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

> **NIH NIH R44** · AUGNITION LABS LLC · 2022 · $1,004,853

## Abstract

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/pro...

## Key facts

- **NIH application ID:** 10545457
- **Project number:** 1R44AG079718-01
- **Recipient organization:** AUGNITION LABS LLC
- **Principal Investigator:** Adele Gilpin
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,004,853
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10545457

## Citation

> US National Institutes of Health, RePORTER application 10545457, Augmem: A Novel Digital Cognitive Assessment for the Early Detection of Alzheimer's Disease (1R44AG079718-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10545457. Licensed CC0.

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