  The center stage of neuro-imaging is currently occupied by studies of
functional correlations between brain regions. These correlations define the
brain functional networks, which are the most frequently used framework to
represent and interpret a variety of experimental findings. In previous work we
first demonstrated that the relatively stronger BOLD activations contain most
of the information relevant to understand functional connectivity and
subsequent work confirmed that a large compression of the original signals can
be obtained without significant loss of information. In this work we revisit
the correlation properties of these epochs to define a measure of nonlinear
dynamic directed functional connectivity (nldFC) across regions of interest. We
show that the proposed metric provides at once, without extensive numerical
complications, directed information of the functional correlations, as well as
a measure of temporal lags across regions, overall offering a different
perspective in the analysis of brain co-activation patterns. In this paper we
provide for a proof of concept, based on replicating and completing existing
results on an Autism database, to discuss the main features and advantages of
the proposed strategy for the study of brain functional correlations. These
results show new interpretations of the correlations found on this sample.
