Dynamic electrophysiological connectomics

The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. For a century we have been developing techniques to non-invasively map these areas and their associated functions, a discipline now known as neuroimaging. In recent years the field has undergone a paradigm shift to investigate how the brain communicates with itself; it is widely regarded that healthy brain function relies upon efficient connectivity between different functional areas, and the neuroimaging field has been revolutionised by our ability to estimate this connectivity. Studies into communication between spatially separate locations in the brain have revealed a series of robust functional networks which govern mental processes. However these studies have been based on the temporal averaging of minutes or even hours of data to give us a generalised ’snapshot’ of connectivity. Increasing evidence shows us that these connections are dynamic in space, time and frequency and so the next generation of of neuroimaging methods, which capture this 5-dimensional connectivity will prove to be key tools in the investigation of brain networks and ultimately their breakdown in disease. In this thesis we introduce novel methods to capture non-stationarity using magnetoencephalography (MEG), an imaging modality which measures the changes in extracranial magnetic fields associated with neuronal current flow. MEG is a direct measurement of neural activity and has an excellent temporal resolution, which makes it attractive for non-invasively tracking dynamic functional connections. However there are many technical limitations which can confound assessment of functional connectivity which have to be addressed. In Chapters 2 and 3 we introduce the theory behind MEG; specifically how it is possible to measure the femtoTelsa changes in magnetic field generated by the brain and how to project these data to generate a 3-dimensional picture of current in the brain. Chapter 4 reviews some of popular methods of assessing functional connectivity and how to control for the influence of artefactual functional connections erroneously produced during source projection. Chapter 5 introduces a pipeline to assess functional connections across time, space and frequency and in Chapter 6 we apply this pipeline to show that resting state networks, measured using ’static’ metrics are in fact comprised of a series of rapidly forming and dissolving subnetwork connections. Finally, Chapter 7 introduces a pipeline to track dynamic network behaviour simultaneously across the entire brain volume and shows that networks can be characterised by their temporal signatures of connectivity

Dynamic electrophysiological connectomics

The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. For a century we have been developing techniques to non-invasively map these areas and their associated functions, a discipline now known as neuroimaging. In recent years the field has undergone a paradigm shift to investigate how the brain communicates with itself; it is widely regarded that healthy brain function relies upon efficient connectivity between different functional areas, and the neuroimaging field has been revolutionised by our ability to estimate this connectivity. Studies into communication between spatially separate locations in the brain have revealed a series of robust functional networks which govern mental processes. However these studies have been based on the temporal averaging of minutes or even hours of data to give us a generalised ’snapshot’ of connectivity. Increasing evidence shows us that these connections are dynamic in space, time and frequency and so the next generation of of neuroimaging methods, which capture this 5-dimensional connectivity will prove to be key tools in the investigation of brain networks and ultimately their breakdown in disease. In this thesis we introduce novel methods to capture non-stationarity using magnetoencephalography (MEG), an imaging modality which measures the changes in extracranial magnetic fields associated with neuronal current flow. MEG is a direct measurement of neural activity and has an excellent temporal resolution, which makes it attractive for non-invasively tracking dynamic functional connections. However there are many technical limitations which can confound assessment of functional connectivity which have to be addressed. In Chapters 2 and 3 we introduce the theory behind MEG; specifically how it is possible to measure the femtoTelsa changes in magnetic field generated by the brain and how to project these data to generate a 3-dimensional picture of current in the brain. Chapter 4 reviews some of popular methods of assessing functional connectivity and how to control for the influence of artefactual functional connections erroneously produced during source projection. Chapter 5 introduces a pipeline to assess functional connections across time, space and frequency and in Chapter 6 we apply this pipeline to show that resting state networks, measured using ’static’ metrics are in fact comprised of a series of rapidly forming and dissolving subnetwork connections. Finally, Chapter 7 introduces a pipeline to track dynamic network behaviour simultaneously across the entire brain volume and shows that networks can be characterised by their temporal signatures of connectivity

Spherical harmonic based noise rejection and neuronal sampling with multi-axis OPMs

In this study we explore the interference rejection and spatial sampling properties of multi-axis Optically Pumped Magnetometer (OPM) data. We use both vector spherical harmonics and eigenspectra to quantify how well an array can separate neuronal signal from environmental interference while adequately sampling the entire cortex. We found that triaxial OPMs have superb noise rejection properties allowing for very high orders of interference (L=6) to be accounted for while minimally affecting the neural space (2dB attenuation for a 60-sensor triaxial system). We show that at least 11th order (143 spatial degrees of freedom) irregular solid harmonics or 95 eigenvectors of the lead field are needed to model the neural space for OPM data (regardless of number of axes measured). This can be adequately sampled with 75-100 equidistant triaxial sensors (225-300 channels) or 200 equidistant radial channels. In other words, ordering the same number of channels in triaxial (rather than purely radial) configuration may give significant advantages not only in terms of external noise rejection but also by minimizing cost, weight and cross-talk

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

How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes

Functional networks obtained from magnetoencephalography (MEG) from different frequency bands show distinct spatial patterns. It remains to be elucidated how distinct spatial patterns in MEG networks emerge given a single underlying structural network. Recent work has suggested that the eigenmodes of the structural network might serve as a basis set for functional network patterns in the case of functional MRI. Here, we take this notion further in the context of frequency band specific MEG networks. We show that a selected set of eigenmodes of the structural network can predict different frequency band specific networks in the resting state, ranging from delta (1–4 Hz) to the high gamma band (40–70 Hz). These predictions outperform predictions based from surrogate data, suggesting a genuine relationship between eigenmodes of the structural network and frequency specific MEG networks. We then show that the relevant set of eigenmodes can be excited in a network of neural mass models using linear stability analysis only by including delays. Excitation of an eigenmode in this context refers to a dynamic instability of a network steady state to a spatial pattern with a corresponding coherent temporal oscillation. Simulations verify the results from linear stability analysis and suggest that theta, alpha and beta band networks emerge very near to the bifurcation. The delta and gamma bands in the resting state emerges further away from the bifurcation. These results show for the first time how delayed interactions can excite the relevant set of eigenmodes that give rise to frequency specific functional connectivity patterns

The effect of physical fatigue on oscillatory dynamics of the sensorimotor cortex

AIM: While physical fatigue is known to arise in part from supraspinal mechanisms within the brain exactly how brain activity is modulated during fatigue is not well understood. Therefore, this study examined how typical neural oscillatory responses to voluntary muscle contractions were affected by fatigue. METHODS: Eleven healthy adults (age 27±4 years) completed two experimental sessions in a randomised crossover design. Both sessions first assessed baseline maximal voluntary isometric wrist-flexion force (MVFb ). Participants then performed an identical series of fourteen test contractions (2 × 100%MVFb , 10 × 40%MVFb , 2 × 100%MVFb ) both before and after one of two interventions: forty 12-s contractions at 55%MVFb (fatigue intervention) or 5%MVFb (control intervention). Magnetoencephalography (MEG) was used to characterise both the movement-related mu and beta decrease (MRMD and MRBD) and the post-movement beta rebound (PMBR) within the contralateral sensorimotor cortex during the 40%MVFb test contractions, while the 100%MVFb test contractions were used to monitor physical fatigue. RESULTS: The fatigue intervention induced a substantial physical fatigue that endured throughout the post-intervention measurements (28.9-29.5% decrease in MVF, P<0.001). Fatigue had a significant effect on both PMBR (ANOVA, session × time-point interaction: P=0.018) and MRBD (P=0.021): the magnitude of PMBR increased following the fatigue but not the control interventions, whereas MRBD was decreased post-control but not post-fatigue. Mu oscillations were unchanged throughout both sessions. CONCLUSION: Physical fatigue resulted in an increased PMBR, and offset attenuations in MRBD associated with task habituation. This article is protected by copyright. All rights reserved

Real-time, model-based magnetic field correction for moving, wearable MEG

Most neuroimaging techniques require the participant to remain still for reliable recordings to be made. Optically pumped magnetometer (OPM) based magnetoencephalography (OP-MEG) however, is a neuroimaging technique which can be used to measure neural signals during large participant movement (approximately 1 m) within a magnetically shielded room (MSR) (Boto et al., 2018; Seymour et al., 2021). Nevertheless, environmental magnetic fields vary both spatially and temporally and OPMs can only operate within a limited magnetic field range, which constrains participant movement. Here we implement real-time updates to electromagnetic coils mounted on-board of the OPMs, to cancel out the changing background magnetic fields. The coil currents were chosen based on a continually updating harmonic model of the background magnetic field, effectively implementing homogeneous field correction (HFC) in real-time (Tierney et al., 2021). During a stationary, empty room recording, we show an improvement in very low frequency noise of 24 dB. In an auditory paradigm, during participant movement of up to 2 m within a magnetically shielded room, introduction of the real-time correction more than doubled the proportion of trials in which no sensor saturated recorded outside of a 50 cm radius from the optimally-shielded centre of the room. The main advantage of such model-based (rather than direct) feedback is that it could allow one to correct field components along unmeasured OPM axes, potentially mitigating sensor gain and calibration issues (Borna et al., 2022)

SARS-CoV-2 infection in the first trimester and the risk of early miscarriage: a UK population-based prospective cohort study of 3041 pregnancies conceived during the pandemic

STUDY QUESTION: Does maternal infection with severe acute respiratory syndrome coronavirus (SARS-CoV-2) in the first trimester affect the risk of miscarriage before 13 week's gestation? SUMMARY ANSWER: Pregnant women with self-reported diagnosis of SARS-CoV-2 in the first trimester had a higher risk of early miscarriage. WHAT IS KNOWN ALREADY: Viral infections during pregnancy have a broad spectrum of placental and neonatal pathology. Data on the effects of the SARS-CoV-2 infection in pregnancy are still emerging. Two systematic reviews and meta-analyses reported an increased risk of preterm birth, caesarean delivery, maternal morbidity and stillbirth. Data on the impact of first trimester infection on early pregnancy outcomes are scarce. This is the first study, to our knowledge, to investigate the rates of early pregnancy loss during the SARS-CoV-2 outbreak among women with self-reported infection. STUDY DESIGN, SIZE, DURATION: This was a nationwide prospective cohort study of pregnant women in the community recruited using social media between 21st May and 31st December, 2020. We recruited 3545 women who conceived during the SARS-CoV-2 pandemic who were less than 13 week's gestation at the time of recruitment. PARTICIPANTS/MATERIALS, SETTING, METHODS: The COVID-19 Contraception and Pregnancy Study (CAP-COVID) was an on-line survey study collecting longitudinal data from pregnant women in the UK aged 18 years or older. Women who were pregnant during the pandemic were asked to complete on-line surveys at the end of each trimester. We collected data on current and past pregnancy complications, their medical history and whether they or anyone in their household had symptoms or been diagnosed with SARS-CoV-2 infection during each trimester of their pregnancy. RT-PCR-based SARS-CoV-2 RNA detection from respiratory samples (e.g., nasopharynx) is the standard practice for diagnosis of SARS-CoV-2 in the UK. We compared rate of self-reported miscarriage in three groups: 'presumed infected' i.e those who reported a diagnosis with SARS-CoV-2 infection in the first trimester; 'uncertain' i.e those who did not report a diagnosis but had symptoms/household contacts with symptoms/diagnosis; and 'presumed uninfected' i.e., those who did not report any symptoms/diagnosis and had no household contacts with symptoms/diagnosis of SARS-CoV-2. MAIN RESULTS AND THE ROLE OF CHANCE: A total of 3545 women registered for the CAP-COVID study at less than 13 weeks gestation and were eligible for this analysis. Data for the primary outcome were available from 3041 women (86%). In the overall sample, the rate of self-reported miscarriage was 7.8% (238/3041 [95% CI, 7-9]). The median gestational age at miscarriage was 9 weeks (interquartile range 8-11). Seventy-seven women were in the 'presumed infected' group (77/3041, 2.5% [95% CI 2 - 3]), 295/3041 were in the uncertain group (9.7%, [95% CI 9-11]) and the rest in the 'presumed uninfected' (87.8%, 2669/3041, [95% CI 87-89]). The rate of early miscarriage was 14% in the 'presumed infected' group, 5% in the 'uncertain' and 8% in the 'presumed uninfected' (11/77 [95% CI 6-22] versus15/295, [95% CI 3-8] versus 212/2669 [95% CI 7-9], p = 0.02). After adjusting for age, BMI, ethnicity, smoking status, gestational age at registration and the number of previous miscarriages, the risk of early miscarriage appears to be higher in the 'presumed infected' group (relative rate 1.7, 95% CI 1.0-3.0, p = 0.06). LIMITATIONS, REASONS FOR CAUTION: We relied on self-reported data on early pregnancy loss and SARS-CoV-2 infection without any means of checking validity. Some women in the 'presumed uninfected' and 'uncertain' groups may have had asymptomatic infections. The number of 'presumed infected' in our study was low and therefore the study was relatively underpowered. WIDER IMPLICATIONS OF THE FINDINGS: This was a national study from the UK, where infection rates were one of the highest in the world. Based on the evidence presented here, women who are infected with SARS-CoV-2 in their first trimester may be at an increased risk of a miscarriage. However, the overall rate of miscarriage in our study population was 8%. This is reassuring and suggests that if there is an effect of SARS-CoV-2 on the risk of miscarriage, this may be limited to those with symptoms substantial enough to lead to a diagnostic test. Further studies are warranted to evaluate a causal association between SARS-CoV-2 infection in early pregnancy and miscarriage risk. Although we did not see an overall increase in the risk of miscarriage, the observed comparative increase in the presumed infected group reinforces the message that pregnant women should continue to exercise social distancing measures and good hygiene throughout their pregnancy to limit their risk of infection. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by a grant from the Elizabeth Garrett Anderson Hospital Charity, (G13-559194). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. JAH is supported by an NIHR Advanced Fellowship. ALD is supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support to JAH and ALD as above; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work. TRIAL REGISTRATION NUMBER: n/a

The effect of physical fatigue on oscillatory dynamics of the sensorimotor cortex

Aim: While physical fatigue is known to arise in part from supraspinal mechanisms within the brain exactly how brain activity is modulated during fatigue is not well understood. Therefore, this study examined how typical neural oscillatory responses to voluntary muscle contractions were affected by fatigue. Methods: Eleven healthy adults (age 27±4 years) completed two experimental sessions in a randomised crossover design. Both sessions first assessed baseline maximal voluntary isometric wrist-flexion force (MVFb). Participants then performed an identical series of fourteen test contractions (2 × 100%MVFb, 10 × 40%MVFb, 2 × 100%MVFb) both before and after one of two interventions: forty 12-s contractions at 55%MVFb (fatigue intervention) or 5%MVFb (control intervention). Magnetoencephalography (MEG) was used to characterise both the movement-related mu and beta decrease (MRMD and MRBD) and the post-movement beta rebound (PMBR) within the contralateral sensorimotor cortex during the 40%MVFb test contractions, while the 100%MVFb test contractions were used to monitor physical fatigue. Results: The fatigue intervention induced a substantial physical fatigue that endured throughout the post-intervention measurements (28.9-29.5% decrease in MVF, P<0.001). Fatigue had a significant effect on both PMBR (ANOVA, session × time-point interaction: P=0.018) and MRBD (P=0.021): the magnitude of PMBR increased following the fatigue but not the control interventions, whereas MRBD was decreased post-control but not post-fatigue. Mu oscillations were unchanged throughout both sessions. Conclusion: Physical fatigue resulted in an increased PMBR, and offset attenuations in MRBD associated with task habituation

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

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