The detrusor, a key component of the urinary bladder wall, is a densely innervated syncytial smooth muscle tissue. Random spontaneous release of neurotransmitter at neuromuscular junctions (NMJs) in the detrusor gives rise to spontaneous excitatory junction potentials (SEJPs). These sub-threshold passive signals not only offer insights into the syncytial nature of the tissue, their spatio-temporal integration is critical to the generation of spontaneous neurogenic action potentials which lead to focal contractions during the filling phase of the bladder. Given the structural complexity and the contractile nature of the tissue, electrophysiological investigations on spatio-temporal integration of SEJPs in the detrusor are technically challenging. Here we report a biophysically constrained computational model of a detrusor syncytium overlaid with spatially distributed innervation, using which we explored salient features of the integration of SEJPs in the tissue and the key factors that contribute to this integration. We validated our model against experimental data, ascertaining that observations were congruent with theoretical predictions. With the help of comparative studies, we propose that the amplitude of the spatio-temporally integrated SEJP is most sensitive to the inter-cellular coupling strength in the detrusor, while frequency of observed events depends more strongly on innervation density. An experimentally testable prediction arising from our study is that spontaneous release frequency of neurotransmitter may be implicated in the generation of detrusor overactivity. Set against histological observations, we also conjecture possible changes in the electrical activity of the detrusor during pathology involving patchy denervation. Our model thus provides a physiologically realistic, heuristic framework to investigate the spread and integration of passive potentials in an innervated syncytial tissue under normal conditions and in pathophysiology.

中文翻译：

---

逼尿肌平滑肌的自发突触驱动：计算研究和对膀胱功能的影响。

逼尿肌是膀胱壁的重要组成部分，是一种紧密支配的合胞体平滑肌组织。逼尿肌中神经肌肉接头（NMJs）处神经递质的随机自发释放会引起自发性兴奋性连接电位（SEJPs）。这些亚阈值以下的被动信号不仅可以洞悉组织的合胞特性，而且它们的时空整合对于自发性神经源性动作电位的产生至关重要，该电位在膀胱充盈阶段导致局灶性收缩。考虑到组织的结构复杂性和收缩特性，对SEJPs在逼尿肌中时空整合的电生理研究在技术上具有挑战性。在这里，我们报告逼尿肌合胞体与空间分布的神经支配的生物物理上受约束的计算模型，利用该模型我们探索了SEJPs在组织中整合的显着特征以及有助于这种整合的关键因素。我们根据实验数据验证了我们的模型，确定观察结果与理论预测一致。在比较研究的帮助下，我们建议时空整合SEJP的幅度对逼尿肌中细胞间的耦合强度最敏感，而观察到的事件的频率则更强烈地取决于神经支配密度。从我们的研究中得出的实验可验证的预测是，神经递质的自发释放频率可能与逼尿肌过度活动的发生有关。与组织学观察相反，我们还推测在涉及斑状神经支配的病理过程中逼尿肌电活动的可能变化。因此，我们的模型提供了一个生理上现实的，启发式的框架，以研究在正常条件下和病理生理学中，神经支配的合胞组织中被动电位的扩散和整合。