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Oracle Compound Decision Rules for False Discovery Rate Control in fMRI studies

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dc.contributor.advisor Wegman, Edward J. Chen, Nan
dc.creator Chen, Nan 2013-05 2013-08-15T20:21:37Z 2018-06-01T06:41:32Z 2013-08-15
dc.description.abstract The recent advance on functional magnetic resonance imaging (fMRI) allows scientists to assess functionality of the brain by measuring the response of blood flow to one or multiple types of stimuli. The analysis of fMRI studies is a very challenging high dimensional problem due to the fact that the statistical analysis will be applied to the measurements from huge volume of voxels. Based on the most recent development of the oracle false discovery rate, this thesis proposes three new approaches to the analysis of the fMRI studies. They are in the areas of 1) noise suppression by wavelet based Oracle False Discovery Rate (OFDR) threshholding; 2) voxel-based signal extraction with Enhanced Oracle False Discovery Rate (EOFDR); 3) group-based signal extraction using Brain-Map-Constrained adaptive oracle False Discovery Rate Approach. The simulation studies show improved performance of the proposed approaches compared to some existing competitive approaches. The proposed approaches are applied to fMRI studies to demonstrate practical usage.
dc.language.iso en_US en_US
dc.rights Copyright 2013 Nan Chen en_US
dc.subject Denoising en_US
dc.subject False discovery rate en_US
dc.subject Functional Magnetic Resonance Imaging en_US
dc.subject Group analysis en_US
dc.subject Multiple testing en_US
dc.subject Wavelet en_US
dc.title Oracle Compound Decision Rules for False Discovery Rate Control in fMRI studies en_US
dc.type Dissertation en
dc.description.note This work is embargoed by the author and will not be available until June 2018. en_US PhD in Computational Sciences and Informatics en_US Doctoral en Computational Sciences and Informatics en George Mason University en

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