Wallace H. Coulter Department of Biomedical Engineering, Emory University and The Georgia Institute of Technology, Atlanta, Georgia, USA.
Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, Georgia, USA.
Exp Physiol. 2024 Jan;109(1):112-124. doi: 10.1113/EP090767. Epub 2023 Jul 10.
Computational models can be critical to linking complex properties of muscle spindle organs to the sensory information that they encode during behaviours such as postural sway and locomotion where few muscle spindle recordings exist. Here, we augment a biophysical muscle spindle model to predict the muscle spindle sensory signal. Muscle spindles comprise several intrafusal muscle fibres with varied myosin expression and are innervated by sensory neurons that fire during muscle stretch. We demonstrate how cross-bridge dynamics from thick and thin filament interactions affect the sensory receptor potential at the spike initiating region. Equivalent to the Ia afferent's instantaneous firing rate, the receptor potential is modelled as a linear sum of the force and rate change of force (yank) of a dynamic bag1 fibre and the force of a static bag2/chain fibre. We show the importance of inter-filament interactions in (i) generating large changes in force at stretch onset that drive initial bursts and (ii) faster recovery of bag fibre force and receptor potential following a shortening. We show how myosin attachment and detachment rates qualitatively alter the receptor potential. Finally, we show the effect of faster recovery of receptor potential on cyclic stretch-shorten cycles. Specifically, the model predicts history-dependence in muscle spindle receptor potentials as a function of inter-stretch interval (ISI), pre-stretch amplitude and the amplitude of sinusoidal stretches. This model provides a computational platform for predicting muscle spindle response in behaviourally relevant stretches and can link myosin expression seen in healthy and diseased intrafusal muscle fibres to muscle spindle function.
计算模型对于将肌肉梭器官的复杂特性与它们在姿势摆动和运动等行为中编码的感觉信息联系起来至关重要，而这些行为中存在的肌梭记录很少。在这里，我们扩展了一个生物物理肌肉梭模型来预测肌肉梭感觉信号。肌肉梭由几个具有不同肌球蛋白表达的内收肌纤维组成，并由在肌肉拉伸时发射的感觉神经元支配。我们展示了粗丝和细丝相互作用的横桥动力学如何影响起始区域的感觉受体潜力。相当于 Ia 传入纤维的瞬时放电率，受体潜力被建模为动态袋 1 纤维的力和力变化（ Yank）的线性总和，以及静态袋 2/链纤维的力。我们展示了细丝间相互作用在（i）在拉伸开始时产生大的力变化，从而驱动初始爆发，以及（ii）在缩短后更快地恢复袋纤维力和受体潜力方面的重要性。我们展示了肌球蛋白附着和脱离率如何定性地改变受体潜力。最后，我们展示了受体潜力更快恢复对循环拉伸-缩短周期的影响。具体来说，该模型预测了肌肉梭受体潜力作为内伸间隔（ISI）、预拉伸幅度和正弦波拉伸幅度的函数的历史依赖性。该模型为预测行为相关拉伸中的肌肉梭反应提供了一个计算平台，并可以将健康和患病的内收肌纤维中观察到的肌球蛋白表达与肌肉梭功能联系起来。
