Dopamine modulation for the treatment of chronic dysfunction due to traumatic brain injury

多巴胺调节治疗创伤性脑损伤引起的慢性功能障碍

• 批准号: 10594159
• 负责人: Cole Vonder Haar
• 金额: $ 24.1万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10594159
• 关键词: Algorithms; Animals; Attention; Behavior; Behavioral; Chronic; Clinical; Clinical Data; Data; Data Set; Decision Making; Discrimination; Dopamine; Experimental Designs; Functional disorder; Goals; Grant; Grouping; Impulsivity; Individual; Injury; Intervention; Machine Learning; Multiple Trauma; Pathology; Pharmacological Treatment; Rattus; Research Personnel; Risk Factors; Severities; Shapes; Standardization; TBI treatment; Techniques; Testing; Training; Traumatic Brain Injury; Validation; base; improved; large datasets; multidimensional data; multiple datasets; novel; placebo group; pre-clinical; resilience; supervised learning; unsupervised learning

Project Summary/Abstract The current supplement to an R01 grant will augment the initial project. As a result of the initial R01 project, we have completed multiple datasets describing deficits in attention, impulsivity, and decision-making after traumatic brain injury (TBI) in rats. This resulted in millions of lines of data across individual studies – a rare phenomenon for animal TBI. The goal of the current supplement is to compile these into two large datasets for multidimensional analytics and apply cutting-edge machine learning techniques to determine if behavior and pathology can discriminate groupings (e.g., TBI from sham) and what factors determine individual vulnerability and resilience to injury. One dataset will comprise risky decision-making and have roughly 1.5 million lines of data, with approximately 70% corresponding to “pure” sham or TBI conditions (i.e., no other interventions). The second dataset will have roughly 850,000 lines of data, with approximately 80% corresponding to “pure” sham or TBI conditions, and with multiple injury severities. We will apply supervised machine learning techniques to validate discrimination of injury from sham groups based on behavior alone, or pathophysiology, and then test the most robust algorithms against smaller subpopulations which received an intervention (e.g., pharmacological treatment). We will also use unsupervised machine learning techniques to identify subpopulations within the TBI group, particularly with reference to vulnerability and resilience. For each of these approaches, we will compare a large battery of algorithms to determine which are strongest or provide the greatest utility. With large datasets such as this, we can subdivide into training, testing, and validation sets to maximize rigor. This is a unique opportunity because robust, standardized behavioral datasets such as this are rare in preclinical TBI. This will allow us to better align clinical and pre-clinical data, identify risk factors and potential treatment avenues, and improve the utility of machine learning for the study and treatment of TBI. The harmonized datasets will be made publicly available to enable other researchers to explore novel questions and shape experimental design.

项目摘要/摘要 R01赠款的当前补充将增加初始项目。由于初始R01项目，我们 已经完成了多个数据集，描述了创伤后注意力，冲动性和决策的定义 大鼠脑损伤（TBI）。这导致了各个研究的数百万个数据 - 一种罕见的现象 用于动物TBI。当前补充的目的是将它们编译为两个大数据集以进行多维 分析并应用尖端的机器学习技术来确定行为和病理是否可以 区分分组（例如，Sham的TBI），哪些因素决定了个人脆弱性和韧性 受伤。一个数据集将完成风险的决策，并具有大约150万行数据，其中 大约70％对应于“纯”假或TBI条件（即没有其他干预措施）。第二个 数据集将具有大约850,000行数据，大约80％对应于“纯”假或TBI 条件，以及多重伤害严重性。我们将应用监督的机器学习技术来验证 仅根据行为或病理生理学来歧视假手术组的伤害，然后测试最多 强大的算法针对接受干预的较小亚群（例如，药理 治疗）。我们还将使用无监督的机器学习技术来识别TBI中的亚群 小组，特别是参考脆弱性和韧性。对于每种方法，我们都会比较 大量的算法来确定哪些算法是强大的或提供最大的实用程序。带有大数据集 这样，我们可以将其细分为培训，测试和验证集，以最大程度地提高严谨性。这是一个独特的 机会是因为在临床前TBI中很少有这样的稳健，标准化的行为数据集。这会 允许我们更好地调整临床和临床前数据，确定危险因素和潜在的治疗途径，以及 改善机器学习对TBI的研究和治疗的效用。将制作协调的数据集 公开可用于使其他研究人员能够探索新颖的问题并塑造实验设计。