Multi-site longitudinal 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多部位纵向综合神经认知和睡眠行为分析仪 (INSPECDS)

• 批准号: 10603714
• 负责人: Chris Berka
• 金额: $ 99.99万
• 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 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10603714
• 关键词: Accelerometer; Address; Algorithms; Alzheimer' s Disease; Architecture; Automatic Data Processing; Behavior assessment; Biological Markers; Brain; Cause of Death; Characteristics; Classification; Clinical Research; Clinical Trials; Clinical Trials Cooperative Group; Consult; Data; Data Analyses; Data Set; Dementia; Dementia with Lewy Bodies; Devices; Diagnosis; Disease Progression; Drug Industry; Electrocardiogram; Electroencephalography; Electrophysiology (science); Frontotemporal Dementia; Goals; Head Movements; Health Personnel; Home; Impaired cognition; Individual; Infrastructure; Knowledge; Machine Learning; Malignant Neoplasms; Measures; Memory; Monitor; Neurocognitive; Neurodegenerative Disorders; Neurologic; Neuropsychological Tests; Parkinson' s Dementia; Patients; Performance; Pharmacologic Substance; Phase; Probability; Process; REM Sleep Behavior Disorder; Reporting; Research; Research Personnel; Secure; Site; Sleep; Sleep Architecture; Small Business Innovation Research Grant; Statistical Models; Stratification; Study Subject; System; Techniques; Technology; Technology Assessment; Testing; Time; Training; Universities; Visit; alertness; base; brain behavior; classification algorithm; clinical diagnostics; cloud based; cognitive function; cohort; commercial application; commercialization; computerized; data acquisition; data repository; diagnostic accuracy; drug discovery; human subject; improved; large datasets; limb movement; machine learning classification; mild cognitive impairment; neurocognitive test; novel; outreach; relating to nervous system; retention rate; scale up; sleep behavior; synucleinopathy; tool; wireless

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.

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 痴呆症的主要原因，其次是同核病，包括路易体痴呆(DLB)， 帕金森痴呆症(PDD)和额颞性痴呆症。有一个紧急的，未得到满足的 需要有效的工具来帮助在最早可发现的阶段对痴呆症亚型进行分类 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 创建神经认知和睡眠-行为综合评价器的昼夜评估技术 关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和内存分析器(AMP)、睡眠 Profiler(SP)和集成的机器学习、分类算法，托管在安全的基于云的 用于自动数据处理、分析和报告的基础设施。AMP在 神经认知测试平台，因为它是唯一一个集成了先进电生理的平台 在执行计算机化神经认知的过程中进行测量(例如，24通道无线脑电/心电) 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。这一能力 允许研究人员探索警觉性波动、离散认知之间的实时关系 功能和特定的神经过程被认为弥补了观察到的表现缺陷 阿尔茨海默病和其他痴呆症。SP是FDA批准的、易于使用的无线脑电设备， 被开发和验证用于测量睡眠结构，用于亚精神状态下的家庭睡眠研究 肌电和无线加速计监测头部和肢体的运动，以量化特征 快动眼-睡眠行为障碍，被认为是突触核病的前驱表现。这个 应用机器学习、分类算法简化了对数据的处理和分析 这些数据可以得出阿尔茨海默病和其他痴呆症亚型的统计概率。这个 当前提交的总体目标是最终确定安全、基于云的 基础设施，以汇编从AMP和SP获得的数据，训练分类算法以 区分阿尔茨海默病和其他痴呆症亚型，验证诊断准确性，以及 将优化的分类器集成到基于云的架构中。INSPECDS系统是第一个 同类临床研究工具，在基于大学的研究环境和 有助于阿尔茨海默病和其他疾病内表型分层的药物临床试验 痴呆症。