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

基本信息

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
项目类别：
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

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

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项目摘要

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个项目摘要 2人类的寿命更长，导致我们的身体超越了我们的大脑。尽管行为变化和更好 3对健康状况的管理可以帮助逆转或减慢大脑功能的过早下降，前 4症状的成年人在这些变化将使最显着的积极变化时得不到足够的早期确定 5影响。有效的干预需要在不可逆的脑损伤发生之前早期检测，但是缺乏 6的客观，可扩展工具评估症状前成年人的大脑功能。 7 8脑年龄（BA）是一种生物标志物，可用于早期检测脑功能检测的检测。 BA反映 9一个人的年龄调整后的结构和/或功能性大脑特征，已被证明可检测 10认知障碍。虽然有效，但当前评估方法的成本和影响（例如MRI， 11多个术语）防止BA作为生物标志物的宽度使用。因此，明显未满足 12评估BA的新方法。 13 使用在家睡眠数据的14个Neurogeneces的BA机器学习（ML）模型将提供一个目标和 15可解释的对年龄调整的大脑功能的测量。在此阶段I STTR项目中，神经源会扩展 16通过进行人类主题研究并验证ML模型的可行性，睡眠记录的数据集 17可以准确地预测使用在家睡觉的脑袋头带的认知健康成年人中的生物BA。