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

项目概要/摘要 在9/11世界贸易中心（WTC）现场进行救援和恢复行动的男女工作人员 在中年时出现认知障碍，比通常基于年龄的认知障碍早几十年。 检测到到目前为止，认知功能障碍和障碍的最一致的危险因素之一， 包括长期暴露于世贸中心灾难现场和创伤后应激障碍的症状 （PTSD）。我们的初步分析表明，与对照组相比， 没有认知障碍的世贸中心急救员虽然这项工作在推进我们的理解方面很有价值 在WTC反应者的认知障碍中，皮质厚度减少到什么程度仍然未知。 这表明一种已知的疾病，迄今为止还没有研究能够可靠地量化这种疾病的程度。 核磁共振成像的模式符合人群标准。我们的团队最近发现了一种高度敏感的生物标志物 功能性“大脑年龄”，我们已经证明，早在40年代后期就可以检测到退化的第一个迹象。 这种被称为大脑"网络稳定性"的测量方法可以在多个大规模的静息状态功能测试中复制， 磁共振成像数据集，并与逐渐的认知能力下降相关。的区别 基于MRI数据的个体的预测年龄（"脑年龄"）与他们的实际年龄的对比提供了以下指标： 加速大脑衰老因此，关键的下一步是表征WTC响应者的大脑年龄， 结构（皮质厚度）和功能（网络稳定性），这可能与观察到的WTC创伤有关 中年认知障碍在目前的工作中，我们建议完成二级数据分析的一个 大规模脑MRI训练数据集（UK Biobank，N = 19，831），用于训练神经生物学的深度学习模型 衰老的特征及其潜在机制。然后，我们将比较WTC中的神经生物学特征 响应这些签名。在目标1中，我们测量了WTC响应者的加速大脑老化， 在没有创伤后应激障碍的情况下，将大脑老化与人口标准以及阿尔茨海默氏症的蛋白质组标记进行比较， 疾病和相关痴呆，包括β-淀粉样蛋白和tau蛋白。在目标2中，我们利用以前的方法 开发人工智能的神经成像数据，以开发特定于关键机制的神经生物学分类器， 与WTC的相关性：颗粒物、糖皮质激素、炎症、焦虑、抑郁和PTSD，以确定 AI是否将WTC大脑分类为与一个或多个特定的神经生物学特征相匹配， 机制等这项研究响应了对受WTC影响的个体的衰老相关研究建议的呼吁 （RFA-OH-21 - 004），并将提高我们对现有神经生物学加速老化的理解。 WTC反应者的神经影像学研究。为了成功预防ADRD，可靠的措施是 这是中年大脑加速老化的亚临床变化所必需的。本研究旨在实现 一种新的大脑年龄测量方法，优化后对中年神经系统变化敏感，并将其与人工智能相结合。 确定暴露可能影响WTC响应者的机制。

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.