Visual analytics of brain networks

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging.

Authors

• Kaiming Li
• Lei Guo
• Carlos Faraco
• Dajiang Zhu
• Hanbo Chen
• Yixuan Yuan The Chinese University of Hong Kong
• Jinglei Lv
• Fan Deng
• Xi Jiang
• Tuo Zhang Department of Computer Science, University of Birmingham
• Xintao Hu School of Automation, Northwestern Polytechnical University
• Degang Zhang
• L. Stephen Miller
• Tianming Liu

Keywords

• Multimodal neuroimaging
• Joint modeling
• Visual analytics
• Visualization and interaction
• Brain networks