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

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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 accelerate the development of novel and effective therapeutics for ALS-FTD.