Hierarchical organization in neuronal functional networks during working memory tasks

Existing studies have shown that neuronal functional networks (NFNs) exhibit small-world properties. However, the issue of whether NFNs have any other complex network topology properties remains unresolved. In this paper, we introduced a new hierarchical clustering-based method that can clearly indicate the hierarchical modular organization of NFNs. Based on the modularity function Q proposed by Newman, we can divide the NFNs into suitable sub-modules. We proposed a new measure function to calculate the correlations between pairs of spike trains without requiring binning of the spike trains through small time windows. This method can be used to analyze the level of synchronization between spike trains and functional connectivity relationships between neurons. We analyzed NFNs constructed from multi-electrode recordings in rat brain cerebral cortexes in vivo. These rats had been trained to perform different working memory cognitive tasks. The results show that NFNs exhibit a clear hierarchical modular organization in rat brains. These results provided evidence confirming that the brain networks are complex. This can also be used as a means of studying the relationship between neuronal functional organization and cognitive behavioral tasks.