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Pattern Visualization of Human Connectome Data.
Guo, Yishi; Wang, Yang; Fang, Shiaofen; Chao, Hongyang; Saykin, Andrew J; Shen, Li.
Afiliação
  • Guo Y; Radiology and Imaging Sciences, Indiana University School of Medicine, 950 W Walnut St R2 E124, Indianapolis, IN 46202, USA.
  • Wang Y; Computer and Information Science, Purdue University School of Science, 723 W. Michigan St SL280, Indianapolis, IN 46202, USA.
  • Fang S; School of Software, Sun Yat-Sen University, 132 Waihuandong Road, Guangzhou, Guangdong 510006, China.
Eurograph IEEE VGTC Symp Vis ; 2012: 78-83, 2012.
Article em En | MEDLINE | ID: mdl-26090521
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eurograph IEEE VGTC Symp Vis Ano de publicação: 2012 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eurograph IEEE VGTC Symp Vis Ano de publicação: 2012 Tipo de documento: Article País de afiliação: Estados Unidos