A method to determine the necessity for global signal regression in resting-state fMRI studies

Abstract

In resting-state functional MRI studies, the global signal (operationally defined as the global average of resting-state functional MRI time courses) is often considered a nuisance effect and commonly removed in preprocessing. This global signal regression method can introduce artifacts, such as false anticorrelated resting-state networks in functional connectivity analyses. Therefore, the efficacy of this technique as a correction tool remains questionable. In this article, we establish that the accuracy of the estimated global signal is determined by the level of global noise (i.e., non-neural noise that has a global effect on the resting-state functional MRI signal). When the global noise level is low, the global signal resembles the resting-state functional MRI time courses of the largest cluster, but not those of the global noise. Using real data, we demonstrate that the global signal is strongly correlated with the default mode network components and has biological significance. These results call into question whether or not global signal regression should be applied. We introduce a method to quantify global noise levels. We show that a criteria for global signal regression can be found based on the method. By using the criteria, one can determine whether to include or exclude the global signal regression in minimizing errors in functional connectivity measures.

Figure 1

Simulation results. (A) The signals in the first cluster (150 voxels) in red, the signals in the second cluster (50 voxels) in blue. The signals of the rest of the 200 voxels are not shown. The global noise is shown in green. (B) The r value among the 400 signals and the global noise (GN) (replicated 50 times for better illustration). (C, F and I) The mixed time courses in the first cluster in red, the mixed time courses in the second cluster in blue, the global noise is shown in green and the global signal is shown in purple with SGNR of 1, 2 and 10 respectively. (D, G and J) The r value among the global noise (GN), global signal (GS), and the 400 voxels' time courses using (top right half) and without using global regression (bottom left half), with SGNR of 1, 2 and 10 respectively. (E, H and K) The r error among the 400 voxels calculated using (top right half) and without using global regression (bottom left half) with SGNR of 1, 2 and 10 respectively.

Figure 2

Global signal connectivity pattern and r error map. (A) Regions that strongly correlated global signal. (B) r error map using global signal regression with SGNR of 1, 7 and 20 respectively. (C) r error map without using global signal regression with SGNR of 1, 7 and 20 respectively.

Figure 3

Relationship between SGNR and mean r error simulated using synthetic data (A) and real subject data (B). In Figure 3B, the dot-dashed line and solid line cross each other at SGNR value of 7.03. Below this SGNR, performing global signal regression induces less errors as opposed to not performing global signal regression. And above this SGNR, performing global signal regression induces more errors.

Figure 4

Relationship between SGNR and the mean global negative index simulated using synthetic data (A) and real subject data (B). The global negative index measurement of the criteria in doing and not doing global signal regression is 3.0 at the SGNR level of 7.03 determined in Figure 3B.

Figure 5

The global negative indices of the 20 subjects. Their GNI is well above the criterial value of 3.0.