Anticorrelations in resting state networks without global signal regression

By Xiaoqian J. Chai, Alfonso Nieto-Castanon, Dost Öngür, Susan L. Whitfield-Gabrieli. NeuroImage, 2011

1,116 citations recorded by the source corpus.

Anticorrelated relationships in spontaneous signal fluctuation have been previously observed in resting-state functional magnetic resonance imaging (fMRI). In particular, it was proposed that there exists two systems in the brain that are intrinsically organized into anticorrelated networks, the default mode network, which usually exhibits task-related deactivations, and the task-positive network, which usually exhibits task-related activations during tasks that demands external attention. However, it is currently under debate whether the anticorrelations observed in resting state fMRI were valid or were instead artificially introduced by global signal regression, a common preprocessing technique to remove physiological and other noise in resting-state fMRI signal. We examined positive and negative correlations in resting-state connectivity using two different preprocessing methods: a component base noise reduction method (CompCor, Behzadi et al., 2007), in which principal components from noise regions-of-interest were removed, and the global signal regression method. Robust anticorrelations between a default mode network seed region in the medial prefrontal cortex and regions of the task-positive network were observed under both methods. Specificity of the anticorrelations was similar between the two methods. Specificity and sensitivity for positive correlations were higher under CompCor compared to the global regression method. Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/eea43fbe1b2059f20249a4f9893a71ebb812ed2e

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, neutral): “These non-neuronal confounding factors were regressed out from the average BOLD TS for each AAL region via the least-squares error minimization scheme (Chai et al., 2012; Kim et al., 2015; Kim et al., 2015; Song et al., 2011).”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “Finally, brain nodes are typically assigned to sensors or sources, but they can also be assigned to ICA components [51].”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “Finally, the inclusion of global signal regression (GSR) (Macey et al., 2004) in confound regression models remains a source of controversy (Fox et al., 2009; Murphy et al., 2009; Chai et al., 2012; Saad et al., 2012; Yan et al., 2013b; Murphy, in press).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “Global signal regression is more controversial due to potential introduction of negative correlations and potential to artifactually impact inter-individual differences in connectivity (Chai et al., 2012; Fox et al., 2009; Murphy et al., 2009; Saad et al., 2012).”
Reduction of motion-related artifacts in resting state fMRI using aCompCor (2014, methodology): “…impact of motion on rs-FC, we used a specificity metric based on the anatomy of known functional networks to determine whether connectivity is high for regions within the same network and low for regions between different networks that are not expected to be strongly connected (Chai et al., 2012).”
Dynamic functional connectivity of the default mode network tracks daydreaming (2014, background): “These images were then thresholded to retain 198 cm3 and 20 cm3 of voxels with the highest probability of beingWMand CSF, respectively (Chai et al., 2012).”
Decreased segregation of brain systems across the healthy adult lifespan (2014, background): “Although it is possible to include negative ties in a network analysis (73), because of the present ambiguity regarding the meaning of negative correlations (63, 74, 75), negative zvalues were excluded from the data matrix.”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “Other than mean signal regression, the CompCor method (Behzadi et al., 2007), which was reported to be advantageous (Chai et al., 2012), is integrated.”
Resting-state FMRI confounds and cleanup (2013, positive): “Many studies have tried to determine whether these anticorrelations are a true reflection of functional connectivity or an artefact of the GSReg technique (Anderson et al., 2011; Chai et al., 2012; Chang and Glover, 2009a; Fox et al., 2009; Fransson, 2005).”