A multi-modal approach to functional neuroimaging

The work undertaken involves the use of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) as separate but complementary non-invasive functional brain imaging modalities. The aim in combining fMRI and MEG is centred around exploitation of the high temporal resolution available in MEG, and the high spatial resolution available in fMRI. However, whilst MEG represents a direct measure of neuronal activity, BOLD fMRI is an indirect measure and this makes the two modalities truly complementary. In both cases, the imaging signals measured are relatively poorly understood and so the fundamental question asked here is: How are the neuromagnetic effects detectable using MEG related to the metabolic effects reflected in the fMRI BOLD response? Initially, a novel technique is introduced for the detection and spatial localisation of neuromagnetic effects in MEG. This technique, based on a beamforming approach to the MEG inverse problem, is shown to yield accurate results both in simulation and using experimental data. The technique introduced is applied to MEG data from a simple experiment involving stimulation of the visual cortex. A number of heterogeneous neuromagnetic effects are shown to be detectable, and furthermore, these effects are shown to be spatially and temporally correlated with the fMRI BOLD response. The limitations to comparing only two measures of brain activity are discussed, and the use of arterial spin labelling (ASL) to make quantitative measurements of physiological parameters supplementing these two initial metrics is introduced. Finally, a novel technique for accurate quantification of arterial cerebral blood volume using ASL is described and shown to produce accurate results. A concluding chapter then speculates on how these aCBV measurements might be combined with those from MEG in order to better understand the fMRI BOLD response

A multi-modal approach to functional neuroimaging

The work undertaken involves the use of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) as separate but complementary non-invasive functional brain imaging modalities. The aim in combining fMRI and MEG is centred around exploitation of the high temporal resolution available in MEG, and the high spatial resolution available in fMRI. However, whilst MEG represents a direct measure of neuronal activity, BOLD fMRI is an indirect measure and this makes the two modalities truly complementary. In both cases, the imaging signals measured are relatively poorly understood and so the fundamental question asked here is: How are the neuromagnetic effects detectable using MEG related to the metabolic effects reflected in the fMRI BOLD response? Initially, a novel technique is introduced for the detection and spatial localisation of neuromagnetic effects in MEG. This technique, based on a beamforming approach to the MEG inverse problem, is shown to yield accurate results both in simulation and using experimental data. The technique introduced is applied to MEG data from a simple experiment involving stimulation of the visual cortex. A number of heterogeneous neuromagnetic effects are shown to be detectable, and furthermore, these effects are shown to be spatially and temporally correlated with the fMRI BOLD response. The limitations to comparing only two measures of brain activity are discussed, and the use of arterial spin labelling (ASL) to make quantitative measurements of physiological parameters supplementing these two initial metrics is introduced. Finally, a novel technique for accurate quantification of arterial cerebral blood volume using ASL is described and shown to produce accurate results. A concluding chapter then speculates on how these aCBV measurements might be combined with those from MEG in order to better understand the fMRI BOLD response

Resting state MEG oscillations show long-range temporal correlations of phase synchrony that break down during finger movement

The capacity of the human brain to interpret and respond to multiple temporal scales in its surroundings suggests that its internal interactions must also be able to operate over a broad temporal range. In this paper, we utilize a recently introduced method for characterizing the rate of change of the phase difference between MEG signals and use it to study the temporal structure of the phase interactions between MEG recordings from the left and right motor cortices during rest and during a finger-tapping task. We use the Hilbert transform to estimate moment-to-moment fluctuations of the phase difference between signals. After confirming the presence of scale-invariance we estimate the Hurst exponent using detrended fluctuation analysis (DFA). An exponent of >0.5 is indicative of long-range temporal correlations (LRTCs) in the signal. We find that LRTCs are present in the α/μ and β frequency bands of resting state MEG data. We demonstrate that finger movement disrupts LRTCs correlations, producing a phase relationship with a structure similar to that of Gaussian white noise. The results are validated by applying the same analysis to data with Gaussian white noise phase difference, recordings from an empty scanner and phase-shuffled time series. We interpret the findings through comparison of the results with those we obtained from an earlier study during which we adopted this method to characterize phase relationships within a Kuramoto model of oscillators in its sub-critical, critical, and super-critical synchronization states. We find that the resting state MEG from left and right motor cortices shows moment-to-moment fluctuations of phase difference with a similar temporal structure to that of a system of Kuramoto oscillators just prior to its critical level of coupling, and that finger tapping moves the system away from this pre-critical state toward a more random state

The relationship between MEG and fMRI

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

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

The effect of isocapnic hyperoxia on neurophysiology as measured with MRI and MEG

The physiological effect of hyperoxia has been poorly characterised, with studies reporting conflicting results on the role of hyperoxia as a vasoconstrictor. It is not clear whether hyperoxia is the primary contributor to vasoconstriction or whether induced changes in CO2 that commonly accompany hyperoxia are a factor. As calibrated BOLD fMRI based on hyperoxia becomes more widely used, it is essential to understand the effects of oxygen on resting cerebral physiology. This study used a RespirActTM system to deliver a repeatable isocapnic hyperoxia stimulus to investigate the independent effect of O2 on cerebral physiology, removing any potential confounds related to altered CO2. T1-independent Phase Contrast MRI was used to demonstrate that isocapnic hyperoxia has no significant effect on carotid blood flow (normoxia 201 ± 11 ml/min, -0.3 ± 0.8 % change during hyperoxia, p = 0.8), whilst Look Locker ASL was used to demonstrate that there is no significant change in arterial cerebral blood volume (normoxia 1.3 ± 0.4 %, -0.5 ± 5 % change during hyperoxia). These are in contrast to significant changes in blood flow observed for hypercapnia (6.8 ± 1.5 %/mmHg CO2). In addition, magnetoencephalography provided a method to monitor the effect of isocapnic hyperoxia on neuronal oscillatory power. In response to hyperoxia, a significant focal decrease in oscillatory power was observed across the alpha, beta and low gamma bands in the occipital lobe, compared to a more global significant decrease on hypercapnia. This work suggests that isocapnic hyperoxia provides a more reliable stimulus than hypercapnia for calibrated BOLD, and that previous reports of vasoconstriction during hyperoxia probably reflect the effects of hyperoxia-induced changes in CO2. However, hyperoxia does induce changes in oscillatory power consistent with an increase in vigilance, but these changes are smaller than those observed under hypercapnia. The effect of this change in neural activity on calibrated BOLD using hyperoxia or combined hyperoxia and hypercapnia needs further investigation

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

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