A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

ABSTRACT: The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of

SUBMITTER: Huertas I 

PROVIDER: S-EPMC5692833 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature