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

Augmem：一种用于早期检测阿尔茨海默病的新型数字认知评估

• 批准号: 10688227
• 负责人: Adele Gilpin
• 金额: $ 96.07万
• 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/10688227
• 关键词: Acceleration; Achievement; 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; Caregiver Burden; Classification; Clinical; Clinical Trials; Clinical Trials Design; Code; Cognitive; Collaborations; Collection; Computer software; Data; Data Analytics; Data Collection; Data Scientist; Dementia; Development; Digital biomarker; Dimensions; Disease; Early Diagnosis; Early Intervention; Elderly; Episodic memory; Evaluation; Goals; Health; Healthcare Systems; Hippocampus; 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; Probability; Process; Property; Psychometrics; Public Health; Qualifying; Regulatory Pathway; Research; Sampling; Secure; Senile Plaques; Small Business Innovation Research Grant; Social Sciences; Stratification; Surveys; Symptoms; Techniques; Technology; Testing; Therapeutic; Training; Treatment Protocols; United States; Work; age group; baby boomer; care costs; clinical care; clinical outcome assessment; cognitive testing; commercialization; 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; neural; novel; novel therapeutics; patient stratification; payment; pre-clinical; predictive modeling; prodromal Alzheimer' s disease; prospective; recruit; 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：目前授予阿尔茨海默氏病（AD）的明确诊断（AD）。可能的广告 诊断是基于临床/认知测量结果的组合，通常通过结构性MRI扫描来证实。 当前神经心理学和临床工具的局限 在广告中，我们专注于开发改进的认知评估的动力，这些评估易于使用 跨平台，年龄段和多元文化群体，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至关重要。 尽管有新的AD治疗选择去世。 Augnition Labs正在开发AugMem™数字 基于Yassa博士及其同事的工作，凭经验证明的生物标志物平台，使用模式 分离任务，海马的主要功能是模式分离 - 歧视的能力 在类似的记忆中，通过使用独特的神经代码存储它们。我们已经开发，验证了，并且 展示了在情节的三个关键维度上的完整图案分离任务的实用性 记忆，（1）发生了什么（对象），（2）发生的地方（空间），以及（3）发生时（时间）。事先的 通过高分辨率成像以及临床验证的，在神经生物学上进行了神经生物学验证 传统的临床记忆措施。在直接到II期SBIR中，我们合并了对象，空间和临时性 具有功能丰富的AI模型的模式分离技术，以生成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。开发和启动安全可扩展的 AUGMEM™平台。我们将开发和实施测试管理体系结构和研究管理 支持数据收集，质量检查和数据分析的模块。商业上准备的前端 用于数字交付评估的接口将进行迭代开发和测试。目标：用户完成 接受招聘的用户角色（研究参与者，研究管理员，数据科学家）和 临床结果评估资格的FDA监管途径的启动。目标2。发展和训练 AI模型用于预测基于认知和生物标志物概况的细微损害。数据收集， 数据清洁，特征提取和选择，模型构建以及模型评估和分析将 合并对象，空间和临时模式分离措施与通过精确收集的数据进行 衰老网络以及直接通过augnition。目标：多达50万参与者的代表样本 在18-85的年龄频谱中，AI发动机培训以及年龄为0.85岁 ROC AUC（分类）和RMSE≤0.3（回归）。成功完成拟议的 开发，我们将在临床前/阿尔茨海默氏病的前瞻性试验中进行前瞻性试验，以充分验证 AUGMEM™平台的预测能力，并启动该软件作为医疗设备FDA调节 AD早期检测，分层和治疗反应预测的途径。