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Research

Network science is interdisciplinary. Focusing on improving current analytical strategies, we 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.

Selected publications by research theme. For the complete list, see the Publications page.

Precision Neuroscience and Brain–Behavior Linking

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

  2. 2025 · Wang, S., Wang, Y., Xu, F. H., Shen, L., Zhao, Y., & Alzheimer's Disease Neuroimaging Initiative — Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling. Medical Image Analysis, 99, 103309. Code: ABCModel ↗

  3. 2025 · Zhang, X., Hulvershorn, L. A., Constable, T., Zhao, Y., & Wang, S. — Cost efficiency of fMRI studies using resting-state vs. task-based functional connectivity. Human Brain Mapping, 46(9), e70260.

  4. 2025 · Xu, W., Wang, S., Gao, S., Tian, X., Tan, C., Shen, X., Luo, W., Constable, T., Li, T., & Zhao, Y. — Supervised brain node and network construction under voxel-level functional imaging. Imaging Neuroscience, 3, IMAG.a.56.

Multi-omics and Transcriptomic Pathways in Brain Aging

  1. 2026 · Sharma, A., Cornelison, C. L., Altan, L., Chandra, K., Wen, Q., Nho, K., ... Saykin, A. J., Wu, Y.-C., & Wang, S. — Proteomics signature of whole-brain microstructural integrity of white matter using LatentSNA (Abstract). Alzheimer's & Dementia. Accepted for publication, December 2026.

  2. 2026 · Altan, L., & Wang, S. — Bayesian latent space co-evolution model to explain AD progression (ENAR 2026 Poster).

  3. 2026 · Cornelison, C. L., Sharma, A., Altan, L., Nho, K., Saykin, A. J., & Wang, S. — Plasma proteomics biomarkers that differentiate asymptomatic from symptomatic Alzheimer's disease (Abstract). Alzheimer's & Dementia. Accepted for publication, December 2026.

  4. 2025 · Powla, P. P., Jayaprakash, T., Cornelison, C. L., Sweet, T., Shen, L., Gao, S., Yu, M., Wu, Y.-C., Zhao, Y., Saykin, A. J., & Wang, S. — Sex differences in the diffusion of tau neurofibrillary tangles across functional brain networks (AAIC 2025 Abstract).

  5. 2024 · Tian, X., Wang, Y., Wang, S., Zhao, Y., & Zhao, Y. — Bayesian mixed model inference for genetic association under related samples with brain network phenotype. Biostatistics, 25(4), 1195–1209.

  6. 2024 · Wang, S., Wang, Y., Xu, F., Tian, X., Fredericks, C. A., Shen, L., Zhao, Y., & Alzheimer's Disease Neuroimaging Initiative — Sex-specific topological structure associated with dementia via latent space estimation. Alzheimer's & Dementia, 20(12), 8387–8401.

Network Science and Graphical AI

  1. 2026 · Lee, H., Sweet, T. M., & Wang, S. — A Multilevel Modeling Approach to Network Diffusion in Alzheimer's Disease (Abstract). Alzheimer's & Dementia. Accepted for publication, December 2026.

  2. 2025 · Sweet, T., & Wang, S. — Network science in psychology. Psychological Methods.

  3. 2025 · Cornelison, C. L., Wen, Q., Powla, P. P., Jayaprakash, T., Risacher, S., Saykin, A. J., Wu, Y.-C., & Wang, S. — Sex differences in structural connectivity-based AD pathology in the Indiana Memory and Aging cohort (AAIC 2025 Abstract).

  4. 2025 · Jayaprakash, T., Cornelison, C. L., Powla, P. P., Zhang, L., Fredericks, C. A., Saykin, A. J., & Wang, S. — Deep learning framework for characterizing tau-PET spatial heterogeneity along the AD spectrum: A self-supervised approach (Abstract). Alzheimer's & Dementia, 21, e109949.

  5. 2024 · Qiu, W., Chu, H., Wang, S., Zuo, H., Li, X., Zhao, Y., & Ying, R. — Learning high-order relationships of brain regions. In Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR 235, 41582–41603.

  6. 2022 · Wang, S., & Edgerton, J. — Resilience to stress in bipartite networks: Application to the Islamic State recruitment network. Journal of Complex Networks, 10(4), cnac017.

Multi-scale and Multimodal Data Integration

  1. 2026 · Osei, G., Gao, S., Saykin, A. J., & Wang, S. — Detecting Early Cognitive Change using Cognitive Change Index in the Indiana Memory and Aging cohort (Abstract). Alzheimer's & Dementia. Accepted for publication, December 2026.

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

  3. 2025 · Wang, S. — Foundational generative embedding models for neuromolecular networks in Alzheimer's Disease (AAIC 2025 Poster).

  4. 2025 · Li, C., Tian, X., Gao, S., Wang, S., Wang, G., Zhao, Y., & Zhao, Y. — Bayesian longitudinal network regression with application to brain connectome genetics. Statistics in Medicine, 44(8-9), e70069.

  5. 2024 · Xu, W., Wang, S., Shen, L., & Zhao, Y. — Collaborative survival analysis on predicting Alzheimer's disease progression. Statistics in Biosciences, 18(3), 797–820.

  6. 2023 · Wang, S., Paul, S., & De Boeck, P. — Joint latent space model for social networks with multivariate attributes. Psychometrika, 88(4), 1197–1227. Code: jlsm ↗