Mapping Sources of Correlation in Resting State FMRI, with Artifact Detection and Removal

By Hang Joon Jo, Ziad S. Saad, W. Kyle Simmons, Lydia A Milbury, Robert W. Cox. NeuroImage, 2010

522 citations recorded by the source corpus.

Many components of resting-state (RS) FMRI show non-random structure that has little to do with neural connectivity but can covary over multiple brain structures. Some of these signals originate in physiology and others are hardware-related. One artifact discussed herein may be caused by defects in the receive coil array or the RF amplifiers powering it. During a scan, this artifact results in small image intensity shifts in parts of the brain imaged by the affected array components. These shifts introduce artifactual correlations in RS time series on the spatial scale of the coil’s sensitivity profile, and can markedly bias RS connectivity results. We show that such a transient artifact can be substantially removed from RS time series by using locally formed regressors from white matter tissue. This is particularly important in arrays with larger numbers of coils, which may generate smaller artifact zones. In such a case, brain-wide average noise estimates would fail to capture the artifact. We also examine the anatomical structure of artifactual variance in RS FMRI time series, by identifying sources that contribute to these signals and where in the brain are they manifested. We consider current methods for reducing confounding sources (or noises) and their effects on connectivity maps, and offer an improved approach (ANATICOR) that can also reduce hardware artifacts. The methods described herein are currently available with AFNI, in addition to tools for rapid, interactive generation of seed based correlation maps at single-subject and group levels.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b46816549abd7d88cb84fe59cf1228f712e2761f

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In Cognitive Neuroscience

Citations in the corpus

Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “There is ongoing debate regarding the adequacy of these techniques (Jo et al., 2010; Power et al., 2016), and more work may be warranted to determine optimal ways to account for age-related physiological confounds.”
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].”
Global connectivity of prefrontal cortex predicts cognitive control and intelligence (2012, methodology): “These resting state functional images were further preprocessed with the following steps: (1) slice-time correction, (2) removal of first 5 images from each run to reach steady state, (3) rigid body motion correction, (4) bandpass filtering (0.009 f(t) 0.08), (5) removal of a set of nuisance regress”
AFNI: What a long strange trip it’s been (2011, background): “My purpose in developing these interactive computational tools is to allow researchers to explore the structure of their data, to aid in forming hypotheses , and to help identify problems such as scanner artifacts (Jo et al., 2010).”
The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity (2013, background): “…2012a), correction for ringing artifacts (Carp, in press) and physiological noise (Birn et al., 2008); optimal statistical methods for modeling noise (Behzadi et al., 2007); time series prewhitening (Christova et al., 2011); and controlling for local variation in the MR signal (Jo et al., 2010).”
The role of physiological noise in resting-state functional connectivity (2012, unknown): “Physiological fluctuations can also be reduced by including nuisance regressors derived from the data itself, for example from regions such as CSF, white matter, or edges of the brain that are unlikely to show neuronal-induced BOLD signal fluctuations (Birn et al., 2009; Jo et al., 2010; Weissenbach”
Methods for cleaning the BOLD fMRI signal (2016, unknown): “Similarly, tissue-based nuisance regressors obtained from CSF of the lateral ventricles, WM or extracerebral soft tissues can also account for cardiac and respiratory-related fluctuations to some extent if these fluctuations are dominant components of the signal in these regions (e.g. Anderson et al”
Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth (2012, background): “Improvements in modeling the effect of motion on the BOLD signal remain an active area of research (Tohka et al., 2008; Jo et al., 2010).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, methodology): “CCS functional preprocessing steps include: 5) EPI volumes from the beginning of each scan were discarded to allow for signal equilibration (numbers of volumes discarded for R-fMRI_std_2000, R-fMRI_std_2500, R-fMRI_mx_1400 and R-fMRI_mx_645 are 0, 2, 4, and 8, respectively, corresponding to about 5 ”
Sources and implications of whole-brain fMRI signals in humans (2016, background): “In fact, in existing publications, the variance in timeseries accounted for by respiratory variables is often a only few percent of the total variance (e.g., (Bianciardi et al., 2009; Jo et al., 2010; Shmueli et al., 2007)).”
Methods to detect, characterize, and remove motion artifact in resting state fMRI (2013, result): “These results are consistent with previous analyses wherein WM and CSF signals explained little variance in the gray matter (Jo et al., 2010; Weissenbacher et al., 2009).”
Data Quality Influences Observed Links Between Functional Connectivity and Behavior (2017, methodology): “Numerous preprocessing and data cleaning approaches have been developed to reduce the impact of imaging artifact (e.g., Jo et al. 2010; Shirer et al. 2015).”
Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression (2012, unknown): “…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., 2004).”
Resting-state FMRI confounds and cleanup (2013, unknown): “Recently, it has been shown that averaged over grey matter, the cardiac and respiratory regressors modelled in RETROICOR account for only a small amount of time course variance, ~5% (Bianciardi et al., 2009; Jo et al., 2010).”
The Influence of Head Motion on Intrinsic Functional Connectivity MRI (2011, unknown): “Other strategies to address motion might include approaches that (1) improve traditional nuisance regression techniques (for a recent example see Jo et al., 2010), (2) prospectively correct for motion during data acquisition (Thesen et al., 2000; Ward et al., 2000), (3) post-hoc eliminate images or…”
Recent progress and outstanding issues in motion correction in resting state fMRI (2014, unknown): “Because local motion effects (type 1 and type 3 disruptions) presumably cause much of the distance-dependent nature of motion artifact, ANATICOR, by deriving and removing local nuisance signals, is in principle well-positioned to address this aspect of motion artifact.”
Multi‐echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals (2017, background): “These artifacts relate to subject head motion, cardiac and respiratory effects, and hardware (Glover et al., 2000; Jo et al., 2010).”