# Novel Story Recall Measures as Indicators of Cognitive Decline Associated with Alzheimer's Disease and Related Disorders Biomarkers: A Collaborative Study of Existing Data

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2021 · $431,713

## Abstract

PROJECT SUMMARY/ABSTRACT
Growing advances in imaging and fluid-based assays of Alzheimer's disease (AD) biomarkers including amyloid,
tau and neurodegeneration, confirm that AD processes begin decades before clinical impairment in cognitive
function. Subtle changes to cognition are also likely to co-occur years before a clinical diagnosis of dementia
due to AD. There is an urgent need to develop sensitive measures of subtle cognitive decline associated with
AD biomarkers, particularly for monitoring response to early intervention treatments in clinical trials. The
proposed investigation is highly innovative and designed to leverage existing data from three longitudinal cohort
studies—Wisconsin Registry for Alzheimer's Prevention, Wisconsin Alzheimer's Disease Research Center, and
BIOCARD–using a classic and widely used measure of cognition: the story recall task. We developed a novel
scoring system that we hypothesize targets semantic and associative memory processes: measures that capture
lexical categories and serial position. Our preliminary data shows that proper name recall and serial position
scores from story recall are significantly associated with beta-amyloid status from positron emission tomography
(PET), while the traditional total score was not related to amyloid status. In this proposal, our central hypothesis
is that item-level analysis of existing story recall data from several longitudinal cohorts will yield one or more new
measures of cognition that are uniquely associated with underlying preclinical AD pathology. The specific aims
are: Aim1: Use data from multiple cohort studies to a) replicate preliminary findings that lexical-level and serial
position markers from delayed story recall are associated with increased risk of amyloid positivity and b) extend
analyses to investigate whether these variables are associated with PET tau, CSF Aβ and tau, or MRI
neurodegeneration measures. Aim 2: Compare concurrent and predictive validity of measures to determine
whether the novel measures are more strongly associated with biomarkers, cognitive decline, or progression to
clinical levels of impairment than traditional total score measures. Aim 3: Enhance the lexical-level and serial
position analysis with computational linguistic analysis of digitally recorded speech from story recall to determine
whether semantic content, speech fluency, error-monitoring, and serial position recall explain unique variance in
levels of amyloid and/or tau pathology. Impact: The proposed project leverages existing data and is expected to
lead to the development of new outcome measures from a classic, commonly used test that has played a central
role in detection of disease. We expect that our higher-level language and process-based measures will be
sensitive to AD biomarkers in preclinical phases of cognitive decline. By utilizing existing resources from differing
cohorts, we can validate our findings without adding participant burden, share these methods...

## Key facts

- **NIH application ID:** 10126350
- **Project number:** 1R01AG070940-01
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Kimberly D Mueller
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $431,713
- **Award type:** 1
- **Project period:** 2021-02-01 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10126350

## Citation

> US National Institutes of Health, RePORTER application 10126350, Novel Story Recall Measures as Indicators of Cognitive Decline Associated with Alzheimer's Disease and Related Disorders Biomarkers: A Collaborative Study of Existing Data (1R01AG070940-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10126350. Licensed CC0.

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