title: Integrated Visualization of Human Brain Connectome Data.
abstract: Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases.
authors.affiliation: Computer and Information Science, Purdue University Indianapolis, IN, USA; Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
authors.forename: Huang
authors.initials: H
authors.lastname: Li
authors.affiliation: Computer and Information Science, Purdue University Indianapolis, IN, USA.
authors.forename: Shiaofen
authors.initials: S
authors.lastname: Fang
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
authors.forename: Joaquin
authors.initials: J
authors.lastname: Goni
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
authors.forename: Joey A
authors.initials: JA
authors.lastname: Contreras
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA; Electrical & Control Engineering, Heilongjiang Univ. of Science & Tech., China.
authors.forename: Yanhua
authors.initials: Y
authors.lastname: Liang
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA; College of Automation, Harbin Engineering University, China.
authors.forename: Chengtao
authors.initials: C
authors.lastname: Cai
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: West
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
authors.forename: Shannon L
authors.initials: SL
authors.lastname: Risacher
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Psychological and Brain Sciences, Indiana University Bloomington, IN, USA.
authors.forename: Olaf
authors.initials: O
authors.lastname: Sporns
authors.affiliation: Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Saykin
authors.affiliation: Computer and Information Science, Purdue University Indianapolis, IN, USA; Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Shen
keywords: Brain connectome; DTI; MRI; fMRI; visualization