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

项目摘要 肌萎缩侧索硬化症(ALS)和额颞叶痴呆是毁灭性的神经退行性变 存在于遗传和机制连续体上的疾病。肌萎缩侧索硬化症是一种运动神经元疾病 在确诊后的3-5年内几乎一致致死。FTD是一种异质的、快速发展的 该综合征是导致早老性痴呆的三大原因之一。 约10%的肌萎缩侧索硬化症病例由 显性遗传性基因缺陷。SOD1和FUS突变导致侵袭性运动神经元病理 而TDP43突变可导致ALS-FTD。此外，野生型FUS和TDP43是异常的成分 在许多FTD病例中存在包涵体，这表明这些疾病之间存在机械联系。早期的表型是 特别令人感兴趣的是，这些可能导致针对疾病根源的有针对性的干预 这可能会阻止目前不可阻挡的疾病发展。阐明了这种早期的，潜在的共享 这些疾病的特征应该在很大程度上得到以下帮助：1)表达家族性疾病的敲入动物模型 2)灵敏、严谨、客观的行为表型方法进行分析比较 在不同的实验室生成的模型。在出版的作品中，合作PI应用了他们的第一代机器 基于视觉的自动表型分型方法，ACBM‘1.0’(自动连续行为监测) 检测和量化Tdp43Q331K敲入小鼠中观察到的最早的表型。这种方法需要 连续视频录制5天，生成&gt；1400万帧/鼠标。这些视频然后由以下人员评分 一个训练有素的计算机视觉系统。除了它的敏感性、客观性和重现性之外，还有一个主要优势 这种方法的特点是能够获取和存档视频记录并分析现场数据，包括 云，远离收购的云。我们将使用Google Cloud TPU超级计算机 全新设计，旨在加速尖端机器学习工作负载，特别关注 深度学习。我们将使用贝叶斯层次样条法模型来分析这些数据，这些模型描述了不同的 老鼠的行为遵循昼夜节律。目前的提议有两个主要目标：1)使用深度学习 改进和应用新一代ACBM-‘2.0’-这将允许更敏感、更广泛和更强大 四个新的敲门模型和特征良好的SOD1G93A的自动行为表型 转基因小鼠。2)建立和验证程序以实现视频记录的远程获取 数据和基于云的分析。我们的愿景是建立敏感、健壮、客观和开源的 基于机器视觉的行为分析工具，将被该领域的研究人员广泛使用。因为所有的 计算机注释的视频数据在ACBM 2.0中被标准化并将被归档，我们设想一个 可搜索的“行为数据库”，可自由挖掘和分析。这样的工具对于加速 ALS-FTD新的有效治疗方法的发展。

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.