Faculty Profile

F. DuBois Bowman

F. DuBois Bowman, PhD

  • Dean, School of Public Health
  • Roderick J. Little Collegiate Professor of Biostatistics
A renowned expert in the statistical analysis of large complex data sets, F. DuBois Bowman joined the University of Michigan School of Public Health as dean in 2018. Under Dr. Bowman’s leadership, Michigan Public Health launched a school-wide interdisciplinary research initiative pursuing innovative solutions to prevent firearm injuries, create healthy and equitable cities, control infectious diseases, and pursue health equity. The school’s research portfolio exceeds $100 million in expenditures annually. The school has also expanded and refined its distinctive educational programs, which support over 1200 students. Partnering with the school community, Dr. Bowman has led efforts to establish a culture of leadership, service, and inclusion.

  • PhD, Biostatistics, University of North Carolina, Chapel Hill, 2000
  • M.S., Biostatistics, University of Michigan, 1995
  • B.S., Mathematics, Morehouse College, 1992

Dr. Bowman’s research program mines massive data sets and has important implications for mental and neurological disorders such as Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, and substance addiction. His work also explores the impact of environmental exposures on brain function and structure in youth. His research has helped reveal brain patterns that reflect disruption from psychiatric diseases, detect biomarkers for neurological diseases, and determine more individualized therapeutic treatments.

Kemmer, P. B., Wang, Y., Bowman, F. D., Mayberg, H., and Guo, Y. (2019). Quantifying the strength of structural connectivity underlying functional brain networks. Brain Connectivity (accepted).

Xue, W., Bowman, F. D., Kang, J. (2018). Predicting Disease Status Using Imaging Data from Various Modalities. Frontiers in Neuroscience, 12:184. doi: 10.3389/fnins.2018.00184.

Cassidy, B., Bowman, F. D., Rae, C., and Solo, V. (2018).  On the Reliability of Individual Resting-state Functional Networks.  IEEE Transactions on Medical Imaging 37(2): 649-662.

Chen-Plotkin, A. S., Albin, R., Alcalay, R., Babcock, D., Bajaj, V., Bowman, F. D., Buko, A., Cedarbaum, J., Chelsky, D., Cooks, G., Cookson, M., Dawson, T., Dewey, R., Foroud, T., et al. (2018). Finding Useful Biomarkers for Parkinson’s Disease, Science Translational Medicine 10(454):  DOI: 10.1126/scitranslmed.aam6003.

Mollenhauer, B., Bowman, F. D., Drake, D., Duong, J., Blennow, K., El-Agnaf, O., Shaw, L. M., Masucci, J., Taylor, P., Umek, R. M., Dunty, J. M., Smith, C. L., Stoops, E., Vanderstichele, H., Schmid, A. W., Moniatte, M., Zhang, J., Kruse, N., Lashuel, H. A., Teunissen, C., Schubert, T., Dave, K. D., Hutten, S. J., Zetterberg, H. (2018).  Antibody-based methods for the measurement of α-synuclein concentration in human cerebrospinal fluid - method comparisons and round robin studies. Journal of Neurochemistry (doi: 10.1111/jnc.14569).

Gwinn, K., David, K., Swanson-Fischer, C., Albin, R., St Hillaire-Clarke, C., Sieber, B., Lungu, C, Bowman, F. D., Alcalay, R. N., Babcock, D., Dawson, T. M., Dewey, R. B., Foroud, T., German, D., Huang, X., Petyuk, V., Potashkin, J. A., Saunders-Pullman, R., Sutherland, M., Walt, D., West, A. B., Zhang, J., Chen-Plotkin, A., Scherzer, C. R., Vaillancourt, D., and Rosenthal, L. S. (2017). Parkinson’s Disease Biomarkers: Perspective from the NINDS Parkinson’s Disease Biomarkers Program. Biomarkers in Medicine 11(6):451-473.

Bowman, F. D., Drake, D., Huddleston, D. (2016). Multimodal Imaging Signatures of Parkinson’s Disease. Frontiers in Neuroscience, 10:131. doi:10.3389/fnins.2016.00131.

Cha, J., Ide, J.S., Bowman, F.D., Simpson, H., Posner, J., Steinglass, J. (2016). Abnormal Reward Circuitry in Anorexia Nervosa: A Longitudinal, Multimodal MRI Study. Human Brain Mapping 37:3835–3846.

Gazes, Y., Bowman, F.D.,  Razlighi, Q.R., Deirdre, O., Stern, Y., Habeck, C.G. (2016). White matter tract covariance patterns predict age-declining cognitive abilities.  NeuroImage 125: 53-60.

Chen, S., Bowman, F.D., and Mayberg, H.S. (2015). A Bayesian Hierarchical Framework for Modeling Brain Connectivity for Neuroimaging Data. Biometrics 72(2): 596-605 (DOI: 10.1111/biom.12433).

Bowman, F. D. (2014).  Brain Imaging Analysis. Annual Review of Statistics and Its Application, vol. 1: 61-85.

Simpson, S., Bowman, F. D., and Laurienti, P. (2013). Analyzing Complex Functional Brain Networks: Fusing Statistics and Network Science to Understand the Brain. Statistics Surveys, 7: 1-36.

Bowman, F. D., Zhang, L., Derado, G., and Chen, S.  (2012). Determining Functional Connectivity using fMRI Data with Diffusion-Based Anatomical Weighting.  NeuroImage, 62: 1769-1779.

Derado, G., Bowman, F. D., and Kilts, C. (2010). Modeling the spatial and temporal dependence in fMRI data. Biometrics, 66: 949-957.

Bowman, F. D., Caffo, B. A, Bassett, S., and Kilts, C. (2008).  Bayesian Hierarchical Framework for Spatial Modeling of fMRI Data. NeuroImage 39: 146–156

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1822 SPH I
1415 Washington Heights
Ann Arbor, Michigan 48109-2029
Phone:  Office: 734-763-2876
Email:  [email protected]

Media inquiries: [email protected]

Areas of Expertise: Aging,  Biostatistics,  Brain Disorders,  Mental Health,  Substance Use