Multivariate extension of phase synchronization improves the estimation of region-to-region source space functional connectivity

  1. Bruña, Ricardo
  2. Pereda, Ernesto 1
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    GRID grid.10041.34

Journal:
Brain Multiphysics

ISSN: 2666-5220

Year of publication: 2021

Volume: 2

Pages: 100021

Type: Article

Export: RIS
DOI: 10.1016/j.brain.2021.100021 GOOGLE SCHOLAR

Abstract

The estimation of functional connectivity (FC) from noninvasive electrophysiological data recorded from sensors outside the skull requires transforming these data into a source space. As the number of sensors is much lower than the number of electrophysiological sources, the brain activity is usually parcellated into anatomical regions, and the FC between each pair of regions is then estimated.In this work, we generate a set of simulated scenarios with different configurations and coupling levels between synthetic time series. Then, this simulated brain activity is converted into simulated MEG sensor-space data and reconstructed back into the source space. Last, we estimated the FC between different regions using different approaches commonly used in the literature and compared them with a novel approach.Our results show that this novel approach, based on using all the information in each region, clearly outperforms classical approaches based on a representative time series. The proposed approach is more sensitive to the level of coupling and the extent of the area synchronized, and the resulting estimate better reflects the underlying FC. Based on these results, we strongly discourage using a representative time series to summarize large brain areas' activity when calculating FC.Statement of significanceFunctional connectivity is the current framework for understanding brain function. EEG and MEG are acquired from outside the head, and when estimating source-level activity, the number of sources is much higher than the number of sensors. As such, the source-level data is highly redundant, hampering the proper estimation of functional connectivity. Using predefined brain areas instead of individual sources bypasses this problem, but the leap from the former to the latter is far from trivial. Here we propose a novel approach that, according to our simulations, correctly estimates the level of functional connectivity between predefined brain areas in several scenarios. At the cost of a slight increase in computational burden, this solution can help unravel how the healthy and pathological brain communicates.

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