Noise removal is a critical step to recover the signal of interest from resting-state fMRI data. Several pre-processing pipelines have been proposed mainly based on nuisance regression or independent component analysis. The aim of this work was to compare the ability of different existing cleaning pipelines in removing spurious non-BOLD signals when applied to a dataset of healthy controls and epilepsy patients. Increased tSNR and power spectral density in the resting-state frequency range (0.01-0.1 Hz) were found for all pre-processing pipelines with respect to the minimally pre-processed data. This suggests a positive gain in terms of temporal properties when optimal cleaning procedures are applied to fMRI data.
A Comparison of Pre-Processing Pipelines for the Analysis of Resting-State Data in Epilepsy
B. De Blasi
;PASETTO, LUCA
;S. Storti
;I. Boscolo Galazzo
;G. Menegaz
2018-01-01
Abstract
Noise removal is a critical step to recover the signal of interest from resting-state fMRI data. Several pre-processing pipelines have been proposed mainly based on nuisance regression or independent component analysis. The aim of this work was to compare the ability of different existing cleaning pipelines in removing spurious non-BOLD signals when applied to a dataset of healthy controls and epilepsy patients. Increased tSNR and power spectral density in the resting-state frequency range (0.01-0.1 Hz) were found for all pre-processing pipelines with respect to the minimally pre-processed data. This suggests a positive gain in terms of temporal properties when optimal cleaning procedures are applied to fMRI data.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
