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dc.contributor.advisorPiegorsch, Walter W.en
dc.contributor.authorFisher, Julia Marie
dc.creatorFisher, Julia Marieen
dc.date.accessioned2016-01-22T18:22:01Zen
dc.date.available2016-01-22T18:22:01Zen
dc.date.issued2015en
dc.identifier.urihttp://hdl.handle.net/10150/594646en
dc.description.abstractClassification analyses are a promising way to localize signal, especially scattered signal, in functional magnetic resonance imaging data. However, there is not yet a consensus on the most effective analysis pathway. We explore the efficacy of k-Nearest Neighbors classifiers on simulated functional magnetic resonance imaging data. We utilize a novel construction of the classification data. Additionally, we vary the spatial distribution of signal, the design matrix of the linear model used to construct the classification data, and the feature set available to the classifier. Results indicate that the k-Nearest Neighbors classifier is not sufficient under the current paradigm to adequately classify neural data and localize signal. Further exploration of the data using k-means clustering indicates that this is likely due in part to the amount of noise present in each data point. Suggestions are made for further research.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.subjectclusteringen
dc.subjectfMRIen
dc.subjectk-Nearest Neighborsen
dc.subjectsimulationen
dc.subjectStatisticsen
dc.subjectclassificationen
dc.titleClassification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parametersen_US
dc.typetexten
dc.typeElectronic Thesisen
thesis.degree.grantorUniversity of Arizonaen
thesis.degree.levelmastersen
dc.contributor.committeememberPiegorsch, Walter W.en
dc.contributor.committeememberBillheimer, Deanen
dc.contributor.committeememberWatkins, Joseph C.en
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineStatisticsen
thesis.degree.nameM.S.en
refterms.dateFOA2018-06-30T21:27:19Z
html.description.abstractClassification analyses are a promising way to localize signal, especially scattered signal, in functional magnetic resonance imaging data. However, there is not yet a consensus on the most effective analysis pathway. We explore the efficacy of k-Nearest Neighbors classifiers on simulated functional magnetic resonance imaging data. We utilize a novel construction of the classification data. Additionally, we vary the spatial distribution of signal, the design matrix of the linear model used to construct the classification data, and the feature set available to the classifier. Results indicate that the k-Nearest Neighbors classifier is not sufficient under the current paradigm to adequately classify neural data and localize signal. Further exploration of the data using k-means clustering indicates that this is likely due in part to the amount of noise present in each data point. Suggestions are made for further research.


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