Saturday, May 17, 2025
5/17/2025
Ahead of the Curve: Early detection and monitoring of learning decrements in Alzheimers disease - Project Summary/Abstract Early detection and sensitive tracking of cognitive changes related to Alzheimer’s disease (AD) are critical for identifying individuals at-risk for decline and assessing treatment response more rapidly. Current gold standards (e.g., paper and pencil measures administered semi-annually) are insufficient, particularly as the field has shifted towards prevention at the preclinical stage of AD where cognitive changes are subtle and where widespread screening at much larger scales is needed. To meet this need, we will leverage web-based cognitive testing which both offers both scalability and which allows for relatively understudied, but promising cognitive paradigms to be explored. More specifically, our preliminary data suggests that diminished learning associated with preclinical AD is observable over several days using a Multi-Day Learning Curve (MDLC) i.e., the trajectory of daily learning on the same memory and processing speed tests administered 10 min/day for 7 days on a personal device. Diminished learning curves, collectible with frequent, repeated assessments may reflect early aberrations in memory consolidation- that is, difficulty transforming temporary, labile memories into more stable, lasting forms. We will use the Boston Remote Assessment for Neurocognitive Health (BRANCH), an investigator-developed, non-proprietary, web-based platform, to capture high-resolution MDLC data. We will capture an initial baseline MDLC (10 min/day over 7 days) and longitudinal MDLCs (10min/day over 7 days every 6 months) for up to 3 years. In Aim 1, we will develop a summary MDLC score (e.g., accuracy and reaction time across several MDLC tests, Day 1 performance, “area under” the learning curve) honed to evidence of AD (i.e., Aβ (PiB) and tau (FTP) deposition on PET imaging) leveraging well-characterized participants (Imaging Cohort; n=250) who span the continuum from cognitively unimpaired (CU; 65+), to subjective cognitive decline (SCD; 60+), to Mild Cognitive Impairment (MCI; 55+). Second, to test the generalizability of this early detection approach, we will replicate findings in a diverse Community Cohort (n=400; >24% from under-represented groups; 65+) with novel AD plasma biomarkers (e.g., ptau217, Aβ42/40). In Aim 2, we will determine whether an individual’s initial learning curve (baseline MDLC) is predictive of their clinical trajectory over years. In Aim 3, we will determine whether change in an individual’s learning curve (longitudinal MDLCs evaluating new learning every 6 months), can sensitively track AD-related decline. Ultimately, we seek to provide a rapidly obtainable, repeatable, high-resolution snapshot of clinically relevant memory declines across the very early AD continuum to facilitate early detection of worrisome memory changes in the general population and to provide a more sensitive method to monitor cognitive change.
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