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APPLICATIONS OF STATISTICAL ANALYSIS TECHNIQUES FOR NEUROIMAGING DATA: RANDOMIZED SINGULAR VALUE DECOMPOSITION FOR PARTIAL LEAST SQUARES ANALYSIS AND THIN PLATE SPLINES FOR SPATIAL NORMALIZATION

Rosario-Rivera, Bedda Lynn (2009) APPLICATIONS OF STATISTICAL ANALYSIS TECHNIQUES FOR NEUROIMAGING DATA: RANDOMIZED SINGULAR VALUE DECOMPOSITION FOR PARTIAL LEAST SQUARES ANALYSIS AND THIN PLATE SPLINES FOR SPATIAL NORMALIZATION. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

This dissertation applies two statistical analysis techniques for neuroimaging data. The first aim of this dissertation is to apply randomized singular value decomposition for the approximation of the top singular vectors of the singular value decomposition of a large matrix. Randomized singular value decomposition is an algorithm that approximates the top singular vectors of a matrix given a subset of its rows or columns. Several statistical applications, such as partial least squares, require the computation of the singular value decomposition of a matrix. Statistical packages have built in functions that can compute the singular value decomposition of a matrix. In many applications, however, computing the SVD of a matrix is not possible because computer memory requirements associated with matrix allocation is high, limiting its use in high-dimensional settings. Neuroimaging studies can generate measurements for hundreds of thousands of voxels from an image. Therefore, performing partial least squares analysis on these datasets is not possible using statistical packages. Simulation studies showed that the randomized singular value decomposition method provides a good approximation of the top singular vectors and therefore a good approximation of the partial least squares summary scores. This method is significant for public health since it allows researchers to perform statistical analysis at a voxel level with only a sample of a large dataset.The second aim is to apply a thin plate spline method for spatial normalization of structural magnetic resonance images. Spatial normalization is the process of standardizing images of different subjects into the same anatomical space. The idea behind this procedure is to match each data volume from a subject to a template, so that specific anatomic structures will occupy the same voxels. Spatial normalization is a critical step in the analysis of brain imaging data since it produces the "raw" data for subsequent statistical analyses.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Rosario-Rivera, Bedda Lynnbrosario@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWeissfeld, Lisalweis@pitt.eduLWEIS
Committee MemberPrice, Juliepricejc@upmc.edu
Committee MemberMazumdar, Satimaz1@pitt.eduMAZ1
Committee MemberAnderson, Stewartsja@pitt.eduSJA
Date: 29 January 2009
Date Type: Completion
Defense Date: 21 November 2008
Approval Date: 29 January 2009
Submission Date: 4 December 2008
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: partial least squares; randomized singular value decomposition; thin plate splines
Other ID: http://etd.library.pitt.edu/ETD/available/etd-12042008-231600/, etd-12042008-231600
Date Deposited: 10 Nov 2011 20:08
Last Modified: 15 Nov 2016 13:53
URI: http://d-scholarship.pitt.edu/id/eprint/10036

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