CORSICA: correction of structured noise in fMRI by automatic identification of ICA components
Magnetic Resonance Imaging
When applied to functional Magnetic Resonance Imaging (fMRI) data, spatial Independent Component Analysis (sICA), a data-driven technique that adresses the blind source separation problem, seems able to extract components specifically related to physiological noise and brain movements. These components should be removed from the data to achieve structured-noise reduction and improve any subsequent detection and analysis of signal fluctuations related to neural activity. We propose a new
... c method called CORSICA to identify the components related to physiological noise, using prior information on the spatial localization of the main physiological fluctuations in fMRI data. As opposed to existing spectral priors, which may be subject to aliasing effects for long-TR datasets (typically acquired with TR > 1 s), such spatial priors can be applied to fMRI data regardless of the TR of the acquisitions. By comparing the proposed automatic selection to a manual selection performed visually by a human operator, we first show that CORSICA is able to identify the noise-related components for long-TR data with a high sensitivity and a specificity of 1. On short-TR datasets, we validate that the proposed method of noise reduction allows a substantial improvement of the signal-to-noise ratio evaluated at the cardiac and respiratory frequencies, even in the gray matter, while preserving the main fluctuations related to neural activity.