A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities.

ABSTRACT: Relating disease status to imaging data stands to increase the clinical significance of neuroimaging studies. Many neurological and psychiatric disorders involve complex, systems-level alterations that manifest in functional and structural properties of the brain and possibly other clinical and biologic measures. We propose a Bayesian hierarchical model to predict disease status, which is able to incorporate information from both functional and structural brain imaging scans. We consider a two-stage whole brain parcellation, partitioning the brain into 282 subregions, and our model accounts for correlations between voxels from different brain regions defined by the parcellations. Our approach models the imaging data and uses posterior predictive probabilities to perform prediction. The est

SUBMITTER: Xue W 

PROVIDER: S-EPMC5879954 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature