Alexandra Badea, PhD

Associate Professor in Radiology
Associate Professor in Neurology
Assistant Professor of Biomedical Engineering
Member of the Center for Brain Imaging and Analysis
Address: Box 3302 Duke University Medic
Durham, NC 27710
Phone: (919) 684-7654
Email: alexandra.badea@duke.edu

Research Interests

I have a joint appointment in Radiology and Neurology and my research focuses on neurological conditions like Alzheimer’s disease. I work on imaging and analysis to provide a comprehensive characterization of the brain. MRI is particularly suitable for brain imaging, and diffusion tensor imaging is an important tool for studying brain microstructure, and the connectivity amongst gray matter regions.  

I am interested in image segmentation, morphometry and shape analysis, as well as in integrating information from MRI with genetics, and behavior. Our approaches  target: 1) phenotyping the neuroanatomy using imaging; 2) uncovering the link between structural and functional changes, the genetic bases, and environmental factors. I am interested in generating methods and tools for comprehensive phenotyping.

We use high-performance cluster computing to accelerate our image analysis. We use compressed sensing image reconstruction, and process large image arrays using deformable registration, perform segmentation based on multiple image contrasts including diffusion tensor imaging, as well as voxel, and graph analysis for connectomics.

At BIAC  my efforts focus on developing multivariate biomarkers and identifying vulnerable networks based on genetic risk for Alzheimer's disease.

My enthusiasm comes from the possibility to extend from single to integrative multivariate and network based analyses to obtain a comprehensive picture of normal development and aging, stages of disease, and the effects of treatments.  I am working on multivariate image analysis and predictive modeling approaches to help better understand early biomarkers for human disease indirectly through mouse models, as well as directly in human studies. 

I am dedicated to supporting an increase in female presence in STEM fields, and love working with students. The Bass Connections teams involve undergraduate students in research, providing them the opportunity to do independent research studies and get involved with the community. These students have for example takes classes such as:

BME 394: Projects in Biomedical Engineering (GE)
BME 493: Projects in Biomedical Engineering (GE)
ECE 899: Special Readings in Electrical Engineering
NEUROSCI 493: Research Independent Study 1

Publications

Nair, K. Saidas, et al. “GLIS1 regulates trabecular meshwork function and intraocular pressure and is associated with glaucoma in humans.Nat Commun, vol. 12, no. 1, Aug. 2021, p. 4877. Pubmed, doi:10.1038/s41467-021-25181-7.

Bryan, Jordan, et al. “Likelihood ratio statistics for gene set enrichment in Alzheimer's disease pathways.Alzheimers Dement, vol. 17, no. 4, Apr. 2021, pp. 561–73. Pubmed, doi:10.1002/alz.12223.

Badea, Alexandra, et al. “Microcephaly with altered cortical layering in GIT1 deficiency revealed by quantitative neuroimaging.Magn Reson Imaging, vol. 76, Feb. 2021, pp. 26–38. Pubmed, doi:10.1016/j.mri.2020.09.023.

Zhuang, Jie, et al. “Cerebral white matter connectivity, cognition, and age-related macular degeneration.Neuroimage Clin, vol. 30, 2021, p. 102594. Pubmed, doi:10.1016/j.nicl.2021.102594.

Slosky, Lauren M., et al. “β-Arrestin-Biased Allosteric Modulator of NTSR1 Selectively Attenuates Addictive Behaviors.Cell, vol. 181, no. 6, June 2020, pp. 1364-1379.e14. Pubmed, doi:10.1016/j.cell.2020.04.053.

Nair, Saidas, et al. “Kruppel-like zinc finger transcription factor GLIS1 regulates intraocular pressure by maintaining trabecular meshwork function.” Investigative Ophthalmology & Visual Science, vol. 61, no. 7, 2020.

Anderson, Robert J., et al. “Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions.Front Phys, vol. 8, Apr. 2020. Pubmed, doi:10.3389/fphy.2020.00088.

Holbrook, M. D., et al. “MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice.Tomography, vol. 6, no. 1, Mar. 2020, pp. 23–33. Pubmed, doi:10.18383/j.tom.2019.00021.