Visualisation and Interaction Techniques for the Exploration of the Fruit Fly’s Neural Structure

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Abstract

In their studies of the brain of the common fruit fly Drosophila melanogaster, neuro-biologists investigate neural connectivity with the goal to discover how complex be-haviour is generated. Gaining information on potential connectivity between neurons is an essential step in their workflow. This thesis presents a way to compute and visualise such potential connectivity information from segmented neurons. It shortens the tedious month-long search for potential connectivity down to a few minutes. Overlaps of arborisations of two or more neurons indicate a potential anatomical connection, and thus a potential functional connection. The computation of this data starts from neuron meshes. The meshes—segmented from confocal light-microscopy images of the fruit fly—are intersected to find overlapping areas, i.e. areas of potential anatomical connectivity. This information can then help to discover actual functional connectivity in a neural circuit. Analysing higher order overlaps, i.e. intersections of more than two arborisations of segmented neuron data in the same location, poses new challenges. The visualisation in 2D sections or 3D is impeded by visual clutter and occlusion. Computation of relevant volumetric information becomes difficult for higher order overlaps, because the number of possible overlaps increases exponentially with the number of arborisations. This makes the pre-computation for all possible combinations infeasible. Previous tools have thus been restricted to the quantification and visualisation of pairwise overlaps. The thesis presents a novel solution addressing these issues for higher order over-laps. A novel abstracting design is coupled with a modern approach for on-demand GPU computations. Our tool calculates for the first time volumetric information of higher order overlaps on the GPU using A-buffers. The thesis addresses the visual com-plexity of the data with the implementation of an innovative novel design created by the graphics designer Judith Moosburner. We realised this design using non-photorealistic rendering techniques and perspicuous user interfaces, including interactive glyphs and linked views on quantitative overlap information. To complement the neuroscientists’ workflows the resulting interactive tool has been integrated into BrainGazer, a software tool for advanced visualisation and exploration of neural images and circuit data. Qual-itative evaluation with neuroscientists and non-expert users demonstrated the utility and usability of the tool.

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BibTeX

@mastersthesis{Swoboda2020,
  title =      "Visualisation and Interaction Techniques for the Exploration
               of the Fruit Fly’s Neural Structure",
  author =     "Nicolas Swoboda",
  year =       "2020",
  abstract =   "In their studies of the brain of the common fruit fly
               Drosophila melanogaster, neuro-biologists investigate neural
               connectivity with the goal to discover how complex
               be-haviour is generated. Gaining information on potential
               connectivity between neurons is an essential step in their
               workflow. This thesis presents a way to compute and
               visualise such potential connectivity information from
               segmented neurons. It shortens the tedious month-long search
               for potential connectivity down to a few minutes. Overlaps
               of arborisations of two or more neurons indicate a potential
               anatomical connection, and thus a potential functional
               connection. The computation of this data starts from neuron
               meshes. The meshes—segmented from confocal
               light-microscopy images of the fruit fly—are intersected
               to find overlapping areas, i.e. areas of potential
               anatomical connectivity. This information can then help to
               discover actual functional connectivity in a neural circuit.
               Analysing higher order overlaps, i.e. intersections of more
               than two arborisations of segmented neuron data in the same
               location, poses new challenges. The visualisation in 2D
               sections or 3D is impeded by visual clutter and occlusion.
               Computation of relevant volumetric information becomes
               difficult for higher order overlaps, because the number of
               possible overlaps increases exponentially with the number of
               arborisations. This makes the pre-computation for all
               possible combinations infeasible. Previous tools have thus
               been restricted to the quantification and visualisation of
               pairwise overlaps. The thesis presents a novel solution
               addressing these issues for higher order over-laps. A novel
               abstracting design is coupled with a modern approach for
               on-demand GPU computations. Our tool calculates for the
               first time volumetric information of higher order overlaps
               on the GPU using A-buffers. The thesis addresses the visual
               com-plexity of the data with the implementation of an
               innovative novel design created by the graphics designer
               Judith Moosburner. We realised this design using
               non-photorealistic rendering techniques and perspicuous user
               interfaces, including interactive glyphs and linked views on
               quantitative overlap information. To complement the
               neuroscientists’ workflows the resulting interactive tool
               has been integrated into BrainGazer, a software tool for
               advanced visualisation and exploration of neural images and
               circuit data. Qual-itative evaluation with neuroscientists
               and non-expert users demonstrated the utility and usability
               of the tool.",
  month =      feb,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  URL =        "/research/publications/2020/Swoboda2020/",
}