title: Predicting brain activity using a Bayesian spatial model.
abstract: Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some shortcomings of the modeling methods used in other neuroimaging settings, by borrowing strength from the spatial correlations present in the data. Our proposed methodology is applicable to data from various imaging modalities including functional magnetic resonance imaging and positron emission tomography, and we provide an illustration here using positron emission tomography data from a study of Alzheimer's disease to predict disease progression.
authors: Center for Biomedical Imaging Statistics, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. gderado@emory.edu
authors: Gordana
authors: G
authors: Derado
authors: F Dubois
authors: FD
authors: Bowman
authors: Lijun
authors: L
authors: Zhang
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging / Q000503:physiopathology; D001499:Bayes Theorem; D056808:Biostatistics; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D016022:Case-Control Studies; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging / Q000503:physiopathology; D003198:Computer Simulation; D018450:Disease Progression; D019788:Fluorodeoxyglucose F18; D059907:Functional Neuroimaging / Q000706:statistics & numerical data*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D008959:Models, Neurological; D016011:Normal Distribution; D049268:Positron-Emission Tomography / Q000706:statistics & numerical data; D019275:Radiopharmaceuticals
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18