Iterative Exploration of Big Brain Network Data
| dc.contributor.author | Ganglberger, Florian | en_US |
| dc.contributor.author | Swoboda, Nicolas | en_US |
| dc.contributor.author | Frauenstein, Lisa | en_US |
| dc.contributor.author | Kaczanowska, Joanna | en_US |
| dc.contributor.author | Haubensak, Wulf | en_US |
| dc.contributor.author | Bühler, Katja | en_US |
| dc.contributor.editor | Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau | en_US |
| dc.date.accessioned | 2018-09-19T15:19:27Z | |
| dc.date.available | 2018-09-19T15:19:27Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | A current quest in neurscience is the understanding of how genes, structure and behavior relate to one another. In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data - brain-network graphs with billions of edges - whose analysis for higher order relations in structural or functional neuroanatomy together with genetic data may reveal novel insights into brain functionality. This creates a need for joint exploration of spatial data, such as gene expression patterns, whole brain gene co-expression correlation, structural and functional connectivities together with neuroanatomical parcellations. Current experimental workflows involve time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales. We propose a web-based framework to explore heterogeneous neurobiological data in an integrated visual analytics workflow. On-demand queries on spatial gene expression and connectivity data enable an interactive dissection of dense network graphs - with of billion-edges on voxel-resolution - in real-time based on their spatial context. In order to take higher order connections between brain regions into account, queries can be executed in a cascading way. Relating data to the hierarchical structure of common anatomical atlases allows experts to quantitatively compare multimodal networks on different scales. Additionally, 3D visualizations have been optimized to accommodate for the domain experts’' need for publishable network figures. We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory/learning functional neuroanatomy in mice. | en_US |
| dc.description.sectionheaders | Head and Brain | |
| dc.description.seriesinformation | Eurographics Workshop on Visual Computing for Biology and Medicine | |
| dc.identifier.doi | 10.2312/vcbm.20181231 | |
| dc.identifier.isbn | 978-3-03868-056-7 | |
| dc.identifier.issn | 2070-5786 | |
| dc.identifier.pages | 77-87 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/vcbm20181231 | |
| dc.identifier.uri | https://doi.org/10.2312/vcbm.20181231 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.title | Iterative Exploration of Big Brain Network Data | en_US |