A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping
Caballero Gaudes, César
Bandettini, Peter A.
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César Caballero-Gaudes, Stefano Moia, Puja Panwar, Peter A. Bandettini, Javier Gonzalez-Castillo, A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping, NeuroImage, Volume 202, 2019, 116081, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2019.116081.
This work introduces a novel algorithm for deconvolution of the BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the apparent transverse relaxation (⁎) without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of ⁎ changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of ⁎ having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map ⁎ associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.