Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
Magn Reson Med. 2019 May;81(5):3262-3271. doi: 10.1002/mrm.27620. Epub 2018 Dec 18.
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.
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.
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.
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.
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越来越多地被使用，但仍然面临着一个主要挑战，即如何从噪声中分离出神经元活动引起的 BOLD 信号波动。在这项研究中，我们研究了一种新提出的去噪方法，即集成多回波 rs-FMRI 分析，对语言映射的影响。
在 12 名受试者的左半球中，采集多带多回波 rs-FMRI 数据和语言任务 fMRI，以识别语言区域。使用 3 种不同方法预处理 rs-FMRI 数据时，通过种子相关分析比较了语言定位给出的语言侧化和特异性：使用集成多回波独立成分分析的多回波数据、使用每个信号分量的 TE 依赖性来判断其来源的去噪、以及使用常规去噪的多回波和单回波数据。自动计算侧化指数，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。
重复测量方差分析和事后检验显示，3 个回波数据的最佳组合成功地增加了目标语言系统内的相关性。通过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与语言系统更特异的同步区域，与常规去噪方法相比，最终提高了系统侧化的识别能力。
通过成功地减少在大脑中扩散的非特异性相关性，集成多回波方法改善了 rs-FMRI 中的语言映射和系统侧化的识别。
