一个α-gamma-motoneurone附带可以缓解在自愿运动期间的速度依赖的伸展反射:一项计算研究
Grace Niyo1, Lama I Almofeez1, Andrew Erwin2,3
1Biomedical Engineering Department, University of Southern California, Los Angeles, CA, USA.
bioRxiv : the preprint server for biology
|December 18, 2023
概括
不调节的肌肉伸展反射扰乱了自愿的手臂运动. 一个计算模型显示,以前未被识别的α-摩托神经元 (α-MN) 附带物有助于抑制这些反射,从而使运动控制和学习更顺.
科学领域:
- 神经科学是一个神经科学.
- 发动机控制器的控制器
- 计算生物学 计算生物学
背景情况:
- 自愿运动依赖于复杂的运动指令,而不仅仅来自于主要的运动皮质.
- 阿尔法运动神经元 (α-MN) 驱动器集成了各种输入,包括伸展反射,必须调节以实现平滑运动.
- 未调节的伸展反射在扰乱运动中的作用,特别是在多关节四肢中,仍然不清楚.
研究的目的:
- 为了研究速度依赖的伸展反射对自愿手臂运动的破坏性影响.
- 探索调节这些反射如何影响运动的准确性和可变性.
- 识别潜在的神经机制,如α-玛 (α-γ) 联合激活或α-MN附带,以抑制伸展反射.
主要方法:
- 研发了一种 rhesus macaque 手臂的计算模型.
- 模拟包括feedforward α-MN命令,并没有速度依赖的伸展反射反.
- 该模型评估了运动中断和反射调节策略的影响.
主要成果:
- 不调节的速度依赖的伸展反射导致模拟的手臂运动发生了显著的,可变的干扰.
- 通过理想化的α-γ联合激活或α-MN附带投射调节这些反射,大大减少了运动中断.
- 该研究确定了α-MN对同名 γ-MN的附带投影作为一个合理的,未被识别的自身脊柱电路.
结论:
- 阿尔法运动神经元 (α-MN) 对马运动神经元 (γ-MN) 的附带投射代表了哺乳动物融合运动系统中的新型自身脊髓电路.
- 这一电路,与α-γ联合激活和骨运动纤维 (β-MN) 一起,有助于实现灵活的融合运动系统,使自愿运动成为可能.
- 这些附带因素可能对调节肢体机制至关重要,通过更高层次的神经机制促进运动学习和适应.
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