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

• Main Authors: DeDora, Daniel J. (Author), Nedic, Sanja (Author), Katti, Pratha (Author), Arnab, Shafique (Author), Wald, Lawrence (Contributor), Takahashi, Atsushi (Contributor), Van Dijk, Koene R. A. (Author), Strey, Helmut H. (Author), Mujica-Parodi, Lilianne R. (Contributor)
• Other Authors: Harvard University- (Contributor), McGovern Institute for Brain Research at MIT (Contributor)
• Format: Article
• Language: English
• Published: Frontiers Media S.A., 2016-08-15T20:37:31Z.
• Subjects: Article
• Online Access: Get fulltext

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 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.
National Institutes of Health (U.S.) (NIDA-1R2DA03846701 LRMP)
National Science Foundation (U.S.) (CBET-1264440 LRMP)