title: Fully Bayesian inference for structural MRI: application to segmentation and statistical analysis of T2-hypointensities.
abstract: Aiming at iron-related T2-hypointensity, which is related to normal aging and neurodegenerative processes, we here present two practicable approaches, based on Bayesian inference, for preprocessing and statistical analysis of a complex set of structural MRI data. In particular, Markov Chain Monte Carlo methods were used to simulate posterior distributions. First, we rendered a segmentation algorithm that uses outlier detection based on model checking techniques within a Bayesian mixture model. Second, we rendered an analytical tool comprising a Bayesian regression model with smoothness priors (in the form of Gaussian Markov random fields) mitigating the necessity to smooth data prior to statistical analysis. For validation, we used simulated data and MRI data of 27 healthy controls (age: [Formula: see text]; range, [Formula: see text]). We first observed robust segmentation of both simulated T2-hypointensities and gray-matter regions known to be T2-hypointense. Second, simulated data and images of segmented T2-hypointensity were analyzed. We found not only robust identification of simulated effects but also a biologically plausible age-related increase of T2-hypointensity primarily within the dentate nucleus but also within the globus pallidus, substantia nigra, and red nucleus. Our results indicate that fully Bayesian inference can successfully be applied for preprocessing and statistical analysis of structural MRI data.
authors.affiliation: Department of Neurology, Technische Universität München, Munich, Germany.
authors.forename: Paul
authors.initials: P
authors.lastname: Schmidt
authors.forename: Volker J
authors.initials: VJ
authors.lastname: Schmid
authors.forename: Christian
authors.initials: C
authors.lastname: Gaser
authors.forename: Dorothea
authors.initials: D
authors.lastname: Buck
authors.forename: Susanne
authors.initials: S
authors.lastname: Bührlen
authors.forename: Annette
authors.initials: A
authors.lastname: Förschler
authors.forename: Mark
authors.initials: M
authors.lastname: Mühlau
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009010:Monte Carlo Method; D055815:Young Adult