Effects of model-based physiological noise correction on default mode network anti-correlations and correlations

By Catie Chang, Gary H. Glover. NeuroImage, 2009

517 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/477f52fbccefe39ad05c63fd589b17b7e533d2a5

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Function in the Human Connectome: Task-fMRI and Individual Differences in Behavior (2013, background): “, 2006), physiological noise modeling (Brooks et al., 2008; Chang and Glover, 2009; Glover et al., 2000), or motion scrubbing (Power et al.”
Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression (2012, background): “Numerous studies since Murphy et al. (2009) have focused on whether or not anticorrelations are solely an artifact of GSReg with conclusions pointing in both directions (Anderson et al., 2011b; Fox et al., 2009; Chai et al., 2012; Chang and Glover, 2009a).”
Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS) (2012, background): “Anticorrelations between regions with apparent opposing functional properties have also been observed (Chang and Glover, 2009; Fox et al., 2005; Fox et al., 2009; Fransson, 2005; Greicius et al., 2003) (Fig.”
Resting state fMRI: A review of methods and clinical applications (2012, unknown): “27 However, some believe that by shifting the distribution of correlations, whole-brain regression produces spurious negative correlations that have no physiologic significance.”
Anticorrelations in resting state networks without global signal regression (2011, unknown): “Future work in resting state connectivity should consider noise correction methods without global signal regression, such as the one presented in the present study, possibly in combination other physiological noise correction methods such as the ones described in Chang and Glover (2009).”
Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data (2010, unknown): “…effect, or rather artifi cially enhance ‘true’ negative relationships existing between cognitive control RSNs, remains contentious, as multiple studies have not reached identical conclusions on this issue (Chang and Glover, 2009; Fox et al., 2009; Weissenbacher et al., 2009; Van Dijk et al., 2010).”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, unknown): “As clarified by our findings for PCC-iFC in the present work, and previously noted (Chang and Glover, 2009; Fox et al., 2009), negative connectivity can”
The Global Signal in fMRI: Nuisance or Information? (2017, unknown): “This finding would be supported by later studies that also demonstrated the existence of anti-correlations without GSR (Chai et al., 2012; Chang and Glover, 2009; Liang et al., 2012; Wong et al., 2012).”
Decoding subject-driven cognitive states with whole-brain connectivity patterns. (2012, unknown): “Keywords: classification, functional connectivity, resting-state, subjectdriven cognition”
EEG correlates of time-varying BOLD functional connectivity (2013, unknown): “…low-frequency respiratory volume and heart rate, convolved with respiration and cardiac response functions, respectively, usingmethods described in (Chang and Glover, 2009); 6 affinemotion parameters; and the time series of ROIs in the white matter and cerebrospinal fluid, which were 3 mm-radius…”
Intrinsic Brain Connectivity in Fibromyalgia is Associated with Chronic Pain Intensity (2010, unknown): “Physiological data were collected simultaneously to the fMRI data, as cardio-respiratory fluctuations are known to influence fMRI intrinsic connectivity estimation within several brain networks (24,25).”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, unknown): “Additionally, we provide insights into the impact of nuisance signal correction approaches (Chang and Glover, 2009; Fox et al., 2009; Jo et al., 2010; Weissenbacher et al., 2009) (e.”
The Development of Human Amygdala Functional Connectivity at Rest from 4 to 23 Years: a cross-sectional study (2014, unknown): “…of modeling physiological noise that requires removal from the data, but whose sources (heart rate and respiration) were not possible to measure directly in this study or parse out with programs like PESTICA (designed to model adult physiological noise) (Chang and Glover, 2009; Fox et al., 2009).”