title: Pattern Visualization of Human Connectome Data.
abstract: The 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.
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, 950 W Walnut St R2 E124, Indianapolis, IN 46202, USA.
authors.forename: Yishi
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Computer and Information Science, Purdue University School of Science, 723 W. Michigan St SL280, Indianapolis, IN 46202, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Software, Sun Yat-Sen University, 132 Waihuandong Road, Guangzhou, Guangdong 510006, China.
authors.forename: Shiaofen
authors.initials: S
authors.lastname: Fang
authors.forename: Hongyang
authors.initials: H
authors.lastname: Chao
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Saykin
authors.forename: Li
authors.initials: L
authors.lastname: Shen