# Digital biomarker for a low cost ambulatory test for early detection of Alzheimer's disease

> **NIH NIH RF1** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2021 · $2,050,969

## Abstract

PROJECT SUMMARY/ABSTRACT
 Presently, no established biomarker exists to robustly predict the clinical manifestation of cognitive symptoms
in persons with Alzheimer’s disease (AD) and AD-related dementias. PET and MRI brain biomarkers are costly
and invasive, thus there is a critical need for a noninvasive, inexpensive, and portable AD screening tool that
can be easily deployed in-home or in residential communities. While non-brain signals characterizing biological
and behavioral traits may prove valuable, two types of brain signal also hold strong promise as digital biomarkers
of early stages of AD: epileptogenic activity (EA), and aberrant functional brain networks. Importantly, both
biomarkers can be collected affordably and reliably with the latest dry-electrode ambulatory
electroencephalography (EEG) technology. AD patients have a tenfold higher seizure prevalence compared to
the general population (Pandis and Scarmeas, 2012); however, the use of EA as an AD digital biomarker is
largely unexplored. It is also well known that amnestic MCI (aMCI) and AD patients show subtle functional
network disruptions that are promising predictors of AD, as shown by our group (e.g Pusil et al., 2019) and
others, but there is no previous research assessing the joint impact between EA and functional networks. The
scientific premise of this proposal is two-fold: (i) a combinatorial EA and functional network biomarker will predict
conversion from aMCI to AD more robustly than a single signal in isolation, and (ii) a novel deep learning model
that performs multimodal (MEG and EEG) learning to find shared signatures of AD, but ultimately yields a model
that needs affordable EEG-only data, will yield a powerful biomarker. This proposal will pursue three specific
aims. 1) Identify specific features of EA that prognosticate aMCI conversion; 2) Design a digital biomarker that
predicts aMCI conversion from EA features and functional brain networks; 3) Extend the digital biomarker to
ambulatory EEG with dry electrode technology. To achieve these aims, we will collect MEG, wet-electrode EEG,
and dry-electrode (ambulatory) EEG data from 200 aMCI patients, evaluate their signals with expert
epileptologists, and monitor the patient’s yearly conversion rate to AD. We will then design and validate a deep
learning model called Siamese Multiple Graph to Gauss (SMG2G), which performs multimodal learning on MEG
and EEG network (graph) data but ultimately yields a model that needs EEG-only data to make predictions of
aMCI conversion. The final product will be a dry-electrode ambulatory EEG digital biomarker that can be readily
measured in home or in a residential facility. The research proposed in this application is innovative because it
is the first to combine EA and functional network signals to design an AD biomarker and achieves this by cutting-
edge machine learning. It is also significant because it will advance the field vertically both scientifically and
clinically by ena...

## Key facts

- **NIH application ID:** 10301875
- **Project number:** 1RF1AG074204-01
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Michael Funke
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2,050,969
- **Award type:** 1
- **Project period:** 2021-09-30 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10301875

## Citation

> US National Institutes of Health, RePORTER application 10301875, Digital biomarker for a low cost ambulatory test for early detection of Alzheimer's disease (1RF1AG074204-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10301875. Licensed CC0.

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