Abstract
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.
Highlights
Unprecedented investment in functional neuroimaging has ushered in a new era of brain research, in which fMRI’s original role in mapping the areas of the brain most “active” under a task, includes task-free characterization of brain connections and circuits
Our dynamic phantom exploits the fact that the magnetic susceptibility of agarose gel is concentration-dependent; varying the concentration of agarose gel present within a voxel over time produces changes in T2* that we experimentally tuned to amplitudes (∼1%) typically observed with blood oxygen level dependent (BOLD) (Olsrud et al, 2008)
The dynamic phantom is constructed from two concentric cylinders coupled with a pneumatic motor and fiber optic feedback system (Figure 1A)
Summary
Unprecedented investment in functional neuroimaging has ushered in a new era of brain research, in which fMRI’s original role in mapping the areas of the brain most “active” under a task, includes task-free characterization of brain connections and circuits. For task-free designs CNR cannot be computed, and normally is replaced by the temporal signal-to-noise ratio (tSNR), defined as the mean of the time-series divided by its standard deviation (Kruger et al, 2001). Both CNR and tSNR compare the amplitude of a signal against a background of undesired physiological, thermal, and scanner noise present in all fMRI studies. We tested the impact of dynamic fidelity, as defined by our phantom, in predicting detection sensitivity to functional connectivity in human data across three different sets of acquisition parameters, chosen to represent a breadth of realistic optimization strategies utilized within the neuroimaging field for human connectivity studies
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