  Neurodegeneration as measured through magnetic resonance imaging (MRI) is
recognized as a potential biomarker for diagnosing Alzheimer's disease (AD),
but is generally considered less specific than amyloid or tau based biomarkers.
Due to a large amount of variability in brain anatomy between different
individuals, we hypothesize that leveraging MRI time series can help improve
specificity, by treating each patient as their own baseline. Here we turn to
conditional variational autoencoders to generate individualized MRI predictions
given the subject's age, disease status and one previous scan. Using serial
imaging data from the Alzheimer's Disease Neuroimaging Initiative, we train a
novel architecture to build a latent space distribution which can be sampled
from to generate future predictions of changing anatomy. This enables us to
extrapolate beyond the dataset and predict MRIs up to 10 years. We evaluated
the model on a held-out set from ADNI and an independent dataset (from Open
Access Series of Imaging Studies). By comparing to several alternatives, we
show that our model produces more individualized images with higher resolution.
Further, if an individual already has a follow-up MRI, we demonstrate a usage
of our model to compute a likelihood ratio classifier for disease status. In
practice, the model may be able to assist in early diagnosis of AD and provide
a counterfactual baseline trajectory for treatment effect estimation.
Furthermore, it generates a synthetic dataset that can potentially be used for
downstream tasks such as anomaly detection and classification.
