We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time vector autoregressive process. Previous FMRI noise models have either been purely temporal, separable or modeling deterministic trends. The specific form of the noise process is determined using model selection techniques. Notably, this results in the need for a spatially nonstationary and temporally stationary spatial component. Within the same full model, we also investigate the variation of the HRF in different areas of the activation, and for different experimental stimuli. We propose a novel HRF model made up of half-cosines, which allows distinct combinations of parameters to represent characteristics of interest. In addition, to adaptively avoid over-fitting we propose the use of automatic relevance determination priors to force certain parameters in the model to zero with high precision if there is no evidence to support them in the data. We apply the model to three datasets and observe matter-type dependence of the spatial and temporal noise, and a negative correlation between activation height and HRF time to main peak (although we suggest that this apparent correlation may be due to a number of different effects).

Original publication

DOI

10.1109/TMI.2003.823065

Type

Journal article

Journal

IEEE Trans Med Imaging

Publication Date

02/2004

Volume

23

Pages

213 - 231

Keywords

Bayes Theorem, Brain, Brain Mapping, Computer Simulation, Evoked Potentials, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Models, Statistical, Neurons, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Stochastic Processes