publications

2023

  1. A brain-wide risk score for psychiatric disorder evaluated in a large adolescent population reveals increased divergence among higher-risk groups relative to controls
    Weizheng Yan, Godfrey Pearlson, Zening Fu, Xinhui Li, Armin Iraji, Jiayu Chen, Jing Sui, Nora Volkow, and Vince Calhoun
    Biological Psychiatry 2023
    Paper
  2. Multimodal subspace independent vector analysis captures latent subspace structures in large multimodal neuroimaging studies
    Xinhui Li, Tulay Adali, Rogers Silva, and Vince Calhoun
    bioRxiv 2023
    Paper
  3. Predictive Sparse Manifold Transform
    Yujia Xie, Xinhui Li, and Vince Calhoun
    Workshop on High-dimensional Learning Dynamics (HLD), International Conference on Machine Learning (ICML) 2023
    Paper
  4. Learning pipeline-invariant representation for robust brain phenotype prediction
    Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, and Vince Calhoun
    Data-centric Machine Learning Research (DMLR) Workshop, International Conference on Machine Learning (ICML) 2023
    Paper
  5. Align with the NMIND consortium for better neuroimaging
    Gregory Kiar, Jon Clucas, Eric Feczko, Mathias Goncalves, Dorota Jarecka, Christopher Markiewicz, Yaroslav Halchenko, Robert Hermosillo, Xinhui Li, Oscar Miranda-Dominguez, Satrajit Ghosh, Russell Poldrack, Theodore Satterthwaite, Michael Milham, and Damien Fair
    Nature Human Behaviour 2023
    Paper Website
  6. Multimodal subspace independent vector analysis better captures hidden relationships in multimodal neuroimaging data
    Xinhui Li, Tulay Adali, Rogers Silva, and Vince Calhoun
    In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) 2023
    Paper Code
  7. Evaluating trade-offs in IVA of multimodal neuroimaging using cross-platform multidataset independent subspace analysis
    Xinhui Li, Daniel Khosravinezhad, Vince Calhoun, and Rogers Silva
    In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) 2023
    Paper Code

2022

  1. Pipeline-Invariant Representation Learning for Neuroimaging
    Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, and Vince Calhoun
    Machine Learning for Health (ML4H) 2022
    Paper
  2. Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder
    Xinhui Li, Eloy Geenjaar, Zening Fu, Sergey Plis, and Vince Calhoun
    In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022
    Paper
  3. The Connected Brain: OHBM Brain-Art SIG 2022
    SIG OHBM Brain-Art, Livio Tarchi, Mirco Marino, Robin Gutzen, Lena Oestreich, and Zoltan Nagy
    2022
    Catalog

2021

  1. Moving Beyond Processing and Analysis-Related Variation in Neuroscience
    Xinhui Li, Nathalia Bianchini Esper, Lei Ai, Steve Giavasis, Hecheng Jin, Eric Feczko, Ting Xu, Jon Clucas, Alexandre Franco, Anibal Sólon Heinsfeld, Azeez Adebimpe, Joshua Vogelstein, Chao-Gan Yan, Oscar Esteban, Russell Poldrack, Cameron Craddock, Damien Fair, Theodore Satterthwaite, Gregory Kiar, and Michael Milham
    bioRxiv 2021
    Paper Code Talk Tutorial
  2. Direct linkage detection with multimodal IVA fusion reveals markers of age, sex, cognition, and schizophrenia in large neuroimaging studies
    Rogers Silva, Eswar Damaraju, Xinhui Li, Peter Kochunov, Aysenil Belger, Judith M. Ford, Sarah McEwen, Daniel H. Mathalon, Bryon A. Mueller, Steven G. Potkin, Adrian Preda, Jessica A. Turner, Theo G.M. Erp, Tulay Adali, and Vince D. Calhoun
    bioRxiv 2021
    Paper
  3. U-net model for brain extraction: Trained on humans for transfer to non-human primates
    Xindi Wang, Xinhui Li, Jae Wook Cho, Brian E. Russ, Nanditha Rajamani, Alisa Omelchenko, Lei Ai, Annachiara Korchmaros, Stephen Sawiak, R. Austin Benn, Pamela Garcia-Saldivar, Zheng Wang, Ned H. Kalin, Charles E. Schroeder, R. Cameron Craddock, Andrew S. Fox, Alan C. Evans, Adam Messinger, Michael P. Milham, and Ting Xu
    NeuroImage 2021
    Paper Code
  4. Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans
    Kaveri A. Thakoor, Xinhui Li, Emmanouil Tsamis, Zane Z. Zemborain, Carlos Gustavo De Moraes, Paul Sajda, and Donald C. Hood
    Translational Vision Science & Technology 2021
    Paper
  5. Toward Automatic Segmentation for Non-human Primates.
    Xinhui Li, Xindi Wang, Kathleen Mantell, Estefania Casillo Cruz, Michael Milham, Alex Opitz, and Ting Xu
    In 2nd International Workshop on Non-invasive Brain Stimulation 2021
    Poster

2020

  1. Evaluating the transferability of deep learning models that distinguish glaucomatous from non-glaucomatous OCT circumpapillary disc scans.
    Xinhui Li, Emmanouil Tsamis, Kaveri A. Thakoor, Zane Z. Zemborain, Carlos Gustavo De Moraes, and Donald C. Hood
    In Investigative Ophthalmology & Visual Science 2020
    abstract

2019

  1. Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks
    Kaveri A. Thakoor, Xinhui Li, Emmanouil Tsamis, Paul Sajda, and Donald C. Hood
    In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019
    Paper Code