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Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardized description impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We discuss how AtOM can facilitate atlas interoperability and data integration, thereby increasing compliance with the FAIR guiding principles. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data. [ABSTRACT FROM AUTHOR]

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature. [ABSTRACT FROM AUTHOR]

In this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as the International Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data be Findable, Accessible, Interoperable, and Reusable (FAIR). As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects. [ABSTRACT FROM AUTHOR]

The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a 'data dump' that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on 'long-tail' preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields. [ABSTRACT FROM AUTHOR]

As information and communication technology has become pervasive in our society, we are increasingly dependent on both digital data and repositories that provide access to and enable the use of such resources. Repositories must earn the trust of the communities they intend to serve and demonstrate that they are reliable and capable of appropriately managing the data they hold. [ABSTRACT FROM AUTHOR]

The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, 'big science' efforts are not the only drivers of data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists, so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders. [ABSTRACT FROM AUTHOR]

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options. [ABSTRACT FROM AUTHOR]

Although new and functional neurons are produced in the adult brain, little is known about how they integrate into mature networks. Here we explored the mechanisms of synaptogenesis on neurons born in the adult mouse hippocampus using confocal microscopy, electron microscopy and live imaging. We report that new neurons, similar to mature granule neurons, were contacted by axosomatic, axodendritic and axospinous synapses. Consistent with their putative role in synaptogenesis, dendritic filopodia were more abundant during the early stages of maturation and, when analyzed in three dimensions, the tips of all filopodia were found within 200 nm of preexisting boutons that already synapsed on other neurons. Furthermore, dendritic spines primarily synapsed on multiple-synapse boutons, suggesting that initial contacts were preferentially made with preexisting boutons already involved in a synapse. The connectivity of new neurons continued to change until at least 2 months, long after the formation of the first dendritic protrusions. [ABSTRACT FROM AUTHOR]

Imaging, from magnetic resonance imaging (MRI) to localization of specific macromolecules by microscopies, has been one of the driving forces behind neuroinformatics efforts of the past decade. Many web-accessible resources have been created, ranging from simple data collections to highly structured databases. Although many challenges remain in adapting neuroscience to the new electronic forum envisioned by neuroinformatics proponents, these efforts have succeeded in formalizing the requirements for effective data sharing and data integration across multiple sources. In this perspective, we discuss the importance of spatial systems and ontologies for proper modeling of neuroscience data and their use in a large-scale data integration effort, the Biomedical Informatics Research Network (BIRN). [ABSTRACT FROM AUTHOR]

The genetic defect in the cardiomyopathic (CM) hamster is a mutation in the glycoprotein-sarcoglycan (a component of the dystrophin-glycoprotein complex). Apoptosis has been identified in skeletal muscle of dystrophin-deficient mice, and therefore the role of myocardial apoptosis in relation to oncosis in causing myocardial necrosis was assessed at the onset of left ventricular (LV) dysfunction in CM hamsters. LV size and function were evaluated in normal and CM hamsters (CHF147 line) by echocardiography at 1, 2, 3, and 5 months (mo) of age. The decrease of LV fractional shortening was found to be most marked (45 %) between 1 and 2 mo of age. Apoptotic nuclei were identified at each time point using in situ end-labeling of DNA strand breaks (TUNEL), together with immunolabeling of myocytes; DNA fragmentation (laddering) and nuclear morphology were also assessed. Myocyte oncotic necrosis was assessed at 2 mo by Evans blue dye (EBD), wheat germ agglutinin, hematoxylin/eosin staining, and electron microscopy. Apoptotic nuclei were not detected in age-matched normal hamsters. In the CM hamsters apoptotic myocyte nuclei comprised an average of 0.041 % of myocyte nuclei between 1 and 5 mo, an increase at 2 mo (to 0.076 %) was not significant, and DNA laddering was not detected. The number of myocyte nuclei per unit area decreased by 32 % between 1 and 2 mo, and in 2 mo old CM hamsters myocardial staining with EBD was positive in 9.82 % of the myocardial cross sectional areas examined, most of which was consistent with sarcolemmal rupture and oncosis with inflammatory cell infiltration. It is concluded that myocyte oncosis provides the major mechanism for the decreased number of myocyte nuclei and the early decrease of cardiac function between 1 and 2 mo of age in the CM hamster, with only a small contribution of myocyte apoptosis. [ABSTRACT FROM AUTHOR]

Enteroviruses such as Coxsackievirus B3 can cause dilated cardiomyopathy, but the mechanism of this pathology is unknown. Mutations in cytoskeletal proteins such as dystrophin cause hereditary dilated cardiomyopathy, but it is unclear if similar mechanisms underlie acquired forms of heart failure. We demonstrate here that purified Coxsackievirus protease 2A cleaves dystrophin in vitro as predicted by computer analysis. Dystrophin is also cleaved during Coxsackievirus infection of cultured myocytes and in infected mouse hearts, leading to impaired dystrophin function. In vivo, dystrophin and the dystrophin-associated glycoproteins α-sarcoglycan and β-dystroglycan are morphologically disrupted in infected myocytes. We suggest a molecular mechanism through which enteroviral infection contributes to the pathogenesis of acquired forms of dilated cardiomyopathy. [ABSTRACT FROM AUTHOR]

A correction to the article "Biological imaging software tools" that was published in the June 28, 2012 issue is presented.