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Characterizing the distribution of neural and non-neural components in multi-echo EPI data across echo times based on tensor-ICA
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Characterizing the distribution of neural and non-neural components in multi-echo EPI data across echo times based on tensor-ICA
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Characterizing the distribution of neural and non-neural components in multi-echo EPI data across echo times based on tensor-ICA
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Journal Article
Characterizing the distribution of neural and non-neural components in multi-echo EPI data across echo times based on tensor-ICA
2025
Overview
•Tensor ICA can decompose multi-echo EPI data in time, space, and echo time domains.•Distribution across TEs separate BOLD and non-BOLD components of tensor ICA.•Elimination of the noise-related components enhances quality and activation patterns.
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.
Publisher
Elsevier Inc,Elsevier Limited,Elsevier
