Multi-site longitudinal Integrated Neurocognitive and Sleep-Behavior Profiler for the Endophenotypic Classification of Dementia Subtypes (INSPECDS)

用于痴呆亚型内表型分类的多部位纵向综合神经认知和睡眠行为分析仪 (INSPECDS)

• 批准号: 10603714
• 负责人: Chris Berka
• 金额: $ 99.99万
• 依托单位: ADVANCED BRAIN MONITORING, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10603714
• 关键词: Accelerometer; Address; Algorithms; Alzheimer' s Disease; Architecture; Automatic Data Processing; Behavior assessment; Biological Markers; Brain; Cause of Death; Characteristics; Classification; Clinical Research; Clinical Trials; Clinical Trials Cooperative Group; Consult; Data; Data Analyses; Data Set; Dementia; Dementia with Lewy Bodies; Devices; Diagnosis; Disease Progression; Drug Industry; Electrocardiogram; Electroencephalography; Electrophysiology (science); Frontotemporal Dementia; Goals; Head Movements; Health Personnel; Home; Impaired cognition; Individual; Infrastructure; Knowledge; Machine Learning; Malignant Neoplasms; Measures; Memory; Monitor; Neurocognitive; Neurodegenerative Disorders; Neurologic; Neuropsychological Tests; Parkinson' s Dementia; Patients; Performance; Pharmacologic Substance; Phase; Probability; Process; REM Sleep Behavior Disorder; Reporting; Research; Research Personnel; Secure; Site; Sleep; Sleep Architecture; Small Business Innovation Research Grant; Statistical Models; Stratification; Study Subject; System; Techniques; Technology; Technology Assessment; Testing; Time; Training; Universities; Visit; alertness; base; brain behavior; classification algorithm; clinical diagnostics; cloud based; cognitive function; cohort; commercial application; commercialization; computerized; data acquisition; data repository; diagnostic accuracy; drug discovery; human subject; improved; large datasets; limb movement; machine learning classification; mild cognitive impairment; neurocognitive test; novel; outreach; relating to nervous system; retention rate; scale up; sleep behavior; synucleinopathy; tool; wireless

It is estimated that Alzheimer’s and other neurodegenerative diseases causing dementia will surpass cancer as the second leading cause of death by the year 2040. Alzheimer’s disease (AD) is the leading cause of dementia, followed by synucleinopathies, including dementia with Lewy bodies (DLB), Parkinson’s disease with dementia (PDD), and Fronto-temporal dementia. There is an urgent, unmet need for effective tools to aid in the classification of dementia subtypes, in the earliest detectable stages of the pathophysiological process. To address this, Advanced Brain Monitoring (ABM) is leveraging day/night assessment technologies to create the Integrated Neurocognitive and Sleep-Behavior Profiler for the Endophenotypic Classification of Dementia Subtypes (INSPECDS) to profile Alzheimer’s and other dementias. The components of the platform are the Alertness and Memory Profiler (AMP), the Sleep Profiler (SP), and integrated machine-learning, classification algorithms, hosted on a secure cloud-based, infrastructure for automated data processing, analysis, & reporting. AMP is unique among neurocognitive testing platforms in that it is the only one that integrates advanced electrophysiological measures (e.g., 24-channel wireless EEG/ECG) during the performance of computerized neurocognitive tasks and has proven effective in characterizing cognitive decline in Alzheimer’s disease. This capability permits researchers to explore real-time relations between fluctuations in alertness, discrete cognitive functions, and specific neural processes believed to subserve observed performance deficits in Alzheimer’s disease and other dementias. The SP is FDA-cleared, easily applied, wireless-EEG device that was developed and validated to measure sleep architecture for in-home sleep studies with submental EMG and wireless accelerometers to monitor head and limb movements to quantify the characteristics of REM-sleep behavior disorder, considered to be a prodromal expression of synucleinopathy. The application of machine-learning, classification algorithms streamlines the processing and analyses of these data to derive statistical probabilities of Alzheimer’s disease and other dementia subtypes. The overarching goal of the current submission is to finalize implementation of a secure, cloud-based infrastructure to compile the data obtained from the AMP and SP, train classification algorithms to discriminate among Alzheimer’s disease and other dementia subtypes, validate diagnostic accuracy, and integrate optimized classifiers within the cloud-based architecture. The INSPECDS system is the first clinical research tool of its kind with application in both university-based research settings and pharmaceutical clinical trials to aid in the endophenotypic stratification of Alzheimer’s disease and other dementias.

据估计，阿尔茨海默氏症和其他神经退行性疾病会导致痴呆症 到2040年，超越癌症是死亡的第二大死亡原因。阿尔茨海默氏病（AD）是 痴呆症的主要原因，其次是突触性疾病，包括Lewy身体（DLB）的痴呆症， 帕金森氏病（PDD）和额叶痴呆症。有一个紧急，未安装的 需要有效的工具以帮助最早可检测到的痴呆症亚型分类 病理生理过程。为了解决这个问题，高级大脑监测（ABM）正在利用 日/夜评估技术以创建综合的神经认知和睡眠行为分析师 为了对痴呆症亚型（INSECD）的内型型分类，以介绍阿尔茨海默氏症和其他人 痴呆症。平台的组成部分是警觉性和记忆分析器（AMP），睡眠 Profiler（SP）和集成的机器学习，分类算法，该算法托管在一个基于安全的云上， 自动数据处理，分析和报告的基础架构。放大器在 神经认知测试平台是唯一整合高级电生理学的平台 在计算机化神经认知过程中，措施（例如24通道无线EEG/ECG） 任务并已证明有效地表征了阿尔茨海默氏病的认知能力下降。这个功能 允许研究人员探索机敏，离散认知的波动之间的实时关系 功能以及被认为可以观察到的性能的特定神经过程定义 阿尔茨海默氏病和其他痴呆症。 SP是FDA清除的，易于应用的，无线EEG的设备 已开发和验证以测量次室内睡眠研究的睡眠体系结构 EMG和无线加速度计监测头和肢体运动以量化特征 REM睡眠行为障碍，被认为是突触核酸的前驱表达。这 机器学习，分类算法的应用简化了处理和分析 这些数据可导致阿尔茨海默氏病和其他痴呆症亚型的统计可能性。 当前提交的总体目标是最终确定基于云的安全的实现 基础架构来编译从AMP和SP，火车分类算法获得的数据 区分阿尔茨海默氏病和其他痴呆症亚型，验证诊断准确性和 基于云的体系结构中的集成优化分类器。 Inspecds系统是第一个 同类的临床研究工具，都在基于大学的研究环境和 药物临床试验，以帮助阿尔茨海默氏病和其他 痴呆症。