A1640
Title: Functional data analysis in neuroimaging data based on semiparametric transformation models
Authors: Haolun Shi - Simon Fraser University (Canada) [presenting]
Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to memory loss, cognitive decline, and behavioral changes, without a known cure. Neuroimages are often collected alongside covariates at baseline to forecast patient prognosis. Identifying regions of interest within neuroimages associated with disease progression is thus of significant clinical importance. Two major complications arise in such analysis: the domain of the brain area in neuroimages is irregular, and the time to AD is interval-censored, as the event can only be observed between two revisit time points. A semiparametric approach models imaging predictors via bivariate splines over triangulation and incorporates them in a flexible class of semiparametric transformation models. Regions of interest are identified by maximizing a penalized likelihood. A computationally efficient expectation-maximization algorithm is devised for parameter estimation. An extensive simulation study evaluates the finite-sample performance of the proposed method, and an illustration with the AD Neuroimaging Initiative dataset is provided.
