AI Article Synopsis
- This technical note presents a new method for estimating respiratory volume changes, improving upon traditional peak-based approaches by utilizing the Hilbert transform from electrophysiology.
- The new method provides higher time resolution and better identifies unusual breathing patterns, ultimately enhancing physiological noise correction in fMRI studies.
- The improved technique is integrated into the PhysIO package of the TAPAS toolbox, which is publicly available for use in fMRI data preprocessing.
Article 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).
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