DOI: 10.3897/rio.7.e68121

Authors: Daniel Mietchen, Lane Rasberry, Thais Morata, John Sadowski, Jeanette Novakovich, James Heilman

Abstract: In this era of information overload and misinformation, it is a challenge to rapidly translate evidence-based health information to the public. Wikipedia is a prominent global source of health information with high traffic, multilingual coverage, and acceptable quality control practices. Viewership data following the Ebola crisis and during the COVID-19 pandemic reveals that a significant number of web users located health guidance through Wikipedia and related projects, including its media repository Wikimedia Commons and structured data complement, Wikidata.The basic idea discussed in this paper is to increase and expedite health institutions' global reach to the general public, by developing a specific strategy to maximize the availability of focused content into Wikimedia’s public digital knowledge archives. It was conceptualized from the experiences of leading health organizations such as Cochrane, the World Health Organization (WHO) and other United Nations Organizations, Cancer Research UK, National Network of Libraries of Medicine, and Centers for Disease Control and Prevention (CDC)'s National Institute for Occupational Safety and Health (NIOSH). Each has customized strategies to integrate content in Wikipedia and evaluate responses.We propose the development of an interactive guide on the Wikipedia and Wikidata platforms to support health agencies, health professionals and communicators in quickly distributing key messages during crisis situations. The guide aims to cover basic features of Wikipedia, including adding key health messages to Wikipedia articles, citing expert sources to facilitate fact-checking, staging text for translation into multiple languages; automating metrics reporting; sharing non-text media; anticipating offline reuse of Wikipedia content in apps or virtual assistants; structuring data for querying and reuse through Wikidata, and profiling other flagship projects from major health organizations.In the first phase, we propose the development of a curriculum for the guide using information from prior case studies. In the second phase, the guide would be tested on select health-related topics as new case studies. In its third phase, the guide would be finalized and disseminated.

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DOI: 10.3897/rio.4.e25312

Authors: Roberto Toro, Rembrandt Bakker, Thierry Delzescaux, Alan Evans, Paul Tiesinga

Abstract: The first days after birth in ferrets provide a privileged view of the development of a complex mammalian brain. Unlike mice, ferrets develop a rich pattern of deep neocortical folds and cortico- cortical connections. Unlike humans and other primates, whose brains are well differentiated and folded at birth, ferrets are born with a very immature and completely smooth neocortex: folds, neocortical regionalisation and cortico-cortical connectivity develop in ferrets during the first postnatal days. After a period of fast neocortical expansion, during which brain volume increases by up to a factor of 4 in 2 weeks, the ferret brain reaches its adult volume at about 6 weeks of age. Ferrets could thus become a major animal model to investigate the neurobiological correlates of the phenomena observed in human neuroimaging. Many of these phenomena, such as the relationship between brain folding, cortico-cortical connectivity and neocortical regionalisation cannot be investigated in mice, but could be investigated in ferrets. Our aim is to provide the research community with a detailed description of the development of a complex brain, necessary to better understand the nature of human neuroimaging data, create models of brain development, or analyse the relationship between multiple spatial scales. We have already started a project to constitute an open, collaborative atlas of ferret brain development, integrating multi-modal and multi-scale data. We have acquired data for 28 ferrets (4 animals per time point from P0 to adults), using high-resolution MRI and diffusion tensor imaging (DTI). We have developed an open-source pipeline to segment and produce – online – 3D reconstructions of brain MRI data. We propose to process the brains of 16 of our specimens (from P0 to P16) using high-throughput 3D histology, staining for cytoarchitectonic landmarks, neuronal progenitors and neurogenesis. This would allow us to relate the MRI data that we have already acquired with multi-dimensional cell-scale information. Brains will be sectioned at 25 μm, stained, scanned at 0.25 μm of resolution, and processed for real-time multi-scale visualisation. We will extend our current web-platform to integrate an interactive multi-scale visualisation of the data. Using our combined expertise in computational neuroanatomy, multi-modal neuroimaging, neuroinformatics, and the development of inter-species atlases, we propose to build an open-source web platform to allow the collaborative, online, creation of atlases of the development of the ferret brain. The web platform will allow researchers to access and visualise interactively the MRI and histology data. It will also allow researchers to create collaborative, human curated, 3D segmentations of brain structures, as well as vectorial atlases. Our work will provide a first integrated atlas of ferret brain development, and the basis for an open platform for the creation of collaborative multi-modal, multi-scale, multi-species atlases.

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DOI: 10.3897/rio.2.e9113

Authors: Katja Heuer, Satrajit Ghosh, Amy Robinson Sterling, Roberto Toro

Abstract:

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DOI: 10.3897/rio.2.e8817

Authors: Arno Klein

Abstract: We will analyze variations in brain anatomy and create the first integrated software environment to extract patterns from brains and target differences related to inter-individual variability, pathology, development, or degeneration. We will evaluate how well these differences can help diagnose and predict treatment outcome for major depressive disorder, which affects millions of Americans, but our work is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain anatomy between any two populations.

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DOI: 10.3897/rio.2.e8813

Authors: Arno Klein

Abstract:

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