Issue No. 01 - Jan. (2017 vol. 23)
Xinsong Yang , Chinese Academy of Sciences, SKLCSInstitute of Software
Lei Shi , Chinese Academy of Sciences, SKLCSInstitute of Software
Madelaine Daianu , Imaging Genetics CenterMark & Mary Stevens Institute for Neuroimaging & InformaticsUniversity of Southern California
Hanghang Tong , School of Computing, Informatics and Decision Systems EngineeringArizona State University
Qingsong Liu , Chinese Academy of Sciences, SKLCSInstitute of Software
Paul Thompson , Imaging Genetics CenterMark & Mary Stevens Institute for Neuroimaging & InformaticsUniversity of Southern California
Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.
Diffusion tensor imaging, Data visualization, Visual analytics, Diseases, Sociology
X. Yang, L. Shi, M. Daianu, H. Tong, Q. Liu and P. Thompson, "Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation," in IEEE Transactions on Visualization & Computer Graphics, vol. 23, no. 1, pp. 181-190, 2017.