The PhysIO toolbox for modeling physiological noise in fMRI data

Kasper, Lars, Bollmann, Steffen, Diaconescu, Andreea O., Hutton, Chloe, Heinzle, Jakob, Iglesias, Sandra, Hauser, Tobias U., Sebold, Miriam, Manjaly, Zina-Mary, Pruessmann, Klaas P. and Stephan, Klaas E. (2017) The PhysIO toolbox for modeling physiological noise in fMRI data. Journal of Neuroscience Methods, 276 56-72. doi:10.1016/j.jneumeth.2016.10.019


Author Kasper, Lars
Bollmann, Steffen
Diaconescu, Andreea O.
Hutton, Chloe
Heinzle, Jakob
Iglesias, Sandra
Hauser, Tobias U.
Sebold, Miriam
Manjaly, Zina-Mary
Pruessmann, Klaas P.
Stephan, Klaas E.
Title The PhysIO toolbox for modeling physiological noise in fMRI data
Journal name Journal of Neuroscience Methods   Check publisher's open access policy
ISSN 1872-678X
0165-0270
Publication date 2017-01-30
Year available 2017
Sub-type Article (original research)
DOI 10.1016/j.jneumeth.2016.10.019
Open Access Status DOI
Volume 276
Start page 56
End page 72
Total pages 17
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Subject 2800 Neuroscience
Abstract Background Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. New methods We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps − from flexible read-in of data formats to GLM regressor/contrast creation − without any manual intervention. Results We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N = 35). Comparison with existing methods The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. Conclusions Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.
Formatted abstract
Background: Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies.

New methods: We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps − from flexible read-in of data formats to GLM regressor/contrast creation − without any manual intervention.

Results: We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N = 35).

Comparison with existing methods: The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data.

Conclusions: Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.
Keyword FMRI
FMRI preprocessing
Heart rate
Physiological noise correction
Respiratory volume
RETROICOR
RVHRCOR
SPM toolbox
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 151641
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Centre for Advanced Imaging Publications
 
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