Resting-state FMRI confounds and cleanup

By Kevin Murphy, Rasmus M. Birn, Peter A. Bandettini. NeuroImage, 2013

658 citations recorded by the source corpus.

The goal of resting-state functional magnetic resonance imaging (FMRI) is to investigate the brain’s functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain “at rest” as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of FMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state FMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO2 concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state FMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state FMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9b80419559c86cf1925ff2b9994f34acad99d082

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Citations in the corpus

Prospective Motion Correction in Functional MRI (2016, background): “In resting-state fMRI, the microscopic sub-voxel movements are a source of large concern (Murphy et al., 2013), introducing potential bias in group”
Methods for cleaning the BOLD fMRI signal (2016, positive): “These time courses can then be regressed out from the fMRI signal with the purpose of minimizing intrinsic fluctuations associated with cerebral autoregulation and changes in blood pressure (Murphy et al., 2013).”
Hand classification of fMRI ICA noise components (2017, positive): “3 Hz, respectively) often becomes aliased into this low-frequency range (Lowe et al., 1998; Murphy et al., 2013).”
Building a Science of Individual Differences from fMRI. (2016, methodology): “There are a number of sources of within-subject, inter-session variance that are well-known, and which fMRI researchers strive to eliminate at acquisition time and/or correct for during preprocessing of the data [84,85].”
Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging (2015, methodology): “…approach that would include the use of optimized MRIacquisition sequences to decrease the effects of headmovement on signal (Bright and Murphy, 2013; Kundu et al., 2013, 2015) and advanced post-processing techniques for motion correction (Murphy et al., 2013; Yan et al., 2013; Pruim et al., 2015).”
Data Quality Influences Observed Links Between Functional Connectivity and Behavior (2017, methodology): “Techniques have been developed to reduce cardio-pulmonary pulsation artifacts in R-fMRI data using pulse oximeter and respiratory belt records (Chang and Glover 2009; Murphy et al. 2013).”
Sources and implications of whole-brain fMRI signals in humans (2016, background): “It is thus known that motion and respiration (among other potential factors, see (Murphy et al., 2013)) can cause potent, brain-wide modulation of fMRI signals.”
Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, background): “, 2017), but there is still no consensus approach for rs-fMRI preprocessing (Murphy et al., 2013; Geerligs et al., 2017; Murphy and Fox, 2017).”
Ten simple rules for predictive modeling of individual differences in neuroimaging (2019, background): “Additionally, state-of-the-art preprocessing strategies can remove many confound and nuisance variables that cannot be accounted for in data collection, such as physiological and head motion artifacts (Murphy et al., 2013; Power et al., 2012; Siegel et al., 2017; Tagliazucchi and Laufs, 2014).”