American Statistical Association
Advances in biomedical imaging technology have led to an increase in health research studies that collect large-scale, multimodal data sets, frequently in conjunction with genomic data, and biologic and clinical measures. Such studies provide an unprecedented opportunity for cross-cutting investigations that stand to gain a deeper understanding of the pathophysiology associated with major diseases. We consider the analysis of multimodal neuroimaging data to investigate neurological disorders such as Parkinson's disease (PD), and such models hold promise for producing imaging-based biomarkers. We develop a Bayesian statistical modeling framework that incorporates imaging data, reflecting both functional and structural characteristics of the brain, and yields classifications of subjects as either PD patients or healthy controls (HCs). We apply our model to data from magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), resting-state functional MRI, and numerous clinical variables. We also consider a penalized regression approach to distinguish PD patients from HC subjects. In both cases, we demonstrate the ability to isolate neural characteristics that reflect accurate signatures of PD and that, upon further investigation, may serve as useful early stage PD biomarkers.
|Date:||Wednesday, November 5, 2014|
|Time:||4:00 - 5:00 P.M.|
Memorial Sloan-Kettering Cancer Center
Department of Epidemiology and Biostatistics
307 East 63rd Street
(between First and Second Avenues)
3rd Floor Conference Room
New York, New York
Note: To gain access to the building, please follow the directions by the telephone in the foyer.