This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308168
Title: Evaluating Functional Autocorrelation Within Spatially Distributed Neural Processing Networks
Author(s): Gordana Derado*+ and DuBois Bowman
Companies: Emory University and Emory University
Address: 1518 Clifton Rd., N.E., , Atlanta, GA, 30322,
Keywords: Spatial autocorrelation ; Moran's I ; neuroimaging ; fMRI ; ICA ; cluster analysis

Data-driven statistical approaches, such as cluster analysis or independent component analysis, applied to functional neuroimaging data help to identify neural processing networks exhibiting similar task-related or resting state patterns of activity. Ideally, the measured brain activity for voxels within such networks should exhibit high autocorrelation. We propose the use of Moran's I statistic to measure the degree of functional autocorrelation within identified neural processing networks and to evaluate the statistical significance of the observed associations. We adapt the conventional definition of Moran's I for applicability to neuroimaging analyses, by defining the global autocorrelation index using network-based neighborhoods. We illustrate our methodology using PET data from a study of working memory in individuals with schizophrenia and fMRI data from a study of depression.

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