Testing covariance models for MEG source reconstruction of hippocampal activity

Beamforming is one of the most commonly used source reconstruction methods for magneto- and electroencephalography (M/EEG). One underlying assumption, however, is that distant sources are uncorrelated and here we tested whether this is an appropriate model for the human hippocampal data. We revised the Empirical Bayesian Beamfomer (EBB) to accommodate specific a-priori correlated source models. We showed in simulation that we could use model evidence (as approximated by Free Energy) to distinguish between different correlated and uncorrelated source scenarios. Using group MEG data in which the participants performed a hippocampal-dependent task, we explored the possibility that the hippocampus or the cortex or both were correlated in their activity across hemispheres. We found that incorporating a correlated hippocampal source model significantly improved model evidence. Our findings help to explain why, up until now, the majority of MEG-reported hippocampal activity (typically making use of beamformers) has been estimated as unilateral

Magnetoencephalography in Stroke Recovery and Rehabilitation

Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility

The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming.

Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation

Localising the auditory N1m with event-related beamformers:localisation accuracy following bilateral and unilateral stimulation

The auditory evoked N1m-P2m response complex presents a challenging case for MEG source-modelling, because symmetrical, phase-locked activity occurs in the hemispheres both contralateral and ipsilateral to stimulation. Beamformer methods, in particular, can be susceptible to localisation bias and spurious sources under these conditions. This study explored the accuracy and efficiency of event-related beamformer source models for auditory MEG data under typical experimental conditions: monaural and diotic stimulation; and whole-head beamformer analysis compared to a half-head analysis using only sensors from the hemisphere contralateral to stimulation. Event-related beamformer localisations were also compared with more traditional single-dipole models. At the group level, the event-related beamformer performed equally well as the single-dipole models in terms of accuracy for both the N1m and the P2m, and in terms of efficiency (number of successful source models) for the N1m. The results yielded by the half-head analysis did not differ significantly from those produced by the traditional whole-head analysis. Any localisation bias caused by the presence of correlated sources is minimal in the context of the inter-individual variability in source localisations. In conclusion, event-related beamformers provide a useful alternative to equivalent-current dipole models in localisation of auditory evoked responses

During visual word recognition, phonology is accessed within 100 ms and may be mediated by a speech production code: evidence from magnetoencephalography

Debate surrounds the precise cortical location and timing of access to phonological information during visual word recognition. Therefore, using whole-head magnetoencephalography (MEG), we investigated the spatiotemporal pattern of brain responses induced by a masked pseudohomophone priming task. Twenty healthy adults read target words that were preceded by one of three kinds of nonword prime: pseudohomophones (e.g., brein–BRAIN), where four of five letters are shared between prime and target, and the pronunciation is the same; matched orthographic controls (e.g., broin–BRAIN), where the same four of five letters are shared between prime and target but pronunciation differs; and unrelated controls (e.g., lopus–BRAIN), where neither letters nor pronunciation are shared between prime and target. All three priming conditions induced activation in the pars opercularis of the left inferior frontal gyrus (IFGpo) and the left precentral gyrus (PCG) within 100 ms of target word onset. However, for the critical comparison that reveals a processing difference specific to phonology, we found that the induced pseudohomophone priming response was significantly stronger than the orthographic priming response in left IFG/PCG at ∼100 ms. This spatiotemporal concurrence demonstrates early phonological influences during visual word recognition and is consistent with phonological access being mediated by a speech production code

Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. Healthy brain function relies upon efficient connectivity between these areas and, in recent years, neuroimaging has been revolutionised by an ability to estimate this connectivity. In this paper we discuss measurement of network connectivity using magnetoencephalography (MEG), a technique capable of imaging electrophysiological brain activity with good (~5mm) spatial resolution and excellent (~1ms) temporal resolution. The rich information content of MEG facilitates many disparate measures of connectivity between spatially separate regions and in this paper we discuss a single metric known as power envelope correlation. We review in detail the methodology required to measure power envelope correlation including i) projection of MEG data into source space, ii) removing confounds introduced by the MEG inverse problem and iii) estimation of connectivity itself. In this way, we aim to provide researchers with a description of the key steps required to assess envelope based functional networks, which are thought to represent an intrinsic mode of coupling in the human brain. We highlight the principal findings of the techniques discussed, and furthermore, we show evidence that this method can probe how the brain forms and dissolves multiple transient networks on a rapid timescale in order to support current processing demand. Overall, power envelope correlation offers a unique and verifiable means to gain novel insights into network coordination and is proving to be of significant value in elucidating the neural dynamics of the human connectome in health and disease

Caffeine-Induced Global Reductions in Resting-State BOLD Connectivity Reflect Widespread Decreases in MEG Connectivity.

In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity. However, because the BOLD signal is an indirect measure of neuronal activity, its complex hemodynamic nature can complicate the interpretation of differences in connectivity that are observed across conditions or subjects. For example, prior studies have shown that caffeine leads to widespread reductions in BOLD connectivity but were not able to determine if neural or vascular factors were primarily responsible for the observed decrease. In this study, we used source-localized magnetoencephalography (MEG) in conjunction with fMRI to further examine the origins of the caffeine-induced changes in BOLD connectivity. We observed widespread and significant (p < 0.01) reductions in both MEG and fMRI connectivity measures, suggesting that decreases in the connectivity of resting-state neuro-electric power fluctuations were primarily responsible for the observed BOLD connectivity changes. The MEG connectivity decreases were most pronounced in the beta band. By demonstrating the similarity in MEG and fMRI based connectivity changes, these results provide evidence for the neural basis of resting-state fMRI networks and further support the potential of MEG as a tool to characterize resting-state connectivity