Abstract
Magnetoencephalography has gained an increasing importance in systems neuroscience thanks to the possibility it offers of unraveling brain networks at time-scales relevant to behavior, i.e., frequencies in the 1–100 Hz range, with sufficient spatial resolution. In the first part of this review, we describe, in a unified mathematical framework, a large set of metrics used to estimate MEG functional connectivity at the same or at different frequencies. The different metrics are presented according to their characteristics: same-frequency or cross-frequency, univariate or multivariate, directed or undirected. We focus on phase coupling metrics given that phase coupling of neuronal oscillations is a putative mechanism for inter-areal communication, and that MEG is an ideal tool to non-invasively detect such coupling. In the second part of this review, we present examples of the use of specific phase methods on real MEG data in the context of resting state, visuospatial attention and working memory. Overall, the results of the studies provide evidence for frequency specific and/or cross-frequency brain circuits which partially overlap with brain networks as identified by hemodynamic-based imaging techniques, such as functional Magnetic Resonance (fMRI). Additionally, the relation of these functional brain circuits to anatomy and to behavior highlights the usefulness of MEG phase coupling in systems neuroscience studies. In conclusion, we believe that the field of MEG functional connectivity has made substantial steps forward in the recent years and is now ready for bringing the study of brain networks to a more mechanistic understanding.
Highlights
IntroductionSystems neuroscience has made it clear that brain functioning requires the cooperation of several spatially separated brain regions to allow for integrative functions (e.g., vision, audition), as well as for higher order functions (e.g., understanding of actions), for a review see e.g., Rizzolatti et al (2018)
In the last decades, systems neuroscience has made it clear that brain functioning requires the cooperation of several spatially separated brain regions to allow for integrative functions, as well as for higher order functions, for a review see e.g., Rizzolatti et al (2018)
Novel analysis methods have contributed to the examination of whole-brain functional connectivity patterns: functional Magnetic Resonance and functional Near Infrared Spectroscopy have investigated the level of co-activation between brain regions as a proxy for functional communication (e.g., Smith et al, 2013; Wang et al, 2017), while electrophysiological techniques such as ElectroEncephaloGraphy (EEG) and MagnetoEncephaloGraphy (MEG), have characterized both the level of co-activation between brain regions and the coupling of their respective signals, i.e., the statistical relationship between time-series of neuronal signals
Summary
Systems neuroscience has made it clear that brain functioning requires the cooperation of several spatially separated brain regions to allow for integrative functions (e.g., vision, audition), as well as for higher order functions (e.g., understanding of actions), for a review see e.g., Rizzolatti et al (2018). MEG is able to track neuronal activity at its characteristic time scale, i.e., milliseconds, and it is ideally suited to assess the faster dynamics of the different brain areas as well as of their functional connectivity. This characteristic makes MEG able to investigate, with high spectral resolution, neural oscillations which are known to subserve brain connectivity and to play important roles in cognitive processes (Varela et al, 2001; Engel et al, 2013). Long range connectivity can be assessed through the characterization of phase-phase coupling (hereinafter referred to as phase coupling) between MEG signals which has been related to the concept of RSNs (e.g., Marzetti et al, 2013)
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