  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.
