Overview of Research

From its historical underpinnings in the social sciences, network analysis is now a central component of data science research, playing important roles in academic, industry and government sectors. Networks are, for example, widely used in the study of cognitive neuroscience, the analysis of genome wide association, as well as the understanding of social dynamics. In government, networks are often used for analyzing international trade, conflict, and suspicious intelligence groups. Networks also play a key role in the development, analysis, and monetization of large social media sites like Twitter, LinkedIn, Facebook, and Google, as well as health monitoring technology for digital phenotyping on wearables and cellular devices.

Generally speaking, my research focuses on the development of scalable and interpretable techniques to analyze complex network data. My work generally falls into four scientific themes:

  • Network modeling and analysis of brain imaging data as it relates to aging, Alzheimer’s disease, depression, schizophrenia, behavior and personality.

  • Modeling and monitoring social dynamics, and trends on social media

  • Developing interpretable and scalable unsupervised learning algorithms

  • Building scalable models to evaluate and predict primary healthcare outcomes and environmental phenomena.

I am particularly interested in understanding the interplay between social dynamics and neuro-biological systems with aging, behavior and disease. For more information about my code or publications, see my Google Scholar Page or my Github Page.

Journal Publications

  1. Royse, S, Snitz, B, Hengenius, J., Huppert, T., Roush, R., Ehrenkranz, R., Wilson, J.D., Bertolet, M., Reese, A., Cisneros, G., Potopenko, K., Becker, J., Cohen, A., and Shaaban, E.C.. Unhealthy white matter connectivity in African American and non-Hispanic White older adults. (2023) Accepted at Alzheimer’s and Dementia.

  2. Parr, T., Hamrick, J., and Wilson, J.D. Nonparametric feature impact and importance. (2023) Accepted at Information Sciences.

  3. Torbati, M.E., Minhas, D.S., Laymon, C.M., Maillard, P., Wilson, J.D., Chen, C-L., Crainiceanu, C.M. DeCarli, C.S., Hwang, S.J. and Tudorascu, D. MISPEL: A deep learning approach for harmonizing multi-scanner matched neuroimaging data. (2023) Online at Medical Imaging Analysis.

  4. Rigdon, S., Stevens, N., Wilson, J.D., and Woodall, W. First to Signal Criterion for Comparing Control Chart Performance. (2023) Online at Quality Engineering.

  5. Wilson, J.D., Gerlach, A., Aizenstein, H., and Andreescu, C. Sex matters: Acute functional connectivity changes as markers of remission in late-life depression differ by sex. (2023) Online at Molecular Psychiatry.

  6. Bloch-Salisbury, E., Wilson, J.D., Rodriguez, R., Bruch, T., McKenna, L., Derbin, M., Glidden, B., Ayturk, D., Aurora, S., Yanowitz, T., Barton B., Vining, M., Beers, S., and Bogen, D. (2023) Efficacy of a vibrating crib mattress to reduce pharmacological treatment in opioid-exposed newborns: A randomized clinical trial. Online at JAMA Pediatrics.

  7. Mayeli, A, LaGoy, A, Smagula, S, Wilson, J.D., Zarbo, C., Rocchetti, M, Starace, F., Zamparini, M, Casiraghi, L, Calza, S, Rota, M., de Girolamo, G., and Ferrarelli, F. (2023) Shared and distinct abnormalities in residential and outpatient Schizophrenia Spectrum Disorder patients. Online at Molecular Psychiatry.

  8. Bagautdinova, J., Mayeli, A., Wilson, J.D., Donati, F., Colacot, R., Meyer, N., F-P., Paolo, and Ferrareli, F. (2022) Sleep abormalities in different clinical stages of psychosis: A systematic review and meta-analysis. Online at JAMA Psychiatry.

  9. Fish, K. N., Rocco, B.R., Wilson, J.D., and Lewis, D.A. (2022) Laminar-specific alterations in calbindin-positive boutons in the prefrontal cortex of subjects with schizophrenia. Online at Biological Psychiatry.

  10. Mayeli, A., Sonnenschein, S. F., Yushmanov, V., Wilson, J. D., Blazer, A., Perica, M., Calabro, F. J., Luna, B., Hetherington, H. P., Sarpal, D., and Ferrarelli, F. Dorsolateral Prefrontal Cortex GABA/Glutamate Abnormalities in Clinical High Risk and First-Episode Schizophrenia: a 7-T Magnetic Resonance Spectroscopic Imaging Study. International Journal of Molecular Sciences 23 (24) 15846.

  11. Mayeli, A., Wilson, J.D., Donati, F.L., LaGoy, A. and Ferrarelli, F. (2022) Sleep Spindle Alterations Relate to Working Memory Deficits in Individuals at Clinical High Risk for Psychosis. Sleep 45, 11.

  12. Yang, S., Wilson, J. D., Lu, Z-L, and Cranmer, S. J. (2022) Functional Connectivity Signatures of Political Ideology. PNAS Nexus 1.3 (2022): pgac066. [Manuscript]

  13. Sarpal, D. K., Blazer, A., Wilson, J. D., Calabro, F. J., Foran, W., Kahn, C. E., Luna, B., and Chengappa, K. N. R. (2022) Relationship between Plasma Clozapine/N-Desmethylclozapine and Changes in Basal Forebrain-Dorsolateral Prefrontal Cortex Coupling in Treatment-Resistant Schizophrenia. Schizophrenia Research 273, 170 - 177. [Manuscript]

  14. Kent, D., Cranmer, S., and Wilson J.D. (2022) A permutation-based changepoint technique for monitoring effect sizes. Political Analysis, 30(2), 167-178. [Manuscript]

  15. Yu, L., Zwetsloot, I. M., Stevens, N. T., Wilson, J. D., and Tsui, K. L. (2022) Monitoring dynamic networks: a simulation-based strategy for comparing monitoring methods and a comparative study. Quality and Reliability Engineering International, 38 (3), 1226-1250. [Manuscript]

  16. Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Parker, P., Paynabar, K. Perry, M. B., Reisi-Gahrooei, M., Sengupta, S., and Sparks, R. (2021) Research in network monitoring: Connections with SPM and new directions. Quality Engineering 33(4), 736 - 748 .

  17. Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Parker, P., Paynabar, K. Perry, M. B., Reisi-Gahrooei, M., Sengupta, S., and Sparks, R. (2021) Foundations of network monitoring: Definitions and applications. Quality Engineering 33(4), 719 - 730.

  18. Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Parker, P., Paynabar, K. Perry, M. B., Reisi-Gahrooei, M., Sengupta, S., and Sparks, R. (2021) Broader impacts of network monitoring: Its role in government, industry, technology, and beyond. Quality Engineering 33(4) 749 - 757.

  19. Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., Parker, P., Paynabar, K. Perry, M. B., Reisi-Gahrooei, M., Sengupta, S., and Sparks, R. (2021) The interdisciplinary nature of network monitoring: Advantages and disadvantages. Quality Engineering 33(4) 731 - 735.

  20. Stevens, N. T. and Wilson, J. D. (2021) The past, present, and future of network monitoring: A panel discussion. Quality Engineering 33(4), 715 - 718.

  21. Parr, T. and Wilson, J. D. (2021) A stratification approach to partial dependence for codependent variables. Machine Learning with Applications, 6, 100146. [Manuscript]

  22. Wilson, J. D., Baybay, M., Sankar, R., and Stillman, P. (2021) Analysis of Population Functional Connectivity Data via Multilayer Network Embeddings. Network Science, 9(1), 99 - 122. [Manuscript]

  23. Siegel, S.R., True, L., Pfeiffer, K.A., Wilson, J.D., Martin, E.M., Branta, C.F., Pacewicz, R., and Battista, R.A. (2021). Recalled age at menarche: A follow-up to the Michigan State University Motor Performance Study. Measurement in Physical Education and Exercise Science, 25(1), 78-86. [Manuscript]

  24. Lee, J., Li, G., and Wilson, J. D. (2020) Varying-coefficient models for dynamic networks. Computational Statistics and Data Analysis, 152, 107052. [Manuscript]

  25. Houghton, I.A., and Wilson, J.D. (2020) El Nino detection via unsupervised clustering of Argo temperature profiles. Journal of Geophysical Research - Oceans, 125(9) e2019JC015947 [Manuscript]

  26. Wilson, J. D., Cranmer, S. J., and Lu, Z.-L. (2020). A hierarchical latent space network model for population studies of functional connectivity. Computational Brain and Behavior, 3, 384-399. [Manuscript]

  27. Stillman, P. E., Wilson, J. D., Denny, M. J., Desmarais, B. A., Cranmer, S. J., and Lu, Z.-L. (2019). A consistent organizational structure across multiple functional subnetworks of the human brain. NeuroImage, 197, 24 - 36. [Manuscript]

  28. Wilson, J. D., Stevens, N. T., and Woodall, W. H. (2019). Modeling and detecting change in temporal networks via the degree corrected stochastic block model. Quality and Reliability Engineering International, 35(5):1363-1378. [Manuscript]

  29. Sparks, R. and Wilson, J. D. (2019). Monitoring communication outbreaks among an unknown team of actors in dynamic networks. Journal of Quality Technology: 51(4), 353-374. [Manuscript]

  30. Wilson, J.D. (2019) Discussion on “Real-time monitoring of events applied to syndromic surveillance". Quality Engineering: 31(1), 91-96. [Manuscript]

  31. Jeske, D. R., Stevens, N. T., Tartakovsky, A. G., and Wilson, J. D. (2018). Statistical methods for network surveillance. Applied Stochastic Models in Business and Industry, 34(4):425 - 445. [Manuscript]

  32. Stillman, P. E., Wilson, J. D., Denny, M. J., Desmarais, B. A., Bhamidi, S., Cranmer, S. J., and Lu, Z.- L. (2017). Statistical modeling of the default mode brain network reveals a segregated highway structure. Scientific reports, 7(1):11694. [Manuscript]

  33. Woodall, W. H., Zhao, M. J., Paynabar, K., Sparks, R., and Wilson, J. D. (2017). An overview and perspective on social network monitoring. IISE Transactions, 49(3):354-365. [Manuscript]

  34. Wilson, J. D., Denny, M. J., Bhamidi, S., Cranmer, S. J., and Desmarais, B. A. (2017). Stochastic weighted graphs: Flexible model specification and simulation. Social Networks, 49:37 - 47. [Manuscript]

  35. Wilson, J. D., Palowitch, J., Bhamidi, S., and Nobel, A. B. (2017). Community extraction in multilayer networks with heterogeneous community structure. The Journal of Machine Learning Research, 18(1):5458-5506. [Manuscript]

  36. Szekely, E., Pappa, I., Wilson, J.D., Bhamidi, S., Jaddoe, V., Verhulst, H.T., and Shaw, P. (2016) Childhood peer network characteristics: genetic influences and links with early mental health trajectories. Journal of Child Psychology and Psychiatry 57(6), 687 - 694. [Manuscript]

  37. Parker, K.S., Wilson, J.D., Marschall, J., Mucha, P.J., and Henderson, J.P. (2015) Network analysis reveals sex- and antibiotic resistance-associated antivirulence targets in clinical uropathogens. American Chemical Society: Infectious Diseases 1(11), 523 - 532. [Manuscript]

  38. Wilson, J. D., Wang, S, Mucha, P. J., Bhamidi, S. and Nobel, A.B. (2014) A testing based extraction algorithm for identifying significant communities in networks. Annals of Applied Statistics, 8(3):1853-1891. [Manuscript]

Other Publications

  1. Jeske, D. R., Stevens, N. T., Tartakovsky, A. G., and Wilson, J. D. (2018). Statistical network surveillance. Wiley StatsRef-Statistics Reference Online.

  2. Wilson, J. D., Bhamidi, S. and Nobel, A.B. (2013) Measuring the statistical significance of local connections in directed networks. Neural Information Processing Systems Workshop on Frontiers of Network Analysis: Methods, Models and Applications.

Under review or Revisions

  1. WIlson, J.D. and Lee, J. Interpretable network representation learning with principal component analysis. Preprint available here.

  2. Gibbs, C., Fosdick, B., and Wilson, J.D. ECoHeN: A Hypothesis Testing Framework for Extracting Communities from Heterogeneous Networks.

Funding

My research has been funded by the National Science Foundation, National Institutes of Health (NIH, NIMH, NIA), and the University of Pittsburgh Alzheimer’s Disease Research Center. Below I list my current and past funding.

Ongoing

Completed

NIH Funding:

  • NIA P30 AG066468: Network modeling of functional connectivity trajectories for Alzheimer’s disease. (PI: James Wilson) 3/1/2022 - 2/28/2023. Total Direct Costs: $75,000.

    Role: Principal Investigator 12% effort

  • NIA R01 AG055389-05: Evaluation of Brain and Cognitive Changes in Older Adults with MCI Taking Lithium to Prevent Alzheimer Type Dementia (PI: Ariel Gildengers) 7/1/2021 - 5/31/2022. Total Direct Costs: $462,485. 

    Role: Co-Investigator 5% effort.

  • NIMH R01 MH113827-05: Characterize Differences in Sleep Spindles between Clinical High Risk and Healthy Controls Longitudinally (PI: Fabio Ferrarelli) 7/1/2021 - 5/31/2022. Total Direct Costs: $279,688.

    Role: Co-Investigator 7.5% effort.

  • NIA R01 AG063752-03: Statistical Methods to Improve Reproducibility and Reduce Technical Variability in Heterogeneous Multimodal Neuroimaging Studies of Alzheimer’s Disease (PI: Dana Tudorascu) 7/1/2021 - 4/30/2024. Total Direct Costs: $418,285. 

    Role: Co-Investigator 20% effort.

  • NIA RF1 AG025516-12A1: Roles of Gray Matter Brain Aging and Small Vessel Disease in AD Pathophysiology (PIs: Howard Aizenstein and William Klunk) 7/1/2021 - 2/29/2024. Total Direct Costs: $4,209,148. 

    Role: Co-Investigator 5% effort.

  • NIDA R01 DA042074-06: Stochastic Vibrotactile Stimulation: A Non-pharmacological Intervention for Abstinence and Drug Withdrawal in Newborn Infants (PI: Elisabeth Salisbury) 7/1/2021 - 6/30/2022. Total Direct Costs: $200,703.  Role: Co-Investigator 40% effort.

NSF Funding:

  • NSF DMS - 1830547: Spatio-Temporal Data Analysis with Dynamic Network Models (PIs: Subhadeep Paul, Kevin Xu, James D. Wilson) 8/1/2018 - 7/30/2021. Total Direct Costs: $200,000.

    Role: Principal Investigator.

  • NSF DMS - 1841307: The Annual Data Institute Conference (PIs: James D. Wilson and David Uminsky) 3/1/2019. Total Direct Costs: $15,000.

    Role: Principal Investigator.

  • NSF DMS -2310950: The Data Institute Conference (James D. Wilson) 3/1/2023. Total Direct Costs: $15,000.

    Role: Principal Investigator.