# Neural mechanisms of gait disturbances as individualized digital biomarker trajectories in preclinical dementia

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $955,432

## Abstract

PROJECT SUMMARY
Alzheimer's disease and related dementias (AD/ADRD) rank prominently among age-associated
neurodegenerative disorders, with Parkinson’s disease trailing as the second most common. Contemporary
studies on AD and Mild Cognitive Impairment (MCI) have unveiled cognitive decline indicators mirrored in
nuances within gait and hand movements. These indications often emerge long before AD or MCI diagnoses
are confirmed. Consistently, research has correlated slowed gait with cognitive deterioration, elevated brain
amyloid levels, and an augmented AD risk. However, the interplay between systems influencing both cognition
and movement has largely been explored in separate studies. The depth of understanding around the cognitive
efforts required for gait initiation or motor planning remains scant. Though dementia screening traditionally
hinges on comprehensive neuropsychological evaluations and neuroimaging biomarker studies, gait and
movement assessment offers a more direct approach. Crucially, motor features remain unaffected by
language, educational background, or cognitive capabilities, positioning them as unbiased and consistent
evaluative instruments. For example, the Short Physical Performance Battery (SPPB) tests can be easily
conducted at home and monitored, even using smartphone applications. In this project, we chart a course to
harness cutting-edge AI and computer vision (CV) tools to aid in distinguishing diverse dementia and MCI
subtypes, such as AD-related MCI versus MCI with PD pathologies. Our approach integrates multi-modal brain
imaging with gait and movement data from videos, aiming to extract nuanced markers for phenotyping
(pre)clinical AD. These markers can then serve as precise digital trackers for both the progression and
distinction of age-related degenerative disorders. Ultimately, we aspire to enhance the understanding of the
intricate links between gait, movement, and cognition in aging and AD/ADRD scenarios.

## Key facts

- **NIH application ID:** 10979759
- **Project number:** 1R01AG089169-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Ehsan Adeli
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $955,432
- **Award type:** 1
- **Project period:** 2024-09-24 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10979759

## Citation

> US National Institutes of Health, RePORTER application 10979759, Neural mechanisms of gait disturbances as individualized digital biomarker trajectories in preclinical dementia (1R01AG089169-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10979759. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*