Author name cluster

Peter G. Morris

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30 papers

1 author row

YNICL Journal 2021 Journal Article

Motor-related oscillatory activity in schizophrenia according to phase of illness and clinical symptom severity

Lauren E. Gascoyne
Matthew J. Brookes
Mohanbabu Rathnaiah
Mohammad Zia Ul Haq Katshu
Loes Koelewijn
Gemma Williams
Jyothika Kumar
James T.R. Walters

Magnetoencephalography (MEG) measures magnetic fields generated by synchronised neural current flow and provides direct inference on brain electrophysiology and connectivity, with high spatial and temporal resolution. The movement-related beta decrease (MRBD) and the post-movement beta rebound (PMBR) are well-characterised effects in magnetoencephalography (MEG), with the latter having been shown to relate to long-range network integrity. Our previous work has shown that the PMBR is diminished (relative to controls) in a group of schizophrenia patients. However, little is known about how this effect might differ in patients at different stages of illness and degrees of clinical severity. Here, we extend our previous findings showing that the MEG derived PMBR abnormality in schizophrenia exists in 29 recent-onset and 35 established cases (i.e., chronic patients), compared to 42 control cases. In established cases, PMBR is negatively correlated with severity of disorganization symptoms. Further, using a hidden Markov model analysis, we show that transient pan-spectral oscillatory "bursts", which underlie the PMBR, differ between healthy controls and patients. Results corroborate that PMBR is associated with disorganization of mental activity in schizophrenia.

YNIMG Journal 2020 Journal Article

The role of transient spectral ‘bursts’ in functional connectivity: A magnetoencephalography study

Zelekha A. Seedat
Andrew J. Quinn
Diego Vidaurre
Lucrezia Liuzzi
Lauren E. Gascoyne
Benjamin A.E. Hunt
George C. O’Neill
Daisie O. Pakenham

Neural oscillations dominate electrophysiological measures of macroscopic brain activity and fluctuations in these rhythms offer an insightful window on cortical excitation, inhibition, and connectivity. However, in recent years the ‘classical’ picture of smoothly varying oscillations has been challenged by the idea that many ‘oscillations’ may actually be formed from the recurrence of punctate high-amplitude bursts in activity, whose spectral composition intersects the traditionally defined frequency ranges (e. g. alpha/beta band). This finding offers a new interpretation of measurable brain activity, however neither the methodological means to detect bursts, nor their link to other findings (e. g. connectivity) have been settled. Here, we use a new approach to detect bursts in magnetoencephalography (MEG) data. We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. However, unlike existing techniques, the HMM looks for specific spectral patterns in timecourse data. We characterise the distribution of burst duration, frequency of occurrence and amplitude across the cortex in resting state MEG data. During a motor task we show how the movement related beta decrease and post movement beta rebound are driven by changes in burst occurrence. Finally, we show that the beta band functional connectome can be derived using a simple measure of burst overlap, and that coincident bursts in separate regions correspond to a period of heightened coherence. In summary, this paper offers a new methodology for burst identification and connectivity analysis which will be important for future investigations of neural oscillations.

YNIMG Journal 2018 Journal Article

Altered temporal stability in dynamic neural networks underlies connectivity changes in neurodevelopment

Matthew J. Brookes
Madeleine J. Groom
Lucrezia Liuzzi
Ryan M. Hill
Helen J.F. Smith
Paul M. Briley
Emma L. Hall
Benjamin A.E. Hunt

Network connectivity is an integral feature of human brain function, and characterising its maturational trajectory is a critical step towards understanding healthy and atypical neurodevelopment. Here, we used magnetoencephalography (MEG) to investigate both stationary (i. e. time averaged) and rapidly modulating (dynamic) electrophysiological connectivity, in participants aged from mid-childhood to early adulthood (youngest participant 9 years old; oldest participant 25 years old). Stationary functional connectivity (measured via inter-regional coordination of neural oscillations) increased with age in the alpha and beta frequency bands, particularly in bilateral parietal and temporo-parietal connections. Our dynamic analysis (also applied to alpha/beta oscillations) revealed the spatiotemporal signatures of 8 dynamic networks; these modulate on a ∼100 ms time scale, and temporal stability in attentional networks was found to increase with age. Significant overlap was found between age-modulated dynamic networks and inter-regional oscillatory coordination, implying that altered network dynamics underlie age related changes in functional connectivity. Our results provide novel insights into brain network electrophysiology, and lay a foundation for future work in childhood disorders.

YNICL Journal 2018 Journal Article

Changes in electrophysiological markers of cognitive control after administration of galantamine

Lauren E. Gascoyne
Karen J. Mullinger
Siân E. Robson
Jyothika Kumar
George C. O'Neill
Lena Palaniyappan
Peter G. Morris
Elizabeth B. Liddle

The healthy brain is able to maintain a stable balance between bottom-up sensory processing and top-down cognitive control. The neurotransmitter acetylcholine plays a substantial role in this. Disruption of this balance could contribute to symptoms occurring in psychosis, including subtle disruption of motor control and aberrant appropriation of salience to external stimuli; however the pathological mechanisms are poorly understood. On account of the role beta oscillations play in mediating cognitive control, investigation of beta oscillations is potentially informative about such mechanisms. Here, we used magnetoencephalography to investigate the effect of the acetylcholinesterase-inhibitor, galantamine, on beta oscillations within the sensorimotor region during both a sensorimotor task and a relevance-modulation task in healthy participants, employing a double blind randomized placebo controlled cross-over design. In the galantamine condition, we found a significant reduction in the post-movement beta rebound in the case of executed movements and also in a planned but not executed movement. In the latter case, the effect was significantly greater following task-relevant compared with irrelevant stimuli. The results suggest that the action of galantamine reduces the influence of top-down cognitive processing relative to bottom-up perceptual processing in a manner resembling changes previously reported in schizophrenia.

YNIMG Journal 2018 Journal Article

Mapping the topological organisation of beta oscillations in motor cortex using MEG

Eleanor L. Barratt
Susan T. Francis
Peter G. Morris
Matthew J. Brookes

The spatial topology of the human motor cortex has been well studied, particularly using functional Magnetic Resonance Imaging (fMRI) which allows spatial separation of haemodynamic responses arising from stimulation of different body parts, individual digits and even spatially separate areas of the same digit. However, the spatial organisation of electrophysiological responses, particularly neural oscillations (rhythmic changes in electrical potential across cellular assemblies) has been less well studied. Mapping the spatial signature of neural oscillations is possible using magnetoencephalography (MEG), however spatial differentiation of responses induced by movement of separate digits is a challenge, because the brain regions involved are separated by only a few millimetres. In this paper we first show, in simulation, how to optimise experimental design and beamformer spatial filtering techniques to increase the spatial specificity of MEG derived functional images. Combining this result with experimental data, we then capture the organisation of the post-movement beta band (13–30 Hz) oscillatory response to movement of digits 2 and 5 of the dominant hand, in individual subjects. By comparing these MEG results to ultra-high field (7T) fMRI, we also show significant spatial agreement between beta modulation and the blood oxygenation level dependent (BOLD) response. Our results show that, when using an optimised inverse solution and controlling subject movement (using custom fitted foam padding) the spatial resolution of MEG can be of order 3–5 mm. The method described offers exciting potential to understand better the cortical organisation of oscillations, and to probe such organisation in patient populations where those oscillations are known to be abnormal.

YNIMG Journal 2017 Journal Article

A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers

Elena Boto
Sofie S. Meyer
Vishal Shah
Orang Alem
Svenja Knappe
Peter Kruger
T. Mark Fromhold
Mark Lim

Advances in the field of quantum sensing mean that magnetic field sensors, operating at room temperature, are now able to achieve sensitivity similar to that of cryogenically cooled devices (SQUIDs). This means that room temperature magnetoencephalography (MEG), with a greatly increased flexibility of sensor placement can now be considered. Further, these new sensors can be placed directly on the scalp surface giving, theoretically, a large increase in the magnitude of the measured signal. Here, we present recordings made using a single optically-pumped magnetometer (OPM) in combination with a 3D-printed head-cast designed to accurately locate and orient the sensor relative to brain anatomy. Since our OPM is configured as a magnetometer it is highly sensitive to environmental interference. However, we show that this problem can be ameliorated via the use of simultaneous reference sensor recordings. Using median nerve stimulation, we show that the OPM can detect both evoked (phase-locked) and induced (non-phase-locked oscillatory) changes when placed over sensory cortex, with signals ~4 times larger than equivalent SQUID measurements. Using source modelling, we show that our system allows localisation of the evoked response to somatosensory cortex. Further, source-space modelling shows that, with 13 sequential OPM measurements, source-space signal-to-noise ratio (SNR) is comparable to that from a 271-channel SQUID system. Our results highlight the opportunity presented by OPMs to generate uncooled, potentially low-cost, high SNR MEG systems.

YNIMG Journal 2017 Journal Article

Activation induced changes in GABA: Functional MRS at 7 T with MEGA-sLASER

Chen Chen
Hilmar P. Sigurdsson
Sophia E. Pépés
Dorothee P. Auer
Peter G. Morris
Paul S. Morgan
Penny A. Gowland
Stephen R. Jackson

Functional magnetic resonance spectroscopy (fMRS) has been used to assess the dynamic metabolic responses of the brain to a physiological stimulus non-invasively. However, only limited information on the dynamic functional response of γ-aminobutyric acid (GABA), the primary inhibitory neurotransmitter in the brain, is available. We aimed to measure the activation-induced changes in GABA unambiguously using a spectral editing method, instead of the conventional direct detection techniques used in previous fMRS studies. The Mescher-Garwood-semi-localised by adiabatic selective refocusing (MEGA-sLASER) sequence was developed at 7T to obtain the time course of GABA concentration without macromolecular contamination. A significant decrease (−12±5%) in the GABA to total creatine ratio (GABA/tCr) was observed in the motor cortex during a period of 10min of hand-clenching, compared to an initial baseline level (GABA/tCr =0. 11±0. 02) at rest. An increase in the Glx (glutamate and glutamine) to tCr ratio was also found, which is in agreement with previous findings. In contrast, no significant changes in NAA/tCr and tCr were detected. With consistent and highly efficient editing performance for GABA detection and the advantage of visually identifying GABA resonances in the spectra, MEGA-sLASER is demonstrated to be an effective method for studying of dynamic changes in GABA at 7T.

YNIMG Journal 2017 Journal Article

Comparing GABA-dependent physiological measures of inhibition with proton magnetic resonance spectroscopy measurement of GABA using ultra-high-field MRI

Katherine Dyke
Sophia E. Pépés
Chen Chen
Soyoung Kim
Hilmar P. Sigurdsson
Amelia Draper
Masud Husain
Parashkev Nachev

Imbalances in glutamatergic (excitatory) and GABA (inhibitory) signalling within key brain networks are thought to underlie many brain and mental health disorders, and for this reason there is considerable interest in investigating how individual variability in localised concentrations of these molecules relate to brain disorders. Magnetic resonance spectroscopy (MRS) provides a reliable means of measuring, in vivo, concentrations of neurometabolites such as GABA, glutamate and glutamine that can be correlated with brain function and dysfunction. However, an issue of much debate is whether the GABA observed and measured using MRS represents the entire pool of GABA available for measurement (i. e. , metabolic, intracellular, and extracellular) or is instead limited to only some portion of it. GABA function can also be investigated indirectly in humans through the use of non-invasive transcranial magnetic stimulation (TMS) techniques that can be used to measure cortical excitability and GABA-mediated physiological inhibition. To investigate this issue further we collected in a single session both types of measurement, i. e. , TMS measures of cortical excitability and physiological inhibition and ultra-high-field (7 T) MRS measures of GABA, glutamate and glutamine, from the left sensorimotor cortex of the same group of right-handed individuals. We found that TMS and MRS measures were largely uncorrelated with one another, save for the plateau of the TMS IO curve that was negatively correlated with MRS-Glutamate (Glu) and intra-cortical facilitation (10ms ISI) that was positively associated with MRS-Glutamate concentration. These findings are consistent with the view that the GABA concentrations measured using the MRS largely represent pools of GABA that are linked to tonic rather than phasic inhibition and thus contribute to the inhibitory tone of a brain area rather than GABAergic synaptic transmission.

YNIMG Journal 2017 Journal Article

Measurement of dynamic task related functional networks using MEG

George C. O’Neill
Prejaas K. Tewarie
Giles L. Colclough
Lauren E. Gascoyne
Benjamin A.E. Hunt
Peter G. Morris
Mark W. Woolrich
Matthew J. Brookes

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.

YNIMG Journal 2016 Journal Article

A multi-layer network approach to MEG connectivity analysis

Matthew J. Brookes
Prejaas K. Tewarie
Benjamin A.E. Hunt
Sian E. Robson
Lauren E. Gascoyne
Elizabeth B. Liddle
Peter F. Liddle
Peter G. Morris

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.

YNICL Journal 2016 Journal Article

Abnormal visuomotor processing in schizophrenia

Siân E. Robson
Matthew J. Brookes
Emma L. Hall
Lena Palaniyappan
Jyothika Kumar
Michael Skelton
Nikolaos G. Christodoulou
Ayaz Qureshi

Subtle disturbances of visual and motor function are known features of schizophrenia and can greatly impact quality of life; however, few studies investigate these abnormalities using simple visuomotor stimuli. In healthy people, electrophysiological data show that beta band oscillations in sensorimotor cortex decrease during movement execution (event-related beta desynchronisation (ERBD)), then increase above baseline for a short time after the movement (post-movement beta rebound (PMBR)); whilst in visual cortex, gamma oscillations are increased throughout stimulus presentation. In this study, we used a self-paced visuomotor paradigm and magnetoencephalography (MEG) to contrast these responses in patients with schizophrenia and control volunteers. We found significant reductions in the peak-to-peak change in amplitude from ERBD to PMBR in schizophrenia compared with controls. This effect was strongest in patients who made fewer movements, whereas beta was not modulated by movement in controls. There was no significant difference in the amplitude of visual gamma between patients and controls. These data demonstrate that clear abnormalities in basic sensorimotor processing in schizophrenia can be observed using a very simple MEG paradigm.

YNIMG Journal 2016 Journal Article

Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach

Prejaas Tewarie
Arjan Hillebrand
Bob W. van Dijk
Cornelis J. Stam
George C. O'Neill
Piet Van Mieghem
Jil M. Meier
Mark W. Woolrich

Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.

YNIMG Journal 2015 Journal Article

Dynamic recruitment of resting state sub-networks

George C. O'Neill
Markus Bauer
Mark W. Woolrich
Peter G. Morris
Gareth R. Barnes
Matthew J. Brookes

Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with evidence suggesting that they are integral to healthy brain function and perturbed in pathology. Despite rapid progress in this area, the temporal dynamics governing the functional connectivities that underlie RSN structure remain poorly understood. Here, we present a framework to help further our understanding of RSN dynamics. We describe a methodology which exploits the direct nature and high temporal resolution of magnetoencephalography (MEG). This technique, which builds on previous work, extends from solving fundamental confounds in MEG (source leakage) to multivariate modelling of transient connectivity. The resulting processing pipeline facilitates direct (electrophysiological) measurement of dynamic functional networks. Our results show that, when functional connectivity is assessed in small time windows, the canonical sensorimotor network can be decomposed into a number of transiently synchronising sub-networks, recruitment of which depends on current mental state. These rapidly changing sub-networks are spatially focal with, for example, bilateral primary sensory and motor areas resolved into two separate sub-networks. The likely interpretation is that the larger canonical sensorimotor network most often seen in neuroimaging studies reflects only a temporal aggregate of these transient sub-networks. Our approach opens new frontiers to study RSN dynamics, showing that MEG is capable of revealing the spatial, temporal and spectral signature of the human connectome in health and disease.

YNIMG Journal 2014 Journal Article

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

Matthew J. Brookes
George C. O'Neill
Emma L. Hall
Mark W. Woolrich
Adam Baker
Sofia Palazzo Corner
Siân E. Robson
Peter G. Morris

The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i. e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe the generation of time–frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial non-uniformity within short time–frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatio-temporal-spectral modes of covariation between the left and right sensorimotor areas.

YNIMG Journal 2014 Journal Article

Methodology for improved detection of low concentration metabolites in MRS: Optimised combination of signals from multi-element coil arrays

Emma L. Hall
Mary C. Stephenson
Darren Price
Peter G. Morris

State of the art magnetic resonance imaging (MRI) scanners are generally equipped with multi-element receive coils; 16 or 32 channel coils are common. Their development has been predominant for parallel imaging to enable faster scanning. Less consideration has been given to localized magnetic resonance spectroscopy (MRS). Multinuclear studies, for example 31P or 13C MRS, are often conducted with a single element coil located over the region of interest. 1H MRS studies have generally employed the same multi-element coils used for MRI, but little consideration has been given as to how the spectroscopic data from the different channels are combined. In many cases it is simply co-added with detrimental effect on the signal to noise ratio. In this study, we derive the optimum method for combining multi-coil data, namely weighting with the ratio of signal to the square of the noise. We show that provided that the noise is uncorrelated, this is the theoretical optimal combination. The method is demonstrated for in vivo proton MRS data acquired using a 32 channel receive coil at 7T in four different brain areas; left motor and right motor, occipital cortex and medial frontal cortex.

YNIMG Journal 2014 Journal Article

tDCS-induced alterations in GABA concentration within primary motor cortex predict motor learning and motor memory: A 7 T magnetic resonance spectroscopy study

Soyoung Kim
Mary C. Stephenson
Peter G. Morris
Stephen R. Jackson

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that alters cortical excitability in a polarity specific manner and has been shown to influence learning and memory. tDCS may have both on-line and after-effects on learning and memory, and the latter are thought to be based upon tDCS-induced alterations in neurochemistry and synaptic function. We used ultra-high-field (7T) magnetic resonance spectroscopy (MRS), together with a robotic force adaptation and de-adaptation task, to investigate whether tDCS-induced alterations in GABA and Glutamate within motor cortex predict motor learning and memory. Note that adaptation to a robot-induced force field has long been considered to be a form of model-based learning that is closely associated with the computation and ‘supervised’ learning of internal ‘forward’ models within the cerebellum. Importantly, previous studies have shown that on-line tDCS to the cerebellum, but not to motor cortex, enhances model-based motor learning. Here we demonstrate that anodal tDCS delivered to the hand area of the left primary motor cortex induces a significant reduction in GABA concentration. This effect was specific to GABA, localised to the left motor cortex, and was polarity specific insofar as it was not observed following either cathodal or sham stimulation. Importantly, we show that the magnitude of tDCS-induced alterations in GABA concentration within motor cortex predicts individual differences in both motor learning and motor memory on the robotic force adaptation and de-adaptation task.

YNIMG Journal 2014 Journal Article

The relationship between MEG and fMRI

Emma L. Hall
Siân E. Robson
Peter G. Morris
Matthew J. Brookes

In recent years functional neuroimaging techniques such as fMRI, MEG, EEG and PET have provided researchers with a wealth of information on human brain function. However none of these modalities can measure directly either the neuro-electrical or neuro-chemical processes that mediate brain function. This means that metrics directly reflecting brain ‘activity’ must be inferred from other metrics (e. g. magnetic fields (MEG) or haemodynamics (fMRI)). To overcome this limitation, many studies seek to combine multiple complementary modalities and an excellent example of this is the combination of MEG (which has high temporal resolution) with fMRI (which has high spatial resolution). However, the full potential of multi-modal approaches can only be truly realised in cases where the relationship between metrics is known. In this paper, we explore the relationship between measurements made using fMRI and MEG. We describe the origins of the two signals as well as their relationship to electrophysiology. We review multiple studies that have attempted to characterise the spatial relationship between fMRI and MEG, and we also describe studies that exploit the rich information content of MEG to explore differing relationships between MEG and fMRI across neural oscillatory frequency bands. Monitoring the brain at “rest” has become of significant recent interest to the neuroimaging community and we review recent evidence comparing MEG and fMRI metrics of functional connectivity. A brief discussion of the use of magnetic resonance spectroscopy (MRS) to probe the relationship between MEG/fMRI and neurochemistry is also given. Finally, we highlight future areas of interest and offer some recommendations for the parallel use of fMRI and MEG.

YNIMG Journal 2013 Journal Article

Using variance information in magnetoencephalography measures of functional connectivity

Emma L. Hall
Mark W. Woolrich
Carlos E. Thomaz
Peter G. Morris
Matthew J. Brookes

The use of magnetoencephalography (MEG) to assess long range functional connectivity across large scale distributed brain networks is gaining popularity. Recent work has shown that electrodynamic networks can be assessed using both seed based correlation or independent component analysis (ICA) applied to MEG data and further that such metrics agree with fMRI studies. To date, techniques for MEG connectivity assessment have typically used a variance normalised approach, either through the use of Pearson correlation coefficients or via variance normalisation of envelope timecourses prior to ICA. Here, we show that the use of variance information (i. e. data that have not been variance normalised) in source space projected Hilbert envelope time series yields important spatial information, and is of significant functional relevance. Further, we show that employing this information in functional connectivity analyses improves the spatial delineation of network nodes using both seed based and ICA approaches. The use of variance is particularly important in MEG since the non-independence of source space voxels (brought about by the ill-posed MEG inverse problem) means that spurious signals can exist in areas of low signal variance. We therefore suggest that this approach be incorporated into future studies.

YNIMG Journal 2011 Journal Article

Changes in brain network activity during working memory tasks: A magnetoencephalography study

Matthew J. Brookes
Jonathan R. Wood
Claire M. Stevenson
Johanna M. Zumer
Thomas P. White
Peter F. Liddle
Peter G. Morris

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 20–40Hz 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.

YNIMG Journal 2011 Journal Article

Measuring functional connectivity using MEG: Methodology and comparison with fcMRI

Matthew J. Brookes
Joanne R. Hale
Johanna M. Zumer
Claire M. Stevenson
Susan T. Francis
Gareth R. Barnes
Julia P. Owen
Peter G. Morris

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.

YNIMG Journal 2011 Journal Article

The effect of hypercapnia on resting and stimulus induced MEG signals

Emma L. Hall
Ian D. Driver
Paula L. Croal
Susan T. Francis
Penny A. Gowland
Peter G. Morris
Matthew J. Brookes

The effect of hypercapnia (an increase in CO2 concentration in the blood) on the functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) haemodynamic response has been well characterised and is commonly used for BOLD calibration. However, relatively little is known of the effect of hypercapnia on the electrical brain processes that underlie the BOLD response. Here, we investigate the effect of hypercapnia on resting and stimulus induced changes in neural oscillations using a feed-forward low gas flow system to deliver a reliable and repeatable level of hypercapnia. Magnetoencephalography (MEG) is used in conjunction with beamformer source localisation algorithms to non-invasively image changes in oscillatory amplitude. At rest, we find robust oscillatory power loss in the alpha (8Hz–13Hz), beta (13Hz–30Hz) and low gamma (30Hz–50Hz) frequency bands in response to hypercapnia. Further, we show that the spatial signature of this power loss differs across frequency bands, with the largest effect being observed for the beta band in sensorimotor cortices. We also measure changes in oscillatory activity induced by visual and motor events, and the effect of hypercapnia on these changes; whilst the percentage change in oscillatory activity on activation was largely unaffected by hypercapnia, the absolute change in oscillatory amplitude differed between normocapnia and hypercapnia. This work supports invasive recordings made in animals, and the results have potential implications for calibrated BOLD studies.

YNIMG Journal 2010 Journal Article

Investigating spatial specificity and data averaging in MEG

Matthew J. Brookes
Johanna M. Zumer
Claire M. Stevenson
Joanne R. Hale
Gareth R. Barnes
Jiri Vrba
Peter G. Morris

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.

YNIMG Journal 2010 Journal Article

Relating BOLD fMRI and neural oscillations through convolution and optimal linear weighting

Johanna M. Zumer
Matthew J. Brookes
Claire M. Stevenson
Susan T. Francis
Peter G. Morris

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.

YNIMG Journal 2009 Journal Article

Source localisation in concurrent EEG/fMRI: Applications at 7T

Matthew J. Brookes
Jiri Vrba
Karen J. Mullinger
Gerða Björk Geirsdóttir
Winston X. Yan
Claire M. Stevenson
Richard Bowtell
Peter G. Morris

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 ∼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.

YNIMG Journal 2008 Journal Article

A novel MR-compatible device for providing forces to the human finger during functional neuroimaging studies

Carl P.T. Jackson
Richard Bowtell
Peter G. Morris
Stephen R. Jackson

Many motor learning experiments involve subjects performing a task while experiencing external force perturbations. However, it is difficult to transfer these tasks to functional magnetic resonance imaging (fMRI) studies, and much of the technology that currently exists to facilitate this is expensive to produce and difficult to use. Here, we report on the design and construction of a novel device (the ‘force coil’) that is simple and inexpensive, and that uses the static magnetic field inside the scanner to provide forces to the human finger. The coil incorporates a potentiometer in the base to allow the recording of angular position. To test whether the magnetic field generated by the current flowing through the coil would interfere with the functional images collected, we compared images from a phantom during the use of the coil at arm's length in a 7T magnet. There was no noticeable interference from the coil at the levels of current used in this experiment, which produced about 10 N of force in a 7T scanner. In conclusion, the force coil is a cheap, easy to operate device which provides forces to the finger inside the scanner without affecting image quality. Designs based on this principle are likely to prove useful in studies of motor learning using fMRI.

YNIMG Journal 2008 Journal Article

Optimising experimental design for MEG beamformer imaging

Matthew J. Brookes
Jiri Vrba
Stephen E. Robinson
Claire M. Stevenson
Andrew M. Peters
Gareth R. Barnes
Arjan Hillebrand
Peter G. Morris

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.

YNIMG Journal 2008 Journal Article

Simultaneous EEG source localisation and artifact rejection during concurrent fMRI by means of spatial filtering

Matthew J. Brookes
Karen J. Mullinger
Claire M. Stevenson
Peter G. Morris
Richard Bowtell

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.

YNIMG Journal 2007 Journal Article

Beamformer reconstruction of correlated sources using a modified source model

Matthew J. Brookes
Claire M. Stevenson
Gareth R. Barnes
Arjan Hillebrand
Michael I.G. Simpson
Susan T. Francis
Peter G. Morris

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.

YNIMG Journal 2005 Journal Article

GLM-beamformer method demonstrates stationary field, alpha ERD and gamma ERS co-localisation with fMRI BOLD response in visual cortex

Matthew J. Brookes
Andrew M. Gibson
Stephen D. Hall
Paul L. Furlong
Gareth R. Barnes
Arjan Hillebrand
Krish D. Singh
Ian E. Holliday

Recently, we introduced a new ‘GLM-beamformer’ technique for MEG analysis that enables accurate localisation of both phase-locked and non-phase-locked neuromagnetic effects, and their representation as statistical parametric maps (SPMs). This provides a useful framework for comparison of the full range of MEG responses with fMRI BOLD results. This paper reports a ‘proof of principle’ study using a simple visual paradigm (static checkerboard). The five subjects each underwent both MEG and fMRI paradigms. We demonstrate, for the first time, the presence of a sustained (DC) field in the visual cortex, and its co-localisation with the visual BOLD response. The GLM-beamformer analysis method is also used to investigate the main non-phase-locked oscillatory effects: an event-related desynchronisation (ERD) in the alpha band (8–13 Hz) and an event-related synchronisation (ERS) in the gamma band (55–70 Hz). We show, using SPMs and virtual electrode traces, the spatio-temporal covariance of these effects with the visual BOLD response. Comparisons between MEG and fMRI data sets generally focus on the relationship between the BOLD response and the transient evoked response. Here, we show that the stationary field and changes in oscillatory power are also important contributors to the BOLD response, and should be included in future studies on the relationship between neuronal activation and the haemodynamic response.

YNIMG Journal 2004 Journal Article

A general linear model for MEG beamformer imaging

Matthew J. Brookes
Andrew M. Gibson
Stephen D. Hall
Paul L. Furlong
Gareth R. Barnes
Arjan Hillebrand
Krish D. Singh
Ian E. Holliday

A new general linear model (GLM) beamformer method is described for processing magnetoencephalography (MEG) data. A standard nonlinear beamformer is used to determine the time course of neuronal activation for each point in a predefined source space. A Hilbert transform gives the envelope of oscillatory activity at each location in any chosen frequency band (not necessary in the case of sustained (DC) fields), enabling the general linear model to be applied and a volumetric T statistic image to be determined. The new method is illustrated by a two-source simulation (sustained field and 20 Hz) and is shown to provide accurate localization. The method is also shown to locate accurately the increasing and decreasing gamma activities to the temporal and frontal lobes, respectively, in the case of a scintillating scotoma. The new method brings the advantages of the general linear model to the analysis of MEG data and should prove useful for the localization of changing patterns of activity across all frequency ranges including DC (sustained fields).