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DySCo: A general framework for dynamic functional connectivity

Fig 4

Application of the DySCo measures to a simulated dataset.

i) simulated signals and the five underlying covariance patterns, corresponding to brain states. ii) The sliding window covariance matrix computed using the DySCo formula. iii) Reconfiguration speed with a lag of 100 frames shows peak corresponding to the switches between brain states (the three colors are the three options to compute distance as defined in the theory, see Distances between dFC operators). iv) Functional Connectivity Dynamics matrices. is the distance between the matrix at time and the matrix at time t_j using the three possible distances proposed in the DySCo framework.

doi: https://doi.org/10.1371/journal.pcbi.1012795.g004