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.

Objective: Interpretation of the Electroencephalographic (EEG) and Magnetoencephalographic (MEG) signals requires off-line artifacts removal. Since artifacts share frequencies with brain activity, filtering is insufficient. Blind source separation, mainly through Independent Component Analysis (ICA), is the gold-standard procedure for the identification of artifacts in multi-dimensional recordings. However, a classification of brain and artifactual Independent Components (ICs) is still required. Since ICs exhibit recognizable patterns, classification is usually performed by experts' visual inspection. This procedure is time-consuming and prone to errors. Automatic ICs classification has been explored, often through complex ICs features extraction prior to classification. Relying on Deep Learning ability of self-extracting the features of interest, we investigated the capabilities of Convolutional Neural Networks (CNNs) for off-line, automatic artifact identification through ICs without feature selection. Methods: A CNN was applied to spectrum and topography of a large data-set of few thousand samples of ICs obtained from multi-channel EEG and MEG recordings acquired during heterogeneous experimental settings and on different subjects. CNN performances, when applied to EEG, MEG, and combined EEG and MEG ICs, were explored and compared with state of the art feature-based automatic classification. Results: Beyond state-of-the-art automatic classification accuracies were demonstrated through cross-validation (92.4% EEG, 95.4% MEG, 95.6% EEG+MEG). Conclusion: High CNN classification performances were achieved through heuristically selection of machinery hyperparameters and through the CNN self-selection of the features of interest. Significance: Considering the large data availability of multi-channel EEG and MEG recordings, CNNs may be suited for classification of ICs of multi-channel brain electrophysiological recordings.

According to several conceptualizations of meditation, the interplay between brain systems associated to self-related processing, attention and executive control is crucial for meditative states and related traits. We used magnetoencephalography to investigate such interplay in a highly selected group of “virtuoso” meditators (Theravada Buddhist monks), with long-term training in the two main meditation styles: focused attention (FA) and open monitoring (OM) meditation. Specifically, we investigated the differences between FA meditation, OM meditation and resting state in the coupling between the posterior cingulate cortex, core node of the Default Mode Network (DMN) implicated in mind wandering and self-related processing, and the whole brain, with a recently developed phase coherence approach. Our findings showed a state dependent coupling of PCC to nodes of the DMN and of the executive control brain network in the alpha frequency band (8-12 Hz), related to different attentional and cognitive control processes in FA and OM meditation, consistently with the putative role of alpha band synchronization in the functional mechanisms for attention and consciousness. The coupling of posterior cingulate cortex with left medial prefrontal cortex and superior frontal gyrus characterized the contrast between the two meditation styles in a way that correlated with meditation expertise. These correlations may be related to a higher mindful observing ability and a reduced identification with ongoing mental activity in more expert meditators. Notably, different styles of meditation and different meditation expertise appeared to modulate the dynamic balance between fronto-parietal and DMN networks. Our results support the idea that the interplay between the DMN and the fronto-parietal network in the alpha band is crucial for the transition from resting state to different meditative states.

It is well known that attentional selection of relevant information relies on local synchronization of alpha band neuronal oscillations in visual cortices for inhibition of distracting inputs. Additionally, evidence for long-range coupling of neuronal oscillations between visual cortices and regions engaged in the anticipation of upcoming stimuli has been more recently provided. Nevertheless, on the one hand the relation between long-range functional coupling and anatomical connections is still to be assessed, and, on the other hand, the specific role of the alpha and beta frequency bands in the different processes underlying visuo-spatial attention still needs further clarification. We address these questions using measures of linear (frequency-specific) and nonlinear (cross-frequency) phase-synchronization in a cohort of 28 healthy subjects using magnetoencephalography. We show that alpha band phase-synchronization is modulated by the orienting of attention according to a parieto-occipital top-down mechanism reflecting behavior, and its hemispheric asymmetry is predicted by volume's asymmetry of specific tracts of the Superior-Longitudinal-Fasciculus. We also show that a network comprising parietal regions and the right putative Frontal-Eye-Field, but not the left, is recruited in the deployment of spatial attention through an alpha-beta cross-frequency coupling. Overall, we demonstrate that the visuospatial attention network features subsystems indexed by characteristic spectral fingerprints, playing different functional roles in the anticipation of upcoming stimuli and with diverse relation to fiber tracts.