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Mechanisms of Purkinje Cell Encoding of Limb Kinematics in Skilled Reach

熟练伸展肢体运动学的浦肯野细胞编码机制

基本信息

批准号：
10178132
负责人：
Dylan Calame
金额：
$ 2.28万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2022-01-31
项目状态：
已结题

项目摘要

Project Summary The cerebellum is an evolutionarily conserved brain structure known to contribute to motor control. A leading hypothesis of cerebellar function is that it generates an internal model to predict upcoming body kinematics allowing the cerebellum to provide feedforward motor control to make movements smooth and accurate. Current theories propose that the sole outputs of the cerebellar cortex –Purkinje cells (PC)– predict future movement kinematics in simple spike (SS) firing rates. These SSs are driven by mossy fiber inputs that indirectly contact PCs via parallel fibers. It is thought that complex spikes (CSs), driven by inferior olivary climbing fibers synapsing onto PCs, drive plasticity of parallel fibers allowing PCs to learn to respond to incoming mossy fiber information. In this way, CSs adjust the SS rate to model upcoming movements. However, how PCs incorporate their two extracerebellar inputs– mossy fibers and climbing fibers– to produce SS predictive encoding in limb movements, like reaching, is unclear. This study will relate the electrophysiological signals of these two inputs– SSs and CSs– during a mouse reaching task. By using multilinear regression models and closed-loop optogenetics, I will test how CS-driven changes in SS encoding lead to accurate predictions of limb position by PCs. I hypothesize that SS kinematic tuning is shaped by ‘encoding error’-triggered CSs. This hypothesis would be distinct from current ‘target error’-based theories of CS firing and would have the ability to explain how PCs shape movements across an entire reach, not just at the endpoint.

项目摘要小脑是一种进化上保守的大脑结构，已知有助于运动控制。领先的小脑功能的假设是，它产生一个内部模型来预测即将到来的身体运动学使小脑提供前馈运动控制，使运动平稳和准确。电流理论认为小脑皮层的唯一输出--浦肯野细胞（PC）--预测未来的运动简单尖峰（SS）放电率的运动学。这些SS由苔藓纤维输入驱动， PC通过并行光纤。据认为，复杂的棘波（CSs），驱动下橄榄攀缘纤维突触在PC上，驱动平行纤维的可塑性，使PC能够学习对传入的苔藓纤维信息做出反应。通过这种方式，CS调整SS速率以模拟即将到来的运动。然而，PC如何将其两个小脑外输入-苔藓纤维和攀爬纤维-在肢体运动中产生SS预测编码，就像到达一样，还不清楚。这项研究将涉及这两个输入的电生理信号-SS和CS- 在一个鼠标达到任务。通过使用多元线性回归模型和闭环光遗传学，我将测试 CS驱动的SS编码变化如何导致PC准确预测肢体位置。我假设 SS运动学调整是由“编码错误”触发的CS形成的。这一假设与目前的假设不同。 “目标误差”为基础的CS射击理论，并有能力解释PC如何在整个运动中塑造运动一个完整的范围，而不仅仅是在终点。