Project Summary/Abstract
Early detection and sensitive tracking of cognitive changes related to Alzheimer’s disease (AD)
pathology (amyloid-Aβ and tau), particularly those that are scaleable to the large at-risk population of older
adults, are urgently needed. Promising digital tools to measure cognitive changes must be validated in
demographically diverse groups to ensure that research advancements are generalizable to those most at-risk
for cognitive decline. Our recent work suggests that diminished learning over repeated evaluations (i.e., testing
memory for the same content for 12min/day for 7 days) reveals subtle Aβ-related memory failures that are
otherwise undetectable using single timepoint measures. More specifically, 29 Aβ+ clinically unimpaired (CU)
older adults failed to improve at the same rate as their 97 Aβ- CU peers when asked to learn and recall
identical content daily with this group difference emerging after ~3 days. We now seek to determine whether
this Personal Learning Curve (PLC) paradigm can be used to detect early memory failures related to AD
biomarkers in a larger and more diverse and representative sample and whether PLCs can sensitively track
longitudinal cognitive decline when repeated bi-annually. We have the unique opportunity to recruit an
extremely well-characterized and representative at-risk sample (i.e., 32% from URG, family history of cognitive
decline, hypertension, geographic diversity, English and Spanish speaking) with AD biomarkers (global Aβ,
temporal tau quantified via Positron Emission Tomography-PET) and annual cognitive testing. Recruited
participants (n=300) will have completed the active phase of U.S. POINTER, a two-year clinical trial of two
different multi-domain lifestyle interventions to protect brain health. We will explore whether a PLC, collected
12min/day for 7 days on personal devices, is a valid and sensitive marker of memory decline in this
representative population and whether repeating a PLC every 6 months for 12 months will reveal AD-relevant
longitudinal cognitive decline. In exploratory analyses, we will examine PLCs in relation to cardiovascular risk
factors (e.g., systolic blood pressure, white matter hyperintensities via Magnetic Resonance (MR) Imaging) and
non-specific markers of neurodegeneration (e.g., entorhinal tau, hippocampal atrophy). If successful, this
proposal will provide a rapidly obtainable, repeatable, high-resolution snapshot of clinically relevant memory
failures to facilitate early detection of cognitive decline as well as provide a novel method to assess treatment
response more rapidly. Doing so in a representative population will ensure that the most promising novel and
accessible digital cognitive tools are generalizable to the broader at-risk population.
