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综合神经认知和睡眠行为分析仪 (INSPECDS)

• 批准号: 9360534
• 负责人: Chris Berka
• 金额: $ 72.77万
• 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 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9360534
• 关键词: Accelerometer; Address; Adult; Algorithms; Alzheimer' s Disease; Architecture; Automatic Data Processing; Behavior; Behavior assessment; Biological Markers; Blinded; Brain; California; Cardiovascular Diseases; Cause of Death; Characteristics; Chin; Classification; Clinic; Clinical; Clinical Research; Clinical Trials; Data; Data Analyses; Databases; Dementia; Depressed mood; Devices; Diagnosis; Disease; Disease Progression; Drug Industry; Economic Burden; Elderly; Electrocardiogram; Electroencephalography; Electrophysiology (science); Enrollment; Etiology; FDA approved; Funding; General Hospitals; Goals; Head Movements; Home environment; Individual; Lewy Body Dementia; Machine Learning; Malignant Neoplasms; Massachusetts; Measures; Memory; Minor; Modification; Monitor; Neurocognitive; Neurodegenerative Disorders; Neuropsychological Tests; Obstructive Sleep Apnea; Parkinson' s Dementia; Participant; Patients; Performance; Phase; Probability; Process; REM Sleep Behavior Disorder; Reporting; Research; Research Infrastructure; Research Personnel; Sampling; Secure; Sleep; Sleep Architecture; Sleep Deprivation; Small Business Innovation Research Grant; Stratification; Stroke; Study Subject; System; Technology; Time; Training; United States National Institutes of Health; Universities; Wireless Technology; alertness; base; brain behavior; cloud based; cognitive function; cohort; computerized; data acquisition; diagnostic accuracy; genetic association; human subject; limb movement; mild cognitive impairment; neurocognitive test; public health relevance; relating to nervous system; synucleinopathy; tool

 DESCRIPTION (provided by applicant): It is estimated that neurodegenerative diseases causing dementia will surpass cancer as the leading cause of death by the year 2040. Alzheimer's disease (AD) is the leading cause of dementia, followed by synucleinopathies, such as dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Among clinical researchers focused on investigating the varying etiologies, genetic associations, biomarkers, and treatment options for these neurodengenerative diseases, there is an urgent need for effective tools to aid in the classification of dementia subtypes, in the earliest detectable stages of the pathophysiological process. To address this unmet need Advanced Brain Monitoring (ABM) proposes to leverage two previously developed technologies to create an Integrated Neurocognitive and Sleep-Behavior Profiler for the Endophenotypic Classification of Dementia Subtypes (INSPECDS). The core components of the INSPECDS platform will be a previously developed Alertness and Memory Profiler (AMP), a recently developed Sleep Profiler, and integrated machine-learning, classification algorithms, hosted on a secure, cloud-based, infrastructure for automated data processing, analysis, and reporting. The AMP was developed and validated during a previous NIH-funded SBIR Phase I/II project for the purpose of detecting the neurocognitive effects of sleep deprivation in adults diagnosed with obstructive sleep apnea. The AMP is truly unique among neurocognitive testing platforms in that it is the only one which integrates advanced, electrophysiological measures (e.g., 24-channel, wireless EEG and ECG) during the performance of computerized neurocognitive tasks. This advanced 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. The Sleep Profiler is an FDA-cleared, easily applied, wireless-EEG device that was developed and validated to measure sleep architecture for in-home sleep studies. With integrated measures of submental (chin) EMG and wireless accelerometers to monitor head and limb movements, the Sleep Profiler is an ideal device for quantifying the characteristics of REM-sleep behavior disorder (RBD), which is considered to be a prodromal expression of synucleinopathy. Furthermore, the application of sophisticated, machine-learning, classification algorithms will streamline the processing and analyses of these data to derive statistical probabilities of various dementia subtypes. The overarching goal of the current, Direct-to-Phase II, SBIR project is to develop a secure, cloud-based infrastructure to compile the data obtained from the AMP and Sleep Profiler, train classification algorithms to discriminate among dementia subtypes, validate diagnostic accuracy, and integrate optimized classifiers within the cloud-based architecture. Once completed, the INSPECDS system will be the first clinical research tool of its kind and find immediate application in both university-based research settings and pharmaceutical industry clinical trials to aid in the endophenotypic stratification of research participants.

 描述（由适用提供）：据估计，神经退行性疾病会导致痴呆症成为2040年的主要死亡原因。阿尔茨海默氏病（AD）是痴呆症的主要原因，其次是突触核心疗法，其次是与Lewy Body（DLB）和Parkinson nistion and Parkinson and Parkinson's Disephise and Dementia（Pd）（Pd）（Pd）（Pd）。在临床研究人员中，专注于研究这些神经传播疾病的不同病因，遗传关联，生物标志物和治疗方案，迫切需要有效的工具来帮助对痴呆症亚型的分类，在病理学过程的最早可检测的阶段。为了解决这一未满足的需求，高级大脑监测（ABM）提案，以利用两种先前开发的技术来创建一种综合的神经认知和睡眠行为剖面，以对痴呆症亚型（ISSECD）进行内型型分类。 Inspecds平台的核心组件将是先前开发的警觉性和内存探照室（AMP），这是最近开发的睡眠剖面和集成的机器学习，分类算法，该算法托管在安全的，基于云的基于云的基础架构上，用于自动数据处理，分析，分析和报告。在先前由NIH资助的SBIR I/II期项目中开发和验证了AMP，目的是检测被诊断为阻塞性睡眠呼吸暂停的成年人的睡眠剥夺的神经认知效应。在神经认知测试平台中，AMP确实是独特的，因为它是唯一在计算机化的神经认知任务执行过程中唯一集成了高级电生理测量（例如24通道，无线EEG和ECG）的方法。这种高级能力允许研究人员探索机敏性波动，离散的认知功能以及被认为可以观察到的性能定义的特定神经过程之间的实时关系。 Sleep Profiler是一种FDA清除，易于应用，无线EEG设备，已开发和验证，以测量用于家庭睡眠研究的睡眠体系结构。借助底物（CHIN）EMG和无线加速度计的综合测量来监测头部和肢体的运动，睡眠剖面是量化REM-囊性行为障碍（RBD）特征的理想装置，这被认为是突触核酸核酸的前驱表达。此外，复杂的机器学习，分类算法的应用将简化这些数据的处理和分析以得出各种痴呆症亚型的统计可能性。当前，直接直接到相的SBIR项目的总体目标是开发一个安全的，基于云的基础架构，以编译从AMP和Sleep Profiler，Train Claber，火车分类算法中获得的数据，以区分痴呆症亚型，验证诊断精度，并在云基于云的架构中进行集成优化的分类器。完成后，InspeCDS系统将成为同类临床研究工具，并在基于大学的研究环境和制药行业临床试验中立即应用，以帮助研究参与者的内植液分层。