The ascending and descending pathways for the control of action inhibition

我们经常被要求取消一个已经启动的动作，例如，由于一辆接近的汽车而突然停止。在这里，过马路是“前进过程”，而取消运动是“停止过程”。去和停的过程从大脑到肌肉。停止过程采取直接路径，称为超直接路径，允许它击败进行过程并在需要时阻止移动。停止障碍会损害健康和社会经济地位，就像成瘾等冲动控制障碍一样。超直接通路切断了从运动皮层到肌肉的兴奋性驱动。但仍存在一些关键问题。首先，尚不清楚超直接途径如何有助于停止肌肉松弛。放松肌肉，例如让伸展的手臂落下，是运动控制的重要组成部分，研究表明，放松引起的运动可以通过肌肉的主动收缩来停止。这不符合现有的超直接途径模型，该模型规定停止包括终止对肌肉的驱动，而不是“增加”肌肉活动。我们的目标是确定在停止肌肉松弛所需的肌肉活动的水平，通过比较额叶和运动皮层的活动在人类使用脑磁图在肌肉收缩和放松停止。这将揭示超直接信号是否导致运动皮层激活肌肉，或者这种活动是否起源于运动皮层之外，也许构成了外周制动机制。我们还将讨论一个相关但更普遍的问题，即超直接通路是否只通过运动皮层输出，或者是否有绕过运动皮层的肌肉分支。一种假设是，停止过程可以采取皮层下的路线，称为周边制动。我们将使用我们最近在人类脊髓和大脑活动同步成像方面的进展来测试这一点。如果使用替代路线，停止过程将在运动皮层中检测到之前在脊髓中检测到。绘制涉及脊髓输出快速调节的替代路线将有助于解释停止过程如何自动控制不同的肌肉激活模式。给定运动的肌肉活动会随着上下文而变化，例如当肢体因姿势改变而变得更重时。在这些动态条件下，动作是如何被取消的，目前的超直接通路模型并没有很好地捕捉到，因为它不包括肌肉反馈。我们将使用机器人设备来解决这个问题，这些设备在停止过程中扰动手臂，并测量当前和未来试验的响应。这将揭示大脑如何使用来自肌肉的上行信息来修改和改善停止过程。我们假设，在停止过程中从肌肉到大脑的反馈也会提高未来的运动准确性。移动很少被完全取消。由此产生的部分肌肉活动可以用于学习，而不会在错误的时间（即停止信号之后）进行运动的负面后果。我们将通过让参与者在取消后重复伸手动作来测试这一点。预测部分响应期间的肌肉活动的模式被反馈到大脑以修改运动皮层，从而提高随后的到达准确性。因此，我们将展示与停止过程相关的上升信号实际上是如何修改未来的行动过程的。因此，通过四组实验，我们的目标是开发一个新的，全面的说明如何停止行动。这个帐户将超越视为一个单一的，静态的和单向的过程停止，而是强调如何停止是由多个途径，可以通过反馈动态调整，允许不同的肌肉活动模式得到控制的基础。

We are often required to cancel an action after it has been initiated, e.g. suddenly stopping because of an approaching car. Here, crossing the road is the 'go process', while cancelling the movement is the 'stop process'. Go and stop processes race from brain to muscle. The stop process takes a direct route, known as the hyperdirect pathway, allowing it to beat the go process and prevent movement if needed. Impaired stopping harms wellbeing and socioeconomic status, as is seen with impulse control disorders like addiction. The hyperdirect pathway cuts excitatory drive from the motor cortex to the muscle. But a number of key questions remain. Firstly, it is unclear how the hyperdirect pathway contributes to the stopping of muscle relaxations. Relaxing a muscle, e.g. letting an outstretched arm fall, is an important part of movement control, and research has shown that movements caused by relaxations can be stopped through active contractions of the muscles. This does not fit with existing models of the hyperdirect pathway, which stipulate that stopping consists of terminating drive to muscles, not 'adding' muscle activity. We aim to determine the level at which muscle activity needed for stopping muscle relaxations originates, by comparing frontal and motor cortex activity in humans using magnetoencephalography during the stopping of muscle contractions and relaxations. This will reveal whether the hyperdirect signaling causes the motor cortex to activate the muscle, or if such activity originates beyond the motor cortex, perhaps constituting a peripheral braking mechanism. We will also address the related but more general question of whether the hyperdirect pathway only outputs via the motor cortex, or has branches to the muscle that bypass the motor cortex. One hypothesis is that the stop process can take a subcortical route, known as peripheral braking. We will test this using our own recent advances in simultaneous imaging of the activity of the human spinal cord and brain. If alternate routes are utilized, the stop process will be detected in the spinal cord before it is detected in the motor cortex. Confirming alternate routes involving rapid modulation of spinal cord output will help explain how disparate patterns of muscle activation are automatically controlled by the stop process. Muscle activity for given a movement changes with context, such as when a limb gets heavier from altered posture. How actions are cancelled under these dynamic conditions is not well captured by current models of the hyperdirect pathway, which do not include muscle feedback. We will address this using robotic devices that perturb the arm during stopping, and measuring the response on the current and future trials. This will reveal how the brain uses ascending information from the muscle to modify and improve the stopping process. We hypothesize that feedback from muscle to brain during stopping also improves future movement accuracy. Movements are rarely fully cancelled. The resulting partial muscle activity could be used for learning, without the negative consequences of making a movement at the wrong time (i.e. after a stop signal). We will test this by having participants repeat reaching movements after cancelations. The pattern of the muscle activity during partial responses is predicted to be fed back to the brain to modify the motor cortex, thereby improving subsequent reach accuracy. As such, we will show how ascending signals related to the stop process actually modify future go processes. Thus, with four sets of experiments we aim to develop a new, comprehensive account of how actions are stopped. This account will go beyond viewing stopping as a single, static and unidirectional process, instead emphasizing how stopping is underpinned by multiple pathways that can be dynamically adjusted by feedback, allowing disparate patterns of muscle activity to be controlled.