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

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 significance

While it is now well established that mechanical instabilities play an important role for cortical folding in the developing human brain, the mechanisms on the cellular scale leading to those macroscopic structural changes remain insufficiently understood. Here, we demonstrate that a two-field mechanical model coupling cell division and migration with volume growth is capable of capturing the spatial and temporal distribution of the cell density and the corresponding cortical folding pattern observed in the human fetal brain. The presented model provides a platform to obtain important insights into the cellular mechanisms underlying normal cortical folding and, even more importantly, malformations of cortical development.