EEG辅助fMRI的回顾性运动矫正：E-REMCOR
EEG-assisted retrospective motion correction for fMRI: E-REMCOR
摘要：
我们提出了一种方法，回顾性运动校正的fMRI数据在同步EEG-fMRI，采用EEG阵列作为一个敏感的运动检测器。EEG运动伪影用于生成运动回归量，该运动回归量描述具有毫秒时间分辨率的旋转头部运动。这些回归器用于对未处理的fMRI数据进行特定切片的运动校正。该方法的性能证明了从5例重度抑郁症，谁表现出头部运动1-3毫米，在休息脑电图功能磁共振成像运行的fMRI数据的校正。使用8到10个基于EEG的运动回归器校正的fMRI数据集显示fMRI时间序列的时间SNR（TSNR）显著改善，特别是在额叶脑区域和大脑表面附近。在单个受试者分析中，对于大的大脑区域，TSNR的改善高达50%，当结果在受试者之间平均时，TSNR的改善高达25%。同时应用基于EEG的运动校正和通过RETROICOR进行的生理噪声校正，对于大的大脑区域，平均TSNR增强高达35%。这些TSNR的改善在随后的fMRI体积配准和fMRI运动参数的回归后在很大程度上得以保留。所提出的EEG辅助的回顾性fMRI运动校正方法（简称E-REMCOR）可用于改善具有严重运动伪影的fMRI数据的质量，并减少由头部运动引起的EEG和fMRI数据之间的虚假相关性。它不需要任何超出标准EEG-fMRI仪器的专用设备，并且可以回顾性地应用于任何现有的EEG-fMRI数据集。
英文摘要：
We propose a method for retrospective motion correction of fMRI data in simultaneous EEG-fMRI that employs the EEG array as a sensitive motion detector. EEG motion artifacts are used to generate motion regressors describing rotational head movements with millisecond temporal resolution. These regressors are utilized for slice-specific motion correction of unprocessed fMRI data. Performance of the method is demonstrated by correction of fMRI data from five patients with major depressive disorder, who exhibited head movements by 1-3 mm during a resting EEG-fMRI run. The fMRI datasets, corrected using eight to ten EEG-based motion regressors, show significant improvements in temporal SNR (TSNR) of fMRI time series, particularly in the frontal brain regions and near the surface of the brain. The TSNR improvements are as high as 50% for large brain areas in single-subject analysis and as high as 25% when the results are averaged across the subjects. Simultaneous application of the EEG-based motion correction and physiological noise correction by means of RETROICOR leads to average TSNR enhancements as high as 35% for large brain regions. These TSNR improvements are largely preserved after the subsequent fMRI volume registration and regression of fMRI motion parameters. The proposed EEG-assisted method of retrospective fMRI motion correction (referred to as E-REMCOR) can be used to improve quality of fMRI data with severe motion artifacts and to reduce spurious correlations between the EEG and fMRI data caused by head movements. It does not require any specialized equipment beyond the standard EEG-fMRI instrumentation and can be applied retrospectively to any existing EEG-fMRI data set.
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