seattle, Washington, have used laser microdissection and microarrays to assess 900 precise subdivisions in brains from two healthy men with 60,000 gene-expression probes. The resulting atlas, freely available at www.brain-map.org, allows comparisons between humans and other animals, and will facilitate studies of human neurological and psychiatric diseases. One early observation from the data is a human-specific pattern — compared with the mouse and rhesus monkey — for the calcium-binding protein CALB1 in the hippocampus.

We present distinct patterns of neurite distribution in the human cerebral cortex using diffusion magnetic resonance imaging (MRI). We analyzed both high-resolution structural (T1w and T2w images) and diffusion MRI data in 505 subjects from the Human Connectome Project. Neurite distributions were evaluated using the neurite orientation dispersion and density imaging (NODDI) model, optimized for gray matter, and mapped onto the cortical surface using a method weighted towards the cortical mid-thickness to reduce partial volume effects. The estimated neurite density was high in both somatosensory and motor areas, early visual and auditory areas, and middle temporal area (MT), showing a strikingly similar distribution to myelin maps estimated from the T1w/T2w ratio. The estimated neurite orientation dispersion was particularly high in early sensory areas, which are known for dense tangential fibers and are classified as granular cortex by classical anatomists. Spatial gradients of these cortical neurite properties revealed transitions that colocalize with some areal boundaries in a recent multi-modal parcellation of the human cerebral cortex, providing mutually supportive evidence. Our findings indicate that analyzing the cortical gray matter neurite morphology using diffusion MRI and NODDI provides valuable information regarding cortical microstructure that is related to but complementary to myeloarchitecture. •Neurite orientation dispersion and density imaging was applied to HCP diffusion MRI.•Cortical neurite density map showed strikingly similar distribution to myelin map.•Cortical neurite orientation dispersion was high in von Economo's granular cortex.

A quantitative computational theory of the operation of the hippocampus as an episodic memory system is described. The CA3 system operates as a single attractor or autoassociation network (1) to enable rapid one-trial associations between any spatial location (place in rodents or spatial view in primates) and an object or reward and (2) to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, which is also important in episodic memory. The dentate gyrus performs pattern separation by competitive learning to create sparse representations producing, for example, neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells generate, by the very small number of mossy fibre connections to CA3, a randomizing pattern separation effect that is important during learning but not recall and that separates out the patterns represented by CA3 firing as being very different from each other. This is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path input to CA3 is quantitatively appropriate for providing the cue for recall in CA3 but not for learning. The CA1 recodes information from CA3 to set up associatively learned backprojections to the neocortex to allow the subsequent retrieval of information to the neocortex, giving a quantitative account of the large number of hippocampo-neocortical and neocortical-neocortical backprojections. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described and support the theory.

The neocortex of the human brain is the seat of higher brain function. Modern imaging techniques, chief among them magnetic resonance imaging (MRI), allow non-invasive imaging of this important structure. Knowledge of the microstructure of the neocortex has classically come from post-mortem histological studies of human tissue, and extrapolations from invasive animal studies. From these studies, we know that the scale of important neocortical structure spans six orders of magnitude, ranging from the size of axonal diameters (microns), to the size of cortical areas responsible for integrating sensory information (centimetres). MRI presents an opportunity to move beyond classical methods, because MRI is non-invasive and MRI contrast is sensitive to neocortical microstructure over all these length scales. MRI thus allows inferences to be made about neocortical microstructure in vivo, i.e. MRI-based in vivo histology. We review recent literature that has applied and developed MRI-based in vivo histology to probe the microstructure of the human neocortex, focusing specifically on myelin, iron, and neuronal fibre mapping. We find that applications such as cortical parcellation (using R1 maps as proxies for myelin content) and investigation of cortical iron deposition with age (using R2* maps) are already contributing to the frontiers of knowledge in neuroscience. Neuronal fibre mapping in the cortex remains challenging in vivo, but recent improvements in diffusion MRI hold promise for exciting applications in the near future. The literature also suggests that utilising multiple complementary quantitative MRI maps could increase the specificity of inferences about neocortical microstructure relative to contemporary techniques, but that further investment in modelling is required to appropriately combine the maps. In vivo histology of human neocortical microstructure is undergoing rapid development. Future developments will improve its specificity, sensitivity, and clinical applicability, granting an ever greater ability to investigate neuroscientific and clinical questions about the human neocortex. •MRI can probe neocortical microstructure in vivo.•In vivo cortical parcellation using myelin markers is possible.•Can detect ageing-related iron accumulation.•Future developments will increase specificity and sensitivity.

The evolutionary increase in size and complexity of the primate neocortex is thought to underlie the higher cognitive abilities of humans. ARHGAP11B is a human-specific gene that, based on its expression pattern in fetal human neocortex and progenitor effects in embryonic mouse neocortex, has been proposed to have a key function in the evolutionary expansion of the neocortex. Here, we study the effects of ARHGAP11B expression in the developing neocortex of the gyrencephalic ferret. In contrast to its effects in mouse, ARHGAP11B markedly increases proliferative basal radial glia, a progenitor cell type thought to be instrumental for neocortical expansion, and results in extension of the neurogenic period and an increase in upper-layer neurons. Consequently, the postnatal ferret neocortex exhibits increased neuron density in the upper cortical layers and expands in both the radial and tangential dimensions. Thus, human-specific ARHGAP11B can elicit hallmarks of neocortical expansion in the developing ferret neocortex. The human brain owes its characteristic wrinkled appearance to its outer layer, the cerebral cortex. All mammals have a cerebral cortex, but its size varies greatly between species. As the brain evolved, the neocortex, the evolutionarily youngest part of the cerebral cortex, expanded dramatically and so had to fold into wrinkles to fit inside the skull. The human neocortex is roughly three times bigger than that of our closest relatives, the chimpanzees, and helps support advanced cognitive skills such as reasoning and language. But how did the human neocortex become so big? The answer may lie in genes that are unique to humans, such as ARHGAP11B. Introducing ARHGAP11B into the neocortex of mouse embryos increases its size and can induce folding. It does this by increasing the number of neural progenitors, the cells that give rise to neurons. But there are two types of neural progenitors in mammalian neocortex: apical and basal. A subtype of the latter – basal radial glia – is thought to drive neocortex growth in human development. Unfortunately, mice have very few basal radial glia. This makes them unsuitable for testing whether ARHGAP11B acts via basal radial glia to enlarge the human neocortex. Kalebic et al. therefore introduced ARHGAP11B into ferret embryos in the womb. Ferrets have a larger neocortex than mice and possess more basal radial glia. Unlike in mice, introducing this gene into the ferret neocortex markedly increased the number of basal radial glia. It also extended the time window during which the basal radial glia produced neurons. These changes increased the number of neurons, particularly of a specific subtype found mainly in animals with large neocortex and thought to be involved in human cognition. Introducing human-specific ARHGAP11B into embryonic ferrets thus helped expand the ferret neocortex. This suggests that this gene may have a similar role in human brain development. Further experiments are needed to determine whether ferrets with the ARHGAP11B gene, and thus a larger neocortex, have enhanced cognitive abilities. If they do, testing these animals could provide insights into human cognition. The animals could also be used to model human brain diseases and to test potential treatments.

Transforming the barrage of sensory signals into a coherent multisensory percept relies on solving the binding problem – deciding whether signals come from a common cause and should be integrated or, instead, segregated. Human observers typically arbitrate between integration and segregation consistent with Bayesian Causal Inference, but the neural mechanisms remain poorly understood. Here, we presented people with audiovisual sequences that varied in the number of flashes and beeps, then combined Bayesian modelling and EEG representational similarity analyses. Our data suggest that the brain initially represents the number of flashes and beeps independently. Later, it computes their numbers by averaging the forced-fusion and segregation estimates weighted by the probabilities of common and independent cause models (i.e. model averaging). Crucially, prestimulus oscillatory alpha power and phase correlate with observers’ prior beliefs about the world’s causal structure that guide their arbitration between sensory integration and segregation. How do we make inferences about the source of sensory signals? Here, the authors use Bayesian causal modeling and measures of neural activity to show how the brain dynamically codes for and combines sensory signals to draw causal inferences.