title: Bayesian model selection for pathological data.
abstract: The detection of abnormal intensities in brain images caused by the presence of pathologies is currently under great scrutiny. Selecting appropriate models for pathological data is of critical importance for an unbiased and biologically plausible model fit, which in itself enables a better understanding of the underlying data and biological processes. Besides, it impacts on one's ability to extract pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a fully unsupervised hierarchical model selection framework for neuroimaging data which permits the stratification of different types of abnormal image atterns without prior knowledge about the subject's pathological status.
authors.forename: Carole H
authors.initials: CH
authors.lastname: Sudre
authors.forename: Manuel Jorge
authors.initials: MJ
authors.lastname: Cardoso
authors.forename: Willem
authors.initials: W
authors.lastname: Bouvy
authors.forename: Geert Jan
authors.initials: GJ
authors.lastname: Biessels
authors.forename: Josephine
authors.initials: J
authors.lastname: Barnes
authors.forename: Sébastien
authors.initials: S
authors.lastname: Ourselin
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity