# Improving an EEG-based neurodiagnostic software platform to detect Alzheimer's Disease in MCI patients

> **NIH NIH R41** · SPARK NEURO INC. · 2022 · $299,786

## Abstract

PROJECT SUMMARY
Alzheimer’s disease (AD) is a progressive, neurodegenerative condition and the most common cause of
dementia. In the United States, an estimated 6.2 million people over the age of 65 are living with AD, 72% of
whom are over 75 years old. Given the country’s aging population, this number is expected to more than triple
by 2050, costing the United States an annual $600 billion in associated healthcare costs. Early diagnosis is
crucial to AD treatment because it allows clinicians more time to find and initiate treatment pathways, which
decreases disease progression and preserves mental capacity. New research suggests that biomarkers can
help diagnose AD years before symptoms appear. Despite recent technological advancements, many tools and
technologies that measure biomarkers are invasive, expensive, and not sensitive or specific enough, particularly
when detecting the disease at earlier stages, limiting their usability.
When combined with advanced machine learning techniques, electroencephalography (EEG) has been shown
to address many of the existing issues related to AD biomarkers. At SPARK Neuro, we aim to unlock the full
potential of EEG through a novel software platform. Combining EEG with the capabilities of machine learning,
our model better assesses cognitive health and neurodegeneration, aiding the diagnosis of AD. SPARK’s
neuroanalytic platform will be a standardized, objective, non-invasive, cost-effective diagnostic tool capable of
highly sensitive and specific detection of cognitive impairment across the entire disease continuum. Our platform
would vastly expand AD screening initiatives and provide neurological insights to aid in the diagnosis and tracking
of disease progression.
During the proposed Phase I research, we will work in collaboration with Mayo Clinic to extend our current
algorithm to assess and differentiate patients in the earlier, mild cognitive impairment stage of the disease, and
provide highly useful and usable reports to clinicians. First, we will optimize the algorithm by incorporating EEG
data collected from Mayo Clinic patients. Next, we will focus on improving the user experience of both EEG data
acquisition and clinical reporting. We will enhance end-user satisfaction and optimize the technology to fit within
current clinical workflows. Participating Mayo Clinic EEG technicians will provide feedback. Once optimized,
SPARK’s approach will constitute the first in-office EEG-based neurodiagnostic tool specifically for diagnosing
and tracking AD. Our non-invasive solution has the potential to accelerate AD screening programs, detect
pathological AD at earlier stages, and provide individualized disease progression insights.

## Key facts

- **NIH application ID:** 10546255
- **Project number:** 1R41AG078039-01A1
- **Recipient organization:** SPARK NEURO INC.
- **Principal Investigator:** Richard J. Caselli
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $299,786
- **Award type:** 1
- **Project period:** 2022-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10546255

## Citation

> US National Institutes of Health, RePORTER application 10546255, Improving an EEG-based neurodiagnostic software platform to detect Alzheimer's Disease in MCI patients (1R41AG078039-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10546255. Licensed CC0.

---

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