CRCNS: Mechanisms and Modeling of the Adaptive Modulation of the Intrinsic Properties of Spinal Motoneurons

这项研究的长期目标是揭示脊髓中控制运动功能的神经元，即脊髓运动神经元的兴奋性的活动依赖性改变所涉及的细胞机制。了解脊髓运动神经元的输出特性如何通过增加和减少活动来改变，这是一个基本的挑战，涉及从运动训练到康复和先进假肢的各种影响。该项目旨在定量了解运动神经元的持续激活如何调节神经元的内在兴奋性，以及这种效应如何反过来影响神经元的输出以驱动肌肉收缩。长期以来，运动神经元一直被认为只是作为运动指令到肌肉激活的中继器。然而，越来越多的证据表明，这些神经元可以经历显著的修饰(可塑性)，从而改变输入和输出之间的关系。最近对运动神经元的研究表明，内在电特性的可塑性可能对学习运动系统很重要。这个项目的目标是确定持续行走时长时间的激活如何改变运动神经元的内在兴奋性。除了实验研究，该项目还包括开发持续激活前后脊髓运动神经元活动的详细计算模型，这些模型以实验工作为基础，但也指导实验工作。特拉华州立大学是一所历史悠久的黑人大学，主要是本科生，招生人数中有75%是非裔美国人。因此，该项目的一个更广泛的影响是培训属于代表性不足群体的学生。受训者被暴露在一个全面的研究环境中，包括代表最先进的电生理学和计算神经科学的技术方法，以及给予职业指导、写作和沟通方面的培训，以及接受资助提案写作的机会，以促进学生作为科学家的专业发展。突触可塑性的机制在中枢神经系统中得到了广泛的研究，但神经元内在属性的可塑性潜力却很少受到关注。这个项目的目标是了解脊髓运动神经元的可塑性，并确定长时间的激活是如何改变其内在兴奋性的，就像持续行走所发生的那样。该项目涉及电生理、免疫组织化学和药理学方法在小鼠脊髓切片中的应用。最重要的假设是，KCNQ/Kv7.2通道功能的改变和轴突起始节段性质的改变是脊髓运动神经元适应长时间网络激活的主要机制，而这些变化需要兴奋性突触输入的激活。该项目包括开发持续激活前后脊髓运动神经元活动的详细计算模型，利用能够同时匹配多个选择标准的多目标进化算法方法，并生成完整的神经元模型集合。计算模型是以实验测量为基础的，而模型反过来又产生了可通过实验验证的假设。因此，实验和模拟在这个项目中紧密交织在一起。

The long-term objective of this research is to uncover the cellular mechanisms involved in activity-dependent modification to the excitability of neurons in the spinal cord that control motor function, i.e., spinal motoneurons. Understanding how spinal motoneuron output properties can be modified by increased as well as decreased activity is a fundamental challenge with implications that span from athletic training to rehabilitation and advanced prosthetics. The project serves to generate a quantitative understanding of how persistent activation of motoneurons modulates the neurons' intrinsic excitability and how this effect, in turn, influences the neurons' output to drive muscle contraction. Motoneurons have long been thought to function simply as relays from motor commands to muscle activation. However, growing evidence demonstrates that these neurons can undergo significant modification (plasticity) that can change the relationship between input and output. Recent work with motoneurons demonstrates that plasticity in intrinsic electrical properties might be important for learning in the motor system. The goal of this project is to determine how prolonged activation, as occurs with sustained walking, changes the intrinsic excitability of motorneurons. Alongside experimental studies, the project includes the development of detailed computational models of spinal motoneuron activity before and after persistent activation that are based on but also guide the experimental work. Delaware State University is a Historically-Black, primarily undergraduate institution, with an enrollment that is 75% African-American. Thus, a broader impact of this project is the training of students who are members of under-represented groups. Trainees are exposed to a comprehensive research environment, including technical approaches representing state-of-the-art electrophysiological and computational neuroscience, as well as given career guidance, training in writing and communication, and exposure to grant proposal writing to foster the students' professional development as scientists.Mechanisms of synaptic plasticity have been intensively studied in the central nervous system, but the potential for plasticity in neurons' intrinsic properties has received little attention. The goal of this project is to understand the plasticity of spinal motoneurons and to determine how prolonged activation, as what occurs with sustained walking, changes their intrinsic excitability. The project involves the application of electrophysiological, immunohistochemical, and pharmacological methods in mouse spinal cord slices. The overarching hypothesis is that alteration of KCNQ/Kv7.2 channel function and changes in axonal initial segment properties are the primary mechanisms of adaptation of spinal motoneurons to prolonged network activation, and that activation of excitatory synaptic inputs is required for these changes. The project includes the development of detailed computational models of spinal motoneuron activity before and after persistent activation, exploiting a multi-objective evolutionary algorithm approach capable of matching multiple selection criteria simultaneously and of generating entire collections of neuronal models. The computational models are based on experimental measurements, and the models in turn generate experimentally testable hypotheses. Thus, experiments and simulations are closely intertwined in this project.