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Alzheimer's Disease and normal aging are both characterized by brain atrophy.The question of whether AD-related brain atrophy represents accelerated agingor a neurodegeneration process distinct from that in normal aging remainsunresolved. Moreover, precisely disentangling AD-related brain atrophy fromnormal aging in a clinical context is complex. In this study, we propose adeformation-based morphometry framework to estimate normal aging andAD-specific atrophy patterns of subjects from morphological MRI scans. We firstleverage deep-learning-based methods to create age-dependent templates ofcognitively normal (CN) subjects. These templates model the normal agingatrophy patterns in a CN population. Then, we use the learned diffeomorphicregistration 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 secondstep. Finally, normal aging and AD-specific scores are estimated by measuringthe alignment of this registration with the one-year normal aging pattern. Themethodology was developed and evaluated on the OASIS3 dataset with 1,014T1-weighted MRI scans. Of these, 326 scans were from CN subjects, and 688 scanswere from individuals clinically diagnosed with AD at different stages ofclinical severity defined by clinical dementia rating (CDR) scores. The resultsshow that ventricles predominantly follow an accelerated normal aging patternin subjects with AD. In turn, hippocampi and amygdala regions were affected byboth normal aging and AD-specific factors. Interestingly, hippocampi andamygdala regions showed more of an accelerated normal aging pattern forsubjects during the early clinical stages of the disease, while the AD-specificscore increases in later clinical stages. Our code is freely available athttps://github.com/Fjr9516/DBM_with_DL.

A deformation-based morphometry framework for disentangling Alzheimer's disease from normal aging using learned normal aging templates