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.

Resting‐state functional magnetic resonance imaging signal variations in aging: The role of neural activity

“…Conversely, the apparent increase in fMRI frequency with age, given the observation that overall fMRI signal fluctuation amplitude decreases with age in both frequency bands, point to a reduced contribution of low‐frequency fluctuations to fMRI in older adults. In the 0.01–0.1 Hz band, the low‐frequency contributions include arterial carbon dioxide (CO 2 ) fluctuations, respiratory variability and heart‐rate variability (HRV) (Attarpour et al, 2021; Chang & Glover, 2009). Very‐low frequency vascular oscillations measured using near‐infrared spectroscopy have been found to decline in amplitude with age (Schroeter, Schmiedel, & von Cramon, 2004) and coincide in frequency with the resting vascular response to arterial CO 2 fluctuations (0.02–0.04 Hz) (Golestani et al, 2015; Liu et al, 2017).…”

“…The previous work, while extremely novel and informative, has left some unanswered questions. There is still very limited investigation into variations in the frequency of the rs‐fMRI signal in aging and the age–sex interaction. Given the known complexity of the signal and noise contributions to the rs‐fMRI signal, including by intrinsic variations in carbon dioxide (CO 2 ) (Chang & Glover, 2009; Golestani, Chang, Kwinta, Khatamian, & Chen, 2015; Wise, Ide, Poulin, & Tracey, 2004), respiration (Birn, Diamond, Smith, & Bandettini, 2006; Chang & Glover, 2009; Golestani et al, 2015; Shams, LeVan, & Chen, 2021) and cardiac pulsation (Attarpour, Ward, & Chen, 2021; Chang et al, 2013; Falahpour, Refai, & Bodurka, 2013; Shmueli et al, 2007), the neuronal associations of the observed frequency shifts remain unclear. Hence, it is still unclear how EEG and fMRI are related in aging. Given the widespread use of Fourier‐transform based methods for studying rs‐fMRI power distributions (Zou et al, 2008), it is important to clarify whether Fourier‐based spectral decomposition can produce results to support the Hilbert‐transform‐based findings. …”

“…2. Given the known complexity of the signal and noise contributions to the rs-fMRI signal, including by intrinsic variations in carbon dioxide (CO 2 ) (Chang & Glover, 2009;Golestani, Chang, Kwinta, Khatamian, & Chen, 2015;Wise, Ide, Poulin, & Tracey, 2004), respiration (Birn, Diamond, Smith, & Bandettini, 2006;Chang & Glover, 2009;Golestani et al, 2015;Shams, LeVan, & Chen, 2021) and cardiac pulsation (Attarpour, Ward, & Chen, 2021;Chang et al, 2013;Falahpour, Refai, & Bodurka, 2013;Shmueli et al, 2007), the neuronal associations of the observed frequency shifts remain unclear. Hence, it is still unclear how EEG and fMRI are related in aging.…”

The Role of Cerebrovascular-Reactivity Mapping in Functional MRI: Calibrated fMRI and Resting-State fMRI

“…The CO 2 response has also been reported to exhibit network structure that coincide with that of conventional rs-fMRI functional networks ( Bright et al, 2020 ). Cardiac pulsatility, which entrains the autonomic nervous system, is also known to contribute to the rs-fMRI signal in a spatially specific manner ( Shmueli et al, 2007 ; Chang et al, 2009 ; Shokri-Kojori et al, 2018 ; Attarpour et al, 2021 ). In fact, a substantial portion of the rs-fMRI signal may stem from cardiogenic vascular oscillations, and such signals can interact with respiration and CO2 in a biofeedback loop ( Attarpour et al, 2021 ).…”

“…Cardiac pulsatility, which entrains the autonomic nervous system, is also known to contribute to the rs-fMRI signal in a spatially specific manner ( Shmueli et al, 2007 ; Chang et al, 2009 ; Shokri-Kojori et al, 2018 ; Attarpour et al, 2021 ). In fact, a substantial portion of the rs-fMRI signal may stem from cardiogenic vascular oscillations, and such signals can interact with respiration and CO2 in a biofeedback loop ( Attarpour et al, 2021 ). Respiratory and cardiac-related physiological networks have recently been documented in detail ( Chen et al, 2020 ).…”