Classification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parameters

Persistent Link:
http://hdl.handle.net/10150/594646
Title:
Classification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parameters
Author:
Fisher, Julia Marie
Issue Date:
2015
Publisher:
The University of Arizona.
Rights:
Copyright © 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.
Abstract:
Classification 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.
Type:
text; Electronic Thesis
Keywords:
clustering; fMRI; k-Nearest Neighbors; simulation; Statistics; classification
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College; Statistics
Degree Grantor:
University of Arizona
Advisor:
Piegorsch, Walter W.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleClassification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parametersen_US
dc.creatorFisher, Julia Marieen
dc.contributor.authorFisher, Julia Marieen
dc.date.issued2015en
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.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.en
dc.typetexten
dc.typeElectronic Thesisen
dc.subjectclusteringen
dc.subjectfMRIen
dc.subjectk-Nearest Neighborsen
dc.subjectsimulationen
dc.subjectStatisticsen
dc.subjectclassificationen
thesis.degree.nameM.S.en
thesis.degree.levelmastersen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorUniversity of Arizonaen
dc.contributor.advisorPiegorsch, Walter W.en
dc.contributor.committeememberPiegorsch, Walter W.en
dc.contributor.committeememberBillheimer, Deanen
dc.contributor.committeememberWatkins, Joseph C.en
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