Title: Cognitive Subscore Trajectory Prediction in Alzheimer's Disease,
Abstract: Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of
multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's
Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple
subscores that quantify different aspects of a patient's cognitive state such
as learning, memory, and language production/comprehension. Although
computer-aided diagnostic techniques for classification of a patient's current
disease state exist, they provide little insight into the relationship between
changes in brain structure and different aspects of a patient's cognitive state
that occur over time in AD. We have developed a Convolutional Neural Network
architecture that can concurrently predict the trajectories of the 13 subscores
comprised by a subject's ADAS-cog examination results from a current minimally
preprocessed structural MRI scan up to 36 months from image acquisition time
without resorting to manual feature extraction. Mean performance metrics are
within range of those of existing techniques that require manual feature
selection and are limited to predicting aggregate scores.