FMRI Correlates of Dynamic Low Frequency Photoplethysmography (PPG) and Arterial Pressure Measures
Poster No:
1355
Submission Type:
Abstract Submission
Authors:
Joshua Dean1, Burak Akin1, Daniel Handwerker1, Sharif Kronemer1, A. Tyler Morgan1, Peter Bandettini1
Institutions:
1National Institute of Mental Health, Bethesda, MD
First Author:
Co-Author(s):
Introduction:
The photoplethysmography (PPG) signal – a measure of light absorption from blood and collected from the fingertip – contains temporal features that resemble fMRI signals (Attarpour et al., 2021; Chang et al., 2013; Tong et al., 2013). The PPG waveform has a non-pulsatile component in the range of < 0.1 Hz, which is associated with sympathetic activity and Mayer waves (Özbay et al., 2019; Tong et al., 2013). These slow wave components are referred to as systemic or low frequency oscillations (LFO). Dynamic mean arterial pressure (MAP), a measure derived from tissue displacement with the heart beat from a finger cuff, also likely has features that are distinct from, yet linked, with PPG, and likely have a distinct temporal and spatial propagation pattern in the brain. In this study, we compare these two externally measured signal sources as regressors for fMRI time series – calculating maps of delay and correlation at the optimal delay.
Methods:
11 healthy adult participants (26.4 ± 3.0 years old, 5 females) underwent a resting-state scan (14.8 ± 2.0 min) using a 7T multiband fMRI acquisition (TR=0.75s, TE=25ms, voxel-size=2mm isotropic, FOV=68 slices). fMRI data were motion-corrected, bandpass filtered (0.01-0.1 Hz), and normalized to MNI space.
PPG traces were recorded using a standard pulse oximeter, BIOPAC MP160. A 4th order Chebyshev filter (fc = 0.1 Hz) was applied to each subject's PPG trace to compute the LFO regressors. Blood pressure signal over time was collected concurrently using CareTaker (BIOPAC). This approach to measuring blood pressure over time has been shown to agree with invasive measures (Baruch et al., 2014; Gratz et al., 2021).
We determined the delay and optimal correlation on a voxel-wise basis of the physiological reference signal (LFO or MAP) with the fMRI signal using the software, Rapidtide (Frederick et al., 2024), iteratively shifting the reference signal in 0.15s increments across a temporal range of -10s to +30s.
PPG traces were recorded using a standard pulse oximeter, BIOPAC MP160. A 4th order Chebyshev filter (fc = 0.1 Hz) was applied to each subject's PPG trace to compute the LFO regressors. Blood pressure signal over time was collected concurrently using CareTaker (BIOPAC). This approach to measuring blood pressure over time has been shown to agree with invasive measures (Baruch et al., 2014; Gratz et al., 2021).
We determined the delay and optimal correlation on a voxel-wise basis of the physiological reference signal (LFO or MAP) with the fMRI signal using the software, Rapidtide (Frederick et al., 2024), iteratively shifting the reference signal in 0.15s increments across a temporal range of -10s to +30s.
Results:
LFO and MAP reference signals are shown for each subject over the duration of the fMRI scan (Fig. 1). LFO and MAP are marginally anti-correlated (r = -0.1552 ± 0.1809), however they may also simply be time-shifted relative to each other.
The delay maps produced using the MAP reference signal are both weaker and show shorter delays than the delay maps produced using the LFO reference signal (Fig. 2a-b). Both delay maps appear to follow perfusion density distributions, but MAP appears to highlight rapidly changing pulsatile regions. The LFO shows a more even distribution of slower changes, perhaps representing perfusion changes with changes in sympathetic tone.
The correlation maps using LFO depict higher correlations in the visual cortex than MAP (Fig. 2c-d), which may suggest that visual input modulates LFO through sympathetic activity and subcortical control mechanisms. LFO is shown to have highest temporal correlation with the gray matter voxels at the more extreme delay times, whereas MAP seems to have its highest cross correlation shortly before 0 seconds (Fig. 2 bottom panel).
The delay maps produced using the MAP reference signal are both weaker and show shorter delays than the delay maps produced using the LFO reference signal (Fig. 2a-b). Both delay maps appear to follow perfusion density distributions, but MAP appears to highlight rapidly changing pulsatile regions. The LFO shows a more even distribution of slower changes, perhaps representing perfusion changes with changes in sympathetic tone.
The correlation maps using LFO depict higher correlations in the visual cortex than MAP (Fig. 2c-d), which may suggest that visual input modulates LFO through sympathetic activity and subcortical control mechanisms. LFO is shown to have highest temporal correlation with the gray matter voxels at the more extreme delay times, whereas MAP seems to have its highest cross correlation shortly before 0 seconds (Fig. 2 bottom panel).
·Fig. 1: LFO (yellow) and MAP (red) reference signals are shown for each subject as recorded over the duration of the fMRI scan.
·Fig. 2: Delay maps for (a) LFO or (b) MAP and correlation maps for (c) LFO or (d) MAP. Cross correlation between the LFO (bottom left) and MAP (bottom right) and the average GM time series is shown.
Conclusions:
The difference in the spatiotemporal signatures of LFO and MAP suggests that these physiologically-coupled fluctuations each provide unique information that may be intrinsically related to vascular density and patency, flow velocity, sympathetic tone, cerebral autoregulation, and other homeostatic mechanisms. Moreover, their distinct spatial patterns resemble structures from the default mode, dorsal attention, visual, and sensorimotor networks. Future research will aim to modulate these hypothesized relationships to perhaps determine causality, lending insight into BOLD fluctuations, opening up clinical applications, and perhaps improving the quality of BOLD connectivity measures by removing nuisance signals more effectively.
Modeling and Analysis Methods:
Exploratory Modeling and Artifact Removal 1
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Neurophysiology of Imaging Signals
Keywords:
Autonomics
Blood
Computational Neuroscience
FUNCTIONAL MRI
Modeling
Motor
NORMAL HUMAN
Other - Blood Pressure
1|2Indicates the priority used for review
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Provide references using APA citation style.
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