Integrated multi-echo denoising strategy improves identification of inherent language laterality
Integrated multi-echo denoising strategy improves identification of inherent language laterality
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DOI：
10.1002/mrm.27620
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发表时间：
2019-05-01
影响因子：
3.3
通讯作者：
Abe, Osamu
中科院分区：
文献类型：
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作者：
Amemiya, Shiori;Yamashita, Hiroshi;Abe, Osamu
目的：尽管静息态功能磁共振成像（rs-FMRI）在神经科学和临床研究中的应用越来越多，但它面临的一个主要挑战仍然是将神经元活动引起的BOLD信号波动与噪声分离开来。在这项研究中，我们研究了一种新提出的去噪方法（集成多回波rs-FMRI分析）对语言映射的影响。方法：采集多频带多回波rs-FMRI数据，沿着语言任务FMRI，识别12名受试者左半球的语言区域。在使用3种不同方法预处理的rs-FMRI数据集之间比较基于种子的相关分析给出的语言侧化和语言映射的特异性：多回波数据与集成的多回波独立分量分析，去噪使用每个信号分量的TE依赖性来判断其起源，以及多回波和单回波数据与常规去噪。偏侧化指数的自动计算无需设置任何阈值，以尽量减少任意性，并确保结果的通用性。结果：重复测量方差分析，事后检验表明，最佳组合的3-回声数据成功地增加了目标语言系统内的相关性。与传统的去噪方法相比，利用物理原理的多回波去噪方法，集成策略进一步成功地揭示了语言系统更特定的同步区域，最终提高了系统偏侧性的识别。通过成功地减少在大脑中传播的非特异性相关性，集成多回波方法改进了语言映射和使用rs-FMRI的系统的偏侧性的识别。
Purpose: Although increasingly used in both neuroscience and clinical studies, a major challenge facing resting-state FMRI (rs-FMRI) still lies in isolating BOLD signal fluctuations resulting from neuronal activity from noise. In this study, we investigated the effect of a newly proposed denoising approach, integrated multi-echo rs-FMRI analysis, on language mapping.Methods: Multiband multi-echo rs-FMRI data were acquired, along with language task FMRI that identified language areas in the left hemisphere of 12 subjects. The language laterality and specificity of the language mapping given by seed-based correlation analysis were compared among the rs-FMRI data sets pre-processed using 3 different approaches: multi-echo data with integrated multi-echo independent component analysis, denoising that uses the TE-dependency of each signal component to judge its origin, and multi-echo and single-echo data with conventional denoising. The laterality index was automatically computed without setting any threshold to minimize the arbitrariness and to ensure the generality of the result.Results: A repeated measures analysis of variance followed by post hoc tests showed that optimal combination of the 3-echo data succeeded in increasing the correlation within the targeted language system. With the physically principled multi-echo denoising approach, the integrated strategy further succeeded in revealing areas of synchronization more specific to the language system compared with conventional denoising approach, which eventually improved the identification of the laterality of the system.Conclusion: By successfully reducing non-specific correlations spreading over the brain, integrated multi-echo approach improved language mapping and identification of the laterality of the system using rs-FMRI.