Journal

  • Combining Multiple Connectomes Improves Predictive Modeling of Phenotypic Measures
    Siyuan Gao, Abigail Greene, R. Todd Constable, Dustin Scheinost
    NeuroImage.
    Download: [In press]
  • Ten Simple Rules for Predictive Modeling of Individual Differences in Neuroimaging
    Dustin Scheinost, Stephanie Noble, Corey Horien, Abigail S Greene, Evelyn Lake, Mehraveh Salehi, Siyuan Gao, Xilin Shen, David O'Connor, Daniel S Barron, Sarah W Yip, Monica D Rosenberg, R. Todd Constable
    NeuroImage.
    Download: [link]
  • Task-induced brain state manipulation improves prediction of individual traits
    Abigail Greene, Siyuan Gao, R. Todd Constable, Dustin Scheinost
    Nature Communications.
    Download: [link]
  • RCLens: Interactive Rare Category Exploration and Identification
    Hanfei Lin, Siyuan Gao, David Gotz, Fan Du, Jingrui He, Nan Cao
    IEEE Transactions on Visualization and Computer Graphics, 2017 (oral presentation).
    Download: [link] [slides]
  • Adaptively Exploring Population Mobility Patterns in Flow Visualization
    Fei Wang, Wei Chen, Ye Zhao, Tianyu Gu, Siyuan Gao, Hujun Bao
    IEEE Transactions on Intelligent Transportation Systems, 2017.
    Download: [link]

Conference

  • Combining Multiple Behavioral Measures and Multiple Connectomes via Multiway Canonical Correlation Analysis
    Siyuan Gao, Xilin Shen, Todd Constable, Dustin Scheinost
    International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
  • A hierarchical manifold learning framework for high-dimensional brain imaging data
    Siyuan Gao, Gal Mishne, Dustin Scheinost
    International Conference on Information Processing in Medical Imaging (IPMI), 2019.
    Download: [link] [code]
  • Hierarchical nonlinear embedding reveals brain states and performance differences during working memory tasks
    Siyuan Gao, Gal Mishne, Dustin Scheinost
    Conference on Cognitive Computational Neuroscience (CCN), 2018.
    Download: [link]
  • Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models
    Siyuan Gao, Abigail Greene, R. Todd Constable, Dustin Scheinost
    International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018
    Download: [link] [code]
  • Task‐induced brain state manipulation improves prediction of individual traits
    Abigail Greene, Siyuan Gao, R. Todd Constable, Dustin Scheinost
    Organization for Human Brain Mapping (OHBM) Annual Meeting, Singapore, 2018 (poster)
  • Task Integration For Connectome-based Prediction Via Canonical Correlation Analysis
    Siyuan Gao, Abigail Greene, R. Todd Constable, Dustin Scheinost
    IEEE International Symposium on Biomedical Imaging (ISBI), 2018
    Download: [link]
  • Brain state perturbation improves connectome-based predictive modeling of related behaviors
    Abigail Greene, Siyuan Gao, R. Todd Constable, Dustin Scheinost
    Society for Neuroscience(SfN), 2017.
  • Connectome-based predictive modeling: the impact of brain state and sex in a developmental cohort
    Abigail Greene, Siyuan Gao, R. Todd Constable, Dustin Scheinost
    Flux Congress, 2017.

Talk & Presentation

  • Department of Computer Science, University of Electronic Science and Technology of China,
    Chengdu, China, Jan 15, 2018
  • IEEE Visualization Conference (VIS 2017),
    Phoenix AZ, Oct 5, 2017