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

基本信息

批准号：
10315319
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
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10315319
关键词：
Affect Age Aging Alzheimer&apos 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 Modeling 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 Structure Subgroup Testing Thick Thinness Training Woman Work World Trade Center disaster aging brain base biobank clinical anxiety cognitive function cohort deep learning improved men method development middle age neuroimaging novel operation post-traumatic symptoms public health priorities secondary analysis tau Proteins trauma centers

项目摘要

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.

项目概要/摘要在 9/11 世界贸易中心 (WTC) 现场从事救援和恢复工作的男男女女中年时出现认知障碍，比年龄认知障碍通常早几十年迄今为止，已发现认知功能障碍和损伤的最一致的危险因素之一。人群包括长期暴露于世贸中心灾难现场并出现创伤后应激障碍的症状（创伤后应激障碍）。我们的初步分析发现，与痴呆症患者相比，皮质厚度有所减少。虽然这项工作对于增进我们的理解很有价值，但认知能力未受损的世贸中心响应者。世贸中心响应者认知障碍的影响，目前尚不清楚皮质厚度减少到什么程度已知疾病的指标，迄今为止还没有研究能够可靠地量化其程度我们的团队最近发现了一种高度敏感的生物标志物。我们已经证明，早在 40 年代末，“大脑年龄”就可以检测到退化的最初迹象。这种测量被称为大脑“网络稳定性”，可在多个大规模静息态功能中复制磁共振成像数据集与逐渐认知能力下降之间的差异。基于 MRI 数据（“大脑年龄”）的个人预测年龄与实际年龄的比较提供了一个衡量标准：因此，下一步关键是确定世贸中心响应者的大脑年龄特征。结构（皮质厚度）和功能（网络稳定性），这可能将 WTC 创伤与观察到的联系起来在目前的工作中，我们建议完成对中年认知障碍的二次数据分析。大规模脑部 MRI 训练数据集（英国生物银行，N=19,831），用于训练神经生物学深度学习模型然后我们将比较 WTC 中看到的衰老特征及其潜在机制。在目标 1 中，我们测量了 WTC 响应者的大脑老化速度，以及没有创伤后应激障碍（PTSD），将大脑衰老与人口正常情况以及阿尔茨海默病的蛋白质组标记进行比较疾病和相关痴呆症，包括 β-淀粉样蛋白和 tau 蛋白在目标 2 中，我们利用了之前的方法。神经影像数据人工智能的发展，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 大脑分类为与其中一个或多个特定的神经生物学特征相匹配这项研究响应了对受世贸中心影响的个体进行与衰老相关的研究提案的呼吁。（RFA-OH-21-004）并将提高我们对现有神经生物学加速衰老的理解为了成功预防 ADRD，需要采取可靠的措施。这项研究旨在实现中年时期大脑加速老化的亚临床变化。一种新的大脑年龄测量方法，经过优化，对中年神经系统变化敏感，并将其与人工智能相结合以确定暴露可能影响世贸中心响应者的机制。