RF1AG079324

Digital Biomarkers for Vascular Cognitive Decline in Patients with Minor Stroke - 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, and institutional support.

Patients with minor stroke have significant potential to fully recover. A biomarker to detect the high likelihood of conversion to VCI will allow us to design, implement, and monitor the effectiveness of targeted interventions to slow or even prevent cognitive decline.

The Johns Hopkins University

TO ENCOURAGE BIOMEDICAL, SOCIAL, AND BEHAVIORAL RESEARCH AND RESEARCH TRAINING DIRECTED TOWARD GREATER UNDERSTANDING OF THE AGING PROCESS AND THE DISEASES, SPECIAL PROBLEMS, AND NEEDS OF PEOPLE AS THEY AGE. THE NATIONAL INSTITUTE ON AGING HAS ESTABLISHED PROGRAMS TO PURSUE THESE GOALS. THE DIVISION OF AGING BIOLOGY EMPHASIZES UNDERSTANDING THE BASIC BIOLOGICAL PROCESSES OF AGING. THE DIVISION OF GERIATRICS AND CLINICAL GERONTOLOGY SUPPORTS RESEARCH TO IMPROVE THE ABILITIES OF HEALTH CARE PRACTITIONERS TO RESPOND TO THE DISEASES AND OTHER CLINICAL PROBLEMS OF OLDER PEOPLE. THE DIVISION OF BEHAVIORAL AND SOCIAL RESEARCH SUPPORTS RESEARCH THAT WILL LEAD TO GREATER UNDERSTANDING OF THE SOCIAL, CULTURAL, ECONOMIC AND PSYCHOLOGICAL FACTORS THAT AFFECT BOTH THE PROCESS OF GROWING OLD AND THE PLACE OF OLDER PEOPLE IN SOCIETY. THE DIVISION OF NEUROSCIENCE FOSTERS RESEARCH CONCERNED WITH THE AGE-RELATED CHANGES IN THE NERVOUS SYSTEM AS WELL AS THE RELATED SENSORY, PERCEPTUAL, AND COGNITIVE PROCESSES ASSOCIATED WITH AGING AND HAS A SPECIAL EMPHASIS ON ALZHEIMER'S DISEASE. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO EXPAND AND IMPROVE THE SBIR PROGRAM, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.

93.866 - Aging Research

National Institute on Aging (NIA) [HHS - NIH]

Baltimore, Maryland 212051832 United States

Single Zip Code

Research on Current Topics in Alzheimer's Disease and Its Related Dementias (R01 Clinical Trial Optional) (PAR-19-070)

Amendment Since initial award the End Date has been extended from 08/31/25 to 08/31/27 and the total obligations have increased 62% from $2,310,293 to $3,752,944.