# An AI-assisted screening platform within a multivariate framework for biomarkers of mild cognitive impairment due to Alzheimer's disease

> **NIH NIH R41** · ISCREEN 2 PREVENT LLC · 2022 · $26,488

## Abstract

PROJECT SUMMARY
In the past decade, the rate of deaths from Alzheimer's disease (AD) and other dementias escalated more than
twice the rate of deaths from heart disease. Unfortunately, there is a lack of low-cost and non-invasive diagnostic
instruments to accurately identify individuals at risk of AD and ADRD. Advanced non-invasive imaging shows
that retinal neurodegeneration and visual deficits occur long before the cognitive decline in AD and ADRD. This
fact raises the possibility of identifying mechanisms that drive retinal pathology in AD/ADRD that could help
develop effective diagnostics tools and therapies that target early disease. The well-characterized organization
of the retina, with powerful non-invasive imaging and electrophysiology techniques to monitor retinal function,
make it an optimal surrogate to study early CNS pathology. The brain shares many similarities with the retina.
This suggests that the retina, a more accessible organ than the cortex, may provide a viable brain biomarker for
testing diagnostics tools and therapies that target early disease and prevention. Notably, we happen to live in a
non-linear world surrounded by objects and processes with the property of fractality and non-linearity. For
example, the deficit of fractal complexity (i.e., fractality) of environmental effects can lead to fractal complexity
distortion in the brain's visual pathways and abnormalities of development or aging. Particularly, non-linear
dynamics of physiological processes involved in neurodegenerative disorders have a strong base of evidence,
which is seen in the impaired fractal regulation of rhythmic activity in aged and diseased brains. Our multivariate
biomarker methodology relies on the fractal complexity of the retinal vasculature as a potential biomarker.
However, the fractality of the time-varying electroretinogram (ERG) signal that arises from different retina layers
is not yet explored. Therefore, we aim to take advantage of the current electrophysiological measurements
acquired in the parent grant to investigate the distortion of fractal complexity in ERG signals correlated to AD
pathology as a possible means to obtain a more comprehensive assessment for the early detection of MCI due
to AD. In this project, we will further innovate our multivariate biomarker methodology by investigating the
fractality of ERG signals. This investigation would make our novel method a more robust tool by incorporating
the combined fractality of the retinal function (ERG signals) and structure (retinal vasculature), which can shed
new light on early pathogenic mechanisms that compromise retinal and brain function much before the onset of
detectable dementia. To this end, we will investigate the distortion of fractality in ERG signals and explore the
discrimination power of ERG's fractality measurements between groups with the receiver operating characteristic
curve, sensitivity, and specificity metrics. We will use the Youden index and the area un...

## Key facts

- **NIH application ID:** 10571773
- **Project number:** 3R41AG073066-01S2
- **Recipient organization:** ISCREEN 2 PREVENT LLC
- **Principal Investigator:** Delia Cabrera-DeBuc
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $26,488
- **Award type:** 3
- **Project period:** 2021-09-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10571773

## Citation

> US National Institutes of Health, RePORTER application 10571773, An AI-assisted screening platform within a multivariate framework for biomarkers of mild cognitive impairment due to Alzheimer's disease (3R41AG073066-01S2). Retrieved via AI Analytics 2026-07-11 from https://api.ai-analytics.org/grant/nih/10571773. Licensed CC0.

---

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