Deep Phenotyping of ALS and ALS-FTD Mouse Models Using Automated Continuous Behavioral Monitoring

Duffy, Amanda Marie

Full Metadata

Overview

Year:: 2018
Contributor:: Duffy, Amanda Marie (creator); Hart, Anne (Reader); Barnea, Gilad (Reader); Bath, Kevin (Reader); Fallon, Justin (Advisor); Hayward, Lawrence (Reader); Brown University. Department of Neuroscience (sponsor)
Genre:: theses
Subject:: Amyotrophic lateral sclerosis; Frontal temporal dementia; Automated Continuous Behavioral Monitoring; Mouse behavior; Novel knock-in model; Mouse Models
Extent:: xxi, 206 p.
DOI: https://doi.org/10.26300/rh3s-sw63

Files

Description

Abstract:: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of the upper and lower motor neurons resulting in weakness, paralysis and death within two to five years. Frontal temporal dementia (FTD) results in neurodegeneration of the frontal, temporal and insular cortices causing behavioral and cognitive deficits. ALS and FTD exist along a spectrum (ALS-FTD). Characterizing early neurodegenerative disease phenotypes is critical in defining epochs of time during which the nervous system is most plastic and receptive to intervention. Such early events reveal pathways that are deranged prior to the onset of neurodegeneration as well as biomarkers indicative of disease. This information offers insight that allows for earlier diagnosis and the identification of new therapeutic targets, providing an opportunity for early intervention. Because neurodegeneration is a late-stage phenomenon, many contributing pathologies are already present. This is particularly a problem in fast-progressing diseases including ALS and FTD. To identify early phenotypes in ALS and FTD, we used a novel deep behavioral phenotyping technology, Automated Continuous Behavioral Monitoring (ACBM), to identify and quantify the behavior of the transgenic SOD1G93A mouse (B6.Cg-Tg(SOD1*G93A)1Gur/J), as well as the TDP-43Q331K knock-in mouse, a novel model of ALS-FTD. ACBM collects video data of individually housed mice over five days. Position, velocity, acceleration and motion features are extracted from the data and classified through a Support Vector Machine Hidden Markov Model (SVMHMM) as one of nine pre-defined behaviors. We first demonstrated that SOD1G93A mice exhibit neuromotor deficits as early as P30 (Chapter 2). To determine whether such deficits are present in the TDP-43Q331K mouse, we conducted five rounds of ACBM and found TDP-43Q331K deficits as early as P120 (earliest age measured) (Chapter 3). Lastly, to assess earlier phenotypes, we conducted ACBM in a second younger cohort of TDP-43Q331K mice at P30 and P120, and found gender-dependent phenotypes at both ages (Chapter 4). This work presents the earliest behavioral phenotypes identified in the SOD1G93A and TDP-43Q331K mice. ACBM can be used in the future to assess treatment efficacy of pharmacological, genetic or environmental approaches in mouse models of neurodegenerative disease.
Notes:: Thesis (Ph. D.)--Brown University, 2018

Content

Access Conditions

Rights: In Copyright
Restrictions on Use: Collection is open for research.

Citation

Duffy, Amanda Marie, "Deep Phenotyping of ALS and ALS-FTD Mouse Models Using Automated Continuous Behavioral Monitoring" (2018). Neuroscience Theses and Dissertations. Brown Digital Repository. Brown University Library. https://doi.org/10.26300/rh3s-sw63

Relations

Collection:

Neuroscience Theses and Dissertations

Theses and Dissertations for the Neuroscience department.
...