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
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.
Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, 4072, Australia.
J Neurosci Methods. 2017 Jan 30;276:56-72. doi: 10.1016/j.jneumeth.2016.10.019. Epub 2016 Nov 8.
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.
生理噪声是功能磁共振成像（fMRI）的主要干扰因素之一。一类常见的校正方法是根据外周测量（如心电图或气动带）对噪声进行建模。然而，由于以下原因，生理噪声校正尚未成为fMRI数据的标准预处理步骤：（1）生理记录的数据质量各不相同；（2）外周数据格式不规范；（3）大型队列研究所需的生理处理和建模缺乏完全自动化。
我们引入了用于生理记录预处理和基于模型的噪声校正的PhysIO工具箱。它实现了多种噪声模型，如RETROICOR、每时间呼吸量和心率变异性响应（RVT/HRV）。该工具箱涵盖了所有中间步骤——从灵活读取数据格式到创建广义线性模型（GLM）回归器/对比——无需任何人工干预。
我们展示了该工具箱的工作流程及其对来自不同供应商、记录设备、场强和受试者群体的数据集的功能。在一项群体研究（N = 35）中报告了生理噪声校正和性能评估的自动化情况。
PhysIO工具箱再现了先前实现的噪声模型的生理噪声模式和校正效果。它通过在生理周期方面优于供应商提供的峰值检测方法，提高了建模的稳健性。最后，该工具箱提供了一个具有完全自动化功能的集成框架，包括性能监测，并且在输入数据方面具有灵活性。
通过其独立于平台的Matlab实现、开源发布和模块化结构，PhysIO工具箱使生理噪声校正成为fMRI数据可访问的预处理步骤。
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