# Naturalistic Event Representation as a Novel Biomarker of Preclinical Alzheimer's Disease

> **NIH NIH R03** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2020 · $157,000

## Abstract

By 2060, the number of Americans 65 and older is projected to more than double from 46 million to 98
million, 24% of the total population. With this comes an increased prevalence of Alzheimer’s disease
(AD), which will create significant burden on our society and government. At present, screening tools
capable of differentiating healthy aging from AD are most effective a decade or more after the
preclinical stage, when potential treatments would be most effective. Thus, discovery of novel and
specific tools for assessing the aging brain are of utmost importance. Typical studies of cognitive ability
involve recognition of learned objects or simple word associations. However, in real-world situations,
the content of an event is segmented from a flow of multimodal information. Segmentation and
representation of events is supported by a posterior-medial network (PMN) of brain areas. Critically,
this very same brain network features the first regions affected by pathological accumulation of amyloid
beta (Aβ), a key characteristic of AD. A recent report from an NIA working group defined asymptomatic
Aβ accumulation as the earliest indicator of preclinical AD. Given the functional role of the PMN, we
propose that this stage of disease may not be truly asymptomatic: subtle functional deficits may be
evident if properly probed. To address this, we have developed a naturalistic paradigm to characterize
event representation in the brain and subsequent memory. We aim to test the novel hypotheses that
the brain’s ability to segment and represent complex events is compromised in preclinical AD, and that
the extent of this disruption is predictive of deficient memory for the experienced events. We will
acquire functional MRI (fMRI) scans while participants view a video narrative depicting naturalistic
scenarios. We will additionally test memory performance related to details of the events depicted in the
video both in and out of the scanner. Using representational similarity analysis (RSA) and machine
learning analyses of functional MRI (fMRI) data, we will examine differences in the way the brain
represents events into advanced aging in participants with and without ‘asymptomatic’ amyloid
deposition (status obtained via existing PET scan data). This combination of approaches is highly
innovative because current translational measures do not assess memory for rich, dynamic events that
make up the majority of real-world experience. This project is expected to significantly improve our
understanding of neural and cognitive disruptions that differentiate healthy aging from preclinical AD.
By studying how the brain chunks and represents events, and how this relates to memory for those
events, we can reveal significant insights into the way AD-related pathology affects day-to-day living.
This can provide a mechanistic framework for understanding subtle, subjective memory complaints.
The results of this work are anticipated to significantly advance our understanding ...

## Key facts

- **NIH application ID:** 9912695
- **Project number:** 5R03AG063224-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Charan Ranganath
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $157,000
- **Award type:** 5
- **Project period:** 2019-04-15 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9912695

## Citation

> US National Institutes of Health, RePORTER application 9912695, Naturalistic Event Representation as a Novel Biomarker of Preclinical Alzheimer's Disease (5R03AG063224-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9912695. Licensed CC0.

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