Unified Surface and Volumetric Inference on Functional Imaging Data

Surface-based analysis methods for functional imaging data have been shown to offer substantial benefits for the study of the human cortex, namely in the localisation of functional areas and the establishment of inter-subject correspondence. A new approach for surface-based parameter estimation via non-linear model fitting on functional time-series data is presented. It treats the different anatomies within the brain in the manner that is most appropriate: surface-based for the cortex, volumetric for white matter, and using regions-of-interest for subcortical grey matter structures. The mapping between these different domains is incorporated using a novel algorithm that accounts for partial volume effects. A variational Bayesian framework is used to perform parameter inference in all anatomies simultaneously rather than separately. This approach, called hybrid inference, has been implemented using stochastic optimisation techniques. A comparison against a conventional volumetric workflow with post-projection on simulated perfusion data reveals improvements parameter recovery, preservation of spatial detail and consistency between spatial resolutions. At 4mm isotropic resolution, the following improvements were obtained: 2.7% in SSD error of perfusion, 16% in SSD error of Z-score perfusion, and 27% in Bhattacharyya distance of perfusion distribution.