Establishing a Brain Health Index

建立大脑健康指数

• 批准号: 10761845
• 负责人: Michael Brandon Westover
• 金额: $ 86.28万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-07 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10761845
• 关键词: Establishing; Brain; Health; Index

Project Abstract: Polysomnographic Biomarkers of Brain Aging Although cognitive decline is a “normal” part of aging, some individuals clearly age better than others. However, the concept of differential aging has been minimally studied for the brain. Electroencephalogram (EEG) oscillations signals carry rich information regarding brain health and brain aging. Alzheimer’s disease (AD) is associated with fragmented sleep and altered sleep oscillations. Clearance of cerebral beta amyloid through the brain's glymphatic drainage system occurs mainly in non-rapid eye movement (NREM) sleep, and depends on EEG slow oscillations. Cortical generators of sleep EEG oscillations overlap with regions of cortical thinning and loss of functional connectivity in AD. Disturbances of NREM disrupt memory consolidation. Finally, deficient REM sleep contributes to dementia. These observations suggest that brain health may be measurable from information contained in the sleep EEG. In preliminary work we have developed EEG-brain age – a machine learning model that predicts a patient’s age based on patterns of overnight sleeping EEG oscillations. This allows prediction of age with a precision of +/- 7 years. Our preliminary data suggest diabetes and hypertension, chronic HIV infection, an MCI or AD are reflected in the EEG as excessive brain age, and that excessive brain age is independently associated with reduced life expectancy. Our central hypothesis is that sleep physiology data can provide sensitive and specific biomarkers of brain health. This hypothesis is based on our prior work showing that BAI is elevated in several clinical populations. BAI can be accurately calculated using frontal EEG signals, making it suitable for implementation on at-home EEG devices. The rationale for the proposed research is that validating sleep EEG-derived biomarkers as measures of brain health at the level of individual patients would lay the ground for use in clinical trials and patient care. We plan to accomplish the central objective by pursuing two complementary aims. In Aim 1, we will take a hypothesis-driven approach, and test for associations of specific sleep features with specific cognitive deficits and specific structural pathology. In Aim 2, we will take ad data-driven approach, and develop optimized biomarkers of brain health using a novel form of machine learning known as multitask learning, which combine multiple features of sleep – including conventional features, as well as data-driven features directly learned from the data – to predict or “explain” variation in cognitive performance and in structural brain MRI measures that are indicative of brain health or disease. The project will take advantage of a large and diverse set of sleep data (>33,000 patients), as well as thousands of brain MRI an cognitive testing results. At the conclusion of this study, we expect to have a better understanding of the role sleep oscillations play in brain health, and clinically useful brain health biomarkers. These outcomes will aid development of interventions to promote brain health.

项目摘要：脑老化的多导睡眠生物标志物 虽然认知能力下降是衰老的一个“正常”部分，但有些人显然比其他人衰老得更好。 然而，差异老化的概念在大脑中的研究很少。 脑电图（EEG）振荡信号携带着丰富的关于大脑健康和大脑衰老的信息。 阿尔茨海默病（AD）与睡眠片段化和睡眠振荡改变有关。清除 大脑β淀粉样蛋白通过大脑的胶质淋巴引流系统主要发生在非快速眼 运动（NREM）睡眠，并依赖于EEG慢振荡。睡眠脑电皮层发生器 振荡与AD中皮质变薄和功能连接丧失的区域重叠。紊乱 非快速眼动扰乱记忆巩固。最后，REM睡眠不足会导致痴呆症。这些观察结果 表明大脑健康可以从睡眠EEG中包含的信息中测量。 在初步工作中，我们开发了EEG-大脑年龄-一种预测患者年龄的机器学习模型。 年龄基于夜间睡眠EEG振荡的模式。这使得预测年龄的精度 +/- 7年。我们的初步数据表明，糖尿病和高血压，慢性HIV感染，MCI或AD是 反映在脑电图过度的大脑年龄，而过度的大脑年龄是独立相关的， 预期寿命缩短。 我们的中心假设是，睡眠生理学数据可以提供敏感和特异的大脑生物标志物， 健康这一假设是基于我们先前的工作，表明BAI在几个临床人群中升高。 BAI可以使用额叶EEG信号准确计算，使其适合在家中实施 脑电图仪这项研究的基本原理是，验证睡眠脑电图衍生的生物标志物， 在个体患者水平上测量大脑健康将为临床试验奠定基础， 病人护理我们计划通过追求两个相辅相成的目标来实现中心目标。目标1： 将采取假设驱动的方法，并测试特定睡眠特征与特定 认知缺陷和特定的结构病理学。在目标2中，我们将采取广告数据驱动的方法，并开发 使用称为多任务学习的新型机器学习形式优化大脑健康的生物标志物， 其结合了联合收割机的多种睡眠特征-包括常规特征以及数据驱动特征 直接从数据中学习-预测或“解释”认知表现和大脑结构的变化 MRI测量表明大脑健康或疾病。该项目将利用一个大型和 这些数据来自不同的睡眠数据集（> 33，000名患者），以及数千份大脑MRI和认知测试结果。 在这项研究的结论，我们希望有一个更好的了解睡眠振荡的作用， 脑健康和临床上有用的脑健康生物标志物。这些成果将有助于发展 促进大脑健康的干预措施。

项目成果

Measuring expertise in identifying interictal epileptiform discharges.

• DOI: 10.1684/epd.2021.1409
• 发表时间: 2022-06-01
• 期刊: EPILEPTIC DISORDERS
• 影响因子: 2.3
• 作者: Harid, Nitish M;Jing, Jin;Hogan, Jacob;Nascimento, Fabio A;Ouyang, An;Zheng, Wei-Long;Ge, Wendong;Zafar, Sahar F;Kim, Jennifer A;Alice, D Lam;Herlopian, Aline;Maus, Douglas;Karakis, Ioannis;Ng, Marcus;Hong, Shenda;Yu, Zhu;Kaplan, Peter W;Cash, Sydney;Shafi, Mouhsin;Martz, Gabriel;Halford, Jonathan J;Westover, Michael Brandon
• 通讯作者: Westover, Michael Brandon

Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing.

使用自然语言处理从非结构化电子健康记录中自动提取中风严重程度。

• DOI: 10.1101/2024.03.08.24304011
• 发表时间: 2024
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Fernandes,Marta;Westover,MBrandon;Singhal,AneeshB;Zafar,SaharF
• 通讯作者: Zafar,SaharF