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  <doc>
    <id>9655</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Multi-species community platform for comparative neuroscience in teleost fish</title>
    <abstract language="eng">Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost community’s research and its easy extensibility will encourage contributions to its continuous expansion.</abstract>
    <parentTitle language="eng">bioRxiv</parentTitle>
    <identifier type="doi">10.1101/2024.02.14.580400</identifier>
    <enrichment key="SubmissionStatus">under review</enrichment>
    <enrichment key="PreprintUrn">publish</enrichment>
    <enrichment key="PeerReviewed">no</enrichment>
    <author>Sumit Kumar Vohra</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Kristian Herrera</author>
    <author>Tinatini Tavhelidse-Suck</author>
    <author>Simon Knoblich</author>
    <author>Ali Seleit</author>
    <author>Jonathan Boulanger-Weill</author>
    <author>Sydney Chambule</author>
    <author>Ariel Aspiras</author>
    <author>Cristina Santoriello</author>
    <author>Owen Randlett</author>
    <author>Joachim Wittbrodt</author>
    <author>Alexander Aulehla</author>
    <author>Jeff W. Lichtman</author>
    <author>Mark Fishman</author>
    <author>Hans-Christian Hege</author>
    <author>Daniel Baum</author>
    <author>Florian Engert</author>
    <author>Yasuko Isoe</author>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
  <doc>
    <id>9918</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Dissecting origins of wiring specificity in dense cortical connectomes</title>
    <abstract language="eng">Wiring specificity in the cortex is observed across scales from the subcellular to the network level. It describes the deviations of connectivity patterns from those expected in randomly connected networks. Understanding the origins of wiring specificity in neural networks remains difficult as a variety of generative mechanisms could have contributed to the observed connectome. To take a step forward, we propose a generative modeling framework that operates directly on dense connectome data as provided by saturated reconstructions of neural tissue. The computational framework allows testing different assumptions of synaptic specificity while accounting for anatomical constraints posed by neuron morphology, which is a known confounding source of wiring specificity. We evaluated the framework on dense reconstructions of the mouse visual and the human temporal cortex. Our template model incorporates assumptions of synaptic specificity based on cell type, single-cell identity, and subcellular compartment. Combinations of these assumptions were sufficient to model various connectivity patterns that are indicative of wiring specificity. Moreover, the identified synaptic specificity parameters showed interesting similarities between both datasets, motivating further analysis of wiring specificity across species.</abstract>
    <parentTitle language="eng">bioRxiv</parentTitle>
    <identifier type="doi">10.1101/2024.12.14.628490</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Philipp Harth</author>
    <submitter>Philipp Harth</submitter>
    <author>Daniel Udvary</author>
    <author>Jan Boelts</author>
    <author>Daniel Baum</author>
    <author>Jakob H. Macke</author>
    <author>Hans-Christian Hege</author>
    <author>Marcel Oberlaender</author>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="persons" number="harth">Harth, Philipp</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="projects" number="PredictingCorticalConnectomes">PredictingCorticalConnectomes</collection>
  </doc>
  <doc>
    <id>8991</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3945</pageFirst>
    <pageLast>3958</pageLast>
    <pageNumber/>
    <edition/>
    <issue>7</issue>
    <volume>30</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Visual Interface for Exploring Hypotheses about Neural Circuits</title>
    <abstract language="eng">One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and  morphologies of individual neurons.&#13;
Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. &#13;
The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.</abstract>
    <parentTitle language="eng">IEEE Transactions on Visualization and Computer Graphics</parentTitle>
    <identifier type="doi">10.1109/TVCG.2023.3243668</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="AcceptedDate">2023-02-04</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-89932</enrichment>
    <author>Sumit Kumar Vohra</author>
    <submitter>Sumit Kumar Vohra</submitter>
    <author>Philipp Harth</author>
    <author>Yasuko Isoe</author>
    <author>Armin Bahl</author>
    <author>Haleh Fotowat</author>
    <author>Florian Engert</author>
    <author>Hans-Christian Hege</author>
    <author>Daniel Baum</author>
    <collection role="persons" number="baum">Baum, Daniel</collection>
    <collection role="persons" number="hege">Hege, Hans-Christian</collection>
    <collection role="projects" number="MultiscaleVirtualFish">MultiscaleVirtualFish</collection>
    <collection role="persons" number="harth">Harth, Philipp</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
    <collection role="persons" number="vohra">Vohra, Sumit Kumar</collection>
  </doc>
</export-example>
