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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Detalles Bibliográficos
Autores principales: Chiang, Sharon, Guindani, Michele, Yeh, Hsiang J., Dewar, Sandra, Haneef, Zulfi, Stern, John M., Vannucci, Marina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5723403/
https://www.ncbi.nlm.nih.gov/pubmed/29259537
http://dx.doi.org/10.3389/fnins.2017.00669

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5723403/
https://www.ncbi.nlm.nih.gov/pubmed/29259537
http://dx.doi.org/10.3389/fnins.2017.00669

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