Coding properties of Vibrissal-Responsive Trigeminal Ganglion Neurons

 描述(申请人提供)：我们看到是因为视网膜神经节细胞对光有反应。我们听到声音是因为螺旋神经节细胞对声音有反应。我们感觉到，是因为初级体感神经元对“触摸”做出反应。但是什么是“触摸”呢？虽然光和声音可以通过物理参数(幅度、频率、相位和偏振)来表征，但触摸的机制以及初级感觉神经元编码触摸参数的方式在很大程度上是无法量化的。这是整个躯体感觉领域的一个明显差距，之所以会出现这种情况，是因为力学很难量化。为了缩小这一差距，我们将使用大鼠触觉(胡须)系统作为模型，将初级感觉神经元的反应与量化的触摸机制直接联系起来。与啮齿动物在遗传学和光遗传学研究中越来越多的使用相平行，啮齿动物触觉阵列已经成为研究触摸和感觉运动整合的越来越重要的模型。在过去的几年里，我们的实验室在表征振动力学方面取得了快速进展，现在我们拥有独特的地位，可以确定在自然搅拌行为中，三叉神经节(VG)初级感觉神经元的放电模式中如何表现3D胡须偏转和振动。我们研究的中心目标是通过适当结合机械信号的三维动态和准静态模型来预测VG神经元在接触和非接触扭动中的反应。我们的三个目标从大鼠的外部向内，从胡须，到毛囊，再到VG神经元。在目标1中，我们将开发基于晶须的机械编码模型，量化接触和非接触搅拌过程中振动基座上的3D机械信号。在目标2中，这些模型将被用来预测毛囊内机械感受器的反应，从而根据它们执行的机械转换来识别VG神经元的类别。最后，在目标3中，我们将量化VG神经元在清醒动物的自然扭动行为中的反应。利用Aim 2中确定的细胞类别，并与Aim1的建模一致，我们将检验VG响应与搅拌过程中的机械信号比它们与搅拌行为的几何和运动学更线性相关的假设。这项拟议的工作将首次记录清醒行为动物的VG神经元，同时充分表征接触和非接触扭动过程中的机械输入。我们的目标是解决三叉神经振动系统的很大一部分“编码问题”。解决这个问题将使我们更好地理解VG棘波对三叉神经系统更中心的阶段意味着什么，包括感觉丘脑和桶状皮质。

 DESCRIPTION (provided by applicant): We see because retinal ganglion cells respond to light. We hear because spiral ganglion cells respond to sound. We feel because primary somatosensory neurons respond to "touch." But what is "touch?" Whereas light and sound can be characterized by physical parameters (amplitude, frequency, phase, and polarization), the mechanics of touch, and the manner in which primary sensory neurons encode the parameters of touch, are largely unquantified. This is a glaring gap within the entire field of somatosensation, and it occurs because mechanics are difficult to quantify. To close this gap we will use the rat vibrissal (whisker) system as a model to directly relate the responses of primary sensory neurons to the quantified mechanics of touch. Paralleling the increased use of rodents in genetic and optogenetic research, the rodent vibrissal array has become an increasingly important model for the study of touch and sensorimotor integration. In the past few years, our laboratory has made rapid progress in characterizing vibrissal mechanics, and we are now uniquely positioned to determine how 3D whisker deflections and vibrations are represented in the firing patterns of primary sensory neurons of the trigeminal ganglion (Vg) during natural whisking behavior. The central goal of our investigation is to predict the responses of Vg neurons during both contact and non-contact whisking by appropriately combining 3D dynamic and quasistatic models of mechanical signals. Our three aims move from the outside of the rat inwards, from whisker, to follicle, to Vg neurons. In Aim 1, we will develop models of mechanical coding by the whisker, quantifying the 3D mechanical signals at the vibrissal base during both contact and non-contact whisking. In Aim 2, these models will be used to predict responses of mechanoreceptors within the follicle and thus to identify classes of Vg neurons based on the mechanical transformation they perform. Finally, in Aim 3 we will quantify the responses of Vg neurons during natural whisking behavior in awake animals. Exploiting the cell classes identified in Aim 2, and consistent with the modeling of Aim1, we will test the hypothesis that Vg responses are more linearly correlated with mechanical signals during whisking than they are with the geometry and kinematics of whisking behavior. The proposed work will be the first to record from Vg neurons in awake behaving animals while fully characterizing the mechanical input during both contact and non-contact whisking. We aim to solve a large portion of the "coding problem" for the vibrissal-trigeminal system. Solving this problem will provide a better understanding of what a Vg spike "means" for more central stages of the trigeminal system, including sensory thalamus and barrel cortex.