Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Department of Physics, University of Gothenburg, Gothenburg, Sweden.
PLoS One. 2020 Feb 19;15(2):e0228334. doi: 10.1371/journal.pone.0228334. eCollection 2020.
The brain works as a large-scale complex network, known as the connectome. The strength of the connections between two brain regions in the connectome is commonly estimated by calculating the correlations between their patterns of activation. This approach relies on the assumption that the activation of connected regions occurs together and at the same time. However, there are delays between the activation of connected regions due to excitatory and inhibitory connections. Here, we propose a method to harvest this additional information and reconstruct the structural brain connectome using delayed correlations. This delayed-correlation method correctly identifies 70% to 80% of connections of simulated brain networks, compared to only 5% to 25% of connections detected by the standard methods; this result is robust against changes in the network parameters (small-worldness, excitatory vs. inhibitory connection ratio, weight distribution) and network activation dynamics. The delayed-correlation method predicts more accurately both the global network properties (characteristic path length, global efficiency, clustering coefficient, transitivity) and the nodal network properties (nodal degree, nodal clustering, nodal global efficiency), particularly at lower network densities. We obtain similar results in networks derived from animal and human data. These results suggest that the use of delayed correlations improves the reconstruction of the structural brain connectome and open new possibilities for the analysis of the brain connectome, as well as for other types of networks.
大脑作为一个大型复杂网络，即连接组，运作。连接组中两个脑区之间的连接强度通常通过计算它们激活模式之间的相关性来估计。这种方法依赖于这样一个假设，即连接区域的激活同时发生。然而，由于兴奋和抑制连接，连接区域之间存在延迟。在这里，我们提出了一种利用这种额外信息的方法，使用延迟相关性来重建结构连接组。与标准方法检测到的 5%至 25%的连接相比，延迟相关方法正确识别了模拟脑网络中 70%至 80%的连接；该结果对网络参数（小世界性、兴奋性与抑制性连接比例、权重分布）和网络激活动力学的变化具有鲁棒性。延迟相关方法更准确地预测了全局网络特性（特征路径长度、全局效率、聚类系数、传递性）和节点网络特性（节点度、节点聚类、节点全局效率），特别是在较低的网络密度下。我们在源自动物和人类数据的网络中获得了类似的结果。这些结果表明，使用延迟相关性可以提高结构连接组的重建，并为大脑连接组的分析以及其他类型的网络开辟新的可能性。
