Department of Biology, Washington and Lee University , Lexington, Virginia 24450.
Institut de Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS UMR 5287 , Université de Bordeaux , 33076 Bordeaux, France.
eNeuro. 2015 Apr 22;2(2). doi: 10.1523/ENEURO.0074-14.2015. eCollection 2015 Mar-Apr.
Neural networks control complex motor outputs by generating several rhythmic neuronal activities, often with different time scales. One example of such a network is the pre-Bötzinger complex respiratory network (preBötC) that can simultaneously generate fast, small-amplitude, monophasic eupneic breaths together with slow, high-amplitude, biphasic augmented breaths (sighs). However, the underlying rhythmogenic mechanisms for this bimodal discharge pattern remain unclear, leaving two possible explanations: the existence of either reconfiguring processes within the same network or two distinct subnetworks. Based on recent in vitro data obtained in the mouse embryo, we have built a computational model consisting of two compartments, interconnected through appropriate synapses. One compartment generates sighs and the other produces eupneic bursts. The model reproduces basic features of simultaneous sigh and eupnea generation (two types of bursts differing in terms of shape, amplitude, and frequency of occurrence) and mimics the effect of blocking glycinergic synapses. Furthermore, we used this model to make predictions that were subsequently tested on the isolated preBötC in mouse brainstem slice preparations. Through a combination of in vitro and in silico approaches we find that (1) sigh events are less sensitive to network excitability than eupneic activity, (2) calcium-dependent mechanisms and the Ih current play a prominent role in sigh generation, and (3) specific parameters of Ih activation set the low sensitivity to excitability in the sigh neuronal subset. Altogether, our results strongly support the hypothesis that distinct subpopulations within the preBötC network are responsible for sigh and eupnea rhythmogenesis.
神经网络通过产生几种节律性神经元活动来控制复杂的运动输出，通常具有不同的时间尺度。这样的网络的一个例子是 pre-Bötzinger 复合呼吸网络 (preBötC)，它可以同时产生快速、小幅度、单相的正常呼吸，以及缓慢、高幅度、双相的增强呼吸（叹息）。然而，这种双峰放电模式的潜在节律发生机制尚不清楚，留下了两种可能的解释：同一个网络中存在重新配置过程，或者存在两个不同的子网络。基于最近在小鼠胚胎中获得的体外数据，我们构建了一个由两个隔室组成的计算模型，通过适当的突触相互连接。一个隔室产生叹息，另一个产生正常呼吸爆发。该模型再现了同时产生叹息和正常呼吸的基本特征（两种爆发在形状、幅度和发生频率方面有所不同），并模拟了阻断甘氨酸能突触的效果。此外，我们使用该模型进行了预测，随后在小鼠脑桥切片制备物的离体 preBötC 中进行了测试。通过体外和计算的组合方法，我们发现：（1）叹息事件比正常呼吸活动对网络兴奋性的敏感性更低，（2）钙依赖性机制和 Ih 电流在叹息产生中起重要作用，（3）Ih 激活的特定参数设定了在叹息神经元亚群中对兴奋性的低敏感性。总之，我们的结果强烈支持这样的假设，即在 preBötC 网络内的不同亚群负责叹息和正常呼吸的节律发生。
