Hannah's Diversity Supplement grant

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Project Summary/Abstract Elucidating how our brain integrates information to elicit appropriate behavioral responses requires mechanistic insights into how our sensory systems are wired to integrate diverse sensory modalities and transform them into the neural codes of motor action. Studies of spinal cord circuits are well-suited to exploring these questions: the direct link between sensory input and motor output (i.e., muscle contraction) affords an exquisite experimental tractability that has been leveraged since Sherrington’s pioneering work on the proprioceptive reflex pathway. Indeed, great progress has been made since then in understanding how proprioceptors (i.e., muscle sensory neurons) shape motor activity. Touch receptors in skin encoding sensory modalities like vibration, indentation, and slip, are also critical for adapting the way we walk in response to changes in our environment. However, the spinal cord integration of touch pathways to sculpt motor activity remains profoundly poorly understood. To address key conceptual and technical challenges in this field, we have built an extensive mouse genetic toolbox to visualize, quantify and manipulate touch-specific spinal cord circuits. In addition, we merge these powerful genetic tools with motor assays involving high-speed cameras, computer vision, and machine learning to quantify somatosensory behavior with unprecedented sensitivity. Combining these technologies, we identified a novel touch-specific premotor network important for sensorimotor function. Our overall hypothesis is that this network represents a critical node for integrating touch and proprioceptive information to influence specific patterns of muscle groups that facilitate both corrective movements during locomotion and motor ‘switching’ during naturalistic behaviors. We interrogate this novel network to address fundamental questions whose answers will enable an understanding of how touch pathways converge to shape movement. In Aims 1 and 2 we combine genetic approaches, high-resolution synaptic analysis, slice electrophysiology and in-vivo muscle recordings to test the hypothesis that this network integrates multimodal sensory information to influence specific muscle responses to sensory input. Aim 3 combines joint and muscle activity recordings to test the hypothesis that this network shapes cutaneous responses to facilitate corrective movements during locomotion. We extend these behavioral studies by implementing computer vision and machine learning to parse out naturalistic behaviors into sub-second movements to test the hypothesis that touch-specific premotor networks sculpt how micro- movements are pieced together into complex motor behaviors . By understanding the final path for movement organization (i.e., the spinal cord) our research will lead to new therapies aimed at improving the quality of life of people suffering from a variety of neurological disorders. Thus, this research lays the critical foundation for novel ways of thinking about modulating spinal circuits for improving motor function.