A multimodal neuroimaging study of somatosensory system

The thesis is the result of a training by the Magnetoencephalography (MEG)-lab by the Center mind/brain science of the university of Trento. Final goal of the analysis was answering the question if MEG is capable to capture activities from the subcortical brain areas and to follow the neural information flow up along the fibers to the cortex. First aim of the thesis is describing the project and developing of an experiment on the somatosensory system that I executed by the CIMeC. The somatosensory system was activated by applying electrical stimulation to the median nerve and MEG signal during this stimulation was recorded. Also MRI and diffusion MRI data of the subject were collected. Further aim of the thesis is to describe the analysis I executed on the collected data. For this purpose the MEG source localization was executed and also Monte-Carlo simulation. The data obtained were integrated with the information obtained from diffusion MRI. Satisfactory results were obtained although we could not prove definitely the result

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies

Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed

Magnetoencephalography for the investigation and diagnosis of Mild Traumatic Brain Injury

Mild Traumatic Brain Injury (mTBI), (or concussion), is the most common type of brain injury. Despite this, it often goes undiagnosed and can cause long term disability—most likely caused by the disruption of axonal connections in the brain. Objective methods for diagnosis and prognosis are needed but clinically available neuroimaging modalities rarely show structural abnormalities, even when patients suffer persisting functional deficits. In the past three decades, new powerful techniques to image brain structure and function have shown promise in detecting mTBI related changes. Magnetoencephalography (MEG), which measures electrical brain activity by detecting magnetic fields outside the head generated by neural currents, is particularly sensitive and has therefore gained interest from researchers. Numerous studies are proposing abnormal low-frequency neural oscillations and functional connectivity—the statistical interdependency of signals from separate brain regions—as potential biomarkers for mTBI. However, typically small sample sizes, the lack of replication between groups, the heterogeneity of the cohorts studied, and the lack of longitudinal studies impedes the adoption of MEG as a clinical tool in mTBI management. In particular, little is known about the acute phase of mTBI. In this thesis, some of these gaps will be addressed by analysing MEG data from individuals with mTBI, using novel as well as conventional methods. The potential future of MEG in mTBI research will also be addressed by testing the capabilities of a wearable MEG system based on optically pumped magnetometers (OPMs). The thesis contains three main experimental studies. In study 1, we investigated the signal dynamics underlying MEG abnormalities, found in a cohort of subjects scanned within three months of an mTBI, using a Hidden Markov Model (HMM), as growing evidence suggests that neural dynamics are (in part) driven by transient bursting events. Applying the HMM to resting-state data, we show that previously reported findings of diminished intrinsic beta amplitude and connectivity in individuals with mTBI (compared to healthy controls) can be explained by a reduction in the beta-band content of pan-spectral bursts and a loss in the temporal coincidence of bursts respectively. Using machine learning, we find the functional connections driving group differences and achieve classification accuracies of 98%. In a motor task, mTBI resulted in reduced burst amplitude, altered modulation of burst probability during movement and decreased connectivity in the motor network. In study 2, we further test our HMM-based method in a cohort of subjects with mTBI and non-head trauma—scanned within two weeks of injury—to ensure specificity of any observed effects to mTBI and replicate our previous finding of reduced connectivity and high classification accuracy, although not the reduction in burst amplitude. Burst statistics were stable over both studies—despite data being acquired at different sites, using different scanners. In the same cohort, we applied a more conventional analysis of delta-band power. Although excess low-frequency power appears to be a promising candidate marker for persistently symptomatic mTBI, insufficient data exist to confirm this pattern in acute mTBI. We found abnormally high delta power to be a sensitive measure for discriminating mTBI subjects from healthy controls, however, similarly elevated delta amplitude was found in the cohort with non-head trauma, suggesting that excess delta may not be specific to mTBI, at least in the acute stage of injury. Our work highlights the need for longitudinal assessment of mTBI. In addition, there appears to be a need to investigate naturalistic paradigms which can be tailored to induce activity in symptom-relevant brain networks and consequently are likely to be more sensitive biomarkers than the resting state scans used to date. Wearable OPM-MEG makes naturalistic scanning possible and may offer a cheaper and more accessible alternative to cryogenic MEG, however, before deploying OPMs clinically, or in pitch-side assessment for athletes, for example, the reliability of OPM-derived measures needs to be verified. In the third and final study, we performed a repeatability study using a novel motor task, estimating a series of common MEG measures and quantifying the reliability of both activity and connectivity derived from OPM-MEG data. These initial findings—presently limited to a small sample of healthy controls—demonstrate the utility of OPM-MEG and pave the way for this technology to be deployed on patients with mTBI

Magnetoencephalography for the investigation and diagnosis of Mild Traumatic Brain Injury

Mild Traumatic Brain Injury (mTBI), (or concussion), is the most common type of brain injury. Despite this, it often goes undiagnosed and can cause long term disability—most likely caused by the disruption of axonal connections in the brain. Objective methods for diagnosis and prognosis are needed but clinically available neuroimaging modalities rarely show structural abnormalities, even when patients suffer persisting functional deficits. In the past three decades, new powerful techniques to image brain structure and function have shown promise in detecting mTBI related changes. Magnetoencephalography (MEG), which measures electrical brain activity by detecting magnetic fields outside the head generated by neural currents, is particularly sensitive and has therefore gained interest from researchers. Numerous studies are proposing abnormal low-frequency neural oscillations and functional connectivity—the statistical interdependency of signals from separate brain regions—as potential biomarkers for mTBI. However, typically small sample sizes, the lack of replication between groups, the heterogeneity of the cohorts studied, and the lack of longitudinal studies impedes the adoption of MEG as a clinical tool in mTBI management. In particular, little is known about the acute phase of mTBI. In this thesis, some of these gaps will be addressed by analysing MEG data from individuals with mTBI, using novel as well as conventional methods. The potential future of MEG in mTBI research will also be addressed by testing the capabilities of a wearable MEG system based on optically pumped magnetometers (OPMs). The thesis contains three main experimental studies. In study 1, we investigated the signal dynamics underlying MEG abnormalities, found in a cohort of subjects scanned within three months of an mTBI, using a Hidden Markov Model (HMM), as growing evidence suggests that neural dynamics are (in part) driven by transient bursting events. Applying the HMM to resting-state data, we show that previously reported findings of diminished intrinsic beta amplitude and connectivity in individuals with mTBI (compared to healthy controls) can be explained by a reduction in the beta-band content of pan-spectral bursts and a loss in the temporal coincidence of bursts respectively. Using machine learning, we find the functional connections driving group differences and achieve classification accuracies of 98%. In a motor task, mTBI resulted in reduced burst amplitude, altered modulation of burst probability during movement and decreased connectivity in the motor network. In study 2, we further test our HMM-based method in a cohort of subjects with mTBI and non-head trauma—scanned within two weeks of injury—to ensure specificity of any observed effects to mTBI and replicate our previous finding of reduced connectivity and high classification accuracy, although not the reduction in burst amplitude. Burst statistics were stable over both studies—despite data being acquired at different sites, using different scanners. In the same cohort, we applied a more conventional analysis of delta-band power. Although excess low-frequency power appears to be a promising candidate marker for persistently symptomatic mTBI, insufficient data exist to confirm this pattern in acute mTBI. We found abnormally high delta power to be a sensitive measure for discriminating mTBI subjects from healthy controls, however, similarly elevated delta amplitude was found in the cohort with non-head trauma, suggesting that excess delta may not be specific to mTBI, at least in the acute stage of injury. Our work highlights the need for longitudinal assessment of mTBI. In addition, there appears to be a need to investigate naturalistic paradigms which can be tailored to induce activity in symptom-relevant brain networks and consequently are likely to be more sensitive biomarkers than the resting state scans used to date. Wearable OPM-MEG makes naturalistic scanning possible and may offer a cheaper and more accessible alternative to cryogenic MEG, however, before deploying OPMs clinically, or in pitch-side assessment for athletes, for example, the reliability of OPM-derived measures needs to be verified. In the third and final study, we performed a repeatability study using a novel motor task, estimating a series of common MEG measures and quantifying the reliability of both activity and connectivity derived from OPM-MEG data. These initial findings—presently limited to a small sample of healthy controls—demonstrate the utility of OPM-MEG and pave the way for this technology to be deployed on patients with mTBI

Source Modelling of the Human Hippocampus for MEG

Magnetoencephalography (MEG) is a neuroimaging technique which gives direct non-invasive measurements of neuronal activity with high temporal resolution. Given its increasing use in cognitive and clinical research, it is important to characterize, and ideally improve upon, its advantages and limitations. For example, it is conventionally assumed to be insensitive to deep structures because of their distance from the sensors. Consequently, knowledge about their signal contribution is limited. One deep structure of particular interest is the hippocampus which plays a key role in memory and learning, and in organising temporal flow of information across regions. A large body of rodent studies have demonstrated quantifiable oscillatory underpinnings of these functions, now waiting to be addressed in humans. Due to its high temporal resolution, MEG is ideally suited for doing so but faces technical challenges. Firstly, the source-to-sensor distance is large, making it difficult to obtain sufficiently high signal-to-noise ratio (SNR) data. Secondly, most generative models (which describe the relationship between sensors and signal) include only the cortical surface. Thirdly, errors in co-registering data to an anatomical image easily obstruct or blur hippocampal sources. This thesis tested the hypotheses that a) identification and optimisation of acquisition parameters which improve the SNR, b) inclusion of the hippocampus in the generative model, and c) minimisation of co-registration error, together enable reliable inferences about hippocampal activity from MEG data. We found the most important empirical factor in detecting hippocampal activity using the extended generative model to be co-registration error; that this can be minimised using flexible head-casts; and that combining anatomical modelling, head-casts, and a spatial memory task, allows hippocampal activity to be reliably observed. Hence the work confirmed the overall hypothesis to be valid. Additionally, simulation results revealed that for a new generation of MEG sensors, ~5-fold sensitivity improvements can be obtained but critically depend on low sensor location errors. These findings set down a new basis for time-resolved examination of hippocampal function

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

Revealing Distinct Neural Signatures in Magnetoencephalography with Hidden Markov Models

Magnetoencephalography (MEG) is a functional neuroimaging method which measures the magnetic fields produced by neural communication in the brain. Specifically, the fields induced by dendritic current flow in assemblies of pyramidal neurons. Because these magnetic fields are generated directly by brain electrophysiology, and are mostly unperturbed by the skull, MEG data are rich in spatial and temporal information. This thesis is chiefly concerned with interpreting these data in a way that produces useful results whilst minimising bias. Hidden Markov modelling (HMM) is a robust statistical method which has been applied to fields as diverse as speech recognition and financial market prediction. It parses data into a number of ‘hidden states’, each with their own unique characteristics, in an unsupervised way. Because it is data-driven, it can create a model unique to each participant’s brain activity and specific to each task. In addition, the HMM framework itself is flexible so it can be applied to both sensor and source-space data and can be applied to multiple channels (multivariate) or to a single time course (univariate). Choice of an observation model allows states to be characterised by amplitude, spatial, or spectral content depending on the research question. The aim of this thesis is to apply hidden Markov modelling (HMM) to whole head MEG data to identify repeated patterns of transient neural activity occurring throughout the brain. Once these patterns were identified, the interaction between these short ‘bursts’ of activity across the cortex was established which provided a unique measure of functional connectivity. Three studies were undertaken: The role of transient spectral bursts in MEG functional connectivity: In recent years, the smoothly varying neural oscillations often studied in MEG (such as those trial-averaged responses in the traditional neurophysiological (such as alpha/beta) frequency bands) have been shown to be made up of single-trial high-amplitude ‘bursts’ of activity. These bursts can be observed in the beta frequency band and are therefore often referred to as beta bursts. In this study, a novel time-delay embedded HMM was used to identif bursts in broadband data based on their spectral content for MEG data from 66 healthy adult participants. The burst amplitude, duration and frequency of occurrence were characterised across the cortex in resting state data, and in a motor task the classic movement-related beta desynchronisation and post movement beta rebound were shown to be made up of changes in burst occurrence. A novel functional connectivity metric was then introduced based on the coincidence of bursts from distal brain regions, allowing the known beta band functional connectome to be reproduced. Bursts coincident across spatially separate brain regions were also shown to correspond to periods of heightened coherence, lending evidence to the communication by coherence (Fries 2005, 2015) hypothesis. Post-stimulus responses across the cortex: During a motor task, both primary (during stimulation) and post stimulus responses (PSR) can be observed. These are well characterised in the literature, but little is known about their functional significance. The PSR in particular is modified in a range of seemingly unrelated neurological conditions with variable symptoms, such as schizophrenia (Robson et al. 2016), autism spectrum disorder (Gaetz et al. 2020) and multiple sclerosis (Barratt et al. 2017), indicating that the PSR is a fundamental neurophysiological process, the disturbance of which has implications on both healthy and pathological brain function. This work therefore tested the hypothesis that the PSR is present across the cortex. MEG data were acquired and analysed from two experiments with 15 healthy adult volunteers each – the first was a right-hand grip task with visual feedback, the second involved passive left visual field stimulation. Both experiments varied stimulus duration (2s, 5s and 10s) with a 30s rest-period between trials to allow characterisation of the full PSR. A univariate 3-state time-delay-embedded hidden Markov model (HMM) was used to characterise the spatial distributions of the primary and PSR across the cortex for both tasks. Results showed that for both tasks, the primary response state was more bilateral over the sensorimotor or visual areas (depending on task) where the PSR state was more unilateral and confined to the contralateral sensorimotor or visual areas (again, dependant on task). A state coincidence metric was then used to investigate the integration of the primary and PSR states across brain regions as a measure of task-related functional connectivity. Hidden Markov modelling of the interictal brain: Epilepsy is a highly heterogeneous disease with variations in the temporal morphology and localisation of epileptiform activity across patients. Unsupervised machine learning techniques like the HMM allow us to take into account this variability and ensure that every model is tailored to each individual. In this work, a multivariate time-delay embedded HMM was used to identify brain states based on their spatial and spectral properties in sensor-level MEG data acquired as part of standard clinical care for patients at the Children’s Hospital of Philadelphia. State allocations were used together with a linearly constrained minimum variance (LCMV) beamformer to produce a 3D map of state variance, hence localising probable epileptogenic foci. Clinical MEG epilepsy data are routinely analysed by excess kurtosis mapping (EKM) and so the performance of the HMM was assessed against this for three patient groups, each with increasingly complex epilepsy manifestation (10 patients in total). The difference in localization of epileptogenic foci for the two methods was 7 ± 2mm (mean ± SD over all 10 patients); and 94 ± 13% of EKM temporal markers were matched by an HMM state visit. It is therefore clear that this method localizes epileptogenic areas in agreement with EKM and in patients with more than one focus the HMM provides additional information about the relationship between them

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