# Next generation machine vision for automated behavioral phenotyping of knock-in ALS-FTD mouse models

> **NIH NIH R21** · BROWN UNIVERSITY · 2020 · $446,875

## Abstract

Project Summary
Amyotrophic lateral sclerosis (ALS) and Frontotemporal Dementia FTD are devastating neurodegenerative
disorders that lie on a genetic and mechanistic continuum. ALS is a disease of motor neurons that that is
almost uniformly lethal within only 3-5 years of diagnosis. FTD is a heterogeneous, rapidly progressing
syndrome that is among the top three causes of presenile dementia.
About 10% of ALS cases are caused by
dominantly transmitted gene defects. SOD1 and FUS mutations cause aggressive motor neuron pathology
while TDP43 mutations cause ALS-FTD. Further, wild type FUS and TDP43 are components of abnormal
inclusions in many FTD cases, suggesting a mechanistic link between these disorders. Early phenotypes are
of particular interest because these could lead to targeted interventions aimed at the root cause of the disorder
that could stem the currently inexorable disease progression. Elucidating such early, potentially shared
characteristics of these disorders should be greatly aided by: 1) knock-in animal models expressing familial
ALS-FTD genes; 2) sensitive, rigorous and objective behavioral phenotyping methods to analyze and compare
models generated in different laboratories. In published work the co-PIs applied their first-generation, machine
vision-based automated phenotyping method, ACBM ‘1.0’ (automated continuous behavioral monitoring) to
detect and quantify the earliest-observed phenotypes in Tdp43Q331K knock-in mice. This method entails
continuous video recording for 5 days to generate >14 million frames/mouse. These videos are then scored by
a trained computer vision system. In addition to its sensitivity, objectivity and reproducibility, a major advantage
of this method is the ability to acquire and archive video recordings and to analyze the data at sites, including
the Cloud, remote from those of acquisition. We will use Google Cloud TPUs supercomputers that have been
designed from the ground up to accelerate cutting-edge machine learning workloads, with a special focus on
deep learning. We will analyze this data using Bayesian hierarchical spline models that describe the different
mouse behaviors along the circadian rhythm. The current proposal has two main goals: 1) Use deep learning
to refine and apply a Next Generation ACBM - ‘2.0’ - that will allow for more sensitive, expansive and robust
automated behavioral phenotyping of four novel knock-in models along with the well characterized SOD1G93A
transgenic mouse. 2) To establish and validate procedures to enable remote acquisition of video recording
data with cloud-based analysis. Our vision is to establish sensitive, robust, objective, and open-source
machine vision-based behavioral analysis tools that will be widely available to researchers in the field. Since all
the computer-annotated video data is standardized in ACBM 2.0 and will be archived, we envision a
searchable ‘behavioral database’, that can be freely mined and analyzed. Such tools are critical to accel...

## Key facts

- **NIH application ID:** 9979408
- **Project number:** 1R21NS112743-01A1
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** JUSTIN R. FALLON
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $446,875
- **Award type:** 1
- **Project period:** 2020-04-01 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9979408

## Citation

> US National Institutes of Health, RePORTER application 9979408, Next generation machine vision for automated behavioral phenotyping of knock-in ALS-FTD mouse models (1R21NS112743-01A1). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/9979408. Licensed CC0.

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