Mesoscale connectomics

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Standardization of a silver stain to reveal mesoscale myelin in histological preparations of the mammalian brain

BACKGROUND

The brain is built of neurons supported by myelin, a fatty substance that improves cellular communication. Noninvasive magnetic resonance imaging (MRI) is now able to measure brain structure like myelin and requires histological validation.

NEW METHOD

Here we present work in small and large biomedical model mammals to standardize a silver impregnation method as a high-throughput histological myelin visualization procedure. Specifically, we built a new staining well plate to increase batch size, and then systematically varied the staining and clearing cycles to describe the staining response curve across taxa and conditions. We compared tissues fixed by immersion or perfusion, mounted versus free-floating, and cut as thicker or thinner slices, with two-weeks of post-fixation.

RESULTS

The staining response curves show optimal staining with a single exposure across taxa when incubation and clearing epochs are held to within 3-9 min. We show that clearing was slower in mounted vs free-floating tissue, and that staining was faster and caused fracturing earlier in thinner sliced and smaller volumes of tissue.

COMPARISON WITH EXISTING METHODS

We developed a batch processing approach to increase throughput while ensuring reproducibility and demonstrate the optimal conditions for fine myelinated fiber morphology visualization with short cycles (<9 minutes).

CONCLUSIONS

We present our optimized protocol to reveal mesoscale neuroanatomical myelin content in histology across mammals. This standard staining procedure will facilitate multiscale analyses of myelin content across development as well as in the presence of injury or disease.

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.

Neuronal Subtypes and Connectivity of the Adult Mouse Paralaminar Amygdala

The paralaminar nucleus of the amygdala (PL) comprises neurons that exhibit delayed maturation. PL neurons are born during gestation but mature during adolescent ages, differentiating into excitatory neurons. These late-maturing PL neurons contribute to the increase in size and cell number of the amygdala between birth and adulthood. However, the function of the PL upon maturation is unknown, as the region has only recently begun to be characterized in detail. In this study, we investigated key defining features of the adult mouse PL; the intrinsic morpho-electric properties of its neurons, and its input and output circuit connectivity. We identify two subtypes of excitatory neurons in the PL based on unsupervised clustering of electrophysiological properties. These subtypes are defined by differential action potential firing properties and dendritic architecture, suggesting divergent functional roles. We further uncover major axonal inputs to the adult PL from the main olfactory network and basolateral amygdala. We also find that axonal outputs from the PL project reciprocally to these inputs and to diverse targets including the amygdala, frontal cortex, hippocampus, hypothalamus, and brainstem. Thus, the adult mouse PL is centrally placed to play a major role in the integration of olfactory sensory information, to coordinate affective and autonomic behavioral responses to salient odor stimuli.

The meso-connectomes of mouse, marmoset, and macaque: network organization and the emergence of higher cognition

The recent publications of the inter-areal connectomes for mouse, marmoset, and macaque cortex have allowed deeper comparisons across rodent vs. primate cortical organization. In general, these show that the mouse has very widespread, "all-to-all" inter-areal connectivity (i.e. a "highly dense" connectome in a graph theoretical framework), while primates have a more modular organization. In this review, we highlight the relevance of these differences to function, including the example of primary visual cortex (V1) which, in the mouse, is interconnected with all other areas, therefore including other primary sensory and frontal areas. We argue that this dense inter-areal connectivity benefits multimodal associations, at the cost of reduced functional segregation. Conversely, primates have expanded cortices with a modular connectivity structure, where V1 is almost exclusively interconnected with other visual cortices, themselves organized in relatively segregated streams, and hierarchically higher cortical areas such as prefrontal cortex provide top-down regulation for specifying precise information for working memory storage and manipulation. Increased complexity in cytoarchitecture, connectivity, dendritic spine density, and receptor expression additionally reveal a sharper hierarchical organization in primate cortex. Together, we argue that these primate specializations permit separable deconstruction and selective reconstruction of representations, which is essential to higher cognition.

The emergence of multiscale connectomics-based approaches in stroke recovery

Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.

Data-driven multiscale computational models of cortical and subcortical regions

Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits have become essential tools in modern brain research. The goal of these multiscale models is to integrate and synthesize information from different levels of brain organization, from cellular properties, dendritic excitability, and synaptic dynamics to microcircuits, mesocircuits, and ultimately behavior. This article surveys recent advances in the genesis of data-driven computational models of mammalian neural networks in cortical and subcortical areas. I discuss the challenges and opportunities in developing data-driven multiscale models, including the need for interdisciplinary collaborations, the importance of model validation and comparison, and the potential impact on basic and translational neuroscience research. Finally, I highlight future directions and emerging technologies that will enable more comprehensive and predictive data-driven models of brain function and dysfunction.

Decomposing cortical activity through neuronal tracing connectome-eigenmodes in marmosets

Deciphering the complex relationship between neuroanatomical connections and functional activity in primate brains remains a daunting task, especially regarding the influence of monosynaptic connectivity on cortical activity. Here, we investigate the anatomical-functional relationship and decompose the neuronal-tracing connectome of marmoset brains into a series of eigenmodes using graph signal processing. These cellular connectome eigenmodes effectively constrain the cortical activity derived from resting-state functional MRI, and uncover a patterned cellular-functional decoupling. This pattern reveals a spatial gradient from coupled dorsal-posterior to decoupled ventral-anterior cortices, and recapitulates micro-structural profiles and macro-scale hierarchical cortical organization. Notably, these marmoset-derived eigenmodes may facilitate the inference of spontaneous cortical activity and functional connectivity of homologous areas in humans, highlighting the potential generalizing of the connectomic constraints across species. Collectively, our findings illuminate how neuronal-tracing connectome eigenmodes constrain cortical activity and improve our understanding of the brain's anatomical-functional relationship.

Vertebral Subluxation and Systems Biology: An Integrative Review Exploring the Salutogenic Influence of Chiropractic Care on the Neuroendocrine-Immune System

In this paper we synthesize an expansive body of literature examining the multifaceted influence of chiropractic care on processes within and modulators of the neuroendocrine-immune (NEI) system, for the purpose of generating an inductive hypothesis regarding the potential impacts of chiropractic care on integrated physiology. Taking a broad, interdisciplinary, and integrative view of two decades of research-documented outcomes of chiropractic care, inclusive of reports ranging from systematic and meta-analysis and randomized and observational trials to case and cohort studies, this review encapsulates a rigorous analysis of research and suggests the appropriateness of a more integrative perspective on the impact of chiropractic care on systemic physiology. A novel perspective on the salutogenic, health-promoting effects of chiropractic adjustment is presented, focused on the improvement of physical indicators of well-being and adaptability such as blood pressure, heart rate variability, and sleep, potential benefits that may be facilitated through multiple neurologically mediated pathways. Our findings support the biological plausibility of complex benefits from chiropractic intervention that is not limited to simple neuromusculoskeletal outcomes and open new avenues for future research, specifically the exploration and mapping of the precise neural pathways and networks influenced by chiropractic adjustment.

High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labeling

Deciphering patterns of connectivity between neurons in the brain is a critical step toward understanding brain function. Imaging-based neuroanatomical tracing identifies area-to-area or sparse neuron-to-neuron connectivity patterns, but with limited throughput. Barcode-based connectomics maps large numbers of single-neuron projections, but remains a challenge for jointly analyzing single-cell transcriptomics. Here, we established a rAAV2-retro barcode-based multiplexed tracing method that simultaneously characterizes the projectome and transcriptome at the single neuron level. We uncovered dedicated and collateral projection patterns of ventromedial prefrontal cortex (vmPFC) neurons to five downstream targets and found that projection-defined vmPFC neurons are molecularly heterogeneous. We identified transcriptional signatures of projection-specific vmPFC neurons, and verified Pou3f1 as a marker gene enriched in neurons projecting to the lateral hypothalamus, denoting a distinct subset with collateral projections to both dorsomedial striatum and lateral hypothalamus. In summary, we have developed a new multiplexed technique whose paired connectome and gene expression data can help reveal organizational principles that form neural circuits and process information.

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.

Neuronal subtypes and connectivity of the adult mouse paralaminar amygdala

The paralaminar nucleus of the amygdala (PL) is comprised of neurons which exhibit delayed maturation. PL neurons are born during gestation but mature during adolescent ages, differentiating into excitatory neurons. The PL is prominent in the adult amygdala, contributing to its increased neuron number and relative size compared to childhood. However, the function of the PL is unknown, as the region has only recently begun to be characterized in detail. In this study, we investigated key defining features of the adult PL; the intrinsic morpho-electric properties of its neurons, and its input and output connectivity. We identify two subtypes of excitatory neurons in the PL based on unsupervised clustering of electrophysiological properties. These subtypes are defined by differential action potential firing properties and dendritic architecture, suggesting divergent functional roles. We further uncover major axonal inputs to the adult PL from the main olfactory network and basolateral amygdala. We also find that axonal outputs from the PL project reciprocally to major inputs, and to diverse targets including the amygdala, frontal cortex, hippocampus, hypothalamus, and brainstem. Thus, the adult PL is centrally placed to play a major role in the integration of olfactory sensory information, likely coordinating affective and autonomic behavioral responses to salient odor stimuli.

Significance Statement

Mammalian amygdala development includes a growth period from childhood to adulthood, believed to support emotional and social learning. This amygdala growth is partly due to the maturation of neurons during adolescence in the paralaminar amygdala. However, the functional properties of these neurons are unknown. In our recent studies, we characterized the paralaminar amygdala in the mouse. Here, we investigate the properties of the adult PL in the mouse, revealing the existence of two neuronal subtypes that may play distinct functional roles in the adult brain. We further reveal the brain-wide input and output connectivity of the PL, indicating that the PL combines olfactory cues for emotional processing and delivers information to regions associated with reward and autonomic states.

giRAff: an automated atlas segmentation tool adapted to single histological slices

Conventional histology of the brain remains the gold standard in the analysis of animal models. In most biological studies, standard protocols usually involve producing a limited number of histological slices to be analyzed. These slices are often selected into a specific anatomical region of interest or around a specific pathological lesion. Due to the lack of automated solutions to analyze such single slices, neurobiologists perform the segmentation of anatomical regions manually most of the time. Because the task is long, tedious, and operator-dependent, we propose an automated atlas segmentation method called giRAff, which combines rigid and affine registrations and is suitable for conventional histological protocols involving any number of single slices from a given mouse brain. In particular, the method has been tested on several routine experimental protocols involving different anatomical regions of different sizes and for several brains. For a given set of single slices, the method can automatically identify the corresponding slices in the mouse Allen atlas template with good accuracy and segmentations comparable to those of an expert. This versatile and generic method allows the segmentation of any single slice without additional anatomical context in about 1 min. Basically, our proposed giRAff method is an easy-to-use, rapid, and automated atlas segmentation tool compliant with a wide variety of standard histological protocols.

Adult-born neurons maintain hippocampal cholinergic inputs and support working memory during aging

Adult neurogenesis is reduced during aging and impaired in disorders of stress, memory, and cognition though its normal function remains unclear. Moreover, a systems level understanding of how a small number of young hippocampal neurons could dramatically influence brain function is lacking. We examined whether adult neurogenesis sustains hippocampal connections cumulatively across the life span. Long-term suppression of neurogenesis as occurs during stress and aging resulted in an accelerated decline in hippocampal acetylcholine signaling and a slow and progressing emergence of profound working memory deficits. These deficits were accompanied by compensatory reorganization of cholinergic dentate gyrus inputs with increased cholinergic innervation to the ventral hippocampus and recruitment of ventrally projecting neurons by the dorsal projection. While increased cholinergic innervation was dysfunctional and corresponded to overall decreases in cholinergic levels and signaling, it could be recruited to correct the resulting memory dysfunction even in old animals. Our study demonstrates that hippocampal neurogenesis supports memory by maintaining the septohippocampal cholinergic circuit across the lifespan.  It also provides a systems level explanation for the progressive nature of memory deterioration during normal and pathological aging and indicates that the brain connectome is malleable by experience.

Spatial cell-type enrichment predicts mouse brain connectivity

A fundamental neuroscience topic is the link between the brain's molecular, cellular, and cytoarchitectonic properties and structural connectivity. Recent studies relate inter-regional connectivity to gene expression, but the relationship to regional cell-type distributions remains understudied. Here, we utilize whole-brain mapping of neuronal and non-neuronal subtypes via the matrix inversion and subset selection algorithm to model inter-regional connectivity as a function of regional cell-type composition with machine learning. We deployed random forest algorithms for predicting connectivity from cell-type densities, demonstrating surprisingly strong prediction accuracy of cell types in general, and particular non-neuronal cells such as oligodendrocytes. We found evidence of a strong distance dependency in the cell connectivity relationship, with layer-specific excitatory neurons contributing the most for long-range connectivity, while vascular and astroglia were salient for short-range connections. Our results demonstrate a link between cell types and connectivity, providing a roadmap for examining this relationship in other species, including humans.

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.

Brain compartmentalization based on transcriptome analyses and its gene expression in Octopus minor

Coleoid cephalopods have a high intelligence, complex structures, and large brain. The cephalopod brain is divided into supraesophageal mass, subesophageal mass and optic lobe. Although much is known about the structural organization and connections of various lobes of octopus brain, there are few studies on the brain of cephalopod at the molecular level. In this study, we demonstrated the structure of an adult Octopus minor brain by histomorphological analyses. Through visualization of neuronal and proliferation markers, we found that adult neurogenesis occurred in the vL and posterior svL. We also obtained specific 1015 genes by transcriptome of O. minor brain and selected OLFM3, NPY, GnRH, and GDF8 genes. The expression of genes in the central brain showed the possibility of using NPY and GDF8 as molecular marker of compartmentation in the central brain. This study will provide useful information for establishing a molecular atlas of cephalopod brain.

Structure and function in artificial, zebrafish and human neural networks

Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.

The fractal brain: scale-invariance in structure and dynamics

The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.

Place cells are nonrandomly clustered by field location in CA1 hippocampus

A challenge in both modern and historic neuroscience has been achieving an understanding of neuron circuits, and determining the computational and organizational principles that underlie these circuits. Deeper understanding of the organization of brain circuits and cell types, including in the hippocampus, is required for advances in behavioral and cognitive neuroscience, as well as for understanding principles governing brain development and evolution. In this manuscript, we pioneer a new method to analyze the spatial clustering of active neurons in the hippocampus. We use calcium imaging and a rewarded navigation task to record from 100 s of place cells in the CA1 of freely moving rats. We then use statistical techniques developed for and in widespread use in geographic mapping studies, global Moran's I, and local Moran's I to demonstrate that cells that code for similar spatial locations tend to form small spatial clusters. We present evidence that this clustering is not the result of artifacts from calcium imaging, and show that these clusters are primarily formed by cells that have place fields around previously rewarded locations. We go on to show that, although cells with similar place fields tend to form clusters, there is no obvious topographic mapping of environmental location onto the hippocampus, such as seen in the visual cortex. Insights into hippocampal organization, as in this study, can elucidate mechanisms underlying motivational behaviors, spatial navigation, and memory formation.

Adult-born neurons maintain hippocampal cholinergic inputs and support working memory during aging

Adult neurogenesis is reduced during aging and impaired in disorders of stress, memory, and cognition though its normal function remains unclear. Moreover, a systems level understanding of how a small number of young hippocampal neurons could dramatically influence brain function is lacking. We examined whether adult neurogenesis sustains hippocampal connections cumulatively across the life span. Long-term suppression of neurogenesis as occurs during stress and aging resulted in an accelerated decline in hippocampal acetylcholine signaling and a slow and progressing emergence of profound working memory deficits. These deficits were accompanied by compensatory reorganization of cholinergic dentate gyrus inputs with increased cholinergic innervation to the ventral hippocampus and recruitment of ventrally projecting neurons by the dorsal projection. While increased cholinergic innervation was dysfunctional and corresponded to overall decreases in cholinergic levels and signaling, it could be recruited to correct the resulting memory dysfunction even in old animals. Our study demonstrates that hippocampal neurogenesis supports memory by maintaining the septohippocampal cholinergic circuit across the lifespan. It also provides a systems level explanation for the progressive nature of memory deterioration during normal and pathological aging and indicates that the brain connectome is malleable by experience.

NRRS: a re-tracing strategy to refine neuron reconstruction

It is crucial to develop accurate and reliable algorithms for fine reconstruction of neural morphology from whole-brain image datasets. Even though the involvement of human experts in the reconstruction process can help to ensure the quality and accuracy of the reconstructions, automated refinement algorithms are necessary to handle substantial deviations problems of reconstructed branches and bifurcation points from the large-scale and high-dimensional nature of the image data. Our proposed Neuron Reconstruction Refinement Strategy (NRRS) is a novel approach to address the problem of deviation errors in neuron morphology reconstruction. Our method partitions the reconstruction into fixed-size segments and resolves the deviation problems by re-tracing in two steps. We also validate the performance of our method using a synthetic dataset. Our results show that NRRS outperforms existing solutions and can handle most deviation errors. We apply our method to SEU-ALLEN/BICCN dataset containing 1741 complete neuron reconstructions and achieve remarkable improvements in the accuracy of the neuron skeleton representation, the task of radius estimation and axonal bouton detection. Our findings demonstrate the critical role of NRRS in refining neuron morphology reconstruction.

Availability and implementation

The proposed refinement method is implemented as a Vaa3D plugin and the source code are available under the repository of vaa3d_tools/hackathon/Levy/refinement. The original fMOST images of mouse brains can be found at the BICCN's Brain Image Library (BIL) (https://www.brainimagelibrary.org). The synthetic dataset is hosted on GitHub (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/Levy/refinement).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Mapping brain-wide excitatory projectome of primate prefrontal cortex at submicron resolution and comparison with diffusion tractography

Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here, we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic, and subcortical areas, relatively weak projections to parietal and insular regions but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative green fluorescent protein-labeled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits.

In the brain is a web of interconnected nerve cells that send messages to one another via spindly projections called axons. These axons join together at junctions called synapses to create circuits of nerve cells which connect neighboring or distant brain regions. Notably, long-range neural connections underpin higher-order cognitive skills (such as planning and emotion regulation) which make humans distinct from our primate relatives. Only by untangling these far-reaching networks can researchers begin to delineate what sets the human brain apart from other species.

Researchers deploy a range of imaging techniques to map neural networks: scanning entire brains using MRI machines, or imaging thin slices of fluorescently labelled brain tissue using powerful microscopes. However, tracing long-range axons at a high resolution is challenging, and has stirred up debate about whether some neural tracts, such as the inferior fronto-occipital fasciculus, are present in all primates or only humans.

To address these discrepancies, Yan, Yu et al. employed a two-pronged approach to map neural circuits in the brains of macaques. First, two techniques – called viral tracing and two-photon microscopy – were used to create a three-dimensional, fine-grain map showing how the ventrolateral prefrontal cortex (vlPFC), which regulates complex behaviors, connects to the rest of the brain. This revealed prominent axons from the vlPFC projecting via a single synapse to distant brain regions involved in higher-order functions, such as encoding memories and processing emotion. However, there were no direct, monosynaptic connections between the vlPFC and the occipital lobe, the brain’s visual processing center at the back of the head.

Next, Yan, Yu et al. used a specialized MRI scanner to create an atlas of neural circuits connected to the vlPFC, and compared these results to a technique tracing axons stained with a fluorescent dye. In general, there was good agreement between the two methods, except for major differences in the rear-end projections that typically form the inferior fronto-occipital fasciculus. This suggests that this long-range neural pathway exists in monkeys, but it connects via multiple synapses instead of a single junction as was previously thought.

The findings of Yan, Yu et al. provide new insights on the far-reaching neural pathways connecting distant parts of the macaque brain. It also suggests that atlases of neural circuits from whole brain scans should be taken with caution and validated using neural tracing experiments.

Computational Concepts for Reconstructing and Simulating Brain Tissue

It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.

Alteration of Neural Pathways and Its Implications in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disease accompanied by cognitive and behavioral symptoms. These AD-related manifestations result from the alteration of neural circuitry by aggregated forms of amyloid-β (Aβ) and hyperphosphorylated tau, which are neurotoxic. From a neuroscience perspective, identifying neural circuits that integrate various inputs and outputs to determine behaviors can provide insight into the principles of behavior. Therefore, it is crucial to understand the alterations in the neural circuits associated with AD-related behavioral and psychological symptoms. Interestingly, it is well known that the alteration of neural circuitry is prominent in the brains of patients with AD. Here, we selected specific regions in the AD brain that are associated with AD-related behavioral and psychological symptoms, and reviewed studies of healthy and altered efferent pathways to the target regions. Moreover, we propose that specific neural circuits that are altered in the AD brain can be potential targets for AD treatment. Furthermore, we provide therapeutic implications for targeting neuronal circuits through various therapeutic approaches and the appropriate timing of treatment for AD.

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

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

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

Whole-brain mesoscale mapping in primates has been hindered by large brain sizes and the relatively low throughput of available microscopy methods. Here, we present an approach that combines primate-optimized tissue sectioning and clearing with ultrahigh-speed fluorescence microscopy implementing improved volumetric imaging with synchronized on-the-fly-scan and readout technique, and is capable of completing whole-brain imaging of a rhesus monkey at 1 × 1 × 2.5 µm³ voxel resolution within 100 h. We also developed a highly efficient method for long-range tracing of sparse axonal fibers in datasets numbering hundreds of terabytes. This pipeline, which we call serial sectioning and clearing, three-dimensional microscopy with semiautomated reconstruction and tracing (SMART), enables effective connectome-scale mapping of large primate brains. With SMART, we were able to construct a cortical projection map of the mediodorsal nucleus of the thalamus and identify distinct turning and routing patterns of individual axons in the cortical folds while approaching their arborization destinations.

A 48-channel receive array coil for mesoscopic diffusion-weighted MRI of ex vivo human brain on the 3 T connectome scanner

In vivo diffusion-weighted magnetic resonance imaging is limited in signal-to-noise-ratio (SNR) and acquisition time, which constrains spatial resolution to the macroscale regime. Ex vivo imaging, which allows for arbitrarily long scan times, is critical for exploring human brain structure in the mesoscale regime without loss of SNR. Standard head array coils designed for patients are sub-optimal for imaging ex vivo whole brain specimens. The goal of this work was to design and construct a 48-channel ex vivo whole brain array coil for high-resolution and high b-value diffusion-weighted imaging on a 3T Connectome scanner. The coil was validated with bench measurements and characterized by imaging metrics on an agar brain phantom and an ex vivo human brain sample. The two-segment coil former was constructed for a close fit to a whole human brain, with small receive elements distributed over the entire brain. Imaging tests including SNR and G-factor maps were compared to a 64-channel head coil designed for in vivo use. There was a 2.9-fold increase in SNR in the peripheral cortex and a 1.3-fold gain in the center when compared to the 64-channel head coil. The 48-channel ex vivo whole brain coil also decreases noise amplification in highly parallel imaging, allowing acceleration factors of approximately one unit higher for a given noise amplification level. The acquired diffusion-weighted images in a whole ex vivo brain specimen demonstrate the applicability and advantage of the developed coil for high-resolution and high b-value diffusion-weighted ex vivo brain MRI studies.

Application of Recombinant Rabies Virus to Xenopus Tadpole Brain

The Xenopus laevis experimental system has provided significant insight into the development and plasticity of neural circuits. Xenopus neuroscience research would be enhanced by additional tools to study neural circuit structure and function. Rabies viruses are powerful tools to label and manipulate neural circuits and have been widely used to study mesoscale connectomics. Whether rabies virus can be used to transduce neurons and express transgenes in Xenopus has not been systematically investigated. Glycoprotein-deleted rabies virus transduces neurons at the axon terminal and retrogradely labels their cell bodies. We show that glycoprotein-deleted rabies virus infects local and projection neurons in the Xenopus tadpole when directly injected into brain tissue. Pseudotyping glycoprotein-deleted rabies with EnvA restricts infection to cells with exogenous expression of the EnvA receptor, TVA. EnvA pseudotyped virus specifically infects tadpole neurons with promoter-driven expression of TVA, demonstrating its utility to label targeted neuronal populations. Neuronal cell types are defined by a combination of features including anatomical location, expression of genetic markers, axon projection sites, morphology, and physiological properties. We show that driving TVA expression in one hemisphere and injecting EnvA pseudotyped virus into the contralateral hemisphere, retrogradely labels neurons defined by cell body location and axon projection site. Using this approach, rabies can be used to identify cell types in Xenopus brain and simultaneously to express transgenes which enable monitoring or manipulation of neuronal activity. This makes rabies a valuable tool to study the structure and function of neural circuits in Xenopus.Significance StatementStudies in Xenopus have contributed a great deal to our understanding of brain circuit development and plasticity, regeneration, and hormonal regulation of behavior and metamorphosis. Here, we show that recombinant rabies virus transduces neurons in the Xenopus tadpole, enlarging the toolbox that can be applied to studying Xenopus brain. Rabies can be used for retrograde labeling and expression of a broad range of transgenes including fluorescent proteins for anatomical tracing and studying neuronal morphology, voltage or calcium indicators to visualize neuronal activity, and photo- or chemosensitive channels to control neuronal activity. The versatility of these tools enables diverse experiments to analyze and manipulate Xenopus brain structure and function, including mesoscale connectivity.

Upscaling X-ray nanoimaging to macroscopic specimens

Upscaling X-ray nanoimaging to macroscopic specimens has the potential for providing insights across multiple length scales, but its feasibility has long been an open question. By combining the imaging requirements and existing proof-of-principle examples in large-specimen preparation, data acquisition and reconstruction algorithms, the authors provide imaging time estimates for howX-ray nanoimaging can be scaled to macroscopic specimens. To arrive at this estimate, a phase contrast imaging model that includes plural scattering effects is used to calculate the required exposure and corresponding radiation dose. The coherent X-ray flux anticipated from upcoming diffraction-limited light sources is then considered. This imaging time estimation is in particular applied to the case of the connectomes of whole mouse brains. To image the connectome of the whole mouse brain, electron microscopy connectomics might require years, whereas optimized X-ray microscopy connectomics could reduce this to one week. Furthermore, this analysis points to challenges that need to be overcome (such as increased X-ray detector frame rate) and opportunities that advances in artificial-intelligence-based 'smart' scanning might provide. While the technical advances required are daunting, it is shown that X-ray microscopy is indeed potentially applicable to nanoimaging of millimetre- or even centimetre-size specimens.

Dual-Viral Transduction Utilizing Highly Efficient Retrograde Lentivirus Improves Labeling of Long Propriospinal Neurons

The nervous system coordinates pathways and circuits to process sensory information and govern motor behaviors. Mapping these pathways is important to further understand the connectivity throughout the nervous system and is vital for developing treatments for neuronal diseases and disorders. We targeted long ascending propriospinal neurons (LAPNs) in the rat spinal cord utilizing Fluoro-Ruby (FR) [10kD rhodamine dextran amine (RDA)], and two dual-viral systems. Dual-viral tracing utilizing a retrograde adeno-associated virus (retroAAV), which confers robust labeling in the brain, resulted in a small number of LAPNs being labeled, but dual-viral tracing using a highly efficient retrograde (HiRet) lentivirus provided robust labeling similar to FR. Additionally, dual-viral tracing with HiRet lentivirus and tracing with FR may preferentially label different subpopulations of LAPNs. These data demonstrate that dual-viral tracing in the spinal cord employing a HiRet lentivirus provides robust and specific labeling of LAPNs and emphasizes the need to empirically optimize viral systems to target specific neuronal population(s).

A simple method defines 3D morphology and axon projections of filled neurons in a small CNS volume: Steps toward understanding functional network circuitry

BACKGROUND

Fundamental to understanding neuronal network function is defining neuron morphology, location, properties, and synaptic connectivity in the nervous system. A significant challenge is to reconstruct individual neuron morphology and connections at a whole CNS scale and bring together functional and anatomical data to understand the whole network.

NEW METHOD

We used a PC controlled micropositioner to hold a fixed whole mount of Xenopus tadpole CNS and replace the stage on a standard microscope. This allowed direct recording in 3D coordinates of features and axon projections of one or two neurons dye-filled during whole-cell recording to study synaptic connections. Neuron reconstructions were normalised relative to the ventral longitudinal axis of the nervous system. Coordinate data were stored as simple text files.

RESULTS

Reconstructions were at 1 μm resolution, capturing axon lengths in mm. The output files were converted to SWC format and visualised in 3D reconstruction software NeuRomantic. Coordinate data are tractable, allowing correction for histological artefacts. Through normalisation across multiple specimens we could infer features of network connectivity of mapped neurons of different types.

COMPARISON WITH EXISTING METHODS

Unlike other methods using fluorescent markers and utilising large-scale imaging, our method allows direct acquisition of 3D data on neurons whose properties and synaptic connections have been studied using whole-cell recording.

CONCLUSIONS

This method can be used to reconstruct neuron 3D morphology and follow axon projections in the CNS. After normalisation to a common CNS framework, inferences on network connectivity at a whole nervous system scale contribute to network modelling to understand CNS function.

Ultra-slow Oscillations in fMRI and Resting-State Connectivity: Neuronal and Vascular Contributions and Technical Confounds

Ultra-slow, ∼0.1-Hz variations in the oxygenation level of brain blood are widely used as an fMRI-based surrogate of "resting-state" neuronal activity. The temporal correlations among these fluctuations across the brain are interpreted as "functional connections" for maps and neurological diagnostics. Ultra-slow variations in oxygenation follow a cascade. First, they closely track changes in arteriole diameter. Second, interpretable functional connections arise when the ultra-slow changes in amplitude of γ-band neuronal oscillations, which are shared across even far-flung but synaptically connected brain regions, entrain the ∼0.1-Hz vasomotor oscillation in diameter of local arterioles. Significant confounds to estimates of functional connectivity arise from residual vasomotor activity as well as arteriole dynamics driven by self-generated movements and subcortical common modulatory inputs. Last, methodological limitations of fMRI can lead to spurious functional connections. The neuronal generator of ultra-slow variations in γ-band amplitude, including that associated with self-generated movements, remains an open issue.

Revealing architectural order with quantitative label-free imaging and deep learning

We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.

Molecular atlas of the adult mouse brain

Brain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain based purely on the unbiased identification of spatially defining features by employing whole-brain spatial transcriptomics. We found that the molecular information was sufficient to deduce the complex and detailed neuroanatomical organization of the brain. The unsupervised (non-expert, data-driven) classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and new subdivisions of striatum. The molecular atlas further supports the characterization of the spatial identity of neurons from their single-cell RNA profile, and provides a resource for annotating the brain using a minimal gene set-a brain palette. In summary, we have established a molecular atlas to formally define the spatial organization of brain regions, including the molecular code for mapping and targeting of discrete neuroanatomical domains.

Light Up the Brain: The Application of Optogenetics in Cell-Type Specific Dissection of Mouse Brain Circuits

The exquisite intricacies of neural circuits are fundamental to an animal’s diverse and complex repertoire of sensory and motor functions. The ability to precisely map neural circuits and to selectively manipulate neural activity is critical to understanding brain function and has, therefore been a long-standing goal for neuroscientists. The recent development of optogenetic tools, combined with transgenic mouse lines, has endowed us with unprecedented spatiotemporal precision in circuit analysis. These advances greatly expand the scope of tractable experimental investigations. Here, in the first half of the review, we will present applications of optogenetics in identifying connectivity between different local neuronal cell types and of long-range projections with both in vitro and in vivo methods. We will then discuss how these tools can be used to reveal the functional roles of these cell-type specific connections in governing sensory information processing, and learning and memory in the visual cortex, somatosensory cortex, and motor cortex. Finally, we will discuss the prospect of new optogenetic tools and how their application can further advance modern neuroscience. In summary, this review serves as a primer to exemplify how optogenetics can be used in sophisticated modern circuit analyses at the levels of synapses, cells, network connectivity and behaviors.

Connect-seq to superimpose molecular on anatomical neural circuit maps

The mouse brain contains about 75 million neurons interconnected in a vast array of neural circuits. The identities and functions of individual neuronal components of most circuits are undefined. Here we describe a method, termed "Connect-seq," which combines retrograde viral tracing and single-cell transcriptomics to uncover the molecular identities of upstream neurons in a specific circuit and the signaling molecules they use to communicate. Connect-seq can generate a molecular map that can be superimposed on a neuroanatomical map to permit molecular and genetic interrogation of how the neuronal components of a circuit control its function. Application of this method to hypothalamic neurons controlling physiological responses to fear and stress reveals subsets of upstream neurons that express diverse constellations of signaling molecules and can be distinguished by their anatomical locations.

High-order couplings in geometric complex networks of neurons

We explore the consequences of introducing higher-order interactions in a geometric complex network of Morris-Lecar neurons. We focus on the regime where traveling synchronization waves are observed from a first-neighbors-based coupling to evaluate the changes induced when higher-order dynamical interactions are included. We observe that the traveling-wave phenomenon gets enhanced by these interactions, allowing the activity to travel further in the system without generating pathological full synchronization states. This scheme could be a step toward a simple phenomenological modelization of neuroglial networks.

Optical Imaging in Brainsmatics

When neuroscience’s focus moves from molecular and cellular level to systems level, information technology mixes in and cultivates a new branch neuroinformatics. Especially under the investments of brain initiatives all around the world, brain atlases and connectomics are identified as the substructure to understand the brain. We think it is time to call for a potential interdisciplinary subject, brainsmatics, referring to brain-wide spatial informatics science and emphasizing on precise positioning information affiliated to brain-wide connectome, genome, proteome, transcriptome, metabolome, etc. Brainsmatics methodology includes tracing, surveying, visualizing, and analyzing brain-wide spatial information. Among all imaging techniques, optical imaging is the most appropriate solution to achieve whole-brain connectome in consistent single-neuron resolution. This review aims to introduce contributions of optical imaging to brainsmatics studies, especially the major strategies applied in tracing and surveying processes. After discussions on the state-of-the-art technology, the development objectives of optical imaging in brainsmatics field are suggested. We call for a global contribution to the brainsmatics field from all related communities such as neuroscientists, biologists, engineers, programmers, chemists, mathematicians, physicists, clinicians, pharmacists, etc. As the leading approach, optical imaging will, in turn, benefit from the prosperous development of brainsmatics.

Functional Geometry of Human Connectomes

Mapping the brain imaging data to networks, where nodes represent anatomical brain regions and edges indicate the occurrence of fiber tracts between them, has enabled an objective graph-theoretic analysis of human connectomes. However, the latent structure on higher-order interactions remains unexplored, where many brain regions act in synergy to perform complex functions. Here we use the simplicial complexes description of human connectome, where the shared simplexes encode higher-order relationships between groups of nodes. We study consensus connectome of 100 female (F-connectome) and of 100 male (M-connectome) subjects that we generated from the Budapest Reference Connectome Server v3.0 based on data from the Human Connectome Project. Our analysis reveals that the functional geometry of the common F&M-connectome coincides with the M-connectome and is characterized by a complex architecture of simplexes to the 14th order, which is built in six anatomical communities, and linked by short cycles. The F-connectome has additional edges that involve different brain regions, thereby increasing the size of simplexes and introducing new cycles. Both connectomes contain characteristic subjacent graphs that make them 3/2-hyperbolic. These results shed new light on the functional architecture of the brain, suggesting that insightful differences among connectomes are hidden in their higher-order connectivity.

Topographical Visualization of the Reciprocal Projection between the Medial Septum and the Hippocampus in the 5XFAD Mouse Model of Alzheimer's Disease

It is widely known that the degeneration of neural circuits is prominent in the brains of Alzheimer’s disease (AD) patients. The reciprocal connectivity of the medial septum (MS) and hippocampus, which constitutes the septo-hippocampo-septal (SHS) loop, is known to be associated with learning and memory. Despite the importance of the reciprocal projections between the MS and hippocampus in AD, the alteration of bidirectional connectivity between two structures has not yet been investigated at the mesoscale level. In this study, we adopted AD animal model, five familial AD mutations (5XFAD) mice, and anterograde and retrograde tracers, BDA and DiI, respectively, to visualize the pathology-related changes in topographical connectivity of the SHS loop in the 5XFAD brain. By comparing 4.5-month-old and 14-month-old 5XFAD mice, we successfully identified key circuit components of the SHS loop altered in 5XFAD brains. Remarkably, the SHS loop began to degenerate in 4.5-month-old 5XFAD mice before the onset of neuronal loss. The impairment of connectivity between the MS and hippocampus was accelerated in 14-month-old 5XFAD mice. These results demonstrate, for the first time, topographical evidence for the degradation of the interconnection between the MS and hippocampus at the mesoscale level in a mouse model of AD. Our results provide structural and functional insights into the interconnectivity of the MS and hippocampus, which will inform the use and development of various therapeutic approaches that target neural circuits for the treatment of AD.

Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method

Neuronal shape reconstruction is a helpful technique for establishing neuron identity, inferring neuronal connections, mapping neuronal circuits, and so on. Advances in optical imaging techniques have enabled data collection that includes the shape of a neuron across the whole brain, considerably extending the scope of neuronal anatomy. However, such datasets often include many fuzzy neurites and many crossover regions that neurites are closely attached, which make neuronal shape reconstruction more challenging. In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover regions, and we verified that our model could robustly reconstruct the neuron shape on a brain-wide scale.

High-density multi-fiber photometry for studying large-scale brain circuit dynamics

Animal behavior originates from neuronal activity distributed across brain-wide networks. However, techniques available to assess large-scale neural dynamics in behaving animals remain limited. Here we present compact, chronically implantable, high-density arrays of optical fibers that enable multi-fiber photometry and optogenetic perturbations across many regions in the mammalian brain. In mice engaged in a texture discrimination task, we achieved simultaneous photometric calcium recordings from networks of 12-48 brain regions, including striatal, thalamic, hippocampal and cortical areas. Furthermore, we optically perturbed subsets of regions in VGAT-ChR2 mice by targeting specific fiber channels with a spatial light modulator. Perturbation of ventral thalamic nuclei caused distributed network modulation and behavioral deficits. Finally, we demonstrate multi-fiber photometry in freely moving animals, including simultaneous recordings from two mice during social interaction. High-density multi-fiber arrays are versatile tools for the investigation of large-scale brain dynamics during behavior.

Myelin Dynamics Throughout Life: An Ever-Changing Landscape?

Myelin sheaths speed up impulse propagation along the axons of neurons without the need for increasing axon diameter. Subsequently, myelin (which is made by oligodendrocytes in the central nervous system) allows for highly complex yet compact circuitry. Cognitive processes such as learning require central nervous system plasticity throughout life, and much research has focused on the role of neuronal, in particular synaptic, plasticity as a means of altering circuit function. An increasing body of evidence suggests that myelin may also play a role in circuit plasticity and that myelin may be an adaptable structure which could be altered to regulate experience and learning. However, the precise dynamics of myelination throughout life remain unclear – does the production of new myelin require the differentiation of new oligodendrocytes, and/or can existing myelin be remodelled dynamically over time? Here we review recent evidence for both de novo myelination and myelin remodelling from pioneering longitudinal studies of myelin dynamics in vivo, and discuss what remains to be done in order to fully understand how dynamic regulation of myelin affects lifelong circuit function.

Gateway reflex: Local neuroimmune interactions that regulate blood vessels

Neuroimmunology is a research field that intersects neuroscience and immunology, with the larger aim of gaining significant insights into the pathophysiology of chronic inflammatory diseases such as multiple sclerosis. Conventional studies in this field have so far mainly dealt with immune responses in the nervous system (i.e. neuroinflammation) or systemic immune regulation by the release of glucocorticoids. On the other hand, recently accumulating evidence has indicated bidirectional interactions between specific neural activations and local immune responses. Here we discuss one such local neuroimmune interaction, the gateway reflex. The gateway reflex represents a mechanism that translates specific neural stimulations into local inflammatory outcomes by changing the state of specific blood vessels to allow immune cells to extravasate, thus forming the gateway. Several types of gateway reflex have been identified, and each regulates distinct blood vessels to create gateways for immune cells that induce local inflammation. The gateway reflex represents a novel therapeutic strategy for neuroinflammation and is potentially applicable to other inflammatory diseases in peripheral organs.