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

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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.