A Joint Modeling Approach for Treatment Response and Baseline Imaging Data-Bei Jiang (加拿大University of Alberta数学与统计学系)

There have been a lot of interests in using neuroimaging approaches to help

 guide clinicians in selecting treatment for patients of major depressive

 disorders and understand placebo response. In this talk, we discuss a

unified Bayesian framework for modeling treatment outcome while exploiting

 image-based features as predictors. Traditional methods usually take two

steps. First, a dimension reduction procedure is conducted to reduce high-

dimensional images to low dimensional features. Second, a regression

analysis is carried out to investigate the relationship between a treatment

 response of interest and the extracted low dimensional imaging features

such that the effect of treatment depends on these imaging features. In

 contrast, our method performs these tasks simultaneously to ultimately

take into account uncertainty in both steps. We also illustrate the

 application of the method on a large placebo-controlled depression

clinical trial using baseline EEG measurements. This is a joint work with

Eva Petkova, Thaddeus Tarpey and R. Todd Ogden.