Heisenberg Research Group of Computational Neuroscience - Modelling Neural Network Function, Institute of Zoology, University of Cologne, Cologne, Germany.
Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich, Germany.
PLoS One. 2019 Aug 6;14(8):e0220767. doi: 10.1371/journal.pone.0220767. eCollection 2019.
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.
动物的行走是由中枢模式生成网络（CPG）、表达腿部位置、速度和产生的力的局部感觉信号以及相邻腿部之间的协调信号之间的复杂相互作用产生的。特别是，CPG 控制运动神经元（MN）池的活动，这些神经元驱动各个腿部的肌肉，从而负责产生有节奏的腿部运动。CPG 的节奏活动及其连接性可以被上述感觉信号修改。然而，CPG 与这些感觉信号之间的相互作用的确切性质在很大程度上仍然未知。旨在找出这些相互作用细节的实验方法通常应用拟胆碱能激动剂（如毛果芸香碱）来诱导 CPG 中的节奏活动。使用这种通用方法，我们消除了感觉信号的影响，并研究了控制不同节段的昆虫腿部上下运动的 CPG 之间的假定连接。实验数据，即测量的 MN 活动，使用动态因果建模（DCM）进行了连接分析。该方法可以揭示节段性 CPG 对之间的潜在耦合结构和强度。对于分析，我们为 DCM 设置了不同的耦合方案（模型），并使用贝叶斯模型选择（BMS）对它们进行了比较。BMS 优先选择每个节段中具有对侧连接和双侧同侧连接以及从meta 到同侧前胸神经节的耦合的模型，而不是测试的所有其他类型的模型。此外，中胸神经节中的节内耦合强度在三个神经节中都是最强和最稳定的。
