Publications

Visualization and Quantification for Interactive Analysis of Neural Connectivity in Drosophila

N. Swoboda, J. Moosburner, S. Bruckner, J. Y. Yu, B. J. Dickson, and K. Bühler

Abstract

Neurobiologists investigate the brain of the common fruit fly Drosophila melanogaster to discover neural circuits and link them to complex behavior. Formulating new hypotheses about connectivity requires potential connectivity information between individual neurons, indicated by overlaps of arborizations of two or more neurons. As the number of higher order overlaps (i.e., overlaps of three or more arborizations) increases exponentially with the number of neurons under investigation, visualization is impeded by clutter and quantification becomes a burden. Existing solutions are restricted to visual or quantitative analysis of pairwise overlaps, as they rely on precomputed overlap data. We present a novel tool that complements existing methods for potential connectivity exploration by providing for the first time the possibility to compute and visualize higher order arborization overlaps on the fly and to interactively explore this information in both its spatial anatomical context and on a quantitative level. Qualitative evaluation by neuroscientists and non-experts demonstrated the utility and usability of the tool

N. Swoboda, J. Moosburner, S. Bruckner, J. Y. Yu, B. J. Dickson, and K. Bühler, "Visualization and Quantification for Interactive Analysis of Neural Connectivity in Drosophila," Computer Graphics Forum, vol. 36, iss. 1, p. 160–171, 2017. doi:10.1111/cgf.12792
[BibTeX]

Neurobiologists investigate the brain of the common fruit fly Drosophila melanogaster to discover neural circuits and link them to complex behavior. Formulating new hypotheses about connectivity requires potential connectivity information between individual neurons, indicated by overlaps of arborizations of two or more neurons. As the number of higher order overlaps (i.e., overlaps of three or more arborizations) increases exponentially with the number of neurons under investigation, visualization is impeded by clutter and quantification becomes a burden. Existing solutions are restricted to visual or quantitative analysis of pairwise overlaps, as they rely on precomputed overlap data. We present a novel tool that complements existing methods for potential connectivity exploration by providing for the first time the possibility to compute and visualize higher order arborization overlaps on the fly and to interactively explore this information in both its spatial anatomical context and on a quantitative level. Qualitative evaluation by neuroscientists and non-experts demonstrated the utility and usability of the tool
@ARTICLE {Swoboda-2017-VQI,
author = "Nicolas Swoboda and Judith Moosburner and Stefan Bruckner and Jai Y. Yu and Barry J. Dickson and Katja B{\"u}hler",
title = "Visualization and Quantification for Interactive Analysis of Neural Connectivity in Drosophila",
journal = "Computer Graphics Forum",
year = "2017",
volume = "36",
number = "1",
pages = "160--171",
month = "jan",
abstract = "Neurobiologists investigate the brain of the common fruit fly Drosophila  melanogaster to discover neural circuits and link them to complex  behavior. Formulating new hypotheses about connectivity requires  potential connectivity information between individual neurons, indicated  by overlaps of arborizations of two or more neurons. As the number  of higher order overlaps (i.e., overlaps of three or more arborizations)  increases exponentially with the number of neurons under investigation,  visualization is impeded by clutter and quantification becomes a  burden. Existing solutions are restricted to visual or quantitative  analysis of pairwise overlaps, as they rely on precomputed overlap  data. We present a novel tool that complements existing methods for  potential connectivity exploration by providing for the first time  the possibility to compute and visualize higher order arborization  overlaps on the fly and to interactively explore this information  in both its spatial anatomical context and on a quantitative level.  Qualitative evaluation by neuroscientists and non-experts demonstrated  the utility and usability of the tool",
pdf = "pdfs/Swoboda-2017-VQI.pdf",
images = "images/Swoboda-2017-VQI.jpg",
thumbnails = "images/Swoboda-2017-VQI.png",
youtube = "https://www.youtube.com/watch?v=bycWGQQpqks",
doi = "10.1111/cgf.12792",
keywords = "visual analysis, neurobiology"
}
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