1. Signal Fluctuation Sensitivity: an improved metric for optimizing detection of resting-state fMRI networks
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DeDora, D. J., Nedic, S., Katti, P., Arnab, S., Wald, L. L., Takahashi, A., Van Dijk, K. R. A., Strey, H. H., and Mujica-Parodi, L. R.
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Quantitative Biology - Neurons and Cognition - 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., Comment: 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, Lilianne.Strey@stonybrook.edu (www.lcneuro.org)
- Published
- 2015
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