# Validation of the Mayo Test Drive Screening Battery Composite and Stricker Learning Span for Early Detection and Monitoring of Cognitive Decline in Preclinical and Prodromal Alzheimer’s Disease

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2024 · $1,453,969

## Abstract

PROJECT SUMMARY/ABSTRACT
The field of Alzheimer's disease (AD) is entering a new era where increasing numbers of novel treatments
targeting the biological underpinnings of AD will be available within the foreseeable future. If we fail to identify
cognitive impairment due to AD early, we will miss an important treatment window. There is a critical need for
easily accessible, scalable, and sensitive cognitive tools that can aid early detection and monitoring of
cognitive impairment and thereby allow earlier intervention to mitigate further decline. High quality, brief
cognitive assessment tools that can be deployed remotely or via self-administration in clinic settings represent
one key component of the future of AD research and clinical practice. These tools will help enrich clinical trials
and aid triage decisions to inform specialty clinic referrals and initiation of treatment in clinics, ideally in
conjunction with plasma biomarkers to address both clinical symptoms and underlying biology. Additional
validation of novel remote cognitive assessment tools is needed. Mayo Test Development through Rapid
Iteration, Validation and Expansion (Mayo Test Drive, MTD) is a cognitive testing platform developed for self-
administered digital cognitive assessment. MTD addresses remote assessment needs and is a multi-device
(smartphone, tablet, PC), flexible and easily accessible platform, with subtests that provide more in-depth
assessment of targeted cognitive domains relative to typical screening tests. The MTD brief cognitive
screening battery takes 15-20 minutes and includes 2 subtests: (1) the Stricker Learning Span (SLS), a novel
computer adaptive word list memory test (learning and delay trials) and (2) Symbols Test, an open-source
measure of visual matching and processing speed/executive function; these are combined into a screening
battery composite (MTD-SBC). The overall goal of this R01 is to establish the validity of the MTD-SBC and the
SLS for several specific contexts of use. Specific aims are to (1) determine cross-sectional diagnostic accuracy
of MTD for clinically defined and PET biomarker-defined groups, (2) demonstrate sensitivity of MTD to amyloid-
related cognitive decline over 30 months, (3) determine utility of MTD for detecting clinical progression over 45
months, and (4) determine whether MTD performance is associated with plasma biomarkers to a similar
degree as in-person neuropsychological measures. Most participants will be recruited from the Mayo Clinic
Study of Aging, with additional recruitment from the Alzheimer's Disease Research Center (Rochester, MN and
Jacksonville, FL) and the Memory Impairment and Neurodegenerative Dementia Center - Mayo Clinic Study of
Aging at the University of Mississippi Medical Center in Jackson, MS (N=2,300 across cohorts, predominantly
remote administration). By increasing the representation of diverse participants and the range of social and
structural determinants of health (SSDoH) with the Jack...

## Key facts

- **NIH application ID:** 10799229
- **Project number:** 1R01AG081955-01A1
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Nikki H Stricker
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,453,969
- **Award type:** 1
- **Project period:** 2023-12-01 → 2028-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10799229

## Citation

> US National Institutes of Health, RePORTER application 10799229, Validation of the Mayo Test Drive Screening Battery Composite and Stricker Learning Span for Early Detection and Monitoring of Cognitive Decline in Preclinical and Prodromal Alzheimer’s Disease (1R01AG081955-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10799229. Licensed CC0.

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