Abstract
Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze non-linear and non-stationary phenomena. For each SIMF, functional connectivity matrices were computed by means of Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high correlation and global efficiency values of the connectivity matrices related to the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, by summing up the first two SIMFs extracted from the fMRI signals, we have automatically excluded the GS which is now voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision in identifying the correct brain network connectivity, our approach demonstrated expected connectivity patterns for the default mode network and task-positive network.
Highlights
Resting-state functional connectivity MRI has considerable potential for mapping functional brain networks (Biswal et al, 1995; Kandel et al, 2000; De Luca et al, 2006; Fox et al, 2006; Shmuel and Leopold, 2008; Friston, 2011)
As seen in the figures, SIMF1 and SIMF2 in all Temporal IMFs (TIMFs) showed low connectivity whereas SIMF3 to SIMF5 in all TIMFs showed high connectivity. They indicate that the magnitude of the correlation does not significantly depend on the TIMFs
We averaged the six connectivity matrices resulting from the summation of TIMF1 to TIMF3 with SIMF1 and SIMF2 (Figure 4) and labeled it as adaptive global signal regression (AGSR) (Figure 5A), and the nine connectivity matrices resulting when combining TIMF1 to TIMF3 with SIMF3 to SIMF5, which we labeled as Adaptive Global Signal (AGS) (Figure 5B)
Summary
Resting-state functional connectivity MRI (rs-fcMRI) has considerable potential for mapping functional brain networks (Biswal et al, 1995; Kandel et al, 2000; De Luca et al, 2006; Fox et al, 2006; Shmuel and Leopold, 2008; Friston, 2011). We show that by applying AGSR, we do not need the traditional low-pass filtering methods as the proposed method exhibits the potential to adaptively remove the physiological noises from high temporal frequency modes of fMRI time series, that are shared in whole brain regions. It has been shown that applying EMD-based methods on fMRI data separate inherent brain oscillations and fundamental modes embedded in BOLD signal Each of these oscillations occupies a unique frequency band and can be used to investigate the frequency characteristics in resting-state brain networks (McGonigle et al, 2010; Zheng et al, 2010; Niazy et al, 2011; Song et al, 2014, 2015; Qian et al, 2015; Lin et al, 2016; Cordes et al, 2018). While the correlation pattern identified by the other methods suffers from a low level of precision, our method demonstrates a high level of accuracy due to its data-driven adaptive nature
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