DeDora, Daniel J.Nedic, SanjaKatti, PrathaArnab, ShafiqueWald, LawrenceTakahashi, AtsushiVan Dijk, Koene R. A.Strey, Helmut H.Mujica-Parodi, Lilianne R.2016-06-142016DeDora, Daniel J., Sanja Nedic, Pratha Katti, Shafique Arnab, Lawrence L. Wald, Atsushi Takahashi, Koene R. A. Van Dijk, Helmut H. Strey, and Lilianne R. Mujica-Parodi. 2016. “Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks.” Frontiers in Neuroscience 10 (1): 180. doi:10.3389/fnins.2016.00180. http://dx.doi.org/10.3389/fnins.2016.00180.1662-4548http://nrs.harvard.edu/urn-3:HUL.InstRepos:27320380Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS—and not tSNR—is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network.en-USMethodsFunctional MRIsignal fluctuation sensitivityresting state connectivitytemporal signal to noise ratiodynamic phantomfidelityJournal Article2016-06-1410.3389/fnins.2016.00180