Tassi E, Maggioni E, Cerutti S, Brambilla P, Bianchi A M
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1718-1721. doi: 10.1109/EMBC44109.2020.9176117.
In this study, a semi-automatic, easy-to-use classification method for the identification and removal of fMRI noise is proposed and tested. The method relies on subject-level spatial independent component analysis (ICA) of fMRI data. Starting from a reference set of labeled independent components (ICs), novel ICs are classified as physiological/artefactual by combining a spatial correlation (SC) analysis with the reference ICs and relative power spectral (PS) analysis. Here, ICs from a task-based fMRI dataset were used as reference. SC and SP thresholds were set using a test dataset (5 subjects, same fMRI protocol) based on Receiving Operating Characteristic curves. The tool performance and versatility were measured on a resting-state fMRI dataset (5 subjects). Our results show that the method can automatically identify noise-related ICs with accuracy, specificity and sensitivity higher than 80% across different fMRI protocols. These findings also suggest that the reference set provided in the present study might be used to mark ICs coming from independent taskrelated or resting-state fMRI datasets.Clinical relevance- The new method will be included in a userfriendly, open-source tool for removal of noisy contributions from fMRI datasets to be used in clinical and research practices.
在本研究中，我们提出并测试了一种用于识别和去除功能磁共振成像（fMRI）噪声的半自动、易于使用的分类方法。该方法依赖于fMRI数据的受试者水平空间独立成分分析（ICA）。从一组标记的独立成分（IC）参考集开始，通过将空间相关性（SC）分析与参考IC以及相对功率谱（PS）分析相结合，将新的IC分类为生理/伪影。在此，基于任务的fMRI数据集中的IC被用作参考。基于接收操作特征曲线，使用测试数据集（5名受试者，相同的fMRI协议）设置SC和SP阈值。在静息态fMRI数据集（5名受试者）上测量该工具的性能和通用性。我们的结果表明，该方法能够自动识别与噪声相关的IC，在不同的fMRI协议中，其准确性、特异性和敏感性均高于80%。这些发现还表明，本研究中提供的参考集可用于标记来自独立任务相关或静息态fMRI数据集的IC。临床相关性——新方法将被纳入一个用户友好的开源工具中，用于去除fMRI数据集中的噪声成分，以应用于临床和研究实践。
