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

 描述（由申请人提供）：据估计，到2040年，导致痴呆的神经退行性疾病将超过癌症，成为导致死亡的主要原因。阿尔茨海默病（AD）是痴呆的主要原因，其次是突触核蛋白病，例如路易体痴呆（DLB）和帕金森病痴呆（PDD）。在专注于研究这些神经退行性疾病的不同病因、遗传关联、生物标志物和治疗方案的临床研究人员中，迫切需要有效的工具来帮助在病理生理过程的最早可检测阶段对痴呆亚型进行分类。为了解决这一未满足的需求，高级脑监测（ABM）提出利用两种先前开发的技术来创建痴呆亚型内表型分类（INSPECDS）的综合神经认知和睡眠行为分析仪。INSPECDS平台的核心组件将是以前开发的Alertness and Memory Profiler（AMP），最近开发的Sleep Profiler，以及集成的机器学习，分类算法，托管在安全的基于云的基础设施上，用于自动数据处理，分析和报告。AMP是在之前NIH资助的SBIR I/II期项目期间开发和验证的，目的是检测睡眠剥夺对诊断患有阻塞性睡眠呼吸暂停的成年人的神经认知影响。AMP在神经认知测试平台中是真正独特的，因为它是唯一一个集成了先进的电生理测量（例如，24通道无线EEG和ECG）。这种先进的能力使研究人员能够探索警觉性波动，离散认知功能和特定神经过程之间的实时关系，这些神经过程被认为有助于观察到的表现缺陷。Sleep Profiler是一款经FDA批准、易于应用的无线EEG设备，经开发和验证，可用于测量家庭睡眠研究的睡眠结构。通过颏下（下巴）EMG和无线加速度计的综合测量来监测头部和肢体运动，Sleep Profiler是量化REM睡眠行为障碍（RBD）特征的理想设备，RBD被认为是突触核蛋白病的前驱表现。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，以得出各种痴呆亚型的统计概率。目前，直接到第二阶段，SBIR项目的首要目标是开发一个安全的，基于云的基础设施，以编译从AMP和睡眠分析器获得的数据，训练分类算法以区分痴呆亚型，验证诊断准确性，并在基于云的架构中集成优化的分类器。一旦完成，INSPECDS系统将成为第一个此类临床研究工具，并立即应用于大学研究环境和制药行业临床试验，以帮助研究参与者的内在表型分层。

 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.