Signal Fluctuation Sensitivity: an improved metric for optimizing detection of resting-state fMRI networks
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.
Keywords
Cite
@article{arxiv.1511.04345,
title = {Signal Fluctuation Sensitivity: an improved metric for optimizing detection of resting-state fMRI networks},
author = {D. J. DeDora and S. Nedic and P. Katti and S. Arnab and L. L. Wald and A. Takahashi and K. R. A. Van Dijk and H. H. Strey and L. R. Mujica-Parodi},
journal= {arXiv preprint arXiv:1511.04345},
year = {2020}
}
Comments
27 pages, 4 figures, 2 tables. Contact Information: Lilianne R. Mujica-Parodi, Laboratory for Computational Neurodiagnostics, Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, [email protected] (www.lcneuro.org)