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- W2890300986 abstract "This work introduces a novel framework for the deconvolution of the BOLD signal in multi-echo functional MRI (ME-fMRI) data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Building upon a physical model of the ME-fMRI signal and using a sparsity-promoting regularized least squares estimator, this algorithm obtains time-varying maps of the changes in the transverse relaxation rate (( R_{2}^{*} )) and the net magnetization (( S_{0} )) of the signal at the subject level without prior knowledge of the timing of the individual BOLD events. Our results with experimental data demonstrate that the maps of ( R_{2}^{*} ) changes obtained with ME-SPFM at the times of the stimulus trials closely resemble the maps obtained with standard model-based methods that are aware of the onsets and durations of the experimental events, and considerably improves the accuracy of the deconvolution compared with the SPFM algorithm developed for single-echo fMRI. Furthermore, this method yields estimates of ( R_{2}^{*} ) changes in physiologically interpretable units (s−1), which is a step towards deciphering the dynamic nature of brain activity in a pseudo-quantitative manner in naturalistic paradigms, resting-state or clinical applications with unknown event-timing." @default.
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- W2890300986 date "2018-01-01" @default.
- W2890300986 modified "2023-10-18" @default.
- W2890300986 title "Quantitative Deconvolution of fMRI Data with Multi-echo Sparse Paradigm Free Mapping" @default.
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- W2890300986 doi "https://doi.org/10.1007/978-3-030-00931-1_36" @default.
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