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

项目总结 我们正在申请补充资金，以获得和应用变革性的，最近验证的 方法识别和研究行为的神经相关因素。大脑是如何转变的 将感官信息转化为复杂的行为？家长提案的目标是确定 整个大脑中的相关神经元是产生相对简单的 学习和识别编码背后的基本原则的激励行为。感官到- 行为回路包含各种神经计算，例如那些确定 感觉到的线索的身份和意义，测量内部状态，记住以前的经验， 并指挥肌肉动作。在家长提案中，我们的目标是确定和研究整个 从大脑的感觉到运动输出的回路。最初的RFA要求有能力 利用和研究神经动力学。在为母基金提供资金时，我们提出了 使用CFO作为代理来监控全脑活动，然而，这只提供了 动物死后大脑活动的瞬间。我们在体内补充了这种方法 纤维光度法对清醒行为动物GCaMP活性的成像。这种方法揭示了 几秒钟的神经动力学，但仅限于对大脑的一小部分进行成像。尽管这些 方法在当时是最先进的，它们都是低吞吐量的实现，而且不 提供整个大脑的神经动力学。最近，功能超声成像(FUSI) 已经过验证，可以报告整个老鼠大脑的动态图像(4次/秒) 分辨率至少为100微米。动态的，全脑的，高分辨率的想象是真正的 具有变革性。FUSI相对容易且成本低，这将减少资源，包括 人员、动物和时间。它不仅会更快、更容易，我们还会学到更多。我们预计 FUSI提供的补充信息，以增加我们的信心和范围 结论。这将使前所未有的活动概况能够告知我们所有的最初目标 包括识别关键的神经回路和节点，节点内和节点之间的信息流 大脑，以及随着个体内部和跨个体的发展和经验而发生的活动变化。 此外，与行为相关的动态脑部活动将增加我们的能力 生成可靠的信息编码模型。更好的车型有望提供新的、 可验证性假说，将有助于我们理解信息编码的感觉和 社会行为和大脑功能的总体上。实现这一点的行政补充 这种方法将使我们的目标更快地实现，并更好地促进 初始RFA。

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.