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dc.contributor.authorSchultz, Thomasen_US
dc.contributor.authorGerber, Peteren_US
dc.contributor.authorSchmidt-Wilcke, Tobiasen_US
dc.contributor.editorMichael Goesele and Thorsten Grosch and Holger Theisel and Klaus Toennies and Bernhard Preimen_US
dc.date.accessioned2013-11-08T10:35:42Z
dc.date.available2013-11-08T10:35:42Z
dc.date.issued2012en_US
dc.identifier.isbn978-3-905673-95-1en_US
dc.identifier.urihttp://dx.doi.org/10.2312/PE/VMV/VMV12/217-218en_US
dc.description.abstractWe use visualization to help analyze a diffusion magnetic resonance imaging study that has investigated the effects of learning how to juggle on nerve fiber microstructure in the human brain. Building on a standard voxel-wise statistical analysis, we perform a more elaborate visual analysis of the affected fiber bundles. Based on the visualization, we hypothesize that brain image data allows us to distinguish learners from controls with better-thanrandom accuracy; we test this hypothesis with a machine learning technique. We believe that our results exemplify the value of more tightly integrating statistical with visual analysis and machine learning in brain imaging studies, in order to complement the group-wise view of traditional analysis with insights about specific individuals.en_US
dc.publisherThe Eurographics Associationen_US
dc.titleJuggling Increases Interhemispheric Brain Connectivity: A Visual and Quantitative dMRI Studyen_US
dc.description.seriesinformationVision, Modeling and Visualizationen_US


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  • VMV12
    ISBN 978-3-905673-95-1

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