Noise Reduction in BOLD-Based fMRI Using Component Analysis

By Christopher G. Thomas, Richard A. Harshman, Ravi S. Menon. NeuroImage, 2002

309 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fad6c07eb061998c6a4def0c8519ced93fbe864f

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

In Signal Processing

Citations in the corpus

Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, methodology): “, 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).”
Hand classification of fMRI ICA noise components (2017, unknown): “Some methods, for example, classify ICs using task-paradigm timing information (Calhoun et al., 2005; Kochiyama et al., 2005; Thomas et al., 2002).”
CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. (2007, methodology): “[14] was used spatial independent component analysis (sICA), which has a great potential for identifying physiological noise patterns in fMRI data sets with a great flexibility [15].”
Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters (2005, methodology): “Several methods have utilized physiological monitoring of the cardiac and respiratory cycle to try to reduce the physiological signal clutter from the fMRI time-course (Glover et al., 2000; Le et al., 1996; Pfeuffer et al., 2002; Thomas et al., 2002).”
Linear discriminant analysis: A detailed tutorial (2017, unknown): “This type of data reduction is suitable for many applications such as visualization [2,40], and noise removal [70].”
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”
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI (2007, methodology): “However, the utility of component-based methods has been limited to BOLD studies with sampling times short enough to clearly differentiate cardiac and respiratory elements from evoked responses (Thomas et al., 2002), in”
Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data (2010, methodology): “These approaches can be divided according to 1) whether ICA is performed on individual fMRI data runs (McKeown, 2000; Calhoun et al., 2001a; Thomas et al., 2002; Moritz et al., 2003; Kochiyama et al., 2005; McKeown et al., 2005; Greicius et al., 2007; Perlbarg et al., 2007; Tohka et al., 2008) or…”
Methods for cleaning the BOLD fMRI signal (2016, unknown): “Defining multiple spatially uncorrelated nuisance regressors based on the principal component analysis (PCA) decomposition of voxels where no BOLD fMRI signals of neuronal origin are expected to originate is another popular approach account for the spatial and temporal variability of the noise sourc”
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.”
Non-white noise in fMRI: Does modelling have an impact? (2006, unknown): “, 2004, p795-796) or (Thomas et al., 2002; Li et al., 2000) there is in general no single frequency band into which the aliasing happens.”
Differentiating BOLD and Non-BOLD Signals in fMRI Time Series Using Multi-Echo EPI (2011, background): “Prior studies have denoised task-based fMRI by removing ICA components that have poor correlation to task reference functions (Thomas et al., 2002).”