Guo, YishiWang, YangFang, ShiaofenChao, HongyangSaykin, Andrew J.Shen, Li2016-07-012016-07-012012Guo, Y., Wang, Y., Fang, S., Chao, H., Saykin, A. J., & Shen, L. (2012). Pattern Visualization of Human Connectome Data. Eurographics/IEEE VGTC Symposium on Visualization : EUROVIS : [proceedings] / Co-Sponsored by IEEE-CS Visualization and Graphics Technical Committee and Eurographics Association in Cooperation with ACM SIGGRAPH. Eurographics/IEEE VGTC S., 2012, 78–83. http://doi.org/10.2312/PE/EuroVisShort/EuroVisShort2012/078-083https://hdl.handle.net/1805/10283The human brain is a complex network with countless connected neurons, and can be described as a "connectome". Existing studies on analyzing human connectome data are primarily focused on characterizing the brain networks with a small number of easily computable measures that may be inadequate for revealing complex relationship between brain function and its structural substrate. To facilitate large-scale connectomic analysis, in this paper, we propose a powerful and flexible volume rendering scheme to effectively visualize and interactively explore thousands of network measures in the context of brain anatomy, and to aid pattern discovery. We demonstrate the effectiveness of the proposed scheme by applying it to a real connectome data set.en-USPublisher PolicyBrainConnectomeBrain AnatomyPattern DiscoveryConference proceedings