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