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deconvolution

fMRI data analyses are often directed to disentangling and understanding the neural processes that occur among brain regions.

Interactions in the brain are expressed, not at the level of hemodynamic responses, but at the neural level. Thus, an intermediate step that estimates the underlying neuronal activity is necessary for such analyses.

Given the nature of the fMRI BOLD signal, the appropriate approximation of the neuronal activity can be obtained by means of deconvolution with an assumed hemodynamic response (Gitelman et al. (2003)).

Simply put, deconvolution methods are capable of blindly estimating neuronal activity with no prior information on the timing of the BOLD events.

Referenced in

deconvolution

Given the nature of the fMRI BOLD signal, the appropriate approximation of the neuronal activity can be obtained by means of deconvolution with an assumed hemodynamic response (Gitelman et al. (2003)).

A Low Rank and Sparse Paradigm Free Mapping Algorithm for Deconvolution of fMRI Data

We propose a new approach (LR+MV-SPFM) for the spatio-temporal deconvolution of fMRI data that can simultaneously estimate global signal fluctuations and neuronal-related activity based on the low-rank plus sparse matrix decomposition method (Otazo et al. (2015)).

deconvolution

Simply put, deconvolution methods are capable of blindly estimating neuronal activity with no prior information on the timing of the BOLD events.

A Low Rank and Sparse Paradigm Free Mapping Algorithm for Deconvolution of fMRI Data

The performance of existing deconvolution approaches can be hampered considerably in presence of global, widespread signal changes due to head jerks, hardware artifacts, or prominent non-neuronal physiological events (e.g. deep breaths) (Power et al. (2017)).

deconvolution