  MRI-based entorhinal cortical thickness (eCT) measurements predict cognitive
decline in Alzheimer's disease (AD) with low cost and minimal invasiveness. Two
prominent imaging paradigms, FreeSurfer (FS) and Advanced Normalization Tools
(ANTs), feature multiple pipelines for extracting region-specific eCT
measurements from raw MRI, but the sheer complexity of these pipelines makes it
difficult to choose between pipelines, compare results between pipelines, and
characterize uncertainty in pipeline estimates. Worse yet, the EC is
particularly difficult to image, leading to variations in thickness estimates
between pipelines that overwhelm physiologicl variations predictive of AD. We
examine the eCT outputs of seven different pipelines on MRIs from the
Alzheimer's Disease Neuroimaging Initiative. Because of both theoretical and
practical limitations, we have no gold standard by which to evaluate them.
Instead, we use a Bayesian hierarchical model to combine the estimates. The
resulting posterior distribution yields high-probability idealized eCT values
that account for inherent uncertainty through a flexible multivariate error
model that supports different constant offsets, standard deviations,
tailedness, and correlation structures between pipelines. Our hierarchical
model directly relates idealized eCTs to clinical outcomes in a way that
propagates eCT estimation uncertainty to clinical estimates while accounting
for longitudinal structure in the data. Surprisingly, even though it
incorporates greater uncertainty in the predictor and regularization provided
by the prior, the combined model reveals a stronger association between eCT and
cognitive capacity than do nonhierarchical models based on data from single
pipelines alone.
