Vascular origins of low-frequency oscillations in the cerebrospinal fluid signal in resting-state fMRI: Interpretation using photoplethysmography

Abstract

Slow and rhythmic spontaneous oscillations of cerebral blood flow are well known to have diagnostic utility, notably frequencies of 0.008-0.03 Hz (B-waves) and 0.05-0.15Hz (Mayer waves or M waves). However, intracranial measurements of these oscillations have been difficult. Oscillations in the cerebrospinal fluid (CSF), which are influenced by the cardiac pulse wave, represent a possible avenue for non-invasively tracking these oscillations using resting-state functional MRI (rs-fMRI), and have been used to correct for vascular oscillations in rs-fMRI functional connectivity calculations. However, the relationship between low-frequency CSF and vascular oscillations is unclear. In this study, we investigate this relationship using fast simultaneous multi-slice rs-fMRI coupled with fingertip photoplethysmography (PPG). We not only extract B-wave and M-wave range spectral power from the PPG signal, but also derive the pulse-intensity ratio (PIR, a surrogate of slow blood-pressure oscillations), the second-derivative of the PPG (SDPPG, a surrogate of arterial stiffness) and heart-rate variability (HRV). The main findings of this study are: (1) signals in different CSF regions (ROIs) are not equivalent in their vascular contributions or in their associations with vascular and tissue rs-fMRI signals; (2) the PPG signal contains the highest signal contribution from the M-wave range, while PIR contains the highest signal contribution from the B-wave range; (3) in the low-frequency range, PIR is more strongly associated with rs-fMRI signal in the CSF than PPG itself, and than HRV and SDPPG; (4) PPG-related vascular oscillations only contribute to < 20% of the CSF signal in rs-fMRI, insufficient support for the assumption that low-frequency CSF signal fluctuations directly reflect vascular oscillations. These findings caution the use of CSF as a monolithic region for extracting physiological nuisance regressors in rs-fMRI applications. They also pave the way for using rs-fMRI in the CSF as a potential tool for tracking cerebrovascular health through, for instance the strong relationship between PIR and the CSF signal.