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

• 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
• 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' s Disease; Alzheimer' s disease model; Alzheimer' s disease patient; Alzheimer' 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

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 临床前的努力，以开发治疗阿尔茨海默氏症的认知障碍，已受到阻碍， 两个障碍：耗时的行为分析（通常缺乏灵敏度）和转基因（TG）小鼠模型 APP过度表达不能准确概括人类发病机制。为了克服第二个 屏障，新开发的人源化App敲入（App-KI）小鼠模型表达AD相关的人类基因， 生理水平。App-KI小鼠具有显著的脑病理学，但不一致、较温和或缺乏行为学， 在许多传统的行为测试中，包括Morris水迷宫（Saito等人，2014年）。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 代表了在新开发的App-KI小鼠中适当开发治疗剂的关键方法学障碍。 如果不解决这个问题，转化和临床前研究很可能难以发展 在早期发病模型或以轻度或轻微行为表型为特征的散发性AD中的治疗 且缺乏明显的临床疾病表现。为了克服行为测试的局限性，我们建议 实现机器学习（ML）方法，提供完整、无偏见和强大的行为 即使是细微的行为表型的表征。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（目标#1），在App-KI和TG AD小鼠模型中验证（目标#2），并应用 将其用于接受新开发的抗A β抗体的小鼠，以在临床前环境中验证我们的方法（目标#3）。 我们最近发布了ML包的第一次迭代，该包开发了VAME神经网络来识别 行为动机将进一步开发VAME，以提供对大型队列的快速检测， 疾病相关的行为缺陷，以及在实验条件下可重复的表型， laboratories.因此，我们的建议解决了标准行为测试和小鼠建模的关键限制， 垂直推进行为神经科学的方法，推出创新的生物技术 发展，并为痴呆症相关研究开辟新视野，包括方法的适应 对人类拟议研究的成功完成将为诊断提供新的临床前工具， 评估和疾病监测。总之，将建立一个新的机器- 学习行为表型分析平台，具有非侵入性识别稳健行为改变的能力 在AD的App-KI模型中，消除了MCI转化研究的关键方法学障碍， 行为研究的价值 该奖项将大力支持PI在当地进行沉浸式创业培训体验 大学和一家专注于开发神经退行性疾病新型疗法的初创公司。