Unravelling intra- and intersegmental neuronal connectivity between central pattern generating networks in a multi-legged locomotor system
Unravelling intra- and intersegmental neuronal connectivity between central pattern generating networks in a multi-legged locomotor system
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DOI：
10.1371/journal.pone.0220767
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发表时间：
2019-08
期刊：
影响因子：
3.7
通讯作者：
S. Daun;Charalampos Mantziaris;T. Tóth;A. Bueschges;N. Rosjat
中科院分区：
文献类型：
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作者：
S. Daun;Charalampos Mantziaris;T. Tóth;A. Bueschges;N. Rosjat
动物行走是由中央模式生成网络（CPG），表达腿中产生的位置，速度和力的局部感觉信号以及相邻腿之间的协调信号的复杂相互作用引起的。特别地，CPG控制运动神经元（MN）池的活动，所述运动神经元（MN）池驱动个体腿部的肌肉，并且由此负责产生有节奏的腿部运动。CPG的节律活动以及它们的连接性可以通过上述感觉信号来修改。然而，CPG和这些感觉信号之间相互作用的确切性质通常仍然是未知的。旨在找出这些相互作用的细节的实验方法通常应用胆碱能激动剂，如毛果芸香碱，以诱导CPG的节律性活动。使用这种一般的方法，我们删除了感觉信号的影响，并调查了CPGs控制的向上/向下运动在不同的腿的竹节虫之间的假定连接。实验数据，即测得的MN活动，使用动态因果模型（DCM）进行连接性分析。这种方法可以揭示潜在的耦合结构和对节段CPG之间的强度。对于分析，我们建立了不同的耦合方案（模型）DCM和比较它们使用贝叶斯模型选择（BMS）。与所有其他类型的测试模型相比，BMS首选每个节段中具有对侧连接和两侧上具有同侧连接的模型，以及从Meta至同侧胸前神经节的耦合。此外，节内耦合强度在中胸神经节是最强和最稳定的所有三个神经节。
Animal walking results from a complex interplay of central pattern generating networks (CPGs), local sensory signals expressing position, velocity and forces generated in the legs, and coordinating signals between neighboring legs. In particular, the CPGs control the activity of motoneuron (MN) pools which drive the muscles of the individual legs and are thereby responsible for the generation of rhythmic leg movements. The rhythmic activity of the CPGs as well as their connectivity can be modified by the aforementioned sensory signals. However, the precise nature of the interaction between the CPGs and these sensory signals has remained generally largely unknown. Experimental methods aiming at finding out details of these interactions often apply cholinergic agonists such as pilocarpine in order to induce rhythmic activity in the CPGs. Using this general approach, we removed the influence of sensory signals and investigated the putative connections between CPGs controlling the upward/downward movement in the different legs of the stick insect. The experimental data, i.e. the measured MN activities, underwent connectivity analysis using Dynamic Causal Modelling (DCM). This method can uncover the underlying coupling structure and strength between pairs of segmental CPGs. For the analysis we set up different coupling schemes (models) for DCM and compared them using Bayesian Model Selection (BMS). Models with contralateral connections in each segment and ipsilateral connections on both sides, as well as the coupling from the meta- to the ipsilateral prothoracic ganglion were preferred by BMS to all other types of models tested. Moreover, the intrasegmental coupling strength in the mesothoracic ganglion was the strongest and most stable in all three ganglia.