ArticleIEEE transactions on visualization and computer graphics2017
Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation.
Article in IEEE transactions on visualization and computer graphics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Neurosurgery Network Pattern Analysis with 2nd Generation Ensembles.Computer graphics forum : journal of the European Association for Computer Graphics · 2026Article
- Effective data visualization strategies in untargeted metabolomics.Natural product reports · 2025Review
- Exploration of resting-state brain functional connectivity as preclinical markers for arousal prediction in prolonged disorders of consciousness: A pilot study based on functional near-infrared spectroscopy.Brain and behavior · 2024Article
- Visualization of Neuronal Structures in Wide-Field Microscopy Brain Images.IEEE transactions on visualization and computer graphics · 2018Article
- A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging.Brain informatics · 2018Review
- Brain explorer for connectomic analysis.Brain informatics · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
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