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. 2017 Feb 1;146:609-625.
doi: 10.1016/j.neuroimage.2016.09.038. Epub 2016 Oct 15.

Sources and Implications of Whole-Brain fMRI Signals in Humans

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Free PMC article

Sources and Implications of Whole-Brain fMRI Signals in Humans

Jonathan D Power et al. Neuroimage. .
Free PMC article

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.

Trial registration: ClinicalTrials.gov NCT01031407.

Conflict of interest statement

The authors have no conflicts of interest to declare with regard to this work.

Figures

Figure 1
Figure 1. All voxel signals in a single subject
At top a head motion (FD) trace is shown to establish when artifactual signals are expected. At bottom right are compartment masks. In the middle panels, signals from all in-brain voxels are shown, organized by mask. The traces are z-scored so that differences in signal magnitudes do not obscure signal (dis)similarities. Over half of all white matter voxels are in the ero0-2 erosion mask (lightest green) and signals at these white matter voxels are highly correlated with cortical gray matter signals (see red arrows), especially when compared with the signals deeper in the white matter (ero4, dark green). At bottom, the table shows correlations in each cohort of mean cortical ribbon signals and mean signals found in the various white matter masks.
Figure 2
Figure 2. Kinds of global signals
The top row shows subjects with very little motion and unobtrusive global signal fluctuations. The second row shows a common signal pattern: prominent global signal fluctuations mostly in the absence of motion. The third row shows a common signal pattern: motion followed by a prominent signal fluctuation. The fourth row shows other signals or less common versions of signals. The signal in NIH38 (purple arrow) is likely due to a head coil malfunction. The signal in ABIDE32 (orange arrow) is due to a large motion. The very low frequency signal modulations in GSP32 are likely due to respiratory artifact. The white bands in RP33 are associated with large motions.
Figure 3
Figure 3. Spatial distribution of global signals
For several scans of Figure 2, maps are shown of each voxel’s correlation with the global signal (GSCORR). The first 3 subjects exhibit minimal fluctuations, the next 3 exhibit obvious fluctuations, the next 3 exhibit obvious fluctuations and head motion, and the final maps show examples of global signal distributions that reflect artifact.
Figure 4
Figure 4. Spatial distribution of global signals in each cohort
At top, an atlas image and the WU modules reported in [Power, 2011]. At middle, the median GSCORR map across each cohort. In the gray rows, published images are paired to the approximately corresponding slices of GSCORR. At bottom, GSCORR in the RP datasets (6.5 hours of data, see Figure S4). The RP data, from a single individual, are easier to view on a surface because the central tendency of dozens of scans is less spatially blurred than in other cohorts and thus contains complex folding patterns. See Figure S6 for further illustrations of RP and WU data, and surface visualizations of modules in these datasets.
Figure 5
Figure 5. Relationships between respiratory cycles, estimated heart rate, and pulse pressures
For 1 minute of data from 3 subjects, top and bottom panels show respiratory belt and pulse oximeter traces (both 50 Hz signals). Instantaneous heart rate is calculated from the peak-to-peak interval and plotted in green at the peak time, as is peak amplitude. Cyclical influences of respiration on heart rate and peak amplitude are evident. Such traces can be seen for all subjects in Videos 3a and 3b. The black traces at right show the correlation between respiratory belt traces and heart rate and peak amplitude, with -5 to 5 seconds of lag applied to the respiratory trace.
Figure 6
Figure 6. Respiratory traces denote many global signal changes
Red traces of FD (mm) show head motion. The middle panels show heart rate (green, beats per minute) and peak amplitude (gray, zscore) data derived from a pulse-oximeter trace. The unprocessed respiratory belt record is shown in blue (arbitrary units). At bottom all fMRI signals in the brain are shown. When green heart rate traces appear to be noisy, for example in the bottom middle subject, this variance is not actually random noise, but rather cyclical modulation of heart rate by respiratory cycle (see Video 3a and 3b to resolve individual cycles). Similar statements apply to the gray peak amplitude trace at upper middle. At bottom right, the respiratory response function defined in the NIH data (see Figure S7 for more details).
Figure 7
Figure 7. Removal of respiratory-related variance during denoising is often incomplete
Data for 3 subjects from Figure 6 are shown. At top, respiratory belt and head motion traces. The five panels below show data after various combinations of denoising. The labels at left denote the kinds and number of regressors used.
Figure 8
Figure 8. Global signals after denoising continue to reflect unwanted influences
The standard deviation of the global signal is correlated, across subjects, with motion, heart rate variability, and respiratory variability. The thin red line fits all the data, the thicker dark line fits only subjects with mean FD < 0.2 mm. The top row reflect undenoised data, the bottom row reflects data after the denoising procedure shown in the 4th row of Figure 7.
Figure 9
Figure 9. Global signals after denoising continue to reflect unwanted influences across a variety of denoising strategies
Global signal variance was modeled, across subjects, as a function of 7 parameters, as in Table 3. The heat maps and tables at left show the percent variance explained and beta values for “typical” (TD) subjects, the timepoints entering the model, and the various denoising strategies tested. At right, mean values across the 14 columns are shown for all NIH subjects, ASD subjects only, and TD subjects only. TT is placed in the mean ASD column to indicate that significant relationships exist in some strategies and in raw data, but the average is a permutation rank of 88%.
Figure 10
Figure 10. GSCORR after denoising continue to reflect unwanted influences
GSCORR calculated after denoising is correlated with RVT variability, across subjects, at each voxel. The top 10% of correlations determined by 10,000 permutations are shown in slices, the ranks of all voxels in the image are shown at right.

Comment in

  • Mixed Signals: On Separating Brain Signal from Noise.
    Uddin LQ. Uddin LQ. Trends Cogn Sci. 2017 Jun;21(6):405-406. doi: 10.1016/j.tics.2017.04.002. Epub 2017 Apr 28. Trends Cogn Sci. 2017. PMID: 28461113 Free PMC article.
  • On Global fMRI Signals and Simulations.
    Power JD, Laumann TO, Plitt M, Martin A, Petersen SE. Power JD, et al. Trends Cogn Sci. 2017 Dec;21(12):911-913. doi: 10.1016/j.tics.2017.09.002. Epub 2017 Sep 19. Trends Cogn Sci. 2017. PMID: 28939332 No abstract available.

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