Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
PLoS Comput Biol. 2012;8(8):e1002653. doi: 10.1371/journal.pcbi.1002653. Epub 2012 Aug 23.
A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.
通过直接电生理检测对全球神经网络连接进行系统评估在技术上仍然不可行，即使在像分离神经元培养物这样的简单系统中也是如此。我们引入了一种基于转移熵的改进算法方法，从通过钙成像监测的网络活动中重建结构连接。我们在这项研究中重点关注兴奋性突触连接的推断。基于信息论，我们的方法不需要对神经元放电和神经元连接的统计数据进行任何先验假设。我们的算法的性能在通过具有已知真实拓扑结构的网络的模拟动力学生成的钙荧光的替代时间序列上进行了基准测试。我们发现，转移熵揭示的功能网络拓扑结构在很大程度上取决于网络的时变动态状态（爆发或非爆发）。因此，通过相对于全局平均活动进行条件化，我们提高了方法的性能。这使我们能够将分析集中在网络的特定动态状态上，其中推断的功能连接由单突触兴奋性连接而不是集体同步性形成。我们的方法可以区分神经元之间的实际因果影响和由于光散射伪影而产生的虚假非因果相关性，这些伪影会固有地影响荧光成像的质量。与其他重建策略（如互相关或格兰杰因果关系方法）相比，我们基于改进的转移熵的方法要准确得多。特别是，它可以很好地估计兴奋性网络聚类系数，从而可以区分弱聚类和强聚类拓扑。最后，我们证明了我们的方法在体外去抑制皮质培养物的实际记录分析中的适用性，我们建议兴奋性连接的聚类水平比随机图（尽管不是极端）高，并且可以明显非局部化。
