A Hilbert-based method for processing respiratory timeseries.
Neuroimage
; 230: 117787, 2021 04 15.
Article
en En
| MEDLINE
| ID: mdl-33516897
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).
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Encéfalo
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Imagen por Resonancia Magnética
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Mecánica Respiratoria
Límite:
Humans
Idioma:
En
Revista:
Neuroimage
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
Año:
2021
Tipo del documento:
Article