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dc.contributor.authorSulek, Thaddeus Robert
dc.date.accessioned2018-02-14T17:56:57Z
dc.date.available2018-02-14T17:56:57Z
dc.date.issued2017-05
dc.identifier.othersulek_thaddeus_r_201705_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/sulek_thaddeus_r_201705_ms
dc.identifier.urihttp://hdl.handle.net/10724/37188
dc.description.abstractIn this thesis, we study the graphical lasso method and apply it to functional magnetic resonance imaging (fMRI) data. The graphical lasso method enables one to construct undirected sparse graphs between variables of interest. The fMRI data concerns subjects’ brain activities while they engage in saccadic eye movement tasks. The datasets are collected before and after they practice certain tasks. Using the graphical lasso procedure, we create undirected graphs that display the connections between the different regions of interest (ROI) in the brain. By controlling the regularization parameter in this lasso procedure, we identify which ROIs are more strongly connected than the others. We compare these undirected graphs before and after the practice and also across different practice groups.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectFunctional Magnetic Resonance Imaging Data
dc.subjectGraphical Model
dc.subjectLasso
dc.subjectRegions of Interest
dc.titleAn application of graphical models to fMRI data using the lasso penalty
dc.typeThesis
dc.description.degreeMS
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorCheolwoo Park
dc.description.committeeCheolwoo Park
dc.description.committeeJennifer McDowell
dc.description.committeeNicole Lazar


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