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Comparison of peripheral near‐infrared spectroscopy low‐frequency oscillations to other denoising methods in resting state functional MRI with ultrahigh temporal resolution
Author(s) -
Hocke Lia M.,
Tong Yunjie,
Lindsey Kimberly P.,
de B. Frederick Blaise
Publication year - 2016
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26038
Subject(s) - resting state fmri , temporal resolution , functional magnetic resonance imaging , nuclear magnetic resonance , chemistry , frequency domain , noise (video) , neuroscience , physics , artificial intelligence , optics , computer science , psychology , computer vision , image (mathematics)
Purpose Functional MRI (fMRI) blood–oxygen level–dependent (BOLD) signals result not only from neuronal activation, but also from nonneuronal physiological processes. These changes, especially in the low‐frequency domain (0.01–0.2 Hz), can significantly confound inferences about neuronal processes. It is crucial to effectively identify these nuisance low‐frequency oscillations (LFOs). Method A high temporal resolution (repetition time, ∼0.5 s) fMRI resting state study was conducted with simultaneous physiological measurements to compare LFOs measured directly by near‐infrared spectroscopy (NIRS) in the periphery and three methods that model LFOs from the respiration or cardiac signal: 1) the respiration volume per time (RVT), 2) the respiratory variation (RVRRF), and 3) the cardiac variation method (HRCRF). The LFO noise regressors from these methods were compared temporally and spatially as well as in their denoising efficiency. Results Methods were not highly correlated with one another, temporally or spatially. The set of two NIRS LFOs combined explained over 13% of BOLD signal variance and explained equal or more variance than HRCRF and RVRRF or RVT combined (in 14 of 16 participants). Conclusion LFOs collected using NIRS in the periphery contain distinct temporal and spatial information about the LFOs in BOLD fMRI that is not contained in current low‐frequency denoising methods derived from respiration and cardiac pulsation. Magn Reson Med 76:1697–1707, 2016. © 2016 International Society for Magnetic Resonance in Medicine