[HTML][HTML] The PhysIO toolbox for modeling physiological noise in fMRI data
Journal of neuroscience methods, 2017•Elsevier
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 …
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 …
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.
Elsevier
