title: Nonparametric variogram modeling with hole effect structure in analyzing the spatial characteristics of fMRI data.
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.
authors.affiliation: Department of Statistics, University of Akron, Akron, OH, United States. Electronic address: jye1@uakron.edu.
authors.forename: Jun
authors.initials: J
authors.lastname: Ye
authors.affiliation: Department of Statistics, University of Georgia, Athens, GA, United States. Electronic address: nlazar@stat.uga.edu.
authors.forename: Nicole A
authors.initials: NA
authors.lastname: Lazar
authors.affiliation: Department of Statistics & Statistical Laboratory, Iowa State University, Ames, IA, United States. Electronic address: yehuali@iastate.edu.
authors.forename: Yehua
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D017711:Nonlinear Dynamics; D012438:Saccades / Q000502:physiology; D018709:Statistics, Nonparametric
keywords: Bessel model; Cross-validation; Kriging; Mean squared deviation ratio