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Alexandra Badea

Alexandra Badea
Associate Professor, Neurology, Biomedical Engineering, BIAC
Associate Professor of Radiology
Email address: Alexandra.Badea@duke.edu

Current Research Interests:

Brain imaging, MRI, connectivity, multivariate biomarkers, image analysis, neurological conditions, Alzheimer's disease

My research has centered on multivariate image based phenotyping, with a focus on neurological conditions like Alzheimer’s disease. I work on imaging and analysis techniques 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 white matter, and the connectivity amongst gray matter regions.  Using such techniques, we have developed high resolution, multivariate population atlases for animal models.

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.
The unique setting of the Center for In Vivo Microscopy (CIVM) provides most imaging modalities for small animals: several MRI systems, micro-CT, SPECT, and multi-photon microscopy, which allow us to integrate imaging data from multiple modalities, and across scales. 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 based analysis, and graph analysis for connectomics. These strategies will help increase the rate at which we grow our current understanding of gray and white matter changes in neurological and psychiatric conditions. 

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 looking forward to continue 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.

Education and Training

PhD: University of Patras (Greece), 2003