Linking Motor Cortex Activity and Movement in the Mouse Orofacial System

通过神经回路的信息流是动态的。参与计算的大脑区域集是 根据行为需求不断变化。大脑中的电路是如何在功能上耦合的 在行为时间尺度上不耦合，以便可以将信息传递到 适当的时间仍然是系统神经科学中的一个重大悬而未决的问题。这个问题很特别 在发动机系统中的关联性。动作的产生是动作最基本的功能之一 在哺乳动物中，灵活的运动依赖于运动皮质。运动中的神经活动是皮质 复杂，由控制信号组成，除了与运动规划相关的活动外，控制信号还驱动运动， 学习和其他认知过程。该宽广的信号阵列在同一电路内被多路复用， 通常，在相同的细胞内。大脑如何调节哪些活动模式被传达给 外周驱动运动，以及哪些被限制在用于局部皮质计算的局部电路？一个 提出了一种有影响力的假说，以解释为什么只有一些活动模式会产生运动--零 空间模型-提供了一种生物学上可信的计算策略，用于将认知信号与 那些被传输到外围以产生运动的东西。零空间模型表明 认知信号仅限于对下游运动区的影响有效抵消的模式- 它们是“输出为空”。初步证据表明，灵长类运动皮质的活动模式可能是 与零空间模型一致，但到目前为止，建立两者之间的因果联系一直具有挑战性 这些神经活动模式和行为。 在这项建议中，我们研究了神经信号从运动皮质到控制运动的运动神经元的流动。 肌肉系统来确定零空间模型是否准确地预测了哪些神经信号具有 在产生运动中的因果作用。强大的、新兴的多区域生理学方法使我们能够 检查沿着这一途径的每个处理阶段的神经活动，这是 了解大脑皮层活动模式如何转化为运动。细胞类型特异光发生 扰动允许我们消除驱动运动的神经信号的歧义，而不是那些简单的 并将有助于建立神经活动和运动之间的因果关系。 了解神经活动与行为的关系最终将帮助我们更好地解释这些缺陷 表达在运动障碍中，并激励改进的脑机接口和仿生控制 在下一代人工系统中使用的策略。

Information flow through neural circuits is dynamic. The set of brain areas that are engaged in computation are ever changing according to behavioral demands. How circuits in the brain are functionally coupled and uncoupled on behavioral time scales so that information can be relayed to the appropriate place at the appropriate time remains a major outstanding question in systems neuroscience. This question is of particular relevance in the motor system. The production of movements is one of the most fundamental functions of the brain, and in mammals, flexible movements depend on the motor cortex. Neural activity in the motor is cortex complex, made up of control signals that drive movements in addition to activity related to motor planning, learning, and other cognitive processes. This wide array of signals is multiplexed within the same circuit and, often, within the same cells. How does the brain regulate which activity patterns are communicated to the periphery to drive movements and which are confined to local circuits for local cortical computation? An influential hypothesis proposed to explain why only some activity patterns generate movements – the null space model – provides a biologically plausible computational strategy for segregating cognitive signals from those that are transmitted to the periphery to produce movement. The null space model suggests that cognitive signals are restricted to patterns whose impact on downstream motor areas effectively cancel out – they are ‘output-null.’ Preliminary evidence suggests that activity patterns in the primate motor cortex may be consistent with the null space model, but thus far it has been challenging to establish a causal link between these neural activity patterns and behavior. In this proposal, we examine the flow of neural signals from the motor cortex to the motor neurons that control the musculature to determine whether the null space model accurately predicts which neural signals have a causal role in generating movements. Powerful, emerging methods for multi-regional physiology allow us to examine neural activity at each processing stage along this pathway, an essential requirement for understanding how cortical activity patterns are transformed into movements. Cell-type specific optogenetic perturbations allow us to disambiguate the neural signals that drive movements from those that are simply a consequence and will help to establish a causal relationship between neural activity and movements. Understanding how neural activity relates to behavior will ultimately help us better interpret the deficits expressed in movement disorders and motivate improved brain-machine interfaces and biomimetic control strategies for use in the next generation of artificial systems.