Feng Tengfei, Baqapuri Halim Ibrahim, Zweerings Jana, Li Huanjie, Cong Fengyu, Mathiak Klaus
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, Aachen 52074, Germany; School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian 116024, China.
Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Pauwelsstr. 30, Aachen 52074, Germany; Mental Health and Neuroscience Research Institute (MHeNs), Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht 6211KL, the Netherlands.
Neuroimage. 2025 May 1;311:121199. doi: 10.1016/j.neuroimage.2025.121199. Epub 2025 Apr 10.
Multi-echo echo-planar imaging (ME-EPI) acquires images at multiple echo times (TEs), enabling the differentiation of BOLD and non-BOLD fluctuations through TE-dependent analysis of transverse relaxation time and initial intensity. Decomposing ME-EPI in tensor space is a promising approach to characterize the distribution of changes across TEs (TE patterns) directly and aid the classification of components by providing information from an additional domain. In this study, the tensorial extension of independent component analysis (tensor-ICA) is used to characterize the TE patterns of neural and non-neural components in ME-EPI data. With the constraints of independent spatial maps, an ME-EPI dataset was decomposed into spatial, temporal, and TE domains to understand the TE patterns of noise or signal-related independent components. Our analysis revealed three distinct groups of components based on their TE patterns. Motion-related and other non-BOLD origin components followed decreased TE patterns. While the long-TE-peak components showed a large overlay on grey matter and signal patterns, the components that peaked at short TEs reflected noise that may be related to the vascular system, respiration, or cardiac pulsation, amongst others. Accordingly, removing short-TE peak components as part of a denoising strategy significantly improved quality control metrics and revealed clearer, more interpretable activation patterns compared to non-denoised data. To our knowledge, this work is the first application of decomposing ME-EPI in a tensor way. Our findings demonstrate that tensor-ICA is efficient in decomposing ME-EPI and characterizing the neural and non-neural TE patterns aiding in classifying components which is important for denoising fMRI data.
多回波平面成像（ME-EPI）在多个回波时间（TE）采集图像，通过对横向弛豫时间和初始强度进行基于TE的分析，能够区分血氧水平依赖（BOLD）波动和非BOLD波动。在张量空间中分解ME-EPI是一种很有前景的方法，可直接表征TE之间变化的分布（TE模式），并通过提供来自另一个域的信息辅助成分分类。在本研究中，独立成分分析的张量扩展（张量独立成分分析）用于表征ME-EPI数据中神经和非神经成分的TE模式。在独立空间图的约束下，将一个ME-EPI数据集分解到空间、时间和TE域，以了解噪声或信号相关独立成分的TE模式。我们的分析基于TE模式揭示了三组不同的成分。与运动相关的以及其他非BOLD起源的成分呈现下降的TE模式。虽然长TE峰值成分在灰质和信号模式上有很大重叠，但在短TE时达到峰值的成分反映的噪声可能与血管系统、呼吸或心脏搏动等有关。因此，作为去噪策略的一部分去除短TE峰值成分，与未去噪数据相比，显著改善了质量控制指标，并揭示了更清晰、更具可解释性的激活模式。据我们所知，这项工作是首次以张量方式分解ME-EPI的应用。我们的研究结果表明，张量独立成分分析在分解ME-EPI和表征神经及非神经TE模式方面是有效的，有助于成分分类，这对功能磁共振成像（fMRI）数据去噪很重要。
