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

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

We 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...

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Bibliographic Details
Main Authors: Sharon Chiang, Michele Guindani, Hsiang J. Yeh, Sandra Dewar, Zulfi Haneef, John M. Stern, Marina Vannucci
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-12-01
Series:Frontiers in Neuroscience
Subjects:
Bayesian hierarchical model
positron emission tomography (PET)
spatially-informed prior
mixture model
variable selection
Pólya-Gamma distribution
Online Access:http://journal.frontiersin.org/article/10.3389/fnins.2017.00669/full

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http://journal.frontiersin.org/article/10.3389/fnins.2017.00669/full

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