  Alzheimer's Disease and normal aging are both characterized by brain atrophy.
The question of whether AD-related brain atrophy represents accelerated aging
or a neurodegeneration process distinct from that in normal aging remains
unresolved. Moreover, precisely disentangling AD-related brain atrophy from
normal aging in a clinical context is complex. In this study, we propose a
deformation-based morphometry framework to estimate normal aging and
AD-specific atrophy patterns of subjects from morphological MRI scans. We first
leverage deep-learning-based methods to create age-dependent templates of
cognitively normal (CN) subjects. These templates model the normal aging
atrophy patterns in a CN population. Then, we use the learned diffeomorphic
registration to estimate the one-year normal aging pattern at the voxel level.
We register the testing image to the 60-year-old CN template in the second
step. Finally, normal aging and AD-specific scores are estimated by measuring
the alignment of this registration with the one-year normal aging pattern. The
methodology was developed and evaluated on the OASIS3 dataset with 1,014
T1-weighted MRI scans. Of these, 326 scans were from CN subjects, and 688 scans
were from individuals clinically diagnosed with AD at different stages of
clinical severity defined by clinical dementia rating (CDR) scores. The results
show that ventricles predominantly follow an accelerated normal aging pattern
in subjects with AD. In turn, hippocampi and amygdala regions were affected by
both normal aging and AD-specific factors. Interestingly, hippocampi and
amygdala regions showed more of an accelerated normal aging pattern for
subjects during the early clinical stages of the disease, while the AD-specific
score increases in later clinical stages. Our code is freely available at
https://github.com/Fjr9516/DBM_with_DL.

Computer Vision and Pattern Recognition