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

 描述(申请人提供)：据估计，到2040年，导致痴呆症的神经退行性疾病将超过癌症成为主要的死亡原因。阿尔茨海默病(AD)是痴呆症的主要原因，其次是突触核病，如路易体痴呆症(DLB)和帕金森氏病痴呆症(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、生物标记物和治疗选择的临床研究人员中，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。为了解决这一未得到满足的需求，高级大脑监测(ABM)建议利用先前开发的两项技术，为痴呆亚型的内表型分类(INSPECDS)创建一个综合的神经认知和睡眠行为简档。INSPECDS平台的核心组件将是以前开发的警觉和记忆简档(AMP)，这是最近开发的睡眠简档，以及集成的机器学习和分类算法，托管在安全的、基于云的基础设施上，用于自动化数据处理、分析和报告。AMP是在NIH资助的SBIR I/II阶段项目中开发和验证的，目的是检测睡眠剥夺对被诊断为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确实是独一无二的，因为它是唯一一个在执行计算机化的神经认知任务期间集成了先进的电生理测量(例如，24通道、无线脑电和心电图)的平台。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，这些过程被认为有助于弥补观察到的表现缺陷。睡眠简档是一种FDA批准的、易于使用的无线脑电设备，开发和验证该设备是为了测量家庭睡眠研究的睡眠结构。通过综合测量智力下(下巴)肌电和无线加速度计来监测头部和肢体的运动，睡眠状况分析仪是量化快速眼动睡眠行为障碍(RBD)特征的理想设备，RBD被认为是突触核病的前驱表现。此外，复杂的机器学习分类算法的应用将简化对这些数据的处理和分析，以得出各种痴呆症亚型的统计概率。目前，直接到第二阶段SBIR项目的总体目标是开发一个安全的、基于云的基础设施，以汇编从AMP和睡眠状况分析器获得的数据，训练分类算法以区分痴呆症亚型，验证诊断准确性，并将优化的分类器集成到基于云的架构中。一旦完成，INSPECDS系统将成为第一个此类临床研究工具，并立即应用于基于大学的研究环境和制药行业临床试验，以帮助研究参与者的内表型分层。

 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.