Frontiers in Neuroscience (May 2016)

Signal Fluctuation Sensitivity: an improved metric for optimizing detection of resting-state fMRI networks

  • Daniel J. DeDora,
  • Sanja eNedic,
  • Pratha eKatti,
  • Shafique eArnab,
  • Lawrence L. Wald,
  • Lawrence L. Wald,
  • Lawrence L. Wald,
  • Atsushi eTakahashi,
  • Koene RA Van Dijk,
  • Koene RA Van Dijk,
  • Helmut H. Strey,
  • Lilianne Rivka Mujica-Parodi,
  • Lilianne Rivka Mujica-Parodi,
  • Lilianne Rivka Mujica-Parodi

DOI
https://doi.org/10.3389/fnins.2016.00180
Journal volume & issue
Vol. 10

Abstract

Read online

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.

Keywords