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）的明确诊断目前被授予尸检。可能的广告