Frequency-specific oscillations and phase-coupling of neuronal populations are essential mechanisms for the coordination of activity between brain areas during cognitive tasks. Therefore, the ongoing activity ascribed to the different functional brain networks should also be able to reorganise and coordinate via similar mechanisms. We develop a novel method for identifying large-scale phase-coupled network dynamics and show that resting networks in magnetoencephalography are well characterised by visits to short-lived transient brain states, with spatially distinct patterns of oscillatory power and coherence in specific frequency bands. Brain states are identified for sensory, motor networks and higher-order cognitive networks. The cognitive networks include a posterior alpha (8–12 Hz) and an anterior delta/theta range (1–7 Hz) network, both exhibiting high power and coherence in areas that correspond to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks have characteristic signatures in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales.

Magnetoencephalography (MEG) is a powerful technique for functional neuroimaging, offering a non-invasive window on brain electrophysiology. MEG systems have traditionally been based on cryogenic sensors which detect the small extracranial magnetic fields generated by synchronised current in neuronal assemblies, however, such systems have fundamental limitations. In recent years, non-cryogenic quantum-enabled sensors, called optically-pumped magnetometers (OPMs), in combination with novel techniques for accurate background magnetic field control, have promised to lift those restrictions offering an adaptable, motion-robust MEG system, with improved data quality, at reduced cost. However, OPM-MEG remains a nascent technology, and whilst viable systems exist, most employ small numbers of sensors sited above targeted brain regions. Here, building on previous work, we construct a wearable OPM-MEG system with ‘whole-head’ coverage based upon commercially available OPMs, and test its capabilities to measure alpha, beta and gamma oscillations. We design two methods for OPM mounting; a flexible (EEG-like) cap and rigid (additively-manufactured) helmet. Whilst both designs allow for high quality data to be collected, we argue that the rigid helmet offers a more robust option with significant advantages for reconstruction of field data into 3D images of changes in neuronal current. Using repeat measurements in two participants, we show signal detection for our device to be highly robust. Moreover, via application of source-space modelling, we show that, despite having 5 times fewer sensors, our system exhibits comparable performance to an established cryogenic MEG device. While significant challenges still remain, these developments provide further evidence that OPM-MEG is likely to facilitate a step change for functional neuroimaging.

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.

Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.

The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking.

Frequency-specific oscillations and phase-coupling of neuronal populations have been proposed as an essential mechanism for the coordination of activity between brain areas during cognitive tasks. To provide an effective substrate for cognitive function, we reasoned that ongoing functional brain networks should also be able to reorganise and coordinate in a similar manner. To test this hypothesis, we use a novel method for identifying repeating patterns of large-scale phase-coupling network dynamics, and show that resting networks in magnetoencephalography are well characterised by visits to shortlived transient brain states, with spatially distinct power and phase-coupling in specific frequency bands. Brain states were identified for sensory, motor networks and higher-order cognitive networks; the latter include a posterior higher-order cognitive network in the alpha range (8-12Hz) and an anterior higher-order cognitive network in the delta/theta range (1-7Hz). Both higher-order cognitive networks exhibit especially high power and coherence, and contain brain areas corresponding to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks operate in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales. 2015). However, the evidence for frequency specific phase-coupling in spontaneous activity at timescales associated with fast cognition is limited.Here, we propose that cortical activity at rest can be described by transient, intermittently reoccurring events in which large-scale networks activate with distinct spectral and phasecoupling features. To identify the possible presence of these events, we use a new analysis approach based on the Hidden Markov Model (HMM; Rabiner, 1989). For the first time, this allows for the identification of brain-wide networks (or brain states) characterised by specific patterns of power and phase-coupling connectivity, which, crucially, are spectrallyresolved (i.e. power and phase-coupling are defined as a function of frequency). These patterns are also temporally-resolved, meaning that the method provides a probabilistic estimation of when the different networks are active (see Fig. 1a). Notably, applying this approach to resting MEG recordings of healthy human subjects revealed the distinct temporal and spectral properties of anterior versus posterior regions of the default mode network. The joint description of the spectral, temporal and spatial properties of ongoing neuronal activity provides new insight into the large-scale circuit organization of the brain (Woolrich and Stephan, 2013).

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The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poorly understood. Recent animal work has demonstrated that electrical activity promotes myelination. Inspired by this, we test a hypothesis that gray-matter myelin is related to electrophysiological connectivity. Using ultra-high field MRI and the principle of structural covariance, we derive a structural network showing how myelin density differs across cortical regions and how separate regions can exhibit similar myeloarchitecture. Building upon recent evidence that neural oscillations mediate connectivity, we use magnetoencephalography to elucidate networks that represent the major electrophysiological pathways of communication in the brain. Finally, we show that a significant relationship exists between our functional and structural networks; this relationship differs as a function of neural oscillatory frequency and becomes stronger when integrating oscillations over frequency bands. Our study sheds light on the way in which cortical microstructure supports functional networks. Further, it paves the way for future investigations of the gray-matter structure/function relationship and its breakdown in pathology.network | functional connectivity | myelination | magnetoencephalography | MRI T he way in which integration of functionally specific brain regions supports ongoing cognition is one of the most important questions in neuroscience, and noninvasive in vivo imaging provides a tool to investigate this interregional connectivity in terms of both brain function and structure. Functional connectivity refers to statistical interdependencies between patterns of brain "activity" measured at separate cortical locations (1) and, even in the "resting state," measured spontaneous brain activity defines nonrandom networks that are related to cognitive processes (2). The way in which these functional networks are supported by structural white-matter pathways is reasonably well understood (3). However, it is likely that the structure-function association extends to graymatter morphology, for which fundamental understanding is lacking. Structural morphology of the cortex is known to vary significantly between individuals, and does so in an organized fashion. For example, individuals with a high cortical volume in Broca's area typically exhibit high cortical volume in Wernicke's area, reflecting a language network (4). Similar observations can be made between other associated cortical regions (5, 6). This is known as structural covariance (7-9) and it allows the formation of matrices showing how structural properties of individual brain regions covary over subjects. In this paper, we assess structural covariance based upon cortical myeloarchitecture and probe its relationship to functional networks that are assessed based upon measured spontaneous brain activity.Noninvasive map...