A Low Rank and Sparse Paradigm Free Mapping Algorithm For Deconvolution of FMRI Data

Current deconvolution algorithms for functional magnetic resonance imaging (fMRI) data are hindered by widespread signal changes arising from motion or physiological processes (e.g. deep breaths) that can be interpreted incorrectly as neuronal-related hemodynamic events. This work proposes a novel deconvolution approach that simultaneously estimates global signal fluctuations and neuronal-related activity with no prior information about the timings of the blood oxygenation level-dependent (BOLD) events by means of a low rank plus sparse decomposition algorithm. The performance of the proposed method is evaluated on simulated and experimental fMRI data, and compared with state-of-the-art sparsity-based deconvolution approaches and with a conventional analysis that is aware of the temporal model of the neuronal-related activity. We demonstrate that the novel low-rank and sparse paradigm free mapping algorithm can estimate global signal fluctuations related to motion in our task, while estimating the neuronal-related activity with high fidelity.