Dr. Elizabeth Sweeney earned her PhD from the Biostatistics department at the Johns Hopkins Bloomberg School of Public Health, under the supervision of Dr. Ciprian Crainiceanu at Johns Hopkins and Dr. Russell Shinohara at the University of Pennsylvania. Elizabeth’s PhD research has made contributions to the improved analysis of neuroimaging data, as evidenced by numerous publications, presentations, and patents. Elizabeth’s interest in this area began with a traineeship at the Nation Institute of Neurological Disease and Stroke, where she did research in Dr. Daniel Reich’s lab on image analysis in multiple sclerosis. Elizabeth is passionate about both research and teaching. Elizabeth has co-taught a number of tutorials and courses on neuroimage data analysis, including a Coursera course to be released this year. She also taught and introductory biostatistics course to masters of public health students at the American University of Armenia.
While imaging studies are widely used in clinical practice and research, the number of neuroimaging-based biomarkers is small. Dr. Elizabeth Sweeney’s research focuses on developing these much-needed biomarkers. Elizabeth has developed algorithms for identifying multiple sclerosis (MS) brain lesions from structural magnetic resonance images (sMRI). Identifying these lesions is important in MS, as this volume is used in clinical practice and as a primary endpoint in clinical trials. She has also developed a biomarker of lesion repair on sMRI. This biomarker provides information about the dynamics of MS lesion beyond lesion volume and will be a secondary endpoint for an upcoming clinical trial.
As a Rice Academy Postdoctoral Fellow, Elizabeth will turn her attention to Alzheimer's disease (AD). With mentors Dr. Genevera Allen (Rice Statistics) and Dr. Joshua Shulman (Baylor College of Medicine Neurology), Elizabeth will develop neuroimaging, epidemiological, and genetic biomarkers of cognitive reserve, or the difference among individuals that enables some to be more resilient to the pathological brain changes associated with AD. The findings from this work will potentially aid in the understanding of the etiology and natural history of AD, allowing for the development of treatments, therapies and interventions to prevent and delay AD onset.