A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

By Yashar Behzadi, Khaled Restom, Joy Liau, Thomas T. Liu. NeuroImage, 2007

4,328 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/694509dccd2831bc7afc13e510ad6a081726e354

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Dynamic functional connectivity of the default mode network tracks daydreaming (2014, methodology): “To remove physiological and scanner-related noise, aCompCor (Behzadi et al., 2007) was applied.”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “RETROICOR, RVHRCOR, PESTICA, CompCor, ME-ICA, censoring/ “scrubbing” (Beall and Lowe, 2007; Behzadi et al., 2007; Chang et al., 2009; Glover et al., 2000; Kundu et al., 2012; Power et al., 2012)), residual noise inevitably remains, and dynamic studies of resting statewould greatly benefit from a dee”
FMRIPrep: a robust preprocessing pipeline for functional MRI (2018, neutral): “For instance, dimensionality reduction methods based on principal components analysis (PCA) or independent components analysis (ICA), such as component-based noise correction (CompCor 10 ) or automatic removal of motion artifacts (ICA-AROMA 11 ).”
Abnormal intrinsic brain functional network dynamics in Parkinson’s disease (2017, methodology): “In addition, nuisance signals including six motion parameters, as well as signals from white matter and CSF voxels were regressed out using a CompCor, componentbased noise correction method (Behzadi et al., 2007) for correcting head motion and physiological noise.”
Alteration of Brain Default Network in Subacute Phase of Injury in Concussed Individuals: Resting-state fMRI study (2011, positive): “White matter, cerebrospinal fluid (CSF), and physiological noise source reduction were taken as confounds, following the implemented CompCor strategy (Behzadi et al., 2007).”
A Correspondence between Individual Differences in the Brain's Intrinsic Functional Architecture and the Content and Form of Self-Generated Thoughts (2014, methodology): “Principal components of physiological noise were estimated using the CompCor strategy [56]: time series from WM and CSF masks as well as voxels of highest variance were used to extract two sets of principal components (also known as aCompCor and tCompCor).”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, background): “While some work has focused on parcellating the brain into a small number of (less than twenty) functional systems (van den Heuvel et al. 2008; Bellec et al. 2010; Power et al. 2011; Lee et al. 2012; Zuo et al. 2012; Hacker et al. 2013), others have focused on finer subdivisions (Cohen et al.”
Brains and algorithms partially converge in natural language processing (2022, methodology): “The resulting BOLD times series were detrended and de-confounded from 18 variables (the six estimated head-motion parameters ( trans x,y,z , rot x,y,z ) and the ﬁ rst six noise components calculated using anatomical CompCorr 90 and six DCT-basis regressors using nilearn ’ s clean_img pipeline and ot”
The Human Thalamus Is an Integrative Hub for Functional Brain Networks (2016, methodology): “WM and CSF signals were regressed using the CompCor approach with five components (Behzadi et al., 2007).”
Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression (2012, neutral): “…of interest, such as motion estimates, or decompositions of signals aggregated over regions not exhibiting BOLD ﬂuctuations of interest (Beall, 2010; Behzadi et al., 2007; Birn et al., 2006, 2008; Chang and Glover, 2009b; Glover et al., 2000; Jo et al., 2010; Shmueli et al., 2007; Wise et al.,…”
Effective learning is accompanied by high-dimensional and efficient representations of neural activity (2017, methodology): “removed using the anatomical CompCor method (aCompCor) [31]; and (iii) an estimate of a local source of noise, estimated by averaging signals derived from the white matter region located within a 15 mm radius from each voxel, using the ANATICOR method [32].”
Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging (2015, methodology): “Nuisance signal regression utilized COMPCOR (Behzadi et al., 2007), followed by spatial smoothing (Gaussian kernel 6 mm FWHM) and temporal filtering (0.”
The Neural Architecture of the Language Comprehension Network: Converging Evidence from Lesion and Connectivity Analyses (2011, methodology): “Confounding signals were estimated from the WM and CSF segments using the CompCor method (Behzadi et al., 2007).”
An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data (2012, methodology): “In particular, approaches such as fully exploratory network ICA (FENICA; Schöpf et al., 2010, 2011) and CompCor (Behzadi et al., 2007; Chai et al., 2011) may help gain traction towards this goal in the future.”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “Somewhat to our surprise, benchmark results for aCompCor (Behzadi et al., 2007; Muschelli et al., 2014) were most similar to models that included GSR.”