Characterizing Alzheimer's Disease with INSPECDS: Integrated Neurocognitive and Sleep-Behavior Profiler for the Endophenotypic Classification of Dementia Subtypes

项目总结和摘要 据估计，阿尔茨海默氏症和其他导致痴呆的神经退行性疾病将超过 到2040年，癌症将成为死亡的主要原因。阿尔茨海默氏症是痴呆症的主要原因，其次是 突触核蛋白病，包括路易体痴呆（DLB）和帕金森病痴呆 (PDD)、额颞叶痴呆和血管性痴呆。在临床研究人员中， 研究阿尔茨海默病的不同病因、遗传关联、生物标志物和治疗方案 疾病，迫切需要有效的工具来帮助痴呆亚型的分类， 病理生理过程的最早可检测阶段。为了满足这一未满足的需求， 监测（ABM）建议利用日夜评估技术， 神经认知和睡眠行为分析仪用于痴呆亚型的内表型分类 （INSPECDS）来分析阿尔茨海默氏症和其他痴呆症。INSPECDS平台的核心组成部分将 是警觉性和记忆分析器（AMP），睡眠分析器，以及集成的机器学习，分类 算法，托管在一个安全的、基于云的基础设施上，用于自动化数据处理、分析和 报告. AMP最初是为了检测神经认知效应而开发和验证的 睡眠剥夺在被诊断患有阻塞性睡眠呼吸暂停的成年人中的应用，但最近被应用于 评估阿尔茨海默氏症和帕金森氏症。AMP在神经认知测试平台中是独一无二的， 它是唯一一种集成了先进的电生理测量（例如，24通道，无线 EEG和ECG），并已被证明在执行计算机化神经认知任务期间有效， 阿尔茨海默氏症认知能力下降的特征这种先进的能力使研究人员能够探索真实的- 警觉性波动、离散认知功能和特定神经过程之间的时间关系 据信有助于阿尔茨海默氏症和其他痴呆症的表现缺陷。睡眠分析仪是 一种FDA批准的、易于应用的无线脑电图设备，被开发和验证用于测量睡眠 使用颏下（下巴）EMG和无线加速度计监测的家庭睡眠研究的架构 头部和肢体运动，以量化REM睡眠行为障碍（RBD）的特征，被认为是 是突触核蛋白病的前驱表现此外，复杂的，机器的应用- 学习，分类算法将简化这些数据的处理和分析， 阿尔茨海默氏症和其他痴呆亚型的概率。当前直接到阶段的总体目标是 二、SBIR项目将最终实施一个安全的、基于云的基础设施来编译数据 从AMP和睡眠分析器获得，训练分类算法来区分阿尔茨海默氏症 和其他痴呆亚型，验证诊断准确性，并在云中集成优化的分类器- 基于架构。一旦完成，INSPECDS系统将成为第一个临床研究工具 并立即应用于大学的研究环境和制药工业的临床 试验，以帮助在阿尔茨海默氏症和其他痴呆症的内在表型分层。

PROJECT SUMMARY AND ABSTRACT It is estimated that Alzheimer's and other neurodegenerative diseases causing dementia will surpass cancer as the leading cause of death by the year 2040. Alzheimer's is the leading cause of dementia, followed by synucleinopathies, including dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD), Fronto-temporal dementia and Vascular dementia. Among clinical researchers focused on investigating the varying etiologies, genetic associations, biomarkers, and treatment options for Alzheimer's disease, 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 day and night assessment technologies to create an Integrated Neurocognitive and Sleep-Behavior Profiler for the Endophenotypic Classification of Dementia Subtypes (INSPECDS) to profile Alzheimer's and other dementias. The core components of the INSPECDS platform will be the Alertness and Memory Profiler (AMP), the 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 intially for the purpose of detecting the neurocognitive effects of sleep deprivation in adults diagnosed with obstructive sleep apnea but has more recently been applied to assess Alzheimer's and Parkinson's disease. The AMP is 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 and has proven effective in characterizing cognitive decline in Alzheimer's. 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 in Alzheimer's and other dementias. 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 submental (chin) EMG and wireless accelerometers to monitor head and limb movements to quantify the characteristics of REM-sleep behavior disorder (RBD), 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 Alzheimer's and other dementia subtypes. The overarching goal of the current, Direct-to-Phase II, SBIR project is to finalize implementation of a secure, cloud-based infrastructure to compile the data obtained from the AMP and Sleep Profiler, train classification algorithms to discriminate among Alzheimer's and other 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 Alzheimer's and other dementias.