Publications

Journal Publications

Submitted

Sun, L., Han, X., Zhang, A., Joint Estimation of Multiple Graphical Models for an fMRI Study of Brain Connectivity Networks. Submitted to Statistical Methods in Medical Research.

Under Preparation

Zhang, A., Wang, Y., Hu, N., Ye, C. ConnectMVR: A Supervised Multimodal Brain Connectivity Analysis Tool for Predicting Clinical Outcomes and Identifying Associated Connectivity Patterns

Zhang, A., Pagliaccio, D., Marsh, R., Lee, S. Decoding Age specific Changes in Brain Functional Connectivity Using a Sliding window Based Clustering Method. bioRxiv , Code

2025

Qu, G., Zhou, Z., Calhoun, V., Zhang, A. , Wang, Y-P. Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks. Medical Image Analysis, 103, 103570. Code

2024

Sun, L., Zhang, A., Liang, F. Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing. Statistics in Medicine.

Zhang, A., Zhu, X., Wrengler, K., Horga, G., Goldberg, T., Lee, S. Altered hierarchical gradients of intrinsic neural timescales in mild cognitive impairment and Alzheimer's disease. Journal of Neuroscience.

Zhang, A., Zhang, G., Cai, B., Stephen, J.M., Wilson, T.W., Calhoun, V.D. and Wang, Y.P. A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence. Network Neuroscience. Code

Conference Publications

Mutu, D., Ji, K., He, X., Lee, S., Sequeira S. and Zhang, A. 2024, May. Associations Between Brain Connectivity and Psychiatric Symptoms in Children: Insights into Adolescent Mental Health. Student Presentation at 2024 Systems and Information Engineering Design Symposium (SIEDS)(pp. 36-41). IEEE.

Zhang, A., Jia, B. and Wang, Y.P., 2018, March. Tracking the development of brain connectivity in adolescence through a fast Bayesian integrative method. In Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications (Vol. 10579, p. 105790O). International Society for Optics and Photonics.

Zhang, A., Fang, J., Calhoun, V.D. and Wang, Y.P., 2018, April. High dimensional latent Gaussian copula model for mixed data in imaging genetics. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)(pp. 105-109). IEEE.

Zhang, A., Calhoun, V.D. and Wang, Y.P., 2019, March. Joint Gaussian copula model for mixed data with application to imaging epigenetics study of schizophrenia. In Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications (Vol. 10954, p. 109540R). International Society for Optics and Photonics.

Zhang, A., Zhang, G., Calhoun, V.D. and Wang, Y.P. (2020, March). Causal brain network in schizophrenia by a two-step Bayesian network analysis. In Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications (Vol. 11318, p. 1131817). International Society for Optics and Photonics.

Zhang, G., Zhang, A., Calhoun, V.D. and Wang, Y.P. (2020, February). A causal brain network estimation method leveraging Bayesian analysis and the PC algorithm. In Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 11317, p. 113170X). International Society for Optics and Photonics.