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: ; range, ). 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.

Fitting large-scale structured additive regression models using Krylov subspace methods

“…In some regions where our effect map reaches local maxima holes can be seen in the corresponding maps obtained by SPM. This was reported previously within different data situations (Schmidt et al, 2013). Examples for this are indicated by red circles in the plot.…”

“…Although Bayesian versions exist that eliminate the necessity for post hoc correction (Friston and Penny, 2003), spatial information is only considered by smoothing the images with a predetermined smoothing parameter, therefore leading to modification of the original data. As with the previous application it would be favorable to include spatial information in the modeling step, especially as it has been shown that this leads to an increase of the signal to noise ratio (Penny et al, 2005) and statistical power (Schmidt et al, 2013). In this application we show that our MCMC approach is able to provide such a solution by fitting a model with millions of parameters with moderate requirements on computational equipment.…”

MRI characterization of temporal lobe epilepsy using rapidly measurable spatial indices with hemisphere asymmetries and gender features

“…The quantitative ICC in reliability testing [56,58,65,66] and uncertainty analysis by Bayesian inference based on the posterior probability distribution [20,[67][68][69] are used for precise interpretation of MRI data and prediction of dynamic structural/functional model analysis. In the present study, the variance among healthy subjects, i.e., within-subject variability and the between-subject variability, had been assessed by the reliability testing through estimation of the ICC with 95 % confidence intervals using one-way ANOVA.…”

Dilated Virchow-Robin Spaces are a Marker for Arterial Disease in Multiple Sclerosis

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