AUTHOR=DeDora Daniel J. , Nedic Sanja , Katti Pratha , Arnab Shafique , Wald Lawrence L. , Takahashi Atsushi , Van Dijk Koene R. A. , Strey Helmut H. , Mujica-Parodi Lilianne R. 

TITLE=Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks

JOURNAL=Frontiers in Neuroscience

VOLUME=10

YEAR=2016

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00180

DOI=10.3389/fnins.2016.00180

ISSN=1662-453X

ABSTRACT=<p>Task-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 <italic>blood oxygen level dependent</italic> (BOLD)-like inputs and fMRI outputs. We found that the <italic>de facto</italic> quality-metric for task-free fMRI, <italic>temporal signal to noise ratio</italic> (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, <italic>signal fluctuation sensitivity</italic> (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.</p>