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

项目总结/摘要 目前对R 01赠款的补充将扩大最初的项目。作为最初的R 01项目的结果， 我已经完成了多个数据集，描述了创伤后注意力、冲动和决策方面的缺陷。 脑损伤（TBI）。这导致了个体研究中数百万行的数据-这是一种罕见的现象 动物创伤性脑损伤当前补充的目标是将这些数据编译成两个大型数据集， 分析和应用尖端的机器学习技术，以确定行为和病理是否可以 区分分组（例如，哪些因素决定了个体的脆弱性和恢复力 受伤。一个数据集将包括风险决策，大约有150万行数据， 大约70%对应于“纯”假手术或TBI条件（即，没有其他发言）。第二 一个数据集将有大约850，000行数据，其中大约80%对应于“纯”假手术或TBI 条件，并与多处损伤的严重程度。我们将应用监督机器学习技术来验证 基于行为或病理生理学，从假手术组中区分损伤，然后测试最 针对接受干预的较小亚群的鲁棒算法（例如，药理 治疗）。我们还将使用无监督机器学习技术来识别TBI中的亚群 特别是在脆弱性和复原力方面。对于每一种方法，我们将比较 一个大的算法电池，以确定哪些是最强的或提供最大的效用。大型数据集 例如，我们可以细分为训练、测试和验证集，以最大限度地提高严谨性。这是一个独特 这是一个很好的机会，因为像这样强大的标准化行为数据集在临床前TBI中很少见。这将 使我们能够更好地调整临床和临床前数据，识别风险因素和潜在的治疗途径， 提高机器学习在TBI研究和治疗中的效用。将制作统一的数据集， 公开提供，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.