Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment

Abstract

We present a method to discover interesting brain regions in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, showing also the benefits when PET scans are in combined use with non-imaging variables. The discriminative brain regions facilitate a better understanding of Alzheimer's disease (AD) progression, and they can also be used for predicting conversion from mild cognitive impairment (MCI) to AD. A survival analysis(Cox regression) and infinite Gaussian mixture model (IGMM) are introduced to identify the informative brain regions, which can be further used to make a prediction of conversion (in two years) from MCI to AD using only the baseline PET scan. Further, the predictive accuracy can be enhanced when non-imaging variables are used together with identified informative brain voxels. The results suggest that PET scan imaging data is more predictive than other non-imaging data, revealing even better performance when both imaging and non-imaging data are combined.