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Activity Number: 249
Type: Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #315237
Title: Nonparametric Variogram Modeling with Hole Effect Structure in Analyzing the Spatial Characteristics of fMRI Data
Author(s): Jun Ye* and Nicole A. Lazar and Yehua Li
Companies: The University of Akron and University of Georgia and Iowa State University
Keywords: Bessel model ; Cross-validation ; Kriging ; Mean squared deviation ratio

When analyzing functional neuroimaging data, it is particularly important to consider the spatial structure of the brain. Some researchers have applied geostatistical methods in the analysis of functional magnetic resonance imaging (fMRI) data to enhance the detection of activated brain regions. In this paper, we propose a nonparametric variogram model for the complicated spatial characteristics of fMRI data. The new periodic variogram model can well describe the fluctuating spatial structure of fMRI data, considering both the nonlinear physical relationship between the proximate voxels and the functional relationship between distant voxels. We demonstrate the effectiveness of the new variogram model using fMRI data from a saccade study.

Authors who are presenting talks have a * after their name.

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