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Abstract Details

Activity Number: 56
Type: Invited
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract - #303553
Title: Functional Analysis of Brain Images: Smoothing or Not Smoothing?
Author(s): Hongtu Zhu*+
Companies: The University of North Carolina at Chapel Hill
Address: Department of Biostatistics, Chapel Hill, NC, ,
Keywords: Functional analysis ; Imaging data ; Kernel Smoothing ; spatial ; adaptive ; multiscale

Motivated by recent work studying massive imaging data in various neuroimaging studies, we have developed various statistical models for adaptively and spatially modeling the relation between high-dimensional imaging measures in a three-dimensional (3D) volume with a set of covariates. We show that several multiscale adaptive estimation and testing procedures can be useful for the functional analysis of brain images. Due to the complexity of these procedures, we discuss the importance of properly tuning parameters underlying these procedures in order to establish the asymptotic properties of estimators obtained from these estimation and testing procedures. We conduct Monte Carlo simulation and real data analysis to examine the finite-sample performance of these procedures. Our results show that it is critical to smooth imaging data in practice.

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