# Digital Biomarkers for Vascular Cognitive Decline in Patients with Minor Stroke

> **NIH NIH RF1** · JOHNS HOPKINS UNIVERSITY · 2022 · $2,310,293

## Abstract

PROJECT SUMMARY/ABSTRACT
Thrombectomy has significantly improved stroke outcomes. Nearly 80% of our clinic population now present
with small strokes and low NIH Stroke Scale scores. However, greater than half endorse significant problems
with attention, executive function, and processing speed. For many, significant recovery is seen by 6 months,
but up to one third experience persistent vascular cognitive impairment. A biomarker to robustly predict who
will exhibit long-standing deficits would enable us to initiate early interventions to slow or even prevent decline.
Our work with MEG suggests global disruption of cognitive networks irrespective of stroke size or location;
however, the compensatory mechanisms that allow some to recover but fail in others are poorly understood.
There is a critical need for a noninvasive, inexpensive screening tool that can be widely implemented.
The scientific premise of this proposal is two-fold: (i) using MEG and EEG we can determine functional network
characteristics affecting both those with transient post-stroke cognitive impairment (psMCI) and persistent
vascular cognitive impairment (VCI) as well as the compensatory mechanisms responsible for recovery, and
(ii) a novel deep learning model that performs multimodal (MEG and EEG) learning to find shared signatures of
VCI, but ultimately yields a model that needs affordable EEG-only data, will yield a powerful biomarker that can
predict conversion of psMCI to VCI early after stroke. This proposal will pursue three specific aims. 1) Identify
neurophysiologic similarities between transient psMCI and persistent VCI; 2) Identify specific features of
functional connectivity that prognosticate conversion to VCI; 3) Design a digital biomarker that predicts
conversion using functional brain networks that can be extended from MEG to EEG. To achieve these aims,
we will collect both MEG and EEG data from 200 patients with minor stroke, evaluate their signals with expert
neurophysiologists, and monitor the patient’s yearly conversion rate to VCI. 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 conversion to VCI. The final product will be an EEG digital biomarker that can be readily
measured and widely employed across the country. The research proposed in this application is innovative
because it is the first to use functional network signals to design a biomarker for VCI that is inexpensive and
widespread, yet robust, and achieves this by cutting edge machine learning. It is also significant because it will
advance the field vertically both scientifically and clinically by enabling large-scale, early detection of VCI. Our
team is well-prepared to undertake this project, with clinical and engineering expertise, strong collaborations,
preliminary data supporting the aims, an...

## Key facts

- **NIH application ID:** 10525918
- **Project number:** 1RF1AG079324-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Elisabeth Breese Marsh
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,310,293
- **Award type:** 1
- **Project period:** 2022-09-10 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10525918

## Citation

> US National Institutes of Health, RePORTER application 10525918, Digital Biomarkers for Vascular Cognitive Decline in Patients with Minor Stroke (1RF1AG079324-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10525918. Licensed CC0.

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