Nonparametric Variogram Modeling with Hole Effect Structure in Analyzing the Spatial Characteristics of fMRI Data

• Activity Number: 249
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• 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

Abstract

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.