@inproceedings{PB-VRVis-2018-036,
author = {Ganglberger, Florian and Swoboda, Nicolas and Frauenstein, Lisa and Kaczanowska, Joanna and Haubensak, Wulf and B{\"u}hler, Katja},
title = {Iterative Exploration of Big Brain Network Data},
year = {2018},
booktitle = {Eurographics Workshop on Visual Computing for Biology and
  Medicine (2018)},
editor = {Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and
  Hotz, Ingrid and Kozlikova, Barbora and V{\'a}zquez, Pere-Pau},
doi = {10.2312/vcbm.20181231},
url = {https://www.vrvis.at/publications/PB-VRVis-2018-036},
publisher = {The Eurographics Association},
isbn = {978-3-03868-056-7},
abstract = {A current quest in neuroscience 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 {\textendash}  brain-network graphs with billions edges {\textendash} 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  volumetric gene expression and connectivity data enable an  interactive dissection of dense network graphs with of  billion-edges on voxel-resolution in real-time, and based on  their spatial context. The queries can be executed in a  cascading way, to take higher order connections between brain  regions into account. Relating data to the hierarchical  organization of common anatomical atlases allows experts to  quantitatively compare multimodal networks on different scales.  Additionally, 3D visualizations have been optimized to  accommodate domain experts’ needs for publishable network  figures. We demonstrate the relevance of our approach for  neuroscience by exploring social-behavior and memory/learning  functional neuroanatomy in mice.},
}