Research

Network science is interdisciplinary. Focusing on improving current analytical strategies, we try to absorb influences of other disciplines to inform and improve understandings about human behaviors with applications in neuroscience, psychiatry, psychology and others. Broadly speaking, we model real-world networks with statistically sound principles and methods—reduce network complexity, perform intuitive visualization and test critical hypotheses with improved statistical power and controlled type I errors. We are particularly interested in the topological structures of networks and the roles they play in (neuro)degeneration, (neuro)development and resilience.

Brain Networks

Wang, S., Zhang, X., Liu, Y., Xu, W., Tian, X., & Zhao, Y. (2026). Latent space-based network analysis for brain–behavior linking in neuroimaging. Nature Methods, 23(1), 225–235.

Zhang, X., Hulvershorn, L. A., Constable, T., Zhao, Y., & Wang, S. (2025). Cost efficiency of fMRI studies using resting-state vs. task-based functional connectivity. Human Brain Mapping, 46(9), e70260.

Xu, W., Wang, S., Gao, S., Tian, X., Tan, C., Shen, X., Luo, W., Constable, T., Li, T., & Zhao, Y. (2025). Supervised brain node and network construction under voxel-level functional imaging. Imaging Neuroscience, 3, IMAG.a.56.

Wang, S., Wang, Y., Xu, F. H., Shen, L., Zhao, Y., & Alzheimer’s Disease Neuroimaging Initiative. (2025). Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling. Medical Image Analysis, 99, 103309.

Wang, S., Wang, Y., Xu, F., Tian, X., Fredericks, C. A., Shen, L., Zhao, Y., & Alzheimer’s Disease Neuroimaging Initiative. (2024). Sex-specific topological structure associated with dementia via latent space estimation. Alzheimer’s & Dementia, 20(12), 8387–8401.

Tian, X., Wang, Y., Wang, S., Zhao, Y., & Zhao, Y. (2024). Bayesian mixed model inference for genetic association under related samples with brain network phenotype. Biostatistics, 25(4), 1195–1209.

Wang, S., Sweet, T. M., & Paul, S. (2026). The co-varying ties between networks and item responses via latent variables. Psychometrika, 91(2), 641–668.

Sweet, T., & Wang, S. (2025). Network science in psychology. Psychological Methods.

Wang, S., Paul, S., & De Boeck, P. (2023). Joint latent space model for social networks with multivariate attributes. Psychometrika, 88(4), 1197–1227.

Bipartite Networks, Multivariate and Psychometric Analysis

Wang, S., De Boeck, P., & Yotebieng, M. (2023). Heywood cases in unidimensional factor models and item response models for binary data. Applied Psychological Measurement, 47(2), 141–154.

Wang, S., & Edgerton, J. (2022). Resilience to stress in bipartite networks: Application to the Islamic State recruitment network. Journal of Complex Networks, 10(4), cnac017.

Wang, S., & De Boeck, P. (2022). Understanding the role of subpopulations and reliability in between-group studies. Behavior Research Methods, 54(5), 2162–2177.

Topics I am currently working on

Network-based neuroimaging methodology development

Statistical network methods for brain–behavior linking, connectome-based inference, trajectory and normative modeling, interplay between functional and structural connectivity, brain-age research, imaging harmonization, and imaging genetics.

Alzheimer's disease, neurodegeneration, and resilience

Modeling the co-evolution of brain connectomes and pathological proteins, understanding individual differences in Alzheimer's disease progression, and identifying network mechanisms underlying cognitive resilience.

Multimodal neuroimaging and molecular data integration

Integrating neuroimaging with genetics, transcriptomics, proteomics, molecular and cellular information, and other biological biomarkers to understand the biological mechanisms underlying brain organization, aging, and neurodegeneration.

Statistical machine learning and AI

Generative and representation-learning methods for high-dimensional biomedical data, including deep learning, embedding, clustering, prediction versus inference, and interpretable statistical machine learning.

Higher-order and complex network modeling

Statistical methods for complex network structures, including hypergraphs, latent space models, network diffusion, and higher-order interactions among brain regions, biological systems, and behavioral outcomes.