Bacik Karol A, Schaub Michael T, Beguerisse-Díaz Mariano, Billeh Yazan N, Barahona Mauricio
Department of Mathematics, Imperial College London, London, United Kingdom.
naXys & Department of Mathematics, University of Namur, Namur, Belgium.
PLoS Comput Biol. 2016 Aug 5;12(8):e1005055. doi: 10.1371/journal.pcbi.1005055. eCollection 2016 Aug.
We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios.
我们利用线虫秀丽隐杆线虫定向神经网络上的流传播来揭示其连接组的动态相关特征。我们发现了不同粒度水平上基于流的神经元分组，这与神经系统的功能和解剖成分相关。对全套单神经元和双神经元消融进行系统的计算机模拟评估，以识别导致多分辨率流结构最严重破坏的缺失。此类消融与功能相关神经元相关，并为进一步的体内研究提出了潜在候选对象。此外，我们利用所有尺度上网络流入和流出的方向模式来识别连接组中神经元的流轮廓，而无需预先设定先验类别。所确定的四种流角色与由生物输入 - 反应情景驱动的信号传播相关。
