# Ahead of the Curve: Early detection and monitoring of learning decrements in Alzheimers disease

> **NIH NIH R01** · BRIGHAM AND WOMEN'S HOSPITAL · 2024 · $867,107

## Abstract

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.

## Key facts

- **NIH application ID:** 10884672
- **Project number:** 1R01AG084017-01A1
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Kathryn Victoria Papp
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $867,107
- **Award type:** 1
- **Project period:** 2024-07-01 → 2029-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10884672

## Citation

> US National Institutes of Health, RePORTER application 10884672, Ahead of the Curve: Early detection and monitoring of learning decrements in Alzheimers disease (1R01AG084017-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10884672. Licensed CC0.

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