The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data

作者：Lars Kasper, Steffen Bollmann, Andreea Oliviana Diaconescu, Chloe Hutton, Jakob Heinzle, Sandra Iglesias, Tobias U. Hauser, Miriam Sebold, Zina‐Mary Manjaly, Klaas P. Pruessmann, Klaas Enno Stephan · 发表于：Journal of Neuroscience Methods · 年份：2016 · DOI：10.1016/j.jneumeth.2016.10.019 · 被引用次数：476 · 研究领域：Functional Brain Connectivity Studies、Heart Rate Variability and Autonomic Control、EEG and Brain-Computer Interfaces

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 toolb...