Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to loca...

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Main Authors: Michalis Kassinopoulos, Georgios D. Mitsis
Format: Article
Language:English
Published: Elsevier 2021-11-01
Series:NeuroImage
Subjects:
CPM
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811921007400
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spelling doaj-58fcbb673659407ea8d844a8d0e94a3a2021-09-05T04:39:48ZengElsevierNeuroImage1095-95722021-11-01242118467Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmographMichalis Kassinopoulos0Georgios D. Mitsis1Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada; Corresponding author.Department of Bioengineering, McGill University, Montreal, QC, CanadaThe blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS – defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.http://www.sciencedirect.com/science/article/pii/S1053811921007400RETROICORCPMfMRI artifactsnoise correction techniquesCardiac pulsationSLFOs
collection DOAJ
language English
format Article
sources DOAJ
author Michalis Kassinopoulos
Georgios D. Mitsis
spellingShingle Michalis Kassinopoulos
Georgios D. Mitsis
Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph
NeuroImage
RETROICOR
CPM
fMRI artifacts
noise correction techniques
Cardiac pulsation
SLFOs
author_facet Michalis Kassinopoulos
Georgios D. Mitsis
author_sort Michalis Kassinopoulos
title Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph
title_short Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph
title_full Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph
title_fullStr Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph
title_full_unstemmed Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph
title_sort physiological noise modeling in fmri based on the pulsatile component of photoplethysmograph
publisher Elsevier
series NeuroImage
issn 1095-9572
publishDate 2021-11-01
description The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS – defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.
topic RETROICOR
CPM
fMRI artifacts
noise correction techniques
Cardiac pulsation
SLFOs
url http://www.sciencedirect.com/science/article/pii/S1053811921007400
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