Diao Yujian, Yin Ting, Gruetter Rolf, Jelescu Ileana O
Animal Imaging and Technology, EPFL, Lausanne, Switzerland.
CIBM Center for Biomedical Imaging, Lausanne, Switzerland.
Front Neurosci. 2021 Mar 26;15:602170. doi: 10.3389/fnins.2021.602170. eCollection 2021.
Resting state functional MRI (rs-fMRI) is a widespread and powerful tool for investigating functional connectivity (FC) and brain disorders. However, FC analysis can be seriously affected by random and structured noise from non-neural sources, such as physiology. Thus, it is essential to first reduce thermal noise and then correctly identify and remove non-neural artifacts from rs-fMRI signals through optimized data processing methods. However, existing tools that correct for these effects have been developed for human brain and are not readily transposable to rat data. Therefore, the aim of the present study was to establish a data processing pipeline that can robustly remove random and structured noise from rat rs-fMRI data. It includes a novel denoising approach based on the Marchenko-Pastur Principal Component Analysis (MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artifact classification and cleaning, and global signal regression (GSR). Our results show that: (I) MP-PCA denoising substantially improves the temporal signal-to-noise ratio, (II) the pre-trained FIX classifier achieves a high accuracy in artifact classification, and (III) both independent component analysis (ICA) cleaning and GSR are essential steps in correcting for possible artifacts and minimizing the within-group variability in control animals while maintaining typical connectivity patterns. Reduced within-group variability also facilitates the exploration of potential between-group FC changes, as illustrated here in a rat model of sporadic Alzheimer's disease.
静息态功能磁共振成像（rs-fMRI）是一种广泛应用且功能强大的工具，用于研究功能连接性（FC）和脑部疾病。然而，FC分析可能会受到来自非神经源（如生理学）的随机和结构化噪声的严重影响。因此，首先降低热噪声，然后通过优化的数据处理方法从rs-fMRI信号中正确识别并去除非神经伪影至关重要。然而，现有的校正这些影响的工具是针对人类大脑开发的，不易应用于大鼠数据。因此，本研究的目的是建立一个数据处理流程，能够稳健地去除大鼠rs-fMRI数据中的随机和结构化噪声。它包括一种基于马尔琴科-帕斯图尔主成分分析（MP-PCA）方法的新型去噪方法、用于自动伪影分类和清理的FMRIB基于独立成分分析的Xnoiseifier（FIX）以及全局信号回归（GSR）。我们的结果表明：（I）MP-PCA去噪显著提高了时间信噪比，（II）预训练的FIX分类器在伪影分类中具有较高的准确率，（III）独立成分分析（ICA）清理和GSR都是校正可能的伪影以及在保持典型连接模式的同时最小化对照动物组内变异性的关键步骤。组内变异性的降低也有助于探索潜在的组间FC变化，如在这里的散发性阿尔茨海默病大鼠模型中所示。
