Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Neuroimage. 2019 May 1;191:21-35. doi: 10.1016/j.neuroimage.2019.02.021. Epub 2019 Feb 8.
Electroencephalography (EEG) recordings performed in magnetic resonance imaging (MRI) scanners are affected by complex artifacts caused by heart function, often termed pulse artifacts (PAs). PAs can strongly compromise EEG data quality, and remain an open problem for EEG-fMRI. This study investigated the properties and mechanisms of PA variability across heartbeats, which has remained largely unaddressed to date, and evaluated its impact on PA correction approaches. Simultaneous EEG-fMRI was performed at 7T on healthy participants at rest or under visual stimulation, with concurrent recordings of breathing and cardiac activity. PA variability was found to contribute to EEG variance with more than 500 μV at 7T, which extrapolates to 92 μV at 3T. Clustering analyses revealed that PA variability not only is linked to variations in head position/orientation, as previously hypothesized, but also, and more importantly, to the respiratory cycle and to heart rate fluctuations. The latter mechanisms are associated to short-timescale variability (even across consecutive heartbeats), and their importance varied across EEG channels. In light of this PA variability, three PA correction techniques were compared: average artifact subtraction (AAS), optimal basis sets (OBS), and an approach based on K-means clustering. All methods allowed the recovery of visual evoked potentials from the EEG data; nonetheless, OBS and K-means tended to outperform AAS, likely due to the inability of the latter in modeling short-timescale variability. Altogether, these results offer novel insights into the dynamics and underlying mechanisms of the pulse artifact, with important consequences for its correction, relevant to most EEG-fMRI applications.
脑电图（EEG）记录在磁共振成像（MRI）扫描仪中受到心脏功能引起的复杂伪影的影响，通常称为脉搏伪影（PA）。PA 会强烈影响 EEG 数据质量，是 EEG-fMRI 的一个未解决的问题。本研究调查了 PA 在心跳之间的变化特性和机制，这在很大程度上尚未得到解决，并评估了其对 PA 校正方法的影响。在 7T 下对健康参与者进行静息或视觉刺激的同时 EEG-fMRI 记录，同时记录呼吸和心脏活动。研究发现，PA 变化导致 EEG 方差超过 7T 时的 500μV，外推至 3T 时为 92μV。聚类分析表明，PA 变化不仅与头位置/方向的变化有关，正如之前假设的那样，而且更重要的是与呼吸周期和心率波动有关。后一种机制与短时间尺度的变化（即使在连续心跳之间）有关，其重要性因 EEG 通道而异。鉴于这种 PA 变化，比较了三种 PA 校正技术：平均伪影减法（AAS）、最优基集（OBS）和基于 K-均值聚类的方法。所有方法都允许从 EEG 数据中恢复视觉诱发电位；然而，OBS 和 K-均值聚类的方法往往优于 AAS，这可能是由于后者无法对短时间尺度的变化进行建模。总的来说，这些结果提供了对脉搏伪影动力学和潜在机制的新见解，对其校正具有重要意义，与大多数 EEG-fMRI 应用相关。
