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Publications

• Sukoff Rizzo, SJ, Homanics, G, Schaeffer, D, Schaeffer, L, Park, JE, Oluoch, J, Zhang, T, Haber, A, Seyfried, N, Patten, B, Greenwood, A, Murai, T, Choi, SH, Huhe, H, Kofler, J, Strick, PL, Carter, GW, and Silva, AC (2023). Bridging the Rodent to Human Translational Gap: Marmosets as Model Systems for the Study of Alzheimer's disease. Alzheimer's & Dementia: Translational Research & Clinical Interventions. Under review.



• Li, S, Wang, Peng, L, Tudorascu, DL, Yan, G, and Zhang, T (2023). Whole-Brain Directed Network Analysis of fMRI Data. Under review.



• Sun, Y, Li, J, Xu, Y, Zhang, T, and Wang, X (2023). Deep Learning versus Conventional Statistical Methods for Missing Data Imputation: A Comparative Study. Expert Systems with Applications, accepted.



• Wang, Y, Yan G, Wang, X, Li, S, Peng, L, Tudorascu, DL, and Zhang, T (2023). A Variational Bayes Approach to Identifying Whole-Brain Directed Networks Using fMRI Data. The Annals of Applied Statistics, 17, 518-538.



• Hu, SS, Liu, L, Li, Q, Ma, W, Guertin, MJ, Meyer, CA, Deng, K, Zhang, T, and Zang, C (2022). Accurate estimation of intrinsic biases for improved analysis of bulk and single-cell chromatin accessibility sequencing data using SELMA. Nature Communications， 13(1):5533.



• Wang, Y, Yan G, Tanabe, S, Liu, C, Moosa, S, Quigg, M, and Zhang, T (2022). High- Dimensional Directional Brain Network Analysis for Focal Epileptic Seizures. arXiv:2208.07991.



• Corliss, BA, Brown, TR, Zhang, T, Janes, KA, Shakeri, H, and Bourne, PE (2022). The most difference in means: A statistic for the strength of null and near-zero results. arXiv:2201.01239.



• Li, H, Wang, Y, Yan G, Sun, Y, Tanabe, S, Liu, C, Quigg, M, and Zhang, T (2021). A Bayesian State-Space Stochastic Block Model for Mapping Epileptic Brain Networks. Journal of the American Statistical Association, 116(536):1637-1647.



• Li, H, Wang, Y, Tanabe, S, Sun, Y, Yan, G, Quigg, M, and Zhang, T (2021). Mapping Epileptic Directional Brain Networks Using Intracranial EEG Data. Biostatistics, 22(3):613-628.



• Zhang, T, Pham, M, Yan, G, Wang, Y, Medina-Devilliers, S, and Coan, JA (2021). Spatial-Temporal Analysis of Multi-Subject Functional Magnetic Resonance Imaging Data. Econometrics and Statistics, https://doi.org/10.1016/j.ecosta.2021.02.006.



• Corliss, BA, Doty, R, Matthews, C, Rohde, G, Yates, P, Zhang, T, Peirce, SM (2020). REAVER: A Program for Improved Image Analysis and Quantification of Vascular Networks Through Multigroup Analysis of Accuracy and Precision. Microcirculation, 27(5):e12618.



• Zhang, T, Sun, Y, Li, H, Yan, G, Tanabe, S, Miao, R, Wang, Y, Caffo, B, and Quigg, M (2020). Bayesian Inference of a Directional Brain Network Model for Intracranial EEG Data. Computational Statistics and Data Analysis, 144:106847.



• Zhang, T, Pham, M, Sun, J, Yan, G, Gonzalez, MZ, and Coan, JA (2018). A Low-Rank Multivariate General Linear Model for Multi-Subject fMRI Data and a Non-Convex Optimization Algorithm for Brain Response Comparison. NeuroImage, 173: 580-591.



• Xu, P, Zhang, T, and Gu, Q (2017). Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). Fort Lauderdale, Florida, USA.



• Zhang, T, Yin, Q, Caffo, B, Sun, Y, and Boatman-Reich, D (2017). Bayesian Inference of High-Dimensional, Cluster-Structured Ordinary Differential Equation Models with Applications to Brain Connectivity Studies. The Annals of Applied Statistics, 11(2): 868-897.



• Zhang, T, Shen, H, and Li, F (2016). Linear and non-linear models for fMRI time series analysis. In Ombao, H, Lindquist, M, Thompson, W, and Aston, J ed.. Handbook of Modern Statistical Methods: Neuroimaging Data Analysis. Chapman and Hall/CRC.



• Li, F*,Zhang, T*, Wang, Q, Gonzalez, MZ, Maresh, EL, and Coan, JA (2015). Spatial Bayesian variable selection and grouping in high-dimensional scalar-on-image regressions. The Annals of Applied Statistics, 9:687-713.
*Equally contributing authors.


• Zhang, T, Wu, J, Li, F, Caffo, B, and Boatman-Reich, D (2015). Dynamic directional model for effective brain connectivity using Electrocorticographic (ECoG) time series. Journal of the American Statistical Association, 110: 93-106.



• Zhang, T, Li, F, Gonzalec, MZ, Maresh, EL, and Coan, JA (2014). A semi-parametric nonlinear model for event-related fMRI. NeuroImage, 97: 178-187.



• Zhang, T, Li, F, Beckes, L, and Coan, JA (2013). A semi-parametric model of the hemodynamic response for multi-subject fMRI data. NeuroImage， 75:136-145.



• Zhang, T, Li, F, Beckes, L, Brown, C, and Coan, JA (2012). Nonparametric inference of hemodynamic response for multi-subject fMRI data. NeuroImage， 63:1754-1765.



• Zhong, W, Zhang, T, Zhu, Y, and Liu, JS (2012). Correlation pursuit: variable selection beyond linear regression. Journal of Royal Statistical Society Series B, 74:849-970.



• Zhang, T and Liu, JS (2012). Nonparametric hierarchical Bayes analysis of binomial data via Bernstein polynomial priors. The Canadian Journal of Statistics, 40:328-344.



• Zhang, T and Kou, S (2010). Nonparametric inference of doubly stochastic Poisson process via kernel method. The Annals of Applied Statistics, 4:1913-1941.



• Shedlock, AM, Botka, CW, Zhao, S, Shetty, J, Zhang, T, Liu, J, Deschavanne, PJ, and Edwards, SV (2007). Phylogenomics of nonavian reptiles and the structure of the ancestral amniote genome. Proceedings of National Academy of Science (USA), 104(8):2767-2772.