Extraction of the patterns of synchronization
This protocol is extracted from research article:
Cognitive chimera states in human brain networks
Sci Adv, Apr 3, 2019; DOI: 10.1126/sciadv.aau8535

To identify emergent cognitive patterns, we first obtained a binarized synchronization matrix (m) such that mij = 1 if systems i and j are identified as synchronized and mij = 0 otherwise. We defined two cognitive systems si and sj to be synchronized if ρsi,sj ≥ ρTh, where ρTh represents a synchronization threshold. For the results discussed throughout the main text, we used ρTh = 0.8 (33) (as indicated in Fig. 2A).

In principle, one can directly use these binarized synchronization matrices to classify the emergent states and patterns. However, we performed community detection on these binarized matrices. This method clusters the group of synchronized systems into a single community, whereas desynchronized systems remain as separate communities. In case of a coherent state, we observed only one community, and in case of a metastable state, we observed nine separate communities, each representing a cognitive system. For chimera states, communities with different distributions of cognitive systems emerged. Thus, applying the community detection algorithm not only allowed us to robustly classify the emergent dynamical states but also let us separate various spatially distributed patterns of chimera states. Community detection was performed using modularity maximization through the generalized Louvain algorithm (53). For community detection, the value of the resolution parameter was varied between 0.8 and 0.95, and a consensus was run to determine the community structure (54).

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