McGee Meredith J, Grill Warren M
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Department of Neurobiology, Duke University, Durham, NC, USA.
J Comput Neurosci. 2016 Jun;40(3):283-96. doi: 10.1007/s10827-016-0597-5. Epub 2016 Mar 11.
Electrical stimulation of the pudendal nerve (PN) is a promising approach to restore continence and micturition following bladder dysfunction resulting from neurological disease or injury. Although the pudendo-vesical reflex and its physiological properties are well established, there is limited understanding of the specific neural mechanisms that mediate this reflex. We sought to develop a computational model of the spinal neural network that governs the reflex bladder response to PN stimulation. We implemented and validated a neural network architecture based on previous neuroanatomical and electrophysiological studies. Using synaptically-connected integrate and fire model neurons, we created a network model with realistic spiking behavior. The model produced expected sacral parasympathetic nucleus (SPN) neuron firing rates from prescribed neural inputs and predicted bladder activation and inhibition with different frequencies of pudendal afferent stimulation. In addition, the model matched experimental results from previous studies of temporal patterns of pudendal afferent stimulation and selective pharmacological blockade of inhibitory neurons. The frequency- and pattern-dependent effects of pudendal afferent stimulation were determined by changes in firing rate of spinal interneurons, suggesting that neural network interactions at the lumbosacral level can mediate the bladder response to different frequencies or temporal patterns of pudendal afferent stimulation. Further, the anatomical structure of excitatory and inhibitory interneurons in the network model was necessary and sufficient to reproduce the critical features of the pudendo-vesical reflex, and this model may prove useful to guide development of novel, more effective electrical stimulation techniques for bladder control.
电刺激阴部神经（PN）是一种很有前景的方法，可用于恢复因神经疾病或损伤导致膀胱功能障碍后的控尿和排尿功能。尽管阴部 - 膀胱反射及其生理特性已得到充分证实，但对于介导该反射的具体神经机制仍了解有限。我们试图构建一个脊髓神经网络的计算模型，该模型可控制膀胱对PN刺激的反射反应。我们基于先前的神经解剖学和电生理学研究，实现并验证了一种神经网络架构。使用突触连接的积分发放模型神经元，我们创建了一个具有逼真发放行为的网络模型。该模型根据规定的神经输入产生预期的骶副交感核（SPN）神经元发放率，并预测了不同频率阴部传入刺激下膀胱的激活和抑制情况。此外，该模型与先前关于阴部传入刺激的时间模式和抑制性神经元的选择性药理学阻断研究的实验结果相匹配。阴部传入刺激的频率和模式依赖性效应由脊髓中间神经元发放率的变化决定，这表明腰骶水平的神经网络相互作用可介导膀胱对不同频率或阴部传入刺激时间模式的反应。此外，网络模型中兴奋性和抑制性中间神经元的解剖结构对于重现阴部 - 膀胱反射的关键特征是必要且充分的，并且该模型可能有助于指导开发用于膀胱控制的新型、更有效的电刺激技术。
