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Dynamic conditional correlation (DCC) is a method that estimates thecorrelation between two time series across time. Although used primarily infinance so far, DCC has been proposed recently as a model-based estimationmethod for quantifying functional connectivity during fMRI experiments. DCCcould also be used to estimate the dynamic correlation between other types oftime series such as local field potentials (LFP's) or spike trains recordedfrom distinct brain areas. DCC has very nice properties compared to otherexisting methods, but its applications for neuroscience are currently limitedbecause of non-optimal performance in the presence of outliers. To address thisissue, we developed a robust nonparametric version of DCC, based on anadaptation of the weighted visibility graph algorithm which converts a timeseries into a weighted graph. The modified DCC demonstrated better performancein the analysis of empirical data sets: one fMRI data set collected from ahuman subject performing a Stroop task; and one LFP data set recorded from anawake rat in resting state. Nonparametric DCC has the potential of enlargingthe spectrum of analytical tools designed to assess the dynamic coupling anduncoupling of activity among brain areas.

Nonparametrically estimating dynamic bivariate correlation using visibility graph algorithm