JSM 2004 - Toronto

Abstract #301928

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Activity Number: 303
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #301928
Title: Spatial Modeling of fMRI Data Using non-Euclidian Distances
Author(s): Yulia R. Gel*+ and Rajesh R. Nandy and Sudip Bose and Dietmar Cordes
Companies: George Washington University and University of Washington and George Washington University and University of Washington
Address: Dept. of Statistics, Washington, DC, 20052,
Keywords: spatio-temporal modeling ; fMRI data ; non-Euclidian metrics ; spatial interpolation
Abstract:

Our objective is to adapt spatio-temporal methods of geostatistics to human-brain-mapping in order to study spatio-temporal variability of fMRI data. Our current focus is on the spatial pattern of fMRI data. First, we apply a segmentation-based approach to isolate gray matter, white matter, and CSF, which makes each individual segment more homogeneous. Therefore, the assumption of stationarity becomes more plausible. However, segmentation has an essential disadvantage of creating holes in the segmented data, which are most visible in gray matter. Thus the obtained data are not anymore simply connected. Therefore we suggest to use non-Euclidian metrics, e.g., the Manhattan distance. Our main interest is extension of semivariance analysis and kriging methods to data with non-Euclidian distances with emphasis on fMRI data.

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