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

2022

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

2021

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

2020

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

2019

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