PROJECT SUMMARY/ABSTRACT Effective drug treatment for Alzheimer’s disease (AD) may well be on the horizon. While the amyloid-tau- neurodegeneration (A/T/N) signature is recommended for the diagnosis of AD, it is not definitive for clinical expression. Recent advance in digital technology provides a potentially low-cost and scalable approach to continuous cognitive assessment. Digital biomarkers will also make relevant, in real time, disease prevention opportunities by monitoring and reporting changes in modifiable disease risk behaviors. The objective of our proposal is to develop a digital cognitive health resource based on the Boston University Alzheimer’s Disease Research Center (BU ADRC). The project is built upon the success of our initial precision brain health platform for continuous cognitive assessment. We will leverage the BU ADRC cohort and its extensive and on-going collection of longitudinal vascular risk factors, AD PET, CSF and plasma biomarkers, adjudicated dementia subtype diagnoses and other clinical data, cognitive measures obtained from traditional paper-pencil neuropsychological tests and neuroanatomic regions of interest extracted from brain MRI scans. We will add to this annual data collection effort concomitant collection of longitudinal digital cognitive phenotypes via digital recorder, digital pen, and smartphone applications. Our project includes three specific aims: 1) Collect the digital cognitive metrics (dCog) and characterize those at high AD risk (A/T/N positive; A/T/N+) compared to those at low AD risk (A/T/N negative, A/T/N-); 2) Assess the relationship of dCog phenotypes with vascular risk factors and neuroanatomic measures; and 3) Build machine learning models from dCog phenotypes in isolation and in combination with vascular risk factors and brain imaging/blood-based biomarkers to predict cognitive health. The outlined strategy will identify and validate novel digital cognitive biomarkers and provide new avenues for better diagn