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