Neural basis of global resting-state fMRI activity

By M. Schölvinck, A. Maier, F. Ye, Jeff H. Duyn, David A. Leopold. Proceedings of the National Academy of Sciences of the United States of America, 2010

929 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9d8012ff3e450e7b35ce39e876dc898a2811921e

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Citations in the corpus

Anticorrelations in resting state networks without global signal regression (2011, neutral): “One possible explanation for the higher positive correlations under aCompCor is that the global signal may contain neural signal (Scholvinck et al., 2010), and therefore removing the global signal will reduce the estimated effects as well as the power to detect positive correlations.”
The Human Thalamus Is an Integrative Hub for Functional Brain Networks (2016, background): “Functional connectivity between two brain regions likely represents the phase-locking of the two regions’ low-frequency oscillations or coherent activity of high-frequency neuronal activity (Scholvinck et al., 2010; Wang et al., 2012).”
Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics (2018, background): “, 2018), including invasive recordings in animal models (Schölvinck et al., 2010; Mateo et al., 2017).”
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, background): “However, negative correlations may also be introduced by global signal removal, a preprocessing step that is currently controversial (Fox et al., 2009; Murphy et al., 2009; Weissenbacher et al., 2009; Schölvinck et al., 2010).”
Functional connectivity hubs in the human brain (2011, positive): “However, recent studies suggests that much of the global signal reflects neuronal activity (Schölvinck et al., 2010).”
Methods for cleaning the BOLD fMRI signal (2016, neutral): “If relevant, results should be shown with and without GSR.”
Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms (2017, background): “…local increases in the power of neural activity, not only in the gamma-frequency band (Logothetis et al., 2001, Leopold et al., 2003, Nir et al., 2007), but also at lower frequencies (He et al., 2008, Scholvinck et al., 2010, Magri et al., 2012, Tagliazucchi et al., 2012b, Keller et al., 2013).”
Resting-state FMRI confounds and cleanup (2013, positive): “Furthermore, it has been demonstrated that local field potentials from a single cortical site inmonkeys at rest exhibit widespread positive correlations with BOLD signals across the entire brain (Scholvinck et al., 2010).”
Reduction of motion-related artifacts in resting state fMRI using aCompCor (2014, background): “…FC metrics compared to regression of explicit models of motioninduced signal change, concerns have been raised about the potential of GSR to remove signal of neuronal origin (Schölvinck et al., 2010) and to introduce additional confounds into rs fMRI data (Murphy et al., 2009; Saad et al., 2012).”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “…“high gamma”; Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001; K. J. Miller, Weaver, & Ojemann, 2009; Mukamel et al., 2005; Nir et al., 2007; Scholvinck, Maier, Ye, Duyn, & Leopold, 2010), and patterns of FC estimated from ﬂuctuations in high-frequency broadband power reliably exhibit…”
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.”