Evaluating Cerebral Cortex Connectivity with Local Information Algorithm

Previous works have analyzed the cluster organization of the cat cortical network using both traditional multidimensional scaling methods and evolutionary optimization algorithms. Interestingly, the evolutionary optimization principle of previous works is based on the modularity measure used to find communities in network with global algorithms. In this paper, we deepen this point taking into account different community-detection algorithms. We compare the performances of Net Explorer, a local information dynamics algorithm for detecting communities in networks, with six well-known community detection algorithms: Info map, Hierarchical Info map, Lou vain, Modularity Optimization, Label Propagation and Oslom. The results indicate that Net Explorer is able to detect the four functional clusters where misattributions of some areas are explained by their multimodal function. Results are discussed in terms of misattributions of brain areas to the different clusters emphasizing connections which are explainable (or not) by a cognitive point of view.