Return to search

Classification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parameters

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.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/594646
Date January 2015
CreatorsFisher, Julia Marie
ContributorsPiegorsch, Walter W., Piegorsch, Walter W., Billheimer, Dean, Watkins, Joseph C.
PublisherThe University of Arizona.
Source SetsUniversity of Arizona
Languageen_US
Detected LanguageEnglish
Typetext, Electronic Thesis
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.

Page generated in 0.0021 seconds