# Using Artificial Intelligence to Identify Accelerated Brain Aging in World Trade Center Responders

> **NIH NIH R21** · STATE UNIVERSITY NEW YORK STONY BROOK · 2021 · $249,999

## Abstract

PROJECT SUMMARY/ABSTRACT
The men and women who worked in rescue and recovery operations at the 9/11 World Trade Center (WTC) site
are developing cognitive impairment at mid-life, decades before age-based cognitive impairment is usually
detected. To date, one of the most consistent risk factors for cognitive dysfunction and impairment in this
population include long-term exposures to the WTC disaster sites and symptoms of posttraumatic stress disorder
(PTSD). Our preliminary analyses identified reduced cortical thickness in responders with dementia compared
to cognitively unimpaired WTC responders. While this work has been valuable in advancing our understanding
of cognitive impairment in WTC responders, it remains unknown to what extent reduced cortical thickness is
indicative of a known disorder, and no studies to date have been able to reliably quantify the extent to which
patterns evident on MRI match population norms. Our team has recently identified a highly sensitive biomarker
for functional “brain age,” which we have shown to detect the first signs of deterioration as early as the late 40’s.
Known as brain “network stability,” this measure replicates across multiple large-scale resting-state functional
magnetic resonance imaging datasets and correlates with gradual cognitive decline. The difference between an
individual’s predicted age based on MRI data (“brain age”) versus their chronological age provides a metric for
accelerated brain aging. Therefore, a critical next step is to characterize WTC responders’ brain ages, both
structurally (cortical thickness) and functionally (network stability), which may relate WTC trauma to observed
cognitive impairment at mid-life. In the present work, we propose to complete secondary data analyses of a
large-scale brain MRI training data set (UK Biobank, N=19,831) to train a deep learning model for neurobiological
signatures of aging and its potential mechanisms. We will then compare neurobiological features seen in WTC
responders to these signatures. In Aim 1, we measure accelerated brain aging for WTC responders with and
without PTSD, using comparing brain aging to population norms, as well as to proteomic markers of Alzheimer’s
Disease and related dementias, including β-amyloid and tau. In Aim 2, we leverage our previous methods
development in AI of neuroimaging data to develop neurobiological classifiers specific to key mechanisms of
relevance to WTC: particulates, glucocorticoids, inflammation, anxiety, depression, and PTSD, to determine
whether AI classifies WTC brains as matching neurobiological signatures specific to one or more of these
mechanisms. This study responds to a call for aging-related research proposals in WTC-affected individuals
(RFA-OH-21-004) and will improve our understanding of accelerated neurobiological aging in an existing
neuroimaging study of WTC responders. For the prevention of ADRD to be successful, reliable measures are
needed for subclinical changes in accelerated b...

## Key facts

- **NIH application ID:** 10315319
- **Project number:** 1R21AG074706-01
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** SEAN CLOUSTON
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $249,999
- **Award type:** 1
- **Project period:** 2021-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10315319

## Citation

> US National Institutes of Health, RePORTER application 10315319, Using Artificial Intelligence to Identify Accelerated Brain Aging in World Trade Center Responders (1R21AG074706-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10315319. Licensed CC0.

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