Combined spatial and non-spatial prior for inference on MRI time-series.

Journal article

When modelling FMRI and other MRI time-series data, a Bayesian approach based on adaptive spatial smoothness priors is a compelling alternative to using a standard generalized linear model (GLM) on presmoothed data. Another benefit of the Bayesian approach is that biophysical prior information can be incorporated in a principled manner; however, this requirement for a fixed non-spatial prior on a parameter would normally preclude using spatial regularization on that same parameter. We have developed a Gaussian-process-based prior to apply adaptive spatial regularization while still ensuring that the fixed biophysical prior is correctly applied on each voxel. A parameterized covariance matrix provides separate control over the variance (the diagonal elements) and the between-voxel correlation (due to off-diagonal elements). Analysis proceeds using evidence optimization (EO), with variational Bayes (VB) updates used for some parameters. The method can also be applied to non-linear forward models by using a linear Taylor expansion centred on the latest parameter estimates. Applying the method to FMRI with a constrained haemodynamic response function (HRF) shape model shows improved fits in simulations, compared to using either the non-spatial or spatial-smoothness prior alone. We also analyse multi-inversion arterial spin labelling data using a non-linear perfusion model to estimate cerebral blood flow and bolus arrival time. By combining both types of prior information, this new prior performs consistently well across a wider range of situations than either prior alone, and provides better estimates when both types of prior information are relevant.

Authors: Groves, A; Chappell, M; Woolrich, M

• Publication status: Published
• Journal: NeuroImage
• Volume: 45
• Issue: 3
• Pages: 795-809
• Publication date: 2009-04-01
• DOI: 10.1016/j.neuroimage.2008.12.027
• EISSN: 1095-9572
• ISSN: 1053-8119
• Language: English
• Keywords: Magnetic Resonance Imaging; Humans; Algorithms; Models, Neurological; Computer Simulation; Image Processing, Computer-Assisted; Brain