A Machine Learning Algorithm to Assess Functional "Brain Age" from an In-Home EEG Sleepband

一种通过家用脑电图睡眠带评估功能性“大脑年龄”的机器学习算法

• 批准号: 10820286
• 负责人: Michael Comerford
• 金额: $ 29.5万
• 依托单位: NEUROGENECES, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-10 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10820286
• 关键词: Acceleration; Adult; Age; Aging; Algorithms; Apnea; Assessment tool; Behavior; Behavioral; Biological; Biological Clocks; Biological Markers; Brain; Brain Injuries; Characteristics; Clinical; Cognitive; Cognitive aging; Computer software; Coupling; Data; Data Scientist; Data Set; Databases; Deterioration; Diabetes Mellitus; Disease Progression; Early Diagnosis; Electroencephalography; Electrophysiology (science); Facebook; Funding; Goals; Grant; Health; Home; Human; Impaired cognition; Individual; Intervention; Legal patent; Life Style; Longevity; Machine Learning; Magnetic Resonance Imaging; Measures; Medical; Mental disorders; Methods; Modeling; Nerve Degeneration; Neurobehavioral Manifestations; Nutritional; Obesity; Performance; Phase; Physicians; Physiological; Polysomnography; Population; Process; Science; Self Administration; Sleep; Sleep Architecture; Sleep Disorders; Sleep Stages; Small Business Innovation Research Grant; Small Business Technology Transfer Research; Smoking; Standardization; Structure; Study Subject; Symptoms; System; Testing; Time; Training; Validation; aged; aging brain; brain based; brain dysfunction; commercialization; cost; density; early detection biomarkers; effective intervention; experience; health management; human data; human subject; indexing; machine learning algorithm; machine learning model; nervous system disorder; neurotransmission; potential biomarker; premature; prevent; screening; sensor; tool; wearable device

1 PROJECT SUMMARY 2 Humans are living longer, resulting in our bodies outliving our brains. Although behavioral changes and better 3 management of health conditions can help reverse or slow down premature decline in brain function, pre- 4 symptomatic adults are not identified early enough when these changes would make the most significant positive 5 impact. Effective intervention requires early detection before irreversible brain damage occurs, but there is a lack 6 of objective, scalable tools to assess brain function in pre-symptomatic adults. 7 8 Brain Age (BA) is a biomarker that can be used for early detection of deterioration in brain function. BA reflects 9 an individual’s age-adjusted structural and/or functional brain characteristics and has been shown to detect 10 cognitive impairment. While effective, the cost and inconvenience of current assessment methods (e.g., MRI, 11 polysomnography) prevent widespread usage of BA as a biomarker. Therefore, there is a clear unmet need for 12 a new method for assessing BA. 13 14 NeuroGeneces’ BA machine learning (ML) model, using at-home sleep data, will provide an objective and 15 interpretable measure of age-adjusted brain function. In this Phase I STTR project, NeuroGeneces will expand 16 the dataset of sleep recordings by conducting a Human Subject Study and validate the feasibility of a ML model 17 that accurately predicts biological BA in cognitively healthy adults using an at-home sleep EEG headband.

1项目概要 2.人类的寿命越来越长，导致我们的身体比我们的大脑更长寿。虽然行为改变和更好的 3健康状况的管理可以帮助扭转或减缓大脑功能的过早衰退， 4有症状的成年人没有足够早地被识别出来，而这些变化会产生最显著的积极影响。 5影响。有效的干预需要在不可逆的脑损伤发生之前早期发现，但缺乏 6客观，可扩展的工具，以评估症状前成人的大脑功能。 7 8脑年龄（BA）是一种生物标志物，可用于早期检测脑功能的恶化。BA反映 9个人的年龄调整的结构和/或功能的大脑特征，并已被证明可以检测 10认知障碍。虽然有效，但目前评估方法的成本和不便（例如，核磁共振成像， 11多导睡眠图）阻止BA作为生物标志物的广泛使用。因此，有一个明确的未满足的需要， 12.一种新的评估BA的方法。 13 14 NeuroGeneces的BA机器学习（ML）模型，使用家庭睡眠数据，将提供一个客观的， 年龄调整后的大脑功能的15个可解释的措施。在这个第一阶段STTR项目中，NeuroGeneces将扩大 16通过进行人类受试者研究的睡眠记录数据集，并验证ML模型的可行性 17，准确地预测生物BA在认知健康的成年人使用在家里的睡眠脑电图头带。