Identifying, manipulating, and studying a complete sensory-to-motor model behavior circuit

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PROJECT SUMMARY We are requesting supplemental funds to obtain and apply transformative, recently validated methods to identify and study the neural correlates of behavior. How does the brain transform sensory information into complex behavior? The objective of the parent proposal is to identify the relevant neurons across the brain that are necessary to produce a relatively simple motivated behavior to study and identify fundamental principles underlying coding. Sensory-to- behavior circuits contain a variety of neural computations such as those that determine the identity and meaning of the sensed cues, gauge internal state, remember previous experience, and command muscle action. In the parent proposal we aim to identify and study an entire circuit from sensation across the brain to motor output. The original RFA called for the ability to leverage and study neural dynamics. At the time of the funding of the parent grant, we proposed to monitor brain-wide activity using cFos as a proxy, however this only provides a snapshot of an instant of brain activity in post-mortem animals. We complemented this approach with in vivo imaging of GCaMP activity by fiber photometry in awake behaving animals. This method reveals neural dynamics over seconds but is limited to image a small subset of the brain. Though these methods were state of the art at the time, they are both low through-put to implement and do not provide neural dynamics across the whole brain. Recently, functional ultrasound imaging (fUSi) has been validated to report dynamic images of the entire mouse brain (4 times/second) with a resolution of at least 100um. Dynamic, whole brain, high resolution imagine is truly transformative. fUSi is relatively easy and low-cost which will reduce resources including personnel, animals, and time. It will not just be faster and easier; we will learn more. We expect the additional information provided by fUSi to increase the confidence and scope of our conclusions. This will enable unprecedented activity profiles to inform all of our original aims including identifying key neural circuits and nodes, information flow within nodes and across the brain, and the change in activity with development and experience in and across individuals. Moreover, dynamic brain wide activity correlated with behavior will increase our ability to generate reliable models of information coding. Better models are expected to provide new, testable hypothesis that will benefit our understanding of information coding of sensory and social behavior and of brain function in general. An administrative supplement to implement this approach will result in more rapid achievement of our aims and better facilitate the goals of the initial RFA.