Quantifying relationships between behavior, neural network and metabolic dysfunction in a genetic mouse model of developmental epileptic encephalopathy - PROJECT SUMMARY/ABSTRACT De novo mutations of voltage-gated ion channels, including hyperpolarization-activated cyclic nucleotide- regulated (HCN) channels, give rise to developmental epileptic encephalopathies (DEEs). This severe form of childhood epilepsy not only results in periods of intense seizure activity, but also causes behavioral comorbidities that persist throughout the lifetime of the patient despite a reduction in seizures with age. The neuronal underpinnings of the behavioral alterations observed in DEE remain unclear. However, in depth studies are possible using genetic mouse models combined with behavioral analysis and optical imaging approaches. Thus, I propose to measure both behavior and cortex-wide neural activity simultaneously to construct an analysis pipeline to better quantify aberrant behaviors and uncover their neural correlates. As this mouse model has been shown to have developmental delays, motor deficits and abnormal neural activity, I hypothesize that compared to age-matched littermate controls HCN1-GD mice will have distinct behavioral motifs and arousal states that coincide with altered patterns of neuronal network activity. In preliminary studies, I imaged neuronal activity and hemodynamics across the cortex of HCN1-GD mice and observed behavioral differences and aberrant cortical activity patterns. Additionally, I measured alterations in the way that blood flow changed in response to a stimulus-evoked increase in neuronal activity in the whisker (barrel) cortex. This finding has led me to suspect an unexplored metabolic component of this disorder. My studies and extensive quantitative analyses across three perspectives – behavior, network activity, and neurometabolic function, will expand the scope of approaches used to understand deficits associated with DEEs. In Aim 1, I will first use machine-learning based methods to characterize spontaneous behaviors of HCN1-GD mice. This will enable me to build a robust analysis pipeline for classifying behaviors and then grouping large- scale imaging datasets to analyze changes in the neuronal network activity of HCN1-GD mice. Aim 2 will determine aspects of neurometabolic activity that may underlie network level activity changes using whisker stimulation paradigms as measured by the evoked neurovascular response. I will then apply the framework constructed in Aim 1 to systematically investigate the neurovascular response in the context of behavior. The proposed work outlines an innovative approach spanning neuroscience and engineering to study DEE by integrating the analysis of behavior, neural activity, and hemodynamics in a clinically relevant mouse model. The insights gained through this study will aid in translating research findings into clinical practice, ultimately aiming to enhance the quality of life for patients.
