# Collaborative Research: SCH: Assessment of Cognitive Decline using Multimodal Neuroimaging with Embedded Artificial Intelligence

> **NIH NIH R01** · VILLANOVA UNIVERSITY · 2024 · $292,236

## Abstract

PROJECT SUMMARY (See instructions): 
The various forms of cognitive decline cost an estimated $305 billion in the United States alone. It 
is anticipated that, by 2050, the number of older adults with cognitive decline will double. The impact 
of cognitive decline goes beyond the financial costs but also presents physical, mental, and 
emotional burdens to older adults, their caregivers, and society. Recent studies reveal that the 
prevalence of mild cognitive impairment (MCI) ranges from 10% to 20% in older adults and no 
medications have been proven to be effective for treating MCI. Thus, accurate diagnosis, assessment, and 
detection of cognitive decline in older adults are essential for developing effective, precise, and 
individualized management and treatment procedures. 
In this project, we will develop a toolchain for the assessment of cognitive decline using 
multimodal neuroimaging and machine learning (ML) methods. We propose three specific aims: (1) 
to develop a comprehensive test battery selective of MCI in a mobile software synchronized with 
multimodal functional near infrared spectroscopy and electroencephalography (fNIRS-EEG) based 
neuroimaging system that can concurrently provide electrophysiological, hemodynamic and behavioral 
measures; (2) to extract, select, and validate the multitude of within and across modality biomarkers 
from fNIRS-EEG data in temporal, spatial, spectral, and complexity domains together with the 
behavioral ones; (3) to develop a comprehensive multimodal ML approach to detect MCI based on 
fNIRS-EEG and behavioral features. 
The finding of this project can lead to an unprecedented transformation to the study, assessment, 
diagnosis and monitoring of cognitive decline in older adults. Developing a mobile application that 
combines functional near-infrared-spectroscopy (fNIRS) and electroencephalography (EEG) on one 
platform that could be used in less expensive and restrictive testing environments to determine 
functional brain alterations in patients with mild cognitive impairment (MCI) is very innovative and will 
have major impact on knowledge. Furthermore, utilizing the mobile application and cutting-edge machine 
learning methods, will allow us to determine novel functional brain biomarkers that distinguish older 
adults with MCI from healthy controls, which in turn can have major impact on diagnostic procedures of 
older adults with MCI.

## Key facts

- **NIH application ID:** 10893408
- **Project number:** 5R01AG077018-03
- **Recipient organization:** VILLANOVA UNIVERSITY
- **Principal Investigator:** Roee Holtzer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $292,236
- **Award type:** 5
- **Project period:** 2022-09-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10893408

## Citation

> US National Institutes of Health, RePORTER application 10893408, Collaborative Research: SCH: Assessment of Cognitive Decline using Multimodal Neuroimaging with Embedded Artificial Intelligence (5R01AG077018-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10893408. Licensed CC0.

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