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

用于敲入 ALS-FTD 小鼠模型自动行为表型分析的下一代机器视觉

基本信息

批准号：
9979408
负责人：
JUSTIN R. FALLON
金额：
$ 44.69万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-01 至 2022-09-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9979408
关键词：
Amyotrophic Lateral Sclerosis Animal Model Archives Behavior Behavior monitoring Behavioral Characteristics Circadian Rhythms Computer Vision Systems Computers Data Data Set Databases Defect Development Diagnosis Disease Disease Progression Expression Profiling Familial Amyotrophic Lateral Sclerosis Frontotemporal Dementia Gene Expression Generations Genes Genetic Goals Hour Human Intervention Knock-in Knock-in Mouse Laboratories Lead Link Machine Learning Methods Modeling Motor Neuron Disease Motor Neurons Mus Mutation Neurodegenerative Disorders Paralysed Pathology Phenotype Plant Roots Presenile Dementia Procedures Publishing Reproducibility Research Research Personnel Respiratory Paralysis Scientist Site Standardization Syndrome TensorFlow Time Training Transgenic Mice Transgenic Organisms Treatment Efficacy Video Recording Vision Work Workload base behavioral phenotyping cloud based data archive deep learning design frontotemporal lobar dementia-amyotrophic lateral sclerosis interest knockin animal machine vision mouse model new therapeutic target next generation novel open source programs protein TDP-43 stem supercomputer superoxide dismutase 1 tool

项目摘要

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.

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。