Abstract
Functional connectivity analysis aims at assessing the strength of functional coupling between the signal responses in distinct brain areas. Usually, functional magnetic resonance imaging (fMRI) time series connections are estimated through zero-lag correlation metrics that quantify the statistical similarity between pairs of regions or spectral measures that assess synchronization at a frequency band of interest. Here, we explored the application of a new metric to assess the functional synchronization in phase space between fMRI time series in a resting state. We applied a complete topological analysis to the resulting connectivity matrix to uncover both the macro-scale organization of the brain and detect the most important nodes. The synchronization metric is also compared with Pearson’s correlation coefficient and spectral coherence to highlight similarities and differences between the topologies of the three functional networks. We found that the individual topological organization of the resulting synchronization-based connectivity networks shows a finer modular organization than that identified with the other two metrics and a low overlap with the modular partitions of the other two networks suggesting that the derived topological information is not redundant and could be potentially integrated to provide a multi-scale description of functional connectivity.
Highlights
A wide range of biological phenomena are characterized by synchronization of systems whose complex cooperation and integration explain the emergence of vital functions [1].Several synchronization metrics have been introduced to quantify the coupling behavior of interacting dynamic systems [2,3]
The three matrices are very different form each other: the synchronization index (SYNC) network is sparser, with only a small number of entries with high synchronization values. This aspect is emphasized by the probability distribution which shows a right long-tailed trend, with few outliers between 0.2 and 1. This finding highlights a fist important difference between the connectivity matrices, i.e., most regions exhibit a very intermittent synchronization behavior, on average the pairs of ROIs are moderately statistically correlated in time and frequency
We analyzed the topological organization of SYNC-based, Pearson correlation and spectral coherence networks of a single subject
Summary
A wide range of biological phenomena are characterized by synchronization of systems whose complex cooperation and integration explain the emergence of vital functions [1].Several synchronization metrics have been introduced to quantify the coupling behavior of interacting dynamic systems [2,3]. There has been a growing body of literature addressing the application of complex network analysis for the characterization of dynamical systems based on time series. These combined approaches have clarified fundamental issues about the organization of nonlinear dynamics in different fields. The human brain is modeled as a complex network composed of anatomical and functional sub-systems whose interactions allow the performance of both high- and low-level cognitive functions.
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