Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI.
Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI.
复制标题
探索同时fMRI期间获得的脑电图数据的运动伪影校正技术的相对功效。
DOI：
10.1002/hbm.24396
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发表时间：
2019-02-01
影响因子：
4.8
通讯作者：
Mullinger KJ
中科院分区：
文献类型：
--
作者：
Daniel AJ;Smith JA;Spencer GS;Jorge J;Bowtell R;Mullinger KJ
同步 EEG-fMRI 可以对大脑功能进行多参数表征，原则上可以更全面地了解大脑反应；不幸的是，恶劣的 MRI 环境严重降低了脑电图数据质量。以前认为，简单地消除包含粗大运动伪影 [MA]（由 EEG 系统和头部在 MRI 扫描仪静态磁场中运动而产生）的数据段就足够了。然而，最近强调了去除所有 MA 的重要性，并开发了新方法。需要对使用不同的 MA 检测方法和后处理算法去除 MA 和保留潜在神经元活动的能力进行系统比较，以指导神经科学界。我们使用头部模型记录 MA，同时使用三种不同的方法监控运动：参考层伪影扣除 (RLAS)、莫尔相位跟踪器 (MPT) 标记和线环运动传感器 (WLMS)。这些脑电图记录与脑电图对 MRI 环境之外的受试者获得的简单视觉任务的反应相结合。然后使用每种方法收集的运动信息并结合不同的分析流程来校正 MA。所有测试的方法都保留了神经元信号。然而，MA 通常没有被充分去除，无法准确检测潜在的神经元信号。我们证明，使用 RLAS 结合使用多通道递归最小二乘 (M-RLS) 算法的后处理可以最好地校正 MA。该方法需要进一步发展以实现实用；因此，WLMS 与 M-RLS 的结合目前提供了 EEG 数据质量和运动检测实用性之间的最佳折衷。
Simultaneous EEG‐fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post‐processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post‐processing using a multichannel, recursive least squares (M‐RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M‐RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.
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影响因子：
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