We propose a novel deconvolution approach to overcome the detrimental effect of global signal fluctuations on the estimates of neuronal-related activity from fMRI data to blindly map single-trial BOLD events without prior information of their timings. We demonstrate that the low-rank and multivariate SPFM algorithm can estimate global signals related to motion in our task, while estimating neuronal-related activity with high fidelity, and benchmark it against sparsity-promoting deconvolution approaches and conventional GLM analysis. This method allows exploring the brain’s functional dynamics during task, naturalistic and resting state paradigms, being less affected by motion and physiologically related fluctuations.
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