{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/82097"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/82097","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A Model-Free Approach for Classification of fMRI Brain Images","abstract":"This dissertation considers the problem of classifying subjects into predefined groups based on functional magnetic resonance imaging (fMRI) data. Classification of subjects into predefined groups, such as patient vs. control, based on their functional MRI data is a potentially useful procedure for ensuring homogeneous research samples and for clinical diagnostic purposes. Unlike other methods addressing the same question that are using either predefined regions of interest or statistical parametric maps, the proposed methodology uses preprocessed time series for the whole brain volume. Using a training set of two groups of subjects the presented methodology identifies spatio-temporal features that distinguish the groups and uses these features to categorize new subjects. The methodology is illustrated using simulations and in vivo data sets.","abstract_html":"This dissertation considers the problem of classifying subjects into predefined groups based on functional magnetic resonance imaging (fMRI) data. Classification of subjects into predefined groups, such as patient vs. control, based on their functional MRI data is a potentially useful procedure for ensuring homogeneous research samples and for clinical diagnostic purposes. Unlike other methods addressing the same question that are using either predefined regions of interest or statistical parametric maps, the proposed methodology uses preprocessed time series for the whole brain volume. Using a training set of two groups of subjects the presented methodology identifies spatio-temporal features that distinguish the groups and uses these features to categorize new subjects. 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