Models of Disease Spectra
This work addresses the challenge of classifying intermediate disease states in neurology, offering a method to improve predictions for patients with ambiguous symptoms, though it is incremental as it applies existing Gaussian Processes to a specific domain.
The authors tackled the problem of modeling brain function in neurological diseases where patients do not clearly fall into case or control groups, using Gaussian Processes regression to create a continuous spectrum of brain activation and predict disease progression profiles from fMRI data, with results showing reduced spatial activity in Alzheimer's Disease and predictive uncertainty indicating which behavioral scores need more training data.
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.