  Multivariate regression models for age estimation are a powerful tool for
assessing abnormal brain morphology associated to neuropathology. Age
prediction models are built on cohorts of healthy subjects and are built to
reflect normal aging patterns. The application of these multivariate models to
diseased subjects usually results in high prediction errors, under the
hypothesis that neuropathology presents a similar degenerative pattern as that
of accelerated aging. In this work, we propose an alternative to the idea that
pathology follows a similar trajectory than normal aging. Instead, we propose
the use of metrics which measure deviations from the mean aging trajectory. We
propose to measure these deviations using two different metrics: uncertainty in
a Gaussian process regression model and a newly proposed age weighted
uncertainty measure. Consequently, our approach assumes that pathologic brain
patterns are different to those of normal aging. We present results for
subjects with autism, mild cognitive impairment and Alzheimer's disease to
highlight the versatility of the approach to different diseases and age ranges.
We evaluate volume, thickness, and VBM features for quantifying brain
morphology. Our evaluations are performed on a large number of images obtained
from a variety of publicly available neuroimaging databases. Across all
features, our uncertainty based measurements yield a better separation between
diseased subjects and healthy individuals than the prediction error. Finally,
we illustrate differences in the disease pattern to normal aging, supporting
the application of uncertainty as a measure of neuropathology.
