Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI.
Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI.
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DOI：
10.1088/1741-2552/ac1037
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发表时间：
2021-07-26
影响因子：
4
通讯作者：
Bodurka J
中科院分区：
文献类型：
--
作者：
Mayeli A;Al Zoubi O;Henry K;Wong CK;White EJ;Luo Q;Zotev V;Refai H;Bodurka J
同步脑电图 - 功能性磁共振成像（EEG - fMRI）记录为研究人类大脑以及理解介导认知和行为过程的潜在机制提供了一种高时空分辨率的方法。然而，脑电图对磁共振成像诱导的伪影的高度敏感性阻碍了这种方法的广泛应用。更具体地说，在功能性磁共振成像采集过程中收集的脑电图数据除了受到生理来源的伪影污染外，还受到磁共振成像梯度和心冲击图伪影的污染。已经有一些通过手动且耗时的预处理来减少这些伪影的尝试，但由于选择步骤顺序、参数以及伪影独立成分分类的差异，可能会导致脑电图数据出现偏差。因此，迫切需要开发一种全自动且全面的流程来减少所有主要的脑电图伪影。在这项工作中，我们引入了一个具有全自动流程的开源工具箱，用于减少与功能性磁共振成像同时采集的脑电图数据中的伪影（简称APPEAR）。
该流程整合了平均模板减法和独立成分分析，以抑制与磁共振成像相关的伪影和生理伪影。为了验证我们的结果，我们在静息状态（n = 48）和基于任务（即事件相关电位（ERPs）；n = 8）范式下从健康对照受试者记录的脑电图数据上测试了APPEAR。所选用的金标准是对脑电图数据库的专家手动审查。
我们使用频率分析和连续小波变换比较了静息状态下手动和自动校正的脑电图数据，发现两种校正之间没有显著差异。在所谓的停止信号任务期间记录的事件相关电位数据（例如幅度测量和信噪比）之间的比较也表明，手动和全自动功能性磁共振成像 - 脑电图校正数据之间没有差异。
APPEAR提供了第一个全面的开源工具箱，它可以加快脑电图分析的进展，并通过避免实验者的偏好来提高可重复性，同时允许研究人员在可管理的时间和精力内处理由数百名受试者组成的大型脑电图 - 功能性磁共振成像队列。
Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR). The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n = 48) and task-based (i.e. event-related-potentials (ERPs); n = 8) paradigms. The chosen gold standard is an expert manual review of the EEG database. We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data. APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters’ preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.
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影响因子：
3.4
作者：
Bullock M;Jackson GD;Abbott DF
通讯作者：
Abbott DF
影响因子：
3.2
作者：
In, Myung H.;Lee, Soo Y.;Ahn, Young B.
通讯作者：
Ahn, Young B.
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。
影响因子：
4.8
作者：
Daniel AJ;Smith JA;Spencer GS;Jorge J;Bowtell R;Mullinger KJ
通讯作者：
Mullinger KJ
DOI：
10.3758/s13415-014-0270-2
发表时间：
2014-12
期刊：
Cognitive, affective & behavioral neuroscience
影响因子：
--
作者：
Carretié L
通讯作者：
Carretié L
影响因子：
3
作者：
Debener, Stefan;Mullinger, Karen J.;Bowtell, Richard W.
通讯作者：
Bowtell, Richard W.