Machine Learning Approaches for Behavioral Phenotyping of Humanized Knock-in Models of Alzheimer's Disease

用于阿尔茨海默病人源化敲入模型行为表型的机器学习方法

基本信息

批准号：
10741685
负责人：
Stephanie Regina Miller
金额：
$ 15.06万
依托单位：
J. DAVID GLADSTONE INSTITUTES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-15 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10741685
关键词：
3-Dimensional AD transgenic mice Acute Address Affect Age Months Aging Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease patient Alzheimer&apos s disease therapeutic Amyloid Amyloid Beta A4 Precursor Protein Amyloid beta-Protein Antibodies Award Behavior Behavior assessment Behavioral Behavioral Assay Behavioral Research Biological Markers Biotechnology Body part Brain Pathology Clinical Cognitive Collection Complex Computer Vision Systems Consumption Custom Data Defect Dementia Development Diagnosis Disease Disease Progression Early Diagnosis Exploratory Behavior Female Genes Goals Grooming Health Height Histology Human Image Impaired cognition Investigational Therapies Knock-in Knock-in Mouse Laboratories Late Onset Alzheimer Disease Machine Learning Methodology Modeling Monitor Monoclonal Antibodies Motion Movement Mus Neurodegenerative Disorders Neurosciences Pathogenesis Pharmaceutical Preparations Phenotype Physiological Pre-Clinical Model Prognostic Factor Publishing Reproducibility Research Side Speed Symptoms Testing Therapeutic Therapeutic Research Time Training Transgenic Mice Transgenic Organisms Translational Research Universities Validation apolipoprotein E-4 artificial intelligence method behavior measurement behavior test behavioral impairment behavioral phenotyping cohort data mining data visualization detection method detection platform experience familial Alzheimer disease improved innovation machine learning method machine learning pipeline male mild cognitive impairment morris water maze mouse model neural network next generation novel novel therapeutics overexpression pharmacologic pre-clinical pre-clinical research rapid testing repository research study sex stem therapeutic development therapy development tool translational barrier translational study

3-Dimensional AD transgenic mice Acute Address Affect Age Months Aging Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease patient Alzheimer&apos s disease therapeutic Amyloid Amyloid Beta A4 Precursor Protein Amyloid beta-Protein Antibodies Award Behavior Behavior assessment Behavioral Behavioral Assay Behavioral Research Biological Markers Biotechnology Body part Brain Pathology Clinical Cognitive Collection Complex Computer Vision Systems Consumption Custom Data Defect Dementia Development Diagnosis Disease Disease Progression Early Diagnosis Exploratory Behavior Female Genes Goals Grooming Health Height Histology Human Image Impaired cognition Investigational Therapies Knock-in Knock-in Mouse Laboratories Late Onset Alzheimer Disease Machine Learning Methodology Modeling Monitor Monoclonal Antibodies Motion Movement Mus Neurodegenerative Disorders Neurosciences Pathogenesis Pharmaceutical Preparations Phenotype Physiological Pre-Clinical Model Prognostic Factor Publishing Reproducibility Research Side Speed Symptoms Testing Therapeutic Therapeutic Research Time Training Transgenic Mice Transgenic Organisms Translational Research Universities Validation apolipoprotein E-4 artificial intelligence method behavior measurement behavior test behavioral impairment behavioral phenotyping cohort data mining data visualization detection method detection platform experience familial Alzheimer disease improved innovation machine learning method machine learning pipeline male mild cognitive impairment morris water maze mouse model neural network next generation novel novel therapeutics overexpression pharmacologic pre-clinical pre-clinical research rapid testing repository research study sex stem therapeutic development therapy development tool translational barrier translational study

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项目摘要

PROJECT SUMMARY Preclinical efforts to develop treatments for cognitive impairment in Alzheimer’s disease have been hindered by two barriers: time-consuming behavioral assays that often lack sensitivity, and transgenic (TG) mouse models with APP overexpression that do not accurately recapitulate human pathogenesis. To overcome the second barrier, newly-developed humanized App knock-in (App-KI) mouse models express AD-related human genes at physiological levels. App-KI mice have prominent brain pathology, but inconsistent, milder or absent behavioral phenotypes in many traditional behavioral tests, including Morris Water maze (Saito et al., 2014). These limitations of standard behavioral testing, including lack of sensitivity, low throughput, and reproducibility represent key methodological barriers to proper development of therapeutics in newly developed App-KI mice. Without a solution to this problem, it is likely that translational and preclinical research will struggle to develop therapies in models of early pathogenesis or sporadic AD characterized by mild or subtle behavioral phenotype and lacking overt clinical disease manifestation. To overcome the limits of behavioral testing, we propose to implement machine-learning (ML) approaches that offer complete, unbiased, and robust behavioral characterization of even subtle behavioral phenotypes. Specifically, we propose to upgrade and refine our novel computer vision ML approach (Aim #1), to validate it in App-KI and TG AD mouse models (Aim #2), and to apply it to mice receiving a newly-developed anti-Aβ antibody to validate our approach in a preclinical setting (Aim #3). We recently published our first iteration of an ML package that developed the VAME neural network to identify behavioral motifs. VAME will be further developed to provide rapid testing of large cohorts, unbiased identification of disease-associated behavioral deficits, and reproducible phenotypes across experimental conditions and laboratories. Our proposal thereby addresses key limitations of standard behavioral testing and mouse modeling, vertically advances the methodology of behavioral neuroscience, launches innovative biotechnological development, and opens new horizons for dementia-related research, including the adaptation of the approaches to humans. Successful completion of the proposed studies will provide a new preclinical tool for diagnosis, assessment, and disease monitoring in mouse models of AD. In conclusion, will establish a novel machine- learning behavioral phenotyping platform with the power to non-invasively identify robust behavioral alterations in App-KI models of AD, removing a key methodological barrier to the translational study of MCI, and increasing the value of behavioral research broadly. This award will critically support the PI to undertake immersive entrepreneurial training experiences at local universities and at a startup company focused on developing novel therapeutics for neurodegenerative diseases.

项目摘要临床前努力为阿尔茨海默氏病认知障碍的治疗措施受到了阻碍两个障碍：耗时的行为测定通常缺乏灵敏度和转基因（TG）小鼠模型使用APP过表达，无法准确概括人类的发病机理。克服第二屏障，新开发的人性化应用程序敲入（APP-KI）小鼠模型表达与广告相关的人类基因生理水平。 APP-KI小鼠具有明显的脑病理学，但不一致，米勒或没有行为许多传统行为测试中的表型，包括莫里斯水迷宫（Saito等，2014）。这些标准行为测试的局限性，包括缺乏灵敏度，低通量和可重复性代表了新开发的APP-KI小鼠适当发展治疗剂的关键方法论障碍。没有解决这个问题的解决方案，翻译和临床前研究可能会努力发展以轻度或微妙的行为表型为特征的早期发病机理或零星AD模型的疗法缺乏明显的临床疾病表现。为了克服行为测试的限制，我们建议实施机器学习（ML）方法，提供完整，公正和稳健的行为甚至微妙的行为表型的表征。具体来说，我们建议升级和完善我们的小说计算机视觉ML方法（AIM＃1），在App-KI和TG AD鼠标模型中验证它（AIM＃2），并应用它是在接受新发育的抗Aβ抗体的小鼠中，以在临床前验证我们的方法（AIM＃3）。我们最近发布了我们的第一次ML软件包的迭代，该软件包开发了VAME神经网络以识别行为图案。将进一步开发vame，以提供对大型队列，公正识别的快速测试与疾病相关的行为定义和可再现的表型跨实验条件和实验室。我们的建议因此解决了标准行为测试和鼠标建模的关键局限性，垂直发展行为神经科学的方法论，启动了创新的生物技术开发，并为痴呆有关的研究开辟了新的视野，包括改编方法给人类。拟议研究的成功完成将为诊断提供一个新的临床前工具，评估和AD小鼠模型中的疾病监测。总之，将建立一个新颖的机器 - 学习行为表型平台，具有非侵入性识别强大的行为改变的能力在AP-KI AD模型中，消除了MCI翻译研究的关键方法论障碍，并增加行为研究的价值广泛。该奖项将严格支持PI，以在当地进行沉浸式企业家培训经验大学和一家初创公司致力于开发新的神经退行性疾病疗法。