# Development of a cost-effective and neurobiologically valid VR assessment tool for   early detection of AD

> **NIH NIH R21** · STANFORD UNIVERSITY · 2021 · $236,100

## Abstract

PROJECT SUMMARY
Alzheimer’s disease (AD) is the most common form of dementia with significant impact on patients, families
and the public health system. At the time of clinical manifestation of dementia, significant irreversible brain
damage is already present, rendering the development of cost-effective, biologically informed assessment
tools for early detection of the disease an urgent prerequisite for potential therapies to delay or prevent
symptoms. While significant advances have been made in characterizing early stages of AD for research,
current standard-of-care measures used for clinical diagnosis of prodromal AD lack the ability to identify AD in
early stages. The overarching goals of the proposed study are to integrate VR, advanced neuroimaging
technologies, and computational techniques to refine, optimize, test and validate a VR-based assessment tool
that is cost-effective and ecologically and neurobiologically valid for early detection of AD. Particularly, we will
integrate performance on a VR-based multidomain cognitive assessment battery combined with real-time
performance data across multiple sources (e.g. kinematic) to identify subtle factors underlying VR task
performance that contribute the most to sensitive detection of prodromal AD. Most importantly, using advanced
quantitative MR measures of brain microstructure, we will test the association between VR measures and early
markers of microstructural changes in brain networks to identify the most biologically valid VR measures of
prodromal AD. Our central hypothesis is that VR measures of episodic memory, spatial navigation, and
visuospatial skills are most sensitive in detecting prodromal AD, and that these VR measures predict AD-
related brain pathology in medial temporal and posterior parietal cortical regions as well as in cingulum and
hippocampal white matter fascicles. Our preliminary data using an in-house, novel, multidomain VR
assessment battery on a sample of individuals with amnestic mild cognitive impairment (aMCI) and older
healthy controls (HC) (N = 23, 17 with aMCI) supported our hypothesis. We propose to refine, optimize, test
and validate our suite of VR measures in a larger sample (N = 50 total, 30 aMCI) to accomplish the following
Specific Aims: To optimize and test a suite of VR assessments for sensitive detection of prodromal AD (Aim 1)
and to test the biological validity of the proposed VR measures in detecting prodromal AD pathology (Aim 2).
Successful validation of the proposed VR battery may lead to development of a cost-effective, ecologically and
biologically valid assessment tool for early detection of AD. Further, these measures can potentially be used as
sensitive behavioral markers for monitoring the response to experimental AD treatments and predicting
cognitive and clinical trajectories.

## Key facts

- **NIH application ID:** 10289512
- **Project number:** 1R21AG073973-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Hadi Hosseini
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $236,100
- **Award type:** 1
- **Project period:** 2021-09-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10289512

## Citation

> US National Institutes of Health, RePORTER application 10289512, Development of a cost-effective and neurobiologically valid VR assessment tool for   early detection of AD (1R21AG073973-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10289512. Licensed CC0.

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