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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.00736

Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

  • 1Hotchkiss Brain Institute, University of Calgary, Canada
  • 2Department of Biomedical Engineering, University of Calgary, Canada
  • 3Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada
  • 4Department of Radiology, University of Calgary, Canada

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 the 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.

Keywords: Global signal, Empirical Mode Decomposition, fMRI, Resting-state fMRI, Functional Connectivity

Received: 30 Jan 2019; Accepted: 02 Jul 2019.

Edited by:

Garth J. Thompson, ShanghaiTech University, China

Reviewed by:

Elmar W. Lang, University of Regensburg, Germany
Ehsan Shokri Kojori, National Institutes of Health (NIH), United States  

Copyright: © 2019 Moradi, Dousty and Sotero. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ms. Narges Moradi, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada,