Inter-areal functional connectivity (FC), neuronal synchronization in particular, is thought to constitute a key systems-level mechanism for coordination of neuronal processing
and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non-invasively
recorded only with magneto- and electroencephalography (MEG/EEG), which have been used to assess FC networks with high temporal resolution and whole-scalp coverage. However, even in source-reconstructed
MEG/EEG data, signal mixing, or "source leakage", is a significant confounder for FC analyses and network localization. Signal mixing leads to two distinct kinds of false-positive observations: artificial
interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics
have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all-to-all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses
using source-reconstructed MEG/EEG data. Our approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges
with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false-positive to true-positive ratio. Finally,
we demonstrate the advantage of edge bundling in the visualization of large-scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions
that are detectable and dissociable in MEG/EEG connectivity analysis.
Neuronal oscillations and their inter-areal synchronization may be instrumental in regulating neuronal communication in distributed networks.
Several lines of research have, however, shown that cognitive tasks engage neuronal oscillations simultaneously in multiple frequency bands that have distinct functional roles in cognitive processing. Gamma oscillations
(30-120 Hz) are associated with bottom-up processing, while slower oscillations in delta (1-4 Hz), theta (4-7 Hz), alpha (8-14 Hz) and beta (14-30 Hz) frequency bands may have roles in executive or top-down controlling functions,
although also other distinctions have been made. Identification of the mechanisms that integrate such spectrally distributed processing and govern neuronal communication among these networks is crucial for understanding how
cognitive functions are achieved in neuronal circuits. Cross-frequency interactions among oscillations have been recognized as a likely candidate mechanism for such integration. We advance here the hypothesis that phase-phase
synchronization of neuronal oscillations in two different frequency bands, cross-frequency phase synchrony (CFS), could serve to integrate, coordinate and regulate neuronal processing distributed into neuronal assemblies
concurrently in multiple frequency bands. A trail of studies over the past decade has revealed the presence of CFS among cortical oscillations and linked CFS with roles in cognitive integration. We propose that CFS could connect
fast and slow oscillatory networks and thereby integrate distributed cognitive functions such as representation of sensory information with attentional and executive functions.
Visuospatial attention prioritizes processing of attended visual stimuli. It is characterized by lateralized alpha-band (8-14 Hz) amplitude suppression in visual cortex and increased neuronal
activity in a network of frontal and parietal areas. It has remained unknown what mechanisms coordinate neuronal processing among frontoparietal network and visual cortices and implement the
attention-related modulations of alpha-band amplitudes and behavior. We investigated whether large-scale network synchronization could be such a mechanism. We recorded human cortical activity
with magnetoencephalography (MEG) during a visuospatial attention task. We then identified the frequencies and anatomical networks of inter-areal phase synchronization from source localized MEG data.
We found that visuospatial attention is associated with robust and sustained long-range synchronization of cortical oscillations exclusively in the high-alpha (10-14 Hz) frequency band. This synchronization
connected frontal, parietal and visual regions and was observed concurrently with amplitude suppression of low-alpha (6-9 Hz) band oscillations in visual cortex. Furthermore, stronger high-alpha phase
synchronization was associated with decreased reaction times to attended stimuli and larger suppression of alpha-band amplitudes. These results thus show that high-alpha band phase synchronization is functionally
significant and could coordinate the neuronal communication underlying the implementation of visuospatial attention.