Kraljič Aleksij, Matkovič Andraž, Purg Nina, Demšar Jure, Repovš Grega
Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia.
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Front Neuroimaging. 2022 Aug 29;1:968363. doi: 10.3389/fnimg.2022.968363. eCollection 2022.
Multimodal neuroimaging using EEG and fMRI provides deeper insights into brain function by improving the spatial and temporal resolution of the acquired data. However, simultaneous EEG-fMRI inevitably compromises the quality of the EEG and fMRI signals due to the high degree of interaction between the two systems. Fluctuations in the magnetic flux flowing through the participant and the EEG system, whether due to movement within the magnetic field of the scanner or to changes in magnetic field strength, induce electrical potentials in the EEG recordings that mask the much weaker electrical activity of the neuronal populations. A number of different methods have been proposed to reduce MR artifacts. We present an overview of the most commonly used methods and an evaluation of the methods using three sets of diverse EEG data. We limited the evaluation to open-access and easy-to-use methods and a reference signal regression method using a set of six carbon-wire loops (CWL), which allowed evaluation of their added value. The evaluation was performed by comparing EEG signals recorded outside the MRI scanner with artifact-corrected EEG signals recorded simultaneously with fMRI. To quantify and evaluate the quality of artifact reduction methods in terms of the spectral content of the signal, we analyzed changes in oscillatory activity during a resting-state and a finger tapping motor task. The quality of artifact reduction in the time domain was assessed using data collected during a visual stimulation task. In the study we utilized hierarchical Bayesian probabilistic modeling for statistical inference and observed significant differences between the evaluated methods in the success of artifact reduction and associated signal quality in both the frequency and time domains. In particular, the CWL system proved superior to the other methods evaluated in improving spectral contrast in the alpha and beta bands and in recovering visual evoked responses. Based on the results of the evaluation study, we proposed guidelines for selecting the optimal method for MR artifact reduction.
使用脑电图（EEG）和功能磁共振成像（fMRI）的多模态神经成像通过提高采集数据的空间和时间分辨率，为大脑功能提供了更深入的见解。然而，同步EEG-fMRI不可避免地会因两个系统之间的高度相互作用而损害EEG和fMRI信号的质量。流经参与者和EEG系统的磁通量波动，无论是由于扫描仪磁场内的运动还是磁场强度的变化，都会在EEG记录中感应出电势，从而掩盖神经元群体弱得多的电活动。已经提出了许多不同的方法来减少磁共振伪影。我们概述了最常用的方法，并使用三组不同的EEG数据对这些方法进行了评估。我们将评估限制在开放获取且易于使用的方法以及使用一组六个碳丝环（CWL）的参考信号回归方法上，这使得能够评估它们的附加值。通过将在MRI扫描仪外部记录的EEG信号与与fMRI同时记录的经伪影校正的EEG信号进行比较来进行评估。为了根据信号的频谱内容量化和评估伪影减少方法的质量，我们分析了静息状态和手指敲击运动任务期间振荡活动的变化。使用在视觉刺激任务期间收集的数据评估时域中伪影减少的质量。在这项研究中，我们利用分层贝叶斯概率模型进行统计推断，并观察到在频率和时域中，评估方法在伪影减少的成功程度和相关信号质量方面存在显著差异。特别是，CWL系统在改善α和β波段的频谱对比度以及恢复视觉诱发反应方面被证明优于其他评估方法。基于评估研究的结果，我们提出了选择最佳磁共振伪影减少方法的指南。
