A graph-based approach for representing, integrating and analysing neuroscience data: the case of the murine basal ganglia

PurposeNeuroscience data are spread across a variety of sources, typically provisioned through ad-hoc and non-standard approaches and formats and often have no connection to the related data sources. These make it difficult for researchers to understand, integrate and reuse brain-related data. The aim of this study is to show that a graph-based approach offers an effective mean for representing, analysing and accessing brain-related data, which is highly interconnected, evolving over time and often needed in combination.Design/methodology/approachThe authors present an approach for organising brain-related data in a graph model. The approach is exemplified in the case of a unique data set of quantitative neuroanatomical data about the murine basal ganglia––a group of nuclei in the brain essential for processing information related to movement. Specifically, the murine basal ganglia data set is modelled as a graph, integrated with relevant data from third-party repositories, published through a Web-based user interface and API, analysed from exploratory and confirmatory perspectives using popular graph algorithms to extract new insights.FindingsThe evaluation of the graph model and the results of the graph data analysis and usability study of the user interface suggest that graph-based data management in the neuroscience domain is a promising approach, since it enables integration of various disparate data sources and improves understanding and usability of data.Originality/valueThe study provides a practical and generic approach for representing, integrating, analysing and provisioning brain-related data and a set of software tools to support the proposed approach.

Novel neuroanatomical integration and scaling define avian brain shape evolution and development

How do large and unique brains evolve? Historically, comparative neuroanatomical studies have attributed the evolutionary genesis of highly encephalized brains to deviations along, as well as from, conserved scaling relationships among brain regions. However, the relative contributions of these concerted (integrated) and mosaic (modular) processes as drivers of brain evolution remain unclear, especially in non-mammalian groups. While proportional brain sizes have been the predominant metric used to characterize brain morphology to date, we perform a high-density geometric morphometric analysis on the encephalized brains of crown birds (Neornithes or Aves) compared to their stem taxa—the non-avialan coelurosaurian dinosaurs and Archaeopteryx. When analyzed together with developmental neuroanatomical data of model archosaurs (Gallus, Alligator), crown birds exhibit a distinct allometric relationship that dictates their brain evolution and development. Furthermore, analyses by neuroanatomical regions reveal that the acquisition of this derived shape-to-size scaling relationship occurred in a mosaic pattern, where the avian-grade optic lobe and cerebellum evolved first among non-avialan dinosaurs, followed by major changes to the evolutionary and developmental dynamics of cerebrum shape after the origin of Avialae. Notably, the brain of crown birds is a more integrated structure than non-avialan archosaurs, implying that diversification of brain morphologies within Neornithes proceeded in a more coordinated manner, perhaps due to spatial constraints and abbreviated growth period. Collectively, these patterns demonstrate a plurality in evolutionary processes that generate encephalized brains in archosaurs and across vertebrates.

Novel neuroanatomical integration and scaling define avian brain shape evolution and development

How do large and unique brains evolve? Historically, comparative neuroanatomical studies have attributed the evolutionary genesis of highly encephalized brains to deviations along, as well as from, conserved scaling relationships among brain regions. However, the relative contributions of these concerted (integrated) and mosaic (modular) processes as drivers of brain evolution remain unclear, especially in non-mammalian groups. While proportional brain sizes have been the predominant metric used to characterize brain morphology to date, we perform a high-density geometric morphometric analysis on the encephalized brains of crown birds (Neornithes or Aves) compared to their stem taxa—the non-avialan coelurosaurian dinosaurs. When analyzed together with developmental neuroanatomical data of model archosaurs (Gallus, Alligator), crown birds exhibit a distinct allometric relationship that dictates their brain evolution and development. Furthermore, analyses by neuroanatomical regions reveal that the acquisition of this derived shape-to-size scaling relationship occurred in a mosaic pattern, where the ‘avian’-grade optic lobe and cerebellum evolved first among non-avialan dinosaurs, followed by major changes to the evolutionary and developmental dynamics of cerebrum shape after the origin of Avialae. Notably, the brain of crown birds is a more integrated structure than non-avialan archosaurs, implying that diversification of brain morphologies within Neornithes proceeded in a more coordinated manner, perhaps due to spatial constraints and abbreviated growth period. Collectively, these patterns demonstrate a plurality in evolutionary processes that generate encephalized brains in archosaurs and across vertebrates.

REHABILITATION AND MODULATION AIMED AT AMELIORATING AWARENESS IN ANOSOGNOSIA FOR HEMIPLEGIA

Anosognosia for hemiplegia is a multifaceted syndrome that has a detrimental impact on the patient. Various theories based on behavioural and neuroanatomical data have been proposed to explain the mechanisms underlying the symptoms. These approaches have resulted in the development of a number of different proce- dures aimed at reducing symptoms or enhancing residual aware- ness. The article reviews rehabilitation attempts and their effects on individual cases and groups of patients. A selection of material was made using indexed articles published between 1987 and 2019. The inclusion criteria were: i) the presence of a neuropsychological assessment and ii) the presence of one or more methods specifically used to reduce AHP symptoms, or to enhance residual forms of awareness. The review indicates that intervention procedures have moved from bottom-up to more cognitive and metacognitive approaches. In fact, initially anosognosia for hemiplegia was considered to be a co-oc- current symptom of other neuropsychological conditions (e.g. spa- tial neglect) and interventions were borrowed from the rehabilitation techniques that had had success in relieving these other disorders. When anosognosia was identified as an independent syndrome and residual forms of awareness were demonstrated, procedures attempting to modulate awareness started to focus on specific components of the disease, such as visual perspective, motor monitoring and the updating of beliefs. Although further research is needed in this field, the most recent approaches seem to give more stable, lasting results than earlier methods. A timeline for interventions relating to anosognosia is suggested, and ethical issues are also discussed.

Neural divergence and hybrid disruption between ecologically isolated Heliconius butterflies

The importance of behavioral evolution during speciation is well established, but we know little about how this is manifest in sensory and neural systems. A handful of studies have linked specific neural changes to divergence in host or mate preferences associated with speciation. However, the degree to which brains are adapted to local environmental conditions, and whether this contributes to reproductive isolation between close relatives that have diverged in ecology, remains unknown. Here, we examine divergence in brain morphology and neural gene expression between closely related, but ecologically distinct, Heliconius butterflies. Despite ongoing gene flow, sympatric species pairs within the melpomene–cydno complex are consistently separated across a gradient of open to closed forest and decreasing light intensity. By generating quantitative neuroanatomical data for 107 butterflies, we show that Heliconius melpomene and Heliconius cydno clades have substantial shifts in brain morphology across their geographic range, with divergent structures clustered in the visual system. These neuroanatomical differences are mirrored by extensive divergence in neural gene expression. Differences in both neural morphology and gene expression are heritable, exceed expected rates of neutral divergence, and result in intermediate traits in first-generation hybrid offspring. Strong evidence of divergent selection implies local adaptation to distinct selective optima in each parental microhabitat, suggesting the intermediate traits of hybrids are poorly matched to either condition. Neural traits may therefore contribute to coincident barriers to gene flow, thereby helping to facilitate speciation.

A Convolutional Network Architecture Driven by Mouse Neuroanatomical Data

Convolutional neural networks trained on object recognition derive some inspiration from the neuroscience of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the hierarchical organization of primates, the visual system of the mouse has flatter hierarchy. Since mice are capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a framework for building a biologically constrained convolutional neural network model of lateral areas of the mouse visual cortex. The structural parameters of the network are derived from experimental measurements, specifically estimates of numbers of neurons in each area and cortical layer, the interareal connec-tome, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. The code is freely available to support such research.

Neural divergence and hybrid disruption between ecologically isolated Heliconius butterflies

SummaryThe importance of behavioural evolution during speciation is well established, but we know little about how this is manifest in sensory and neural systems. Although a handful of studies have linked specific neural changes to divergence in host or mate preferences associated with speciation, how brains respond to broad environmental transitions, and whether this contributes to reproductive isolation, remains unknown. Here, we examine divergence in brain morphology and neural gene expression between closely related, but ecologically distinct, Heliconius butterflies. Despite on-going gene flow, sympatric species pairs within the melpomene-cydno complex are consistently separated across a gradient of open to closed forest and decreasing light intensity. By generating quantitative neuroanatomical data for 107 butterflies, we show that H. melpomene and H. cydno have substantial shifts in brain morphology across their geographic range, with divergent structures clustered in the visual system. These neuroanatomical differences are mirrored by extensive divergence in neural gene expression. Differences in both morphology and gene expression are heritable, exceed expected rates of neutral divergence, and result in intermediate traits in first generation hybrid offspring. This likely disrupts neural system function, leading to a mismatch between the environment and the behavioral response of hybrids. Our results suggest that disruptive selection on both neural function and external morphology result in coincident barriers to gene flow, thereby facilitating speciation.

The natverse, a versatile toolbox for combining and analysing neuroanatomical data

To analyse neuron data at scale, neuroscientists expend substantial effort reading documentation, installing dependencies and moving between analysis and visualisation environments. To facilitate this, we have developed a suite of interoperable open-source R packages called the <monospace>natverse</monospace>. The <monospace>natverse</monospace> allows users to read local and remote data, perform popular analyses including visualisation and clustering and graph-theoretic analysis of neuronal branching. Unlike most tools, the <monospace>natverse</monospace> enables comparison across many neurons of morphology and connectivity after imaging or co-registration within a common template space. The <monospace>natverse</monospace> also enables transformations between different template spaces and imaging modalities. We demonstrate tools that integrate the vast majority of Drosophila neuroanatomical light microscopy and electron microscopy connectomic datasets. The <monospace>natverse</monospace> is an easy-to-use environment for neuroscientists to solve complex, large-scale analysis challenges as well as an open platform to create new code and packages to share with the community.