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Establishing a Brain Health Index

建立大脑健康指数

基本信息

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
项目类别：
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

项目摘要

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：脑老化的多导睡眠图生物标志物

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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。