DySCo: A general framework for dynamic functional connectivity
Fig 2
Summary of the DySCo Pipeline.
This schema illustrates the main steps involved in the DySCo framework as well as important methodological decisions that must be made when using the framework. After input of raw data and appropriate pre-processing there are multiple dFC matrices as described in Theory (The DySCo theory). Based upon the choice of dFC matrix, which we define as C ( t ) , subsequent processing steps are employed (such as window size adjustment or extraction of phase) to express these dFC matrices into a single equation (). We next calculate the eigenvalues and eigenvectors associated with the dFC matrices using the Temporal Covariance EVD. The eigenvalue-eigenvector representation contains all the information needed to perform the dFC analyses, and to compute the DySCo measures described in Theory. The three main measures are Norms, Distances, and Entropy (see DySCo measures). From them we can obtain derived measures: from the norm it is possible to compute metastability (see Norm metastability), from the distance it is possible to compute the FCD matrix and the reconfiguration speed (see Distances between dFC operators).