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

使用人工智能识别世贸中心急救人员的大脑加速老化情况

• 批准号: 10474467
• 负责人: SEAN CLOUSTON
• 金额: $ 25万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10474467
• 关键词: Affect; Age; Aging; Alzheimer' s disease related dementia; Amyloid beta-Protein; Anxiety; Artificial Intelligence; Biological Markers; Brain; Brain-Derived Neurotrophic Factor; C-reactive protein; Chronic stress; Chronology; Code; Cognitive; Computer software; Data; Data Analyses; Data Set; Dementia; Deterioration; Disease; Emotional Stress; Exposure to; Functional Magnetic Resonance Imaging; Genetic; Glucocorticoids; Impaired cognition; Individual; Inflammation; Institutes; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Link; Magnetic Resonance Imaging; Measures; Mental Depression; Mind; Neurobiology; Neurologic; Neurosciences; Occupations; Particulate; Pattern; Plasma; Population; Post-Traumatic Stress Disorders; Prevention; Proteomics; Recovery; Research; Research Proposals; Rest; Risk Factors; Single Nucleotide Polymorphism; Site; Subgroup; Testing; Thick; Thinness; Training; Woman; Work; World Trade Center disaster; aging brain; base; biobank; brain magnetic resonance imaging; clinical anxiety; cognitive function; cohort; deep learning model; improved; men; method development; middle age; neuroimaging; novel; operation; post-traumatic symptoms; public health priorities; secondary analysis; tau Proteins; trauma centers

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.

项目摘要/摘要 在9/11世界贸易中心（WTC）网站上从事救援和恢复业务工作的男人和女人 在基于年龄的认知障碍之前的几十年中，正在发展认知障碍 检测到。迄今 人口包括长期暴露于WTC灾难现场和创伤后应激障碍的症状 （PTSD）。我们的初步分析确定痴呆症的反应者的皮质厚度减少了 给认知无限制的WTC响应者。尽管这项工作对于促进我们的理解非常有价值 在WTC响应者中的认知障碍中，尚不清楚皮质厚度在多大程度上是 指示已知疾病，迄今为止尚无研究能够可靠地量化 关于MRI匹配人口规范的模式证据。我们的团队最近确定了高度敏感的生物标志物 对于功能性的“大脑时代”，我们已证明它可以检测到最早在40年代后期确定的第一个迹象。 该措施被称为大脑“网络稳定”，在多个大型静止状态功能上复制 磁共振成像数据集并与等级认知下降相关。一个 个人基于MRI数据（“脑年龄”）与年龄为年龄的人的预测年龄为指标提供了指标 加速大脑衰老。因此，下一步的关键是表征WTC响应者的大脑年龄，两者都 结构（皮质厚度）和功能（网络稳定性），这可能与WTC创伤有关 中年的认知障碍。在目前的工作中，我们建议完成A 大型大脑MRI训练数据集（英国生物银行，n = 19,831），以训练神经生物学的深度学习模型 衰老及其潜在机制的签名。然后，我们将比较WTC中看到的神经生物学特征 响应者对这些签名。在AIM 1中，我们测量了与WTC响应者的加速大脑衰老和 没有PTSD，将大脑衰老与人口规范进行比较，以及阿尔茨海默氏症的蛋白质组学标志物 疾病和相关痴呆症，包括β-淀粉样蛋白和tau。在AIM 2中，我们利用以前的方法 在神经影像数据的AI中开发，以开发针对关键机制的神经生物学分类器 与WTC相关：成分，糖皮质激素，感染，焦虑，抑郁和PTSD，以确定 WTC大脑作为与其中一个或多个特有的神经生物学特定的匹配的神经生物学特征 机制。这项研究对与WTC影响的个体有关与衰老相关的研究建议的呼吁做出了回应 （RFA-OH-21-004），并将提高我们对现有神经生物学衰老的理解 WTC响应者的神经影像学研究。为了预防ADRD成功，可靠的措施是 中年发生的加速大脑衰老的亚临床变化所需。这项研究旨在实施 优化的大脑年龄的一种新颖测量，对中年神经系统变化敏感，并将其与AI结合 确定暴露可能影响WTC响应者的机制。