CORSICA: correction of structured noise in fMRI by automatic identification of ICA components.

By Vincent Perlbarg, Lune P Bellec, Jean-Luc Anton, Mélanie Pélégrini-Issac, Julien Doyon, Habib Benali. 2007

212 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “It remains one of the most prolific methods used for fMRI data analysis Xu et al. (2009) McKeown and Sejnowski (1998) Smith et al. (2004) Svenséen et al. (2002) Stone et al. (2002) Perlbarg et al. (2007).”
Resting-state fMRI in the Human Connectome Project (2013, unknown): “Previous methods (De Martino et al., 2007; Perlbarg et al., 2007; Tohka et al., 2008) used a two-stage approach.”
Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers (2014, background): “Perlbarg et al. (2007) proposed an approach that characterises the activity of the voxels in certain regions of interest (ROIs) that are known a priori to correspond to noisy behaviour.”
Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data (2010, methodology): “Three recently reported methods for automated labeling of ICs (Perlbarg et al., 2007; Tohka et al., 2008; Sui et al., 2009) were validated in part by comparing the results of automated labeling with those derived from visual inspection.”
The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? (2008, unknown): “Other techniques have been developed that do not require physiological recordings (Beall and Lowe, 2007; Chuang and Chen, 2001; Perlbarg et al., 2007).”
Reliable Intrinsic Connectivity Networks: Test-Retest Evaluation Using ICA and Dual Regression Approach (2009, unknown): “ICA-based denoising is fully data-driven, automatic (Thomas et al., 2002; Perlbarg et al., 2007; Tohka et al., 2008) and relatively unaffected by different temporal sampling rates (DeLuca et al.”
Differentiating BOLD and Non-BOLD Signals in fMRI Time Series Using Multi-Echo EPI (2011, background): “Other studies have proposed de-noising resting state fMRI by removing those ICA components that do not correspond to atlases for “functional networks” (De Martino et al., 2007; Perlbarg et al., 2007).”
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data (2015, unknown): “Multiple methods have been developed to automatically identify noise components based on temporal and/or spatial features (Thomas et al., 2002; Kochiyama et al., 2005; De Martino et al., 2007; Perlbarg et al., 2007; Tohka et al., 2008; Kundu et al., 2012; Bhaganagarapu et al., 2013; Rummel et al., 2”
Hand classification of fMRI ICA noise components (2017, unknown): “Perlbarg et al. (2007) used manually defined regions of interest (ROIs) to define typical time courses of structured noise in fMRI data, which were used as regressors for the BOLD signals, while Rummel et al. (2013) proposed a fully automatic time course-based filtering procedure to detect…”
The role of physiological noise in resting-state functional connectivity (2012, unknown): “CORSICA is a technique that uses spatial independent component analysis (ICA) to identify and remove signal fluctuations that match spatially with patterns of known physiological noise (Perlbarg et al., 2007).”
Resting-state FMRI confounds and cleanup (2013, unknown): “CORSICA uses spatial ICA to identify and remove signal fluctuations that match known spatial patterns of physiological noise (Perlbarg et al., 2007).”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, background): “, 2014) or alternative ICA-based strategies (Thomas et al., 2002; Kochiyama et al., 2005; De Martino et al., 2007; Perlbarg et al., 2007; Tohka et al., 2008; Kundu et al., 2012; Rummel et al., 2013; Storti et al., 2013; Sochat et al., 2014).”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “Each dataset was corrected of inter-slice difference in acquisition time, rigid body motion, slow time drifts (high-pass filter with a 0.01 Hz cut-off) and physiological noise (Perlbarg et al., 2007).”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “In the latter cases, referring to “graphs”, which do not make explicit assumptions on the nature of the links but rather emphasize the aspect of mathematical modeling, seems to be more appropriate and can be used to improve the interdisciplinary exchange.”