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1,248 claims from 785 papers are on the record. 45 have been checked so far; the other 1,203 have no check with a result yet.

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Status: Unchecked ×Keyword: global signal regression ×Clear all

11 claims from 7 papers

1. Neuroscience › Functional Brain Connectivity Studies

   Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks

   Whitfield‐Gabrieli and Nieto-Castañón · Brain Connectivity · 2012

   The authors present Conn, a toolbox for analysing functional connectivity MRI data using CompCor noise correction, with examples of use and interscan reliability estimates for its measures.

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   1. UncheckedUnlike global signal regression, the CompCor noise-reduction method does not remove the global signal, so negative correlations between brain regions can be interpreted.“Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal.”

2. Neuroscience › Functional Brain Connectivity Studies

   Methods to detect, characterize, and remove motion artifact in resting state fMRI

   Power, Mitra, Laumann, Snyder, Schlaggar and Petersen · NeuroImage · 2013

   The paper examines how head motion alters fMRI signal and connectivity correlations, tests ways to remove it, and shows that censoring high-motion volumes reduces motion-related group differences to chance levels.

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   1. UncheckedSignal changes caused by head motion, during and after movement, raise resting-state fMRI correlations in a way that depends on the distance between brain regions.“These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner.”

3. Neuroscience › Functional Brain Connectivity Studies

   The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

   Murphy, Birn, Handwerker, Jones and Bandettini · NeuroImage · 2008

   The paper asks whether removing the global fMRI signal creates apparent anti-correlated resting-state networks, using mathematics, simulations, a breath-holding and visual task experiment, and resting-state data.

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   1. UncheckedAfter global signal regression, a seed voxel's correlations with all other brain voxels must sum to a negative value.“Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value.”

4. Neuroscience › Functional Brain Connectivity Studies

   A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics

   Yan, Cheung, Kelly et al. · NeuroImage · 2013

   The study mapped how small head movements affect the resting-state fMRI signal, tested motion-correction methods, and examined how motion affects the test-retest reliability of many brain-connectivity measures.

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   1. Unchecked“Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level.”
   2. UncheckedHead motion generally lowered test-retest reliability of resting-state fMRI measures, except those based on frequency, notably ALFF.“Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics - particularly, amplitude of low frequency fluctuations (ALFF).”

5. Neuroscience › Functional Brain Connectivity Studies

   Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity

   Ćirić, Wolf, Power et al. · NeuroImage · 2017

   The paper compared 14 participant-level confound regression methods in 393 youths on four benchmarks and found trade-offs, so different methods may suit different scientific goals.

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   1. UncheckedIn 393 youths, denoising methods using global signal regression minimised the link between motion and connectivity but left distance-dependent artifact.“First, methods that include global signal regression minimize the relationship between connectivity and motion, but result in distance-dependent artifact.”
   2. UncheckedCensoring methods reduced both motion artifact and its distance-dependence in connectivity, but used up additional degrees of freedom in the data.“In contrast, censoring methods mitigate both motion artifact and distance-dependence, but use additional degrees of freedom.”
   3. UncheckedConfound-regression methods that remove motion artefact less well also fail to reveal the brain's modular network structure in functional connectivity data.“Importantly, less effective de-noising methods are also unable to identify modular network structure in the connectome.”

6. Neuroscience › Functional Brain Connectivity Studies

   Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior

   Li, Kong, Liégeois et al. · NeuroImage · 2019

   The study tested whether global signal regression, a debated fMRI clean-up step, strengthens links between resting-state brain connectivity and behaviour, finding that it did for most measures in young healthy adults.

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   1. Unchecked“By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR.”
   2. UncheckedGlobal signal regression raised behavioural prediction accuracy from brain connectivity by an average of 64% in the GSP data and 12% in the HCP data.“GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively.”

7. Neuroscience › Functional Brain Connectivity Studies

   Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms

   Cao, McEwen, Forsyth et al. · Cerebral Cortex · 2018

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   1. Unchecked“Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration.”