Visualizing dynamical neural assemblies with a fuzzy synchronization clustering analysis

Phase synchrony has been proposed as a possible communication mechanism between cerebral regions. The participation index method (PIM) may be used to investigate integrating structures within an oscillatory network, based on the eigenvalue decomposition of matrix of bivariate synchronization indices. However, eigenvector orthogonality between clusters may result in categorization difficulties for hub oscillators and pseudoclustering phenomenon. Here, we propose a method of fuzzy synchronization clustering analysis (FSCA) to avoid the constraint of orthogonality by combining the fuzzy c-means algorithm with the phase-locking value. Following mathematical derivation, we cross-validated the FSCA and the PIM using the same multichannel phase time series of event-related EEG from a subject performing a working memory task. Both clustering methods produced consistent findings for the qualitatively salient configuration of the original network-illustrated here by a visualization technique. In contrast to PIM, use of common virtual oscillatory centroids enabled the FSCA to reveal multiple dynamical neural assemblies as well as the unitary phase information within each assembly.