Augmem: A Novel Digital Cognitive Assessment for the Early Detection of Alzheimer's Disease

Augmem：用于早期检测阿尔茨海默病的新型数字认知评估

• 批准号: 10545457
• 负责人: Adele Gilpin
• 金额: $ 100.49万
• 依托单位: AUGNITION LABS LLC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10545457
• 关键词: Administrator; Aducanumab; Adult; Age; Aging; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s Disease Pathway; Alzheimer' s disease care; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; Alzheimer' s disease risk; American; Amyloid; Architecture; Autopsy; Biological; Biological Markers; Brain; Caregivers; Classification; Clinical; Clinical Trials; Clinical Trials Design; Code; Cognitive; Collaborations; Collection; Computer software; Data; Data Analytics; Data Collection; Data Scientist; Dementia; Development; Dimensions; Disease; Early Diagnosis; Early Intervention; Educational Status; Elderly; Episodic memory; Evaluation; Goals; Health; Healthcare Systems; Hippocampus (Brain); Immunotherapy; Impaired cognition; Impairment; Infrastructure; Intelligence; MRI Scans; Marketing; Measures; Medicaid; Medical Device; Medicare; Memory; Modeling; Monitor; Neurobiology; Neurofibrillary Tangles; Neuropsychology; Participant; Patient-Focused Outcomes; Patients; Pattern; Performance; Pharmaceutical Preparations; Phase; Population; Prediction of Response to Therapy; Prevention; Process; Property; Psychometrics; Public Health; Regulatory Pathway; Research; Sampling; Secure; Senile Plaques; Small Business Innovation Research Grant; Social Sciences; Stratification; Surveys; Symptoms; Techniques; Technology; Testing; Therapeutic; Time; Training; Treatment Protocols; United States; Work; age group; baby boomer; base; care costs; clinical care; clinical outcome assessment; cognitive testing; data cleaning; diagnostic tool; digital; digital delivery; early detection biomarkers; economic cost; effective therapy; evidence base; feature extraction; feature selection; health application; high resolution imaging; human old age (65+); improved; innovation; large scale data; model building; novel; novel therapeutics; patient stratification; payment; pre-clinical; predictive modeling; prodromal Alzheimer' s disease; prospective; recruit; relating to nervous system; standard of care; tau Proteins; tool; treatment response

Summary: Definitive diagnosis of Alzheimer’s Disease (AD) is currently conferred upon autopsy. Probable AD diagnosis is based on a combination of clinical/cognitive measures, often corroborated by structural MRI scans. Limitations of current neuropsychological and clinical tools for precise and early indications of cognitive decline in AD provide the impetus for our focus on developing improved cognitive assessments that are easy to use across platforms, age groups, and diverse cultural groups, and provide an earlier and more accurate indication of preclinical disease. Early diagnosis and intervention are critical for therapeutics to be maximally effective despite the dearth of new therapeutic options for AD. Augnition Labs is developing the Augmem™ digital biomarker platform based on work by Dr. Yassa and colleagues that empirically demonstrated, using a pattern separation task, that the chief function of the hippocampus is pattern separation – the ability to discriminate among similar memories by storing them using unique neural codes. We have developed, validated, and demonstrated the utility of a full suite of pattern separation tasks across the three key dimensions of episodic memory, (1) what happened (object), (2) where it happened (spatial), and (3) when it happened (temporal). Prior work has been neurobiologically validated with high resolution imaging as well as clinically validated against traditional clinical memory measures. In this Direct to Phase II SBIR, we incorporate object, spatial, and temporal pattern separation techniques with feature-rich AI models to produce a more effective digital biomarker for the early prediction of cognitive decline and treatment response. Aim 1. Develop and launch secure and scalable Augmem™ platform. We will develop and implement test management architecture and study administration modules in support of data collection, quality checks, and data analytics. A commercially ready front-end interface for digital delivery of assessments will be iteratively developed and tested. Goal: Completion of User Acceptance Testing with recruited user personas (study participant, study administrator, data scientist), and initiation of FDA regulatory pathway for Clinical Outcome Assessment qualification. Aim 2. Develop and train AI models for predicting subtle impairments based on cognitive and biomarker profiles. Data collection, data cleaning, feature extraction and selection, model building, and model evaluation and analysis will incorporate object, spatial, and temporal pattern separation measures from data collected through the Precision Aging Network as well as directly by Augnition. Goal: A representative sample of up to 500,000 participants across the age spectrum of 18-85, AI engine training, and achievement of predictive accuracy for age of 0.85 ROC AUC (classification) and RMSE ≤ 0.3 (regression). Upon successful completion of the proposed development, we will conduct prospective trials in preclinical/prodromal Alzheimer’s disease to fully validate the predictive power of the Augmem™ platform and initiate the Software as a Medical Device FDA regulatory pathway for AD early detection, stratification, and prediction of treatment response.

摘要：目前，阿尔茨海默病 (AD) 的最终诊断是通过尸检得出的。可能的AD 诊断基于临床/认知测量的结合，通常由结构 MRI 扫描证实。 当前神经心理学和临床工具对于认知能力下降的精确和早期指示的局限性 AD 领域的研究为我们专注于开发易于使用的改进认知评估提供了动力 跨平台、跨年龄段、跨文化群体，提供更早、更准确的指示 的临床前疾病。早期诊断和干预对于治疗发挥最大效果至关重要 尽管缺乏新的 AD 治疗选择。 Augnition Labs 正在开发 Augmem™ 数字 生物标志物平台基于 Yassa 博士及其同事的工作，使用一种模式凭经验证明 分离任务，海马体的主要功能是模式分离——辨别能力 通过使用独特的神经代码存储相似的记忆。我们已经开发、验证和 展示了跨情景三个关键维度的全套模式分离任务的实用性 记忆，(1) 发生了什么（对象），(2) 发生地点（空间），以及（3）何时发生（时间）。事先的 工作已经通过高分辨率成像进行了神经生物学验证，并针对 传统的临床记忆测量。在直接进入第二阶段 SBIR 中，我们将对象、空间和时间结合起来 模式分离技术与功能丰富的人工智能模型相结合，为生物制品产生更有效的数字生物标志物 早期预测认知能力下降和治疗反应。目标 1. 开发并推出安全且可扩展的 Augmem™ 平台。我们将开发和实施测试管理架构和研究管理 支持数据收集、质量检查和数据分析的模块。商业化的前端 将迭代开发和测试用于数字化交付评估的界面。目标：完成用户 使用招募的用户角色（研究参与者、研究管理员、数据科学家）进行验收测试，以及 启动 FDA 临床结果评估资格监管途径。目标 2. 开发和培训 用于根据认知和生物标志物概况预测细微损伤的人工智能模型。数据收集， 数据清洗、特征提取和选择、模型构建、模型评估和分析 将通过 Precision 收集的数据进行对象、空间和时间模式分离测量 老化网络以及直接由 Augnition 提供。目标：最多 500,000 名参与者的代表性样本 覆盖18-85岁年龄段，AI引擎训练，年龄预测准确率达到0.85 ROC AUC（分类）和 RMSE ≤ 0.3（回归）。成功完成建议的工作后 开发过程中，我们将对临床前/前驱阿尔茨海默病进行前瞻性试验，以充分验证 Augmem™ 平台的预测能力，并将该软件作为医疗器械启动 FDA 监管 AD 早期检测、分层和治疗反应预测的途径。