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

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 到2040年超过癌症成为第二大死因。阿尔茨海默病（AD）是一种 痴呆的主要原因，其次是突触核蛋白病，包括路易体痴呆（DLB）， 帕金森病伴痴呆（PDD）和额颞叶痴呆。有一个紧迫的、未得到满足的 需要有效的工具来帮助在最早的可检测阶段对痴呆亚型进行分类 的病理生理过程。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），以描述阿尔茨海默氏症和其他 痴呆症该平台的组件是警觉性和记忆分析器（AMP），睡眠 分析器（SP）和集成的机器学习，分类算法，托管在一个安全的基于云的， 用于自动化数据处理、分析和报告的基础设施。AMP是独一无二的， 神经认知测试平台，因为它是唯一一个集成了先进的电生理学 措施（例如，24-通道无线EEG/ECG）在计算机化神经认知性能期间 任务，并已被证明有效地表征阿尔茨海默病的认知能力下降。这种能力 允许研究人员探索警觉性波动，离散认知 功能和特定的神经过程，据信有助于观察到的性能缺陷， 阿尔茨海默氏症和其他痴呆症。SP是FDA批准的，易于应用的无线EEG设备， 被开发和验证，以测量睡眠结构的家庭睡眠研究与颏下 EMG和无线加速度计监测头部和肢体运动，以量化特征 REM睡眠行为障碍，被认为是突触核蛋白病的前驱表现。的 机器学习的应用，分类算法简化了处理和分析， 这些数据来得出阿尔茨海默病和其他痴呆亚型的统计概率。的 当前提交的总体目标是完成安全的、基于云的 基础设施来编译从AMP和SP获得的数据，训练分类算法， 区分阿尔茨海默病和其他痴呆亚型，验证诊断准确性， 在基于云的架构中集成优化的分类器。INSPECDS系统是第一个 临床研究工具，在大学的研究环境中应用， 药物临床试验，以帮助在阿尔茨海默病和其他疾病的内在表型分层 痴呆症

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.