Developing a novel EEG-based index for evaluating amyloid and tau burden in Alzheimer's Disease

开发一种基于脑电图的新型指数来评估阿尔茨海默病中淀粉样蛋白和 tau 蛋白的负担

• 批准号: 10602059
• 负责人: Che Lucero
• 金额: $ 131.5万
• 依托单位: SPARK NEURO INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10602059
• 关键词: Acceleration; Accounting; Affect; Age; Algorithms; Alzheimer disease screening; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease diagnostic; Alzheimer' s disease model; Alzheimer' s disease pathology; Amyloid; Amyloid beta-42; Amyloid beta-Protein; Behavioral Symptoms; Biological Markers; Cerebrospinal Fluid; Classification; Clinical; Clinical Research; Cognitive; Computer software; Consumption; Country; Data; Dementia; Detection; Devices; Diagnosis; Diagnostic; Disease; Disease Progression; Elderly; Electroencephalography; Ensure; Health; Health Care Costs; Human; Impaired cognition; Machine Learning; Measures; Medical; Methods; Monitor; Nerve Degeneration; Neurologic; Neurologist; Outcome Measure; Output; Pathologic; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Performance; Persons; Phase; Physicians; Radiologic Health; Reporting; Research; Rest; Scanning; Severity of illness; Small Business Innovation Research Grant; Standardization; Symptoms; System; Technology; Testing; Tissues; Training; United States; Universities; Update; Work; aging population; burden of illness; cloud based; commercialization; cost; cost effective; diagnostic tool; drug development; human old age (65+); improved; indexing; insight; machine learning algorithm; novel; performance tests; predictive modeling; research and development; satisfaction; success; symptom management; tau Proteins; tau-1; tool; usability; user-friendly; β-amyloid burden

PROJECT SUMMARY Alzheimer’s disease (AD) is a progressive, neurodegenerative condition that impacts 44 million people globally. It is the most common cause of dementia—accounting for 80% of all dementia diagnoses—and predominantly affects older people. To date, AD has no cure. Thus, it is imperative to diagnose patients as early as possible to maximize the efficacy of symptom management strategies. However, existing AD diagnostic and monitoring options remain subpar, leaving urgent need for sensitive, reliable, and convenient methods to measure AD. New research suggests that electroencephalography (EEG) -based biomarkers can assess neural degeneration caused by AD long before tissue loss or behavioral symptoms appear. SPARK Neuro aims to unlock the full potential of EEG through a novel software platform: SPARK Scan. Combining EEG with the capabilities of machine learning, SPARK Scan better assesses cognitive health and neurodegeneration, aiding the diagnosis of AD. In the proposed Direct-to-Phase II SBIR project, the company will enhance the existing SPARK platform by developing a novel Alzheimer’s Disease Pathology Index that tracks patient amyloid and tau burden. Developed with CSF data collected from Johns Hopkins University, the Pathology Index will be a continuous score that corresponds to a patient’s pathological disease burden. After updating the software, the SPARK team will employ Human Factors testing to enhance the usability and utility of the updated platform and clinical report before submitting findings for FDA De Novo approval. This work will progress the SPARK platform by providing a granular measure of AD-related pathology, extending beyond current capabilities.

项目总结 阿尔茨海默病(AD)是一种进行性的神经退行性疾病，全球有4400万人受到影响。 它是导致痴呆症的最常见原因--占所有痴呆症诊断的80%--而且主要是 影响老年人。到目前为止，AD还没有治愈的方法。因此，尽早对患者进行诊断是当务之急。 最大限度地发挥症状管理策略的功效。但是，现有的AD诊断和监控 选择仍然低于平均水平，迫切需要敏感、可靠和方便的方法来衡量AD。 新的研究表明，基于脑电(EEG)的生物标记物可以评估神经退行性变 在组织丢失或行为症状出现之前很久就由阿尔茨海默病引起。Spark Neuro旨在全面解锁 通过一种新的软件平台：火花扫描来挖掘脑电的潜力。将脑电与脑电的能力相结合 机器学习，火花扫描更好地评估认知健康和神经退化，帮助诊断 公元一代的。在拟议的直接至第二期SBIR项目中，该公司将增强现有的Spark平台 通过开发一种新的阿尔茨海默病病理指数来跟踪患者的淀粉样蛋白和tau负担。 根据约翰·霍普金斯大学收集的脑脊液数据开发的病理指数将是连续的 与患者的病理疾病负担相对应的分数。更新软件后，Spark团队 将采用人为因素测试来增强更新后的平台和临床报告的可用性和实用性 在将调查结果提交FDA de Novo批准之前。这项工作将通过提供 AD相关病理的细粒度测量，超出了当前的能力。