The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data.
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.



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.



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).



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.



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.
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland; Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland. Electronic address: kasper@biomed.ee.ethz.ch.
Lars
L
Kasper
Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, 4072, Australia.
Steffen
S
Bollmann
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland.
Andreea O
AO
Diaconescu
Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK.
Chloe
C
Hutton
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland.
Jakob
J
Heinzle
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland.
Sandra
S
Iglesias
Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1 B 5EH, UK.
Tobias U
TU
Hauser
Department of Psychiatry and Psychotherapy, Campus Mitte, Charité Universitätsmedizin, 10117 Berlin, Germany.
Miriam
M
Sebold
Department of Neurology, Schulthess Clinic, 8008 Zurich, Switzerland.
Zina-Mary
ZM
Manjaly
Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Gloriastrasse 35, 8092 Zurich, Switzerland.
Klaas P
KP
Pruessmann
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Wilfriedstrasse 6, 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck Institute for Metabolism Research, 50931 Cologne, Germany.
Klaas E
KE
Stephan
D000465:Algorithms; D016477:Artifacts; D001289:Attention Deficit Disorder with Hyperactivity / Q000503:physiopathology; D001921:Brain / Q000000981:diagnostic imaging* / Q000502:physiology* / Q000503:physiopathology; D003198:Computer Simulation; D004562:Electrocardiography / Q000295:instrumentation; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D000067570:Social Learning / Q000502:physiology; D012984:Software
Heart rate; Physiological noise correction; RETROICOR; RVHRCOR; Respiratory volume; SPM toolbox; fMRI; fMRI preprocessing