A Comparison of Denoising Approaches for Spoken Word Production Related Artefacts in Continuous Multiband fMRI Data.

IF 3.6 Q1 LINGUISTICS
Neurobiology of Language Pub Date : 2024-09-11 eCollection Date: 2024-01-01 DOI:10.1162/nol_a_00151
Angelique Volfart, Katie L McMahon, Greig I de Zubicaray
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引用次数: 0

Abstract

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.

针对连续多波段 fMRI 数据中与口语发音相关的伪影的去噪方法比较。
使用梯度回波回声平面成像(EPI)序列进行的 fMRI 实验表明,明显的语言产生会带来信号伪影,从而影响任务相关反应的准确检测。多年来,设计和后处理(去噪)技术已被提出并实施,以减少各种噪声源。最近,针对语音生成的 fMRI 研究开始采用多波段 EPI 序列,这种序列具有更好的信噪比(SNR)和时间分辨率,可对生理噪声源(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,我们展示了在命名到定义任务中对连续多波段 fMRI 数据应用多种噪声估计和去除方法的影响,包括刚体运动回归和离群值剔除、去除脑脊液(CSF)/边缘相关噪声成分的主成分分析,以及全局 fMRI 信号回归(使用两种不同的方法),并与单独的重新对齐和解压缩基线进行了比较。我们的研究结果表明,生理噪声最强、空间范围最广的来源是呼吸和肌肉运动产生的全局信号波动以及 CSF/边缘相关噪声成分,而残余的刚体运动产生的方差相对较小。有趣的是,去噪方法往往会分别减少和增强与任务相关的 BOLD 信号增加和减少。通过对未掩蔽数据估算出的全局信号进行体素线性模型的全局信号回归,可显著提高时间信噪比。总之,这些研究结果表明了结合连续多波段 EPI 序列和去噪方法来研究语音产生的神经生物学的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurobiology of Language
Neurobiology of Language Social Sciences-Linguistics and Language
CiteScore
5.90
自引率
6.20%
发文量
32
审稿时长
17 weeks
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