Abstract #301192

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JSM 2003 Abstract #301192
Activity Number: 474
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Graphics
Abstract - #301192
Title: Brain Image Analysis Using the Joint Distribution of Intensity and Spatial Extent
Author(s): Satoru Hayasaka*+ and Thomas E. Nichols
Companies: University of Michigan and University of Michigan
Address: Dept. of Biostatistics, Ann Arbor, MI, 48109-2009,
Keywords: brain imaging ; image analysis ; fMRI ; permutation test ; multivariate permutation test ; bivariate hypothesis test
Abstract:

In analysis of brain image data, such as fMRI images, inference is typically made based on image intensity or spatial extent of signals. The intensity-based test provides great spatial specificity, whereas the extent-based test can have better sensitivity. There have been some methods, parametric and non-parametric, developed to combine intensity and extent information in order to benefit from the strengths of both. However, the parametric approach requires strong assumptions which may not be practical in real data, and the non-parametric approach, based on exceedance mass, may not be a consistent test due to the way intensity and extent are combined. In this work, we investigate consistent combining functions and compare them based on the form of their rejection region, by examining the joint distribution of intensity and extent. We also attempt to calibrate the intensity-extent combined inference by using a weighted combining function, which lets us adjust sensitivity and specificity according to signals of interest. We implement these combining functions in a permutation test framework.

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