# Decoding Early Signs of Alzheimer's Disease in The Lateral Entorhinal Cortex Using Machine Learning

> **NIH NIH R21** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $202,500

## Abstract

The lateral entorhinal cortex (LEC) is one of the first regions in the brain to be affected in Alzheimer’s
disease, and is important for object recognition, odor discrimination and episodic memory. Hence, early
AD symptoms such as misplacing objects, forgetting events and loss of smell could be due to LEC
dysfunction. In order to understand how Aβ and tau accumulation impacts the LEC neurons, we will use
two mouse models of AD: APP knockin (APP-KI) mice- expressing physiological levels of APP and EC-
APP/Tau mice- expressing elevated levels of APP and tau in the EC. Both mouse models show
selectively vulnerability in the LEC, making them ideal candidates to probe LEC function. Our preliminary
data shows behavioral impairment in the EC-APP/Tau mice at 24 months and data on APP-KI show
impairment at 18 months. In the proposal we will evaluate LEC function in the younger mice in order to
detect neuronal changes prior to behavioral deficits. We will record LEC activity with silicon probes and
test responses towards objects, odors and passage of time. Using computational approach such as
machine learning, we will determine if ensemble properties of LEC neurons are affected by tau and Aβ.
We hypothesize that APP in the LEC of APP-KI mice will make the neurons dysfunctional which will be
evident with poor decoding accuracy for objects, odors and temporal epochs. In the EC-APP/Tau mice,
combined effect of Aβ and tau will make the dysfunction worse and affect the decoding accuracy further
allowing better prediction of early symptoms of Alzheimer’s disease.
The proposal brings together diverse fields (electrophysiology, pathology and computational
neuroscience) applying large-scale recording techniques to record ensemble populations of neurons and
develop analytical and predictive computational tests to interrogate function in a vulnerable brain region
that is dysfunctional in Alzheimer’s disease.

## Key facts

- **NIH application ID:** 10017142
- **Project number:** 5R21AG066168-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** S. Abid Hussaini
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $202,500
- **Award type:** 5
- **Project period:** 2019-09-15 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017142

## Citation

> US National Institutes of Health, RePORTER application 10017142, Decoding Early Signs of Alzheimer's Disease in The Lateral Entorhinal Cortex Using Machine Learning (5R21AG066168-02). Retrieved via AI Analytics 2026-05-31 from https://api.ai-analytics.org/grant/nih/10017142. Licensed CC0.

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