  Resting state functional connectivity estimates from MRI measures has become
a promising tool to characterize human brain networks. There are, however,
limitations in the method since several sources of errors have been seen to
significantly affect the final estimates. This has lead to a great interest in
the field to do systematic investigations that help determine the most reliable
and robust strategies to perform functional connectivity analysis. In the
present study, we examine the influence of two aspects of data pre- processing
in resting state functional connectivity analysis: the effect of criteria used
to select nodes in the default mode network (DMN) for the computation of
connectivity, and the effect of using or not physiological noise correction.
Three different strategies of region of interest (ROI) selection were compared
to define DMN node coordinates: (1) ROIs centered on atlas-based coordinates,
(2) ROIs based on the result of group independent component analysis, and (3)
ROIs based on the estimated DMN of each individual. The study was done using
data of 15 healthy volunteers, which had resting state data available from a
separate project. We found that both effects, ROI selection criteria for DMN
nodes and physiological noise correction, have significant effects on the
functional connectivity estimates. In particular, our results show that
physiological noise correction introduces small but significant reductions in
functional connectivity, consistent with a reduction of artifactual non-neural
correlations introduced by physiological effects. Further, selecting DMN nodes
based on the single subject ICA results introduced small but significant
increases in functional connectivity, consistent with higher subject
specificity of the node selection.
