Volfart Angelique, McMahon Katie L, de Zubicaray Greig I
Faculty of Health, School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia.
Faculty of Health, School of Clinical Sciences, Queensland University of Technology, Brisbane, Australia.
Neurobiol Lang (Camb). 2024 Sep 11;5(4):901-921. doi: 10.1162/nol_a_00151. eCollection 2024.
It is well-established from fMRI experiments employing gradient echo echo-planar imaging (EPI) sequences that overt speech production introduces signal artefacts compromising accurate detection of task-related responses. Both design and post-processing (denoising) techniques have been proposed and implemented over the years to mitigate the various noise sources. Recently, fMRI studies of speech production have begun to adopt multiband EPI sequences that offer better signal-to-noise ratio (SNR) and temporal resolution allowing adequate sampling of physiological noise sources (e.g., respiration, cardiovascular effects) and reduced scanner acoustic noise. However, these new sequences may also introduce additional noise sources. In this study, we demonstrate the impact of applying several noise-estimation and removal approaches to continuous multiband fMRI data acquired during a naming-to-definition task, including rigid body motion regression and outlier censoring, principal component analysis for removal of cerebrospinal fluid (CSF)/edge-related noise components, and global fMRI signal regression (using two different approaches) compared to a baseline of realignment and unwarping alone. Our results show the strongest and most spatially extensive sources of physiological noise are the global signal fluctuations arising from respiration and muscle action and CSF/edge-related noise components, with residual rigid body motion contributing relatively little variance. Interestingly, denoising approaches tended to reduce and enhance task-related BOLD signal increases and decreases, respectively. Global signal regression using a voxel-wise linear model of the global signal estimated from unmasked data resulted in dramatic improvements in temporal SNR. Overall, these findings show the benefits of combining continuous multiband EPI sequences and denoising approaches to investigate the neurobiology of speech production.
使用梯度回波平面回波成像(EPI)序列的功能磁共振成像(fMRI)实验已充分证实,明显的言语产生会引入信号伪影,从而影响对任务相关反应的准确检测。多年来,人们提出并实施了设计和后处理(去噪)技术,以减轻各种噪声源的影响。最近,言语产生的fMRI研究开始采用多频段EPI序列,该序列具有更好的信噪比(SNR)和时间分辨率,能够对生理噪声源(如呼吸、心血管效应)进行充分采样,并降低扫描仪的声学噪声。然而,这些新序列也可能引入额外的噪声源。在本研究中,我们展示了将几种噪声估计和去除方法应用于在命名定义任务期间采集的连续多频段fMRI数据的影响,包括刚体运动回归和异常值检查、去除脑脊液(CSF)/边缘相关噪声成分的主成分分析,以及与仅进行重排和去扭曲的基线相比的全局fMRI信号回归(使用两种不同方法)。我们的结果表明,生理噪声最强且空间分布最广的来源是呼吸和肌肉活动引起的全局信号波动以及CSF/边缘相关噪声成分,而残留的刚体运动贡献的方差相对较小。有趣的是,去噪方法往往分别减少和增强与任务相关的血氧水平依赖(BOLD)信号的增加和减少。使用从未经掩蔽的数据估计的全局信号的体素级线性模型进行全局信号回归,可显著提高时间SNR。总体而言,这些发现表明了结合连续多频段EPI序列和去噪方法来研究言语产生的神经生物学的益处。