Sources and implications of whole-brain fMRI signals in humans
By Jonathan D. Power, Mark H. Plitt, Timothy O. Laumann, Alex Martin. NeuroImage, 2016
543 citations recorded by the source corpus.
Abstract Whole‐brain fMRI signals are a subject of intense interest: variance in the global fMRI signal (the spatial mean of all signals in the brain) indexes subject arousal, and psychiatric conditions such as schizophrenia and autism have been characterized by differences in the global fMRI signal. Further, vigorous debates exist on whether global signals ought to be removed from fMRI data. However, surprisingly little research has focused on the empirical properties of whole‐brain fMRI signals. Here we map the spatial and temporal properties of the global signal, individually, in 1000+ fMRI scans. Variance in the global fMRI signal is strongly linked to head motion, to hardware artifacts, and to respiratory patterns and their attendant physiologic changes. Many techniques used to prepare fMRI data for analysis fail to remove these uninteresting kinds of global signal fluctuations. Thus, many studies include, at the time of analysis, prominent global effects of yawns, breathing changes, and head motion, among other signals. Such artifacts will mimic dynamic neural activity and will spuriously alter signal covariance throughout the brain. Methods capable of isolating and removing global artifactual variance while preserving putative “neural” variance are needed; this paper adopts no position on the topic of global signal regression. HighlightsDemonstrates brain‐wide (global) fMRI signals, individually, in 1000+ scans from 8 sites.Global signals often reflect artifact caused by head motion, respiration, or hardware problems.Most existing fMRI denoising methods do not adequately remove global artifacts.Global artifacts mimic dynamic neural activity and modulate signal correlations.Studies reporting global fMRI signal effects must carefully account for artifact.
Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/6f098e83955e9343c577356dc84b3003b7035fb2
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In Cognitive Neuroscience
Citations in the corpus
- Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics (2018, unknown): “We used regression analysis to test whether the improved rs-FC fit of the heterogeneous model, relative to the homogenous model, may be driven by fitting of FC components attributable to non-neural confounding factors such as head motion, heart rate variations, and respiration (Power et al., 2017).”
- The Global Signal in fMRI: Nuisance or Information? (2017, unknown): “GSR has also been found to perform better than other state-of-theart correction approaches, such as those based on independent component analysis, for removing globally distributed motion-related artifacts (Burgess et al., 2016; Power et al., 2016).”
- Deconstructing multivariate decoding for the study of brain function (2017, background): “These include physiological noise such as head motion and noise fluctuations related to the cardiac / respiratory cycle, and separating those from neural sources of variability is difficult as demonstrated in the analysis of functional connectivity (Power et al., 2017).”