  Case vs control comparisons have been the classical approach to the study of
neurological diseases. However, most patients will not fall cleanly into either
group. Instead, clinicians will typically find patients that cannot be
classified as having clearly progressed into the disease state. For those
subjects, very little can be said about their brain function on the basis of
analyses of group differences. To describe the intermediate brain function
requires models that interpolate between the disease states. We have chosen
Gaussian Processes (GP) regression to obtain a continuous spectrum of brain
activation and to extract the unknown disease progression profile. Our models
incorporate spatial distribution of measures of activation, e.g. the
correlation of an fMRI trace with an input stimulus, and so constitute
ultra-high multi-variate GP regressors. We applied GPs to model fMRI image
phenotypes across Alzheimer's Disease (AD) behavioural measures, e.g. MMSE, ACE
etc. scores, and obtained predictions at non-observed MMSE/ACE values. The
overall model confirmed the known reduction in the spatial extent of activity
in response to reading versus false-font stimulation. The predictive
uncertainty indicated the worsening confidence intervals at behavioural scores
distance from those used for GP training. Thus, the model indicated the type of
patient (what behavioural score) that would need to included in the training
data to improve models predictions.
