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

TL;DR

This paper introduces Signal Fluctuation Sensitivity (SFS), a new metric that better captures true neural connectivity in resting-state fMRI by dissociating signal dynamics from scanner artifacts, outperforming traditional tSNR.

Contribution

The study develops and validates SFS as an improved metric for optimizing resting-state fMRI analysis, addressing limitations of tSNR in reflecting true neural signals.

Findings

SFS correlates with enhanced detection of brain connectivity.

tSNR inversely correlates with dynamic fidelity.

SFS outperforms tSNR in identifying meaningful neural signals.

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.