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Interpretation of the blood oxygen level dependent (BOLD) response measured using functional magnetic resonance imaging (fMRI) requires an understanding of the underlying neuronal activity. Here we report on a study using both magnetoencephalography (MEG) and BOLD fMRI, to measure the brain's functional response to electrical stimulation of the median nerve in a paired pulse paradigm. Interstimulus Intervals (ISIs) of 0.25, 0.5, 0.75, 1.0, 1.5 and 2.0 s are used to investigate how the MEG detected neural response to a second pulse is affected by that from a preceding pulse and if these MEG modulations are reflected in the BOLD response. We focus on neural oscillatory activity in the β-band (13–30 Hz) and the P35m component of the signal averaged evoked response in the sensorimotor cortex. A spatial separation of β ERD and ERS following each pulse is demonstrated suggesting that these two effects arise from separate neural generators, with ERS exhibiting a closer spatial relationship with the BOLD response. The spatial distribution and extent of BOLD activity were unaffected by ISI, but modulations in peak amplitude and latency were observed. Non-linearities in both induced oscillatory activity ERS and in the signal averaged evoked response are found for ISIs of up to 2 s when the signal averaged evoked response has returned to baseline, with the P35m component displaying paired pulse depression effects. The β-band ERS magnitude was modulated by ISI, however the ERD magnitude was not. These results support the assumption that BOLD non-linearity arises not only from a non-linear vascular response to neural activity but also a non-linear neural response to the stimulus with ISI up to 2 s.

Functional connectivity (FC) between brain regions is thought to be central to the way in which the brain processes information. Abnormal connectivity is thought to be implicated in a number of diseases. The ability to study FC is therefore a key goal for neuroimaging. Functional connectivity (fc) MRI has become a popular tool to make connectivity measurements but the technique is limited by its indirect nature. A multimodal approach is therefore an attractive means to investigate the electrodynamic mechanisms underlying hemodynamic connectivity. In this paper, we investigate resting state FC using fcMRI and magnetoencephalography (MEG). In fcMRI, we exploit the advantages afforded by ultra high magnetic field. In MEG we apply envelope correlation and coherence techniques to source space projected MEG signals. We show that beamforming provides an excellent means to measure FC in source space using MEG data. However, care must be taken when interpreting these measurements since cross talk between voxels in source space can potentially lead to spurious connectivity and this must be taken into account in all studies of this type. We show good spatial agreement between FC measured independently using MEG and fcMRI; FC between sensorimotor cortices was observed using both modalities, with the best spatial agreement when MEG data are filtered into the β band. This finding helps to reduce the potential confounds associated with each modality alone: while it helps reduce the uncertainties in spatial patterns generated by MEG (brought about by the ill posed inverse problem), addition of electrodynamic metric confirms the neural basis of fcMRI measurements. Finally, we show that multiple MEG based FC metrics allow the potential to move beyond what is possible using fcMRI, and investigate the nature of electrodynamic connectivity. Our results extend those from previous studies and add weight to the argument that neural oscillations are intimately related to functional connectivity and the BOLD response., Research highlights ► The utility of beamforming as a source space projection algorithm for use with functional connectivity (FC) measurements is explored. ► Simulations are described and prove to be a robust methodology to eliminate spurious FC from source space MEG measurements. ► The spatial signature of resting state FC between left and right sensorimotor cortices can be measured independently using MEG and fMRI. ► Excellent agreement between motor cortex FC measured using both MEG and fcMRI. ► Multiple MEG FC metrics exploit the direct nature of MEG.

The exact relationship between neural activity and BOLD fMRI is unknown. However, several recent findings, recorded invasively in both humans and monkeys, show a positive correlation of BOLD to high-frequency (30-150 Hz) oscillatory power changes and a negative correlation to low-frequency (8-30 Hz) power changes arising from cortical areas. In this study, we computed the time series correlation between BOLD GE-EPI fMRI at 7 T and neural activity measures from noninvasive MEG, using a time-frequency beam former for source localisation. A sinusoidal drifting grating was presented visually for 4 s followed by a 20 s rest period in both recording modalities. The MEG time series were convolved with either a measured or canonical haemodynamic response function (HRF) for comparison with the measured BOLD data, and the BOLD data were deconvolved with either a measured or a canonical HRF for comparison with the measured MEG. In the visual cortex, the higher frequencies (mid-gamma=52-75 Hz and high-gamma=75-98 Hz) were positively correlated with BOLD whilst the lower frequencies (alpha=8-12 Hz and beta=12-25 Hz) were negatively correlated with BOLD. Furthermore, regression including all frequency bands predicted BOLD better than stimulus timing alone, although no individual frequency band predicted BOLD as well as stimulus timing. For this paradigm, there was, in general, no difference between using the SPM canonical HRF compared to the subject-specific measured HRF. In conclusion, MEG replicates findings from invasive recordings with regard to time series correlations with BOLD data. Conversely, deconvolution of BOLD data provides a neural estimate which correlates well with measured neural effects as a function of neural oscillation frequency.

This paper investigates the application of source reconstruction methodologies to EEG data recorded in concurrent EEG/fMRI experiments at 7T. An EEG phantom containing a dipolar current source is described and used to investigate the accuracy of source localisation. Both dipole fitting and beamformer algorithms are shown to yield accurate locations for the dipole within the phantom. Source reconstruction methodologies are also shown to reduce significantly the level of interference in the recorded EEG, caused by the MR scanner. A comparison between beamformer and dipole fitting approaches is made and it is shown that, due to its adaptive weighting parameters, the beamformer provides better suppression of interference when compared to the dipole fit. In addition it is shown that, in the case of the beamformer, use of a high EEG channel density improves the level of interference reduction, and the ratio of measured signal to interference can be improved by a factor of approximately 1.6 if the number of EEG electrodes is increased from 32 to 64. The interference reduction properties of source localisation are shown theoretically, in simulation, and in phantom data. Finally, in-vivo experiments conducted at 7T show that effects in the gamma band can be recorded using simultaneous EEG/fMRI. These results are achieved by application of beamformer methodology to 64 channel EEG data.

In recent years, the use of beamformers for source localisation has significantly improved the spatial accuracy of magnetoencephalography. In this paper, we examine techniques by which to optimise experimental design, and ensure that the application of beamformers yields accurate results. We show that variation in the experimental duration, or variation in the bandwidth of a signal of interest, can significantly affect the accuracy of a beamformer reconstruction of source power. Specifically, power will usually be underestimated if covariance windows are made too short, or bandwidths too narrow. The accuracy of spatial localisation may also be reduced. We conclude that for optimum accuracy, experimenters should aim to collect as much data as possible, and use a bandwidth spanning the entire frequency distribution of the signal of interest. This minimises distortion to reconstructed source images, time courses and power estimation. In the case where experimental duration is short, and small covariance windows are therefore used, we show that accurate power estimation can be achieved by matrix regularisation. However, large amounts of regularisation cause a loss in the spatial resolution of the MEG beamformer, hence regularisation should be used carefully, particularly if multiple sources in close proximity are expected.

This paper introduces a lead field formulation for use in beamformer analysis of MEG data. This 'dual source beamformer' is a technique to image two temporally correlated sources using beamformer methodology. We show that while the standard, single source beamformer suppresses the reconstructed power of two spatially separate but temporally correlated sources, the dual source beamformer allows for their accurate reconstruction. The technique is proven to be accurate using simulations. We also show that it can be used to image accurately the auditory steady state response, which is correlated between the left and right auditory cortices. We suggest that this technique represents a useful way of locating correlated sources, particularly if a seed location can be defined a priori for one of the two sources. Such a priori information could be based on previous studies using similar paradigms, or from other functional neuroimaging techniques.

In this study, we elucidate the changes in neural oscillatory processes that are induced by simple working memory tasks. A group of eight subjects took part in modified versions of the N-back and Sternberg working memory paradigms. Magnetoencephalography (MEG) data were recorded, and subsequently processed using beamformer based source imaging methodology. Our study shows statistically significant increases in θ oscillations during both N-back and Sternberg tasks. These oscillations were shown to originate in the medial frontal cortex, and further to scale with memory load. We have also shown that increases in θ oscillations are accompanied by decreases in β and γ band oscillations at the same spatial coordinate. These decreases were most prominent in the 20Hz – 40Hz frequency range, although spectral analysis showed that γ band power decrease extends up to at least 80Hz. β/γ power decrease also scales with memory load. Whilst θ increases were predominately observed in the medial frontal cortex, β/γ decreases were associated with other brain areas, including nodes of the default mode network (for the N-back task) and areas associated with language processing (for the Sternberg task). These observations are in agreement with intracranial EEG and fMRI studies. Finally, we have shown an intimate relationship between changes in β/γ band oscillatory power at spatially separate network nodes, implying that activity in these nodes is not reflective of uni-modal task driven changes in spatially separate brain regions, but rather represents correlated network activity. The utility of MEG as a non-invasive means to measure neural oscillatory modulation has been demonstrated and future studies employing this technology have the potential to gain a better understanding of neural oscillatory processes, their relationship to functional and effective connectivity, and their correspondence to BOLD fMRI.

The simultaneous application of functional MRI and EEG represents an attractive, non-invasive technique for the combined measurement of electrical and haemodynamic activity in the human brain. Simultaneous EEG/fMRI provides a brain imaging modality with millimeter spatial accuracy, and millisecond temporal resolution. However, simultaneously acquired measurements are difficult due to the artifacts that are induced in the EEG by both the temporally varying field gradients used in MRI, and also blood flow effects. In this paper we apply an EEG beamformer spatial filter to EEG data recorded simultaneously with fMRI. We show, using this technique, that it is possible to localise accurately electrical effects in the brain, and that the localisation of driven oscillatory responses in the human visual cortex are spatially co-incident with the fMRI BOLD response. We also show how the beamformer can be used to extract timecourses of electrical activity from areas of interest in the brain. Such timecourses have millisecond time resolution. Finally, we show that in addition to source localisation, the beamformer spatial filter acts to reject interference in EEG signals, thus increasing the effective signal to noise ratio of electrical measurements. We show that the EEG-beamformer can eliminate effectively the ballistocardiogram artifact as well as residual gradient artifacts that remain in EEG data following correction using averaged artifact subtraction techniques.

The relationship between neuronal events and haemodynamic changes measured with blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI) is still unknown, although many recent studies have provided a qualitative corres pondence. The local neuromagnetic fields generated by the dendrites of cortical pyramidal cells can be measured non-invasively at the scalp with magnetoencephalography (MEG); these fields have been shown to have a spatial overlap with BOLD [1] and to be the cause of the majority of metabolic demand which is thought to drive the BOLD response [2]. Recent studies have also examined the relationship between BOLD and the power of neural activity across frequency bands. Using invasive recordings and comparing to fMRI, Mukamel

This study shows that the spatial specificity of MEG beamformer estimates of electrical activity can be affected significantly by the way in which covariance estimates are calculated. We define spatial specificity as the ability to extract independent timecourse estimates of electrical brain activity from two separate brain locations in close proximity. Previous analytical and simulated results have shown that beamformer estimates are affected by narrowing the time frequency window in which covariance estimates are made. Here we build on this by both experimental validation of previous results, and investigating the effect of data averaging prior to covariance estimation. In appropriate circumstances, we show that averaging has a marked effect on spatial specificity. However the averaging process results in ill-conditioned covariance matrices, thus necessitating a suitable matrix regularisation strategy, an example of which is described. We apply our findings to an MEG retinotopic mapping paradigm. A moving visual stimulus is used to elicit brain activation at different retinotopic locations in the visual cortex. This gives the impression of a moving electrical dipolar source in the brain. We show that if appropriate beamformer optimisation is applied, the moving source can be tracked in the cortex. In addition to spatial reconstruction of the moving source, we show that timecourse estimates can be extracted from neighbouring locations of interest in the visual cortex. If appropriate methodology is employed, the sequential activation of separate retinotopic locations can be observed. The retinotopic paradigm represents an ideal platform to test the spatial specificity of source localisation strategies. We suggest that future comparisons of MEG source localisation techniques (e.g. beamformer, minimum norm, Bayesian) could be made using this retinotopic mapping paradigm.