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Activity Number:
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204
- Recent Development in Statistical Methods for Analyzing Big and Complex Neuroimaging Data
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Type:
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Invited
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Date/Time:
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Monday, July 31, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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Abstract #322215
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View Presentation
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Title:
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Functional Data Modeling of Dynamic PET Data
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Author(s):
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R. Todd Ogden* and Yakuan Chen and Jeff Goldsmith
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Companies:
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Department of Biostatistics, Columbia University and Columbia University and Columbia University
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Keywords:
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PET imaging ;
functional data ;
brain imaging
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Abstract:
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One major goal of dynamic positron emission tomography (PET) imaging, with particular relevance to the study of mental and neurological disorders, is the estimation of the spatial distribution of specific molecules throughout the brain. Current analysis strategies involve applying parametric models that require fairly strong assumptions, reducing information for each subject and each voxel/region into a single scalar-valued summary, and modeling each subject and each voxel/region sequentially. We will describe extensions of the analysis in three different directions: a nonparametric approach to the modeling of the observed PET data; a functional data analytic (FDA) approach to modeling the impulse response function; and the ability to consider observed PET data from multiple subjects in a single function-on-scalar regression model. We demonstrate the application of this approach and compare the results with those derived from standard parametric approaches.
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Authors who are presenting talks have a * after their name.
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