Abstract
In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline.Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).
Highlights
There has been much recent interest in the impact of global artefacts on functional magnetic resonance imaging (fMRI) data (Burgess et al, 2016; Ciric et al, 2017; Liu et al, 2018; Murphy and Fox, 2017; Satterthwaite et al, 2013; Schölvinck et al, 2010), with many studies finding a link between physiological processes— breathing—and these global signals (Byrge and Kennedy, 2018; Power et al, 2017a; 2020; 2019; 2017b)
We introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings
We introduce a new estimator for respiratory volume per unit time (RVT) that does not require peak detection—a process that often requires post hoc manual intervention—and demonstrate that this accurately captures atypical breathing events
Summary
There has been much recent interest in the impact of global artefacts on fMRI data (Burgess et al, 2016; Ciric et al, 2017; Liu et al, 2018; Murphy and Fox, 2017; Satterthwaite et al, 2013; Schölvinck et al, 2010), with many studies finding a link between physiological processes— breathing—and these global signals (Byrge and Kennedy, 2018; Power et al, 2017a; 2020; 2019; 2017b). Despite the fact that models for these physiological processes and their impact on fMRI are well established (Birn et al, 2006; 2008; Chang et al, 2009; Chang and Glover, 2009; Glover et al, 2000; Murphy et al, 2013), much recent work has focused on improved data-driven methods for artefact removal (Aquino et al, 2020; Glasser et al, 2018; Power et al, 2018). For a much more detailed discussion of both breathing and breathing-related issues pertaining to fMRI than space affords in this technical note, we refer the reader to the excellent overview in Power et al (2020)
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