Journal

  • Smooth graph learning for functional connectivity estimation
    Siyuan Gao*, Xinyue Xia*, Dustin Scheinost, Gal Mishne
    NeuroImage, 2021
    Download: [link] [code]
  • Non-linear manifold learning in fMRI uncovers a low-dimensional space of brain dynamics
    Siyuan Gao, Gal Mishne, Dustin Scheinost
    Human Brain Mapping, 2021
    Download: [link] [code]
  • Using functional connectivity models to characterize relationships between working and episodic memory
    Gigi F. Stark, Emily W. Avery, Monica D. Rosenberg, Abigail S. Greene, Siyuan Gao, Dustin Scheinost, R. Todd Constable, Marvin M. Chun, Kwangsun Yoo
    Brain and Behavior, 2021
    Download: [link]
  • Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders
    Daniel S Barron, Siyuan Gao, Javid Dadashkarimi, Abigail S Greene, Marisa N Spann, Stephanie Noble, Evelyn M R Lake, John H Krystal, R Todd Constable, Dustin Scheinost
    Cerebral Cortex, 2021
    Download: [link]
  • A hitchhiker’s guide to working with large, open-source neuroimaging datasets
    Corey Horien, Stephanie Noble, Abigail S. Greene, Kangjoo Lee, Daniel S. Barron, Siyuan Gao, David O’Connor, Mehraveh Salehi, Javid Dadashkarimi, Xilin Shen, Evelyn M. R. Lake, R. Todd Constable & Dustin Scheinost
    Nature Human Behaviour, 2021
    Download: [link]
  • How tasks change whole-brain functional organization to reveal brain-phenotype relationships
    Abigail S Greene, Siyuan Gao, Stephanie Noble, Dustin Scheinost, R Todd Constable
    Cell Reports, 2020
    Download: [link]
  • Distributed patterns of functional connectivity predict working memory performance in novel healthy and memory-impaired individuals
    Emily W Avery, Kwangsun Yoo, Monica D Rosenberg, Abigail S Greene, Siyuan Gao, Duk L Na, Dustin Scheinost, Todd R Constable, Marvin M Chun
    Journal of Cognitive Neuroscience, 2020
    Download: [link]
  • Combining Multiple Connectomes Improves Predictive Modeling of Phenotypic Measures
    Siyuan Gao, Abigail Greene, R. Todd Constable, Dustin Scheinost
    NeuroImage, 2019
    Download: [link]
  • 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, 2019
    Download: [link]
  • Task-induced brain state manipulation improves prediction of individual traits
    Abigail Greene, Siyuan Gao, R. Todd Constable, Dustin Scheinost
    Nature Communications, 2018
    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

  • Poincaré embedding reveals edge-based functional networks of the brain
    Siyuan Gao, Gal Mishne, Dustin Scheinost
    International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020
    Download: [link]
  • Inference of Dynamic Graph Changes for Functional Connectome
    Dingjue Ji, Junwei Lu, Yiliang Zhang, Siyuan Gao, Hongyu Zhao
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
    Download: [link]
  • A Mass Multivariate, Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection of Group Differences
    Javid Dadashkarimi, Siyuan Gao, Erin Yeagle, Stephanie Noble, Dustin Scheinost
    3rd Workshop on Connectomics in NeuroImaging (CNI), 2019
    Download: [link]
  • 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
    Download: [link]
  • 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