# Optogenetic dissection of cellular and circuit mechanisms of network dysfunction and amyloid deposition in mouse models of Alzheimer's disease in vivo

> **NIH NIH RF1** · J. DAVID GLADSTONE INSTITUTES · 2021 · $213,379

## Abstract

SUMMARY
A contributing factor to the failure of clinical trials has been the translational limitations of transgenic (TG)
overexpression models in which physiological and endogenously-regulated pathogenic interactions are not
achieved. In addition, standard behavioral tests may lack sensitivity to identify robust and reproducible behavioral
deficits in newly develop knock-in (KI) models. To address or mitigate these limitations, we propose to study the
next generation of newly developed KI mouse models of AD without transgene overexpression using the latest
machine learning approaches for behavioral phenotyping and chronic wireless EEG/EMG recordings.
Specifically, we propose to study knock-in (KI) mice that express humanized Ab under the control of the mouse
App locus with or without FAD mutations, including AppAβ/Aβ, AppNL-F/NL-F, and AppNL-G-F/NL-G-F mice using state-of-
the-art chronic in vivo electrophysiological recordings and machine learning approaches for behavioral
phenotyping. The parent grant heavily focuses on the J20 TG model of AD, because App-KI mice show no
prominent cognitive deficits in standard behavioral tests, including the Morris water maze test,10 and because it
is unknown if App-KI have robust network abnormalities described in J20 and other TG-APP mouse models and
AD patients, including altered gamma oscillations, theta-gamma coupling, epileptiform spikes and seizures.1,3,9,11-
15. The proposed experiments and genotypes expand the focus of our parent grant and will develop tools and
procedures that will be directly used by the parent grant to improve behavioral and brain network characterization
of TG and KI models of AD. We propose the following aims: Aim 1. Develop and apply machine learning
approaches to identify behavioral alterations in late-onset AppAβ/Aβ and early-onset AppNL-F/NL-F and AppNL-G-F/NL-
G-F AD mice. Aim 2. Determine electrophysiological phenotypes by chronic wireless EEG/EMG recordings during
aging and disease progression in late-onset AppAβ/Aβ and early-onset AppNL-F/NL-F and AppNL-G-F/NL-G-F AD mice.
Aim 3. Determine relationships between behavioral alterations and neuronal network dysfunction in late-onset
AppAβ/Aβ and early-onset AppNL-F/NL-F and AppNL-G-F/NL-G-F AD mice.
In addition, this diversity supplement grant will significantly enhance the research potential of the candidate and
further her ability to pursue a research career. The candidate will gain valuable relevant experience with
electrophysiological data analysis and mouse behavioral assays during the funding period. These novel in vivo
approaches and technical skills will help the candidate to address unexplored questions of AD pathogenesis,
thereby creating a technical and conceptual path towards independence. The proposed supplement will provide
the conceptual and technological foundation needed to support the production of preliminary data for the
candidate’s future F32 grant application.

## Key facts

- **NIH application ID:** 10395099
- **Project number:** 3RF1AG062234-01S1
- **Recipient organization:** J. DAVID GLADSTONE INSTITUTES
- **Principal Investigator:** Jorge J Palop
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $213,379
- **Award type:** 3
- **Project period:** 2018-09-30 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10395099

## Citation

> US National Institutes of Health, RePORTER application 10395099, Optogenetic dissection of cellular and circuit mechanisms of network dysfunction and amyloid deposition in mouse models of Alzheimer's disease in vivo (3RF1AG062234-01S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10395099. Licensed CC0.

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