A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection

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dc.citation.articleNumber669en_US
dc.citation.journalTitleFrontiers in Neuroscienceen_US
dc.citation.volumeNumber11en_US
dc.contributor.authorChiang, Sharonen_US
dc.contributor.authorGuindani, Micheleen_US
dc.contributor.authorYeh, Hsiang J.en_US
dc.contributor.authorDewar, Sandraen_US
dc.contributor.authorHaneef, Zulfien_US
dc.contributor.authorStern, John M.en_US
dc.contributor.authorVannucci, Marinaen_US
dc.date.accessioned2018-07-11T19:50:56Zen_US
dc.date.available2018-07-11T19:50:56Zen_US
dc.date.issued2017en_US
dc.description.abstractWe develop an integrative Bayesian predictive modeling framework that identifies individual pathological brain states based on the selection of fluoro-deoxyglucose positron emission tomography (PET) imaging biomarkers and evaluates the association of those states with a clinical outcome. We consider data from a study on temporal lobe epilepsy (TLE) patients who subsequently underwent anterior temporal lobe resection. Our modeling framework looks at the observed profiles of regional glucose metabolism in PET as the phenotypic manifestation of a latent individual pathologic state, which is assumed to vary across the population. The modeling strategy we adopt allows the identification of patient subgroups characterized by latent pathologies differentially associated to the clinical outcome of interest. It also identifies imaging biomarkers characterizing the pathological states of the subjects. In the data application, we identify a subgroup of TLE patients at high risk for post-surgical seizure recurrence after anterior temporal lobe resection, together with a set of discriminatory brain regions that can be used to distinguish the latent subgroups. We show that the proposed method achieves high cross-validated accuracy in predicting post-surgical seizure recurrence.en_US
dc.identifier.citationChiang, Sharon, Guindani, Michele, Yeh, Hsiang J., et al.. "A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection." <i>Frontiers in Neuroscience,</i> 11, (2017) Frontiers Media S.A.: https://doi.org/10.3389/fnins.2017.00669.en_US
dc.identifier.doihttps://doi.org/10.3389/fnins.2017.00669en_US
dc.identifier.urihttps://hdl.handle.net/1911/102405en_US
dc.language.isoengen_US
dc.publisherFrontiers Media S.A.en_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the originalen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.subject.keywordBayesian hierarchical modelen_US
dc.subject.keywordpositron emission tomography (PET)en_US
dc.subject.keywordspatially-informed prioren_US
dc.subject.keywordmixture modelen_US
dc.subject.keywordvariable selectionen_US
dc.subject.keywordPólya-Gamma distributionen_US
dc.titleA Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resectionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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