A multi-layer network approach to MEG connectivity analysis

Abstract

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.

Multiscale energy reallocation during low‐frequency steady‐state brain response

“…Enhanced variability reflects greater network complexity, increased dynamic range, and the capacity for the system to explore different states (Garrett et al, ). The energy reallocation associated with lfSSBR, therefore, is related to network reorganization and state transfer among frequency bands, in keeping with the multilayer network hypothesis (Brookes et al, ; Wang et al, ). The reorganization of functional systems induced by face recognition across frequency bands and across brain regions is also in line with the idea of reorganization of functional networks and functional fingerprints during cognition (Ponce‐Alvarez, He, Hagmann, & Deco, ; Siegel, Donner, & Engel, ).…”

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

“…1. Previous studies (humans and primates) have demonstrated the validity and functional significance of these synchronous envelope amplitude modulations (Brookes et al, 2011, 2016; Vidal et al, 2012; Wang et al, 2012; Colclough et al, 2016) for both oscillatory and broadband signals.…”

Functional and effective reorganization of the aging brain during unimanual and bimanual hand movements

“…Lastly, our choice of processing pipeline may have precluded the identification of modulatory effects that were not time‐locked to the production of isometric handgrips. While our trial averaged connectivity analyses may have led to nonphase‐locked task‐related signal loss in alpha and beta frequency bands (Brookes et al, ), our analytical framework was primarily designed to target oscillations that were directly evoked by the production of isometric handgrips while also suppressing non‐task‐related signals as well as physiological and measurement noise (David et al, ).…”