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The ascending and descending pathways for the control of action inhibition

The ascending and descending pathways for the control of action inhibition
控制动作抑制的上升和下降途径
批准号:
BB/X008614/1
负责人:
Sven Bestmann
金额:
$68.45万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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中文摘要
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英文摘要
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.
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Establishing a trans-atlantic partnership for studying the neural networks for motor skill learning in the human brain
  • 批准号:
    BB/I026162/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $6.18万
  • 财政年份:
    2011
  • 负责人:
    Sven Bestmann
  • 依托单位:
Biasing influences on the motor system during action preparation: a multimodal neuroimaging-computationally informed approach
  • 批准号:
    BB/F02424X/1
  • 项目类别:
    Fellowship
  • 资助金额:
    $93.85万
  • 财政年份:
    2008
  • 负责人:
    Sven Bestmann
  • 依托单位:
海外基金