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：阿尔茨海默病（AD）的早期诊断目前主要依靠尸检。疑似AD 诊断基于临床/认知测量的组合，通常由结构MRI扫描证实。 当前神经心理学和临床工具用于精确和早期指示认知衰退的局限性 为我们专注于开发易于使用的改进的认知评估提供了动力 跨平台、跨年龄组和跨文化群体，并提供更早、更准确的指示 临床前疾病早期诊断和干预对于治疗最大限度地发挥作用至关重要 尽管缺乏新的治疗AD的选择。Augnition Labs正在开发Augnition ™数字 生物标志物平台基于Yassa博士及其同事的工作，使用一种模式进行了经验证明 分离任务，海马的主要功能是模式分离-区分能力 通过独特的神经编码来储存相似的记忆。我们已经开发、验证和 展示了一套完整的模式分离任务在情节的三个关键维度上的实用性 记忆，（1）发生了什么（对象），（2）发生在哪里（空间），（3）何时发生（时间）。之前 这项工作已经通过高分辨率成像进行了神经生物学验证， 传统的临床记忆测试在这个直接到第二阶段SBIR，我们将对象，空间和时间 模式分离技术与功能丰富的人工智能模型相结合，为患者产生更有效的数字生物标志物 认知能力下降和治疗反应的早期预测。目标1.开发并推出安全且可扩展的 August ™平台。我们将开发和实施考试管理架构和学习管理 支持数据收集、质量检查和数据分析的模块。商业化的前端 将反复开发和测试数字化交付评估的接口。目标：完成用户 招募用户角色（研究参与者、研究管理员、数据科学家）的验收测试，以及 启动FDA临床结局评估资格认证的监管途径。目标2.发展和培训 基于认知和生物标志物特征预测细微损伤的AI模型。数据收集， 数据清理、特征提取和选择、模型建立以及模型评估和分析将 将对象、空间和时间模式分离措施与通过Precision收集的数据相结合 老化网络以及直接由增强。目标：最多50万参与者的代表性样本 在18-85岁的年龄范围内，人工智能引擎训练，并实现0.85岁的预测准确率 ROC AUC（分类）和RMSE ≤ 0.3（回归）。在成功完成拟议的 为了开发，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 August ™平台的预测能力，并启动该软件作为医疗器械FDA监管 AD早期检测、分层和预测治疗反应的途径。