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Resting state functional connectivity estimates from MRI measures has becomea promising tool to characterize human brain networks. There are, however,limitations in the method since several sources of errors have been seen tosignificantly affect the final estimates. This has lead to a great interest inthe field to do systematic investigations that help determine the most reliableand robust strategies to perform functional connectivity analysis. In thepresent study, we examine the influence of two aspects of data pre- processingin resting state functional connectivity analysis: the effect of criteria usedto select nodes in the default mode network (DMN) for the computation ofconnectivity, and the effect of using or not physiological noise correction.Three different strategies of region of interest (ROI) selection were comparedto 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 usingdata of 15 healthy volunteers, which had resting state data available from aseparate project. We found that both effects, ROI selection criteria for DMNnodes and physiological noise correction, have significant effects on thefunctional connectivity estimates. In particular, our results show thatphysiological noise correction introduces small but significant reductions infunctional connectivity, consistent with a reduction of artifactual non-neuralcorrelations introduced by physiological effects. Further, selecting DMN nodesbased on the single subject ICA results introduced small but significantincreases in functional connectivity, consistent with higher subjectspecificity of the node selection.

Functional Connectivity in Default Mode Network During Resting State: An Evaluation of the Effects of Data Pre-processing