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 brain aging that occur in midlife. This study seeks to implement a novel measure of brain age optimized to be sensitive to midlife neurological changes and combines it with AI to identify the mechanisms through which exposures may have affected WTC responders.

PROJECT NARRATIVE 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. This study seeks to implement a novel measure of brain age, optimized to be sensitive to midlife neurological changes, and combines it with artificial intelligence to understand the mechanisms through which exposures may have affected WTC responders. 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.