PROJECT SUMMARY / ABSTRACT The goal of this study is to evaluate the utility of non-invasive and cost-effective digital cognitive biomarkers for concurrent prediction of amyloid positivity in pre-clinical stages of Alzheimer’s disease (AD). As AD research has shifted its focus to earlier stages of the disease course, overcoming the economic and logistical barriers of identifying cognitively normal subjects with accumulating AD biomarkers (e.g., amyloid and tau) is of paramount importance. The current gold standard method of identifying cognitively normal subjects with accumulating AD pathology includes invasive and costly biomarker imaging or lumbar punctures, which result in high screen failure rates for biomarker positivity and unnecessarily long lead times for clinical trial enrollment. In our preliminary study, we used Hierarchical Bayesian Cognitive Processing (HBCP) models to analyze baseline item response data from wordlist memory (WLM) tests, and we generated digital biomarkers that distinguished between amyloid positive and amyloid negative groups. This study was conducted using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including ADAS-Cog WLM tests and amyloid measurement by PET and cerebrospinal fluid (CSF). In the proposed study, we will replicate previous results and further evaluate the utility of HBCP model- generated digital cognitive biomarkers (DCBs) for concurrent prediction of amyloid positivity in pre-clinical stages of AD. For this study, we will use CSF and PiB-PET biomarker data plus Auditory-Verbal Learning Test (AVLT) WLM item response data from the ADNI database. Replicating the preliminary results and further refining those DCBs will enable a pragmatic and cost-effective approach to identifying cognitively normal but amyloid positive subjects who are in the pre-clinical stages of AD. While physical biomarkers will remain as industry standards for the foreseeable future, DCBs could play an important complementary role in the screening process. This would significantly expedite clinical trial enrollment and bring new AD therapies more quickly to market, while also enabling a scalable approach to identifying patients who might benefit from disease-modifying therapies once approved.