Neuroscience Convergence Center, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
Department of Neuroscience, College of Medicine, Gachon University, 1198 Kuwol-dong, Namdong-gu, Incheon, 405-760, Republic of Korea.
J Neurosci Methods. 2021 Jul 15;359:109218. doi: 10.1016/j.jneumeth.2021.109218. Epub 2021 May 7.
Generally, the analysis of functional magnetic resonance imaging (fMRI) using echo-planar imaging (EPI) data is based on independent component analysis (ICA) and the general linear model (GLM). The application of these two approaches is highly independent, like GLM is for task-related activation mapping, and ICA is for resting-state imaging. Herein, we propose white noise-removed T*-variation mapping as a new analysis method for fMRI that integrates the two conventional mapping approaches.
We derived the standard deviation to the mean-square ratio of the true T* signal from the multi-echo EPI (ME-EPI) dataset. For the true T*-variation-based value, we removed the S (initial signal intensity) and white noise component from the variation in the EPI signal using signal-coherence analysis of the echo time (TE) dataset and slope analysis of the TE-variated coefficient of variation of the ME-EPI dataset.
The activation mapping for a visual task and resting-state imaging by the proposed method showed the reliable activation map in the visual cortex area and area for the typical default mode network, with white noise and the S component removed.
Conventional analyses for fMRI cannot be applied to both activation mapping and resting-state imaging, with white noise removed, while the proposed method can be applied.
We demonstrated white noise-removed true T*-variation-based mapping as a new functional brain analysis approach. We expect the method allows studying in which that the association between task timing and brain activity is somewhat uncertain, such as studies of emotion and awareness.
通常，使用 echo-planar 成像 (EPI) 数据对功能磁共振成像 (fMRI) 的分析基于独立成分分析 (ICA) 和广义线性模型 (GLM)。这两种方法的应用高度独立，例如 GLM 用于任务相关的激活映射，而 ICA 用于静息态成像。在此，我们提出了一种新的 fMRI 分析方法，即去除白噪声的 T*-变异性映射，它集成了这两种传统的映射方法。
我们从多回波 EPI (ME-EPI) 数据集推导出真实 T信号的均方比标准差。对于基于真实 T-变异性的值，我们使用回波时间 (TE) 数据集的信号相干性分析和 ME-EPI 数据集的 TE 变化变异性斜率分析，从 EPI 信号的变化中去除 S（初始信号强度）和白噪声成分。
所提出的方法进行的视觉任务激活映射和静息态成像显示了在视觉皮层区域和典型默认模式网络区域可靠的激活图，其中去除了白噪声和 S 成分。
传统的 fMRI 分析不能应用于激活映射和去除白噪声的静息态成像，而所提出的方法可以应用。
我们展示了基于去除白噪声的真实 T*-变异性的映射作为一种新的功能脑分析方法。我们期望该方法能够研究任务时间和大脑活动之间的关联有些不确定的情况，例如情感和意识的研究。
