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

项目摘要/摘要 目前对R01赠款的补充将增加最初的项目。作为最初的R01项目的结果，我们 完成了多个数据集，描述了创伤后注意力、冲动和决策能力的缺陷 大鼠脑损伤(TBI)。这导致了个别研究中数百万行的数据--这是一种罕见的现象 用于动物脑损伤。本附录的目标是将这些数据汇编成两个大型数据集，用于多维 分析并应用尖端机器学习技术来确定行为和病理是否可以 区分分组(例如，TBI和Sham)以及哪些因素决定个人的脆弱性和复原力 致伤。一个数据集将包括高风险的决策，大约有150万行数据，其中 大约70%对应于“纯粹的”假的或脑损伤的情况(即，没有其他干预)。第二 数据集将有大约850,000行数据，其中大约80%对应于“纯”假的或TBI 伤情，并有多处伤情。我们将应用有监督的机器学习技术来验证 仅根据行为或病理生理学区分损伤和假手术组，然后测试最多 针对接受干预(例如，药理学)的较小子群体的稳健算法 治疗)。我们还将使用无监督机器学习技术来识别TBI中的子种群 特别是在脆弱性和复原力方面。对于这些方法中的每一种，我们将比较 一大组算法，用来确定哪些是最强大的或提供最大的实用价值。使用大型数据集 这样，我们可以细分为训练、测试和验证集，以最大限度地提高严密性。这是一个独一无二的 机会，因为像这样的健壮、标准化的行为数据集在临床前脑损伤中是罕见的。这将是 使我们能够更好地协调临床和临床前数据，确定风险因素和潜在的治疗途径，以及 提高机器学习在脑外伤研究和治疗中的实用性。将生成协调的数据集 向公众开放，使其他研究人员能够探索新问题并塑造实验设计。

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.