Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.Peer reviewe

Methods for noninvasive localization of focal epileptic activity with magnetoencephalography

Magnetoencephalography (MEG) is a noninvasive brain signal acquisition technique that provides excellent temporal resolution and a whole-head coverage allowing the spatial mapping of sources. These characteristics make MEG an appropriate technique to localize the epileptogenic zone (EZ) in the preoperative evaluation of refractory epilepsy. Presurgical evaluation with MEG can guide the placement of intracranial EEG (iEEG), the current gold standard in the clinical practice, and even supply sufficient information for a surgical intervention without invasive recordings, reducing invasiveness, discomfort, and cost of the presurgical epilepsy diagnosis. However, MEG signals have low signal-to-noise ratio compared with iEEG and can sometimes be affected by noise that masks or distorts the brain activity. This may prevent the detection of interictal epileptiform discharges (IEDs) and high-frequency oscillations (HFOs), two important biomarkers used in the preoperative evaluation of epilepsy. In this thesis, the reduction of two kinds of interference is aimed to improve the signal-to-noise ratio of MEG signals: metallic artifacts mask the activity of IEDs; and the high-frequency noise, that masks HFO activity. Considering the large number of MEG channels and the long duration of the recordings, reducing noise and marking events manually is a time-consuming task. The algorithms presented in this thesis provide automatic solutions aimed at the reduction of interferences and the detection of HFOs. Firstly, a novel automatic BSS-based algorithm to reduce metallic interference is presented and validated using simulated and real MEG signals. Three methods are tested: AMUSE, a second-order BSS technique; and INFOMAX and FastICA, based on high-order statistics. The automatic detection algorithm exploits the known characteristics of metallic-related interferences. Results indicate that AMUSE performes better when recovering brain activity and allows an effective removal of artifactual components.Secondly, the influence of metallic artifact filtering using the developed algorithm is evaluated in the source localization of IEDs in patients with refractory focal epilepsy. A comparison between the resulting positions of equivalent current dipoles (ECDs) produced by IEDs is performed: without removing metallic interference, rejecting only channels with large metallic artifacts, and after BSS-based reduction. The results show that a significant reduction on dispersion is achieved using the BSS-based reduction procedure, yielding feasible locations of ECDs in contrast to the other approaches. Finally, an algorithm for the automatic detection of epileptic ripples in MEG using beamformer-based virtual sensors is developed. The automatic detection of ripples is performed using a two-stage approach. In the first step, beamforming is applied to the whole head to determine a region of interest. In the second step, the automatic detection of ripples is performed using the time-frequency characteristics of these oscillations. The performance of the algorithm is evaluated using simultaneous intracranial EEG recordings as gold standard.The novel approaches developed in this thesis allow an improved noninvasive detection and localization of interictal epileptic biomarkers, which can help in the delimitation of the epileptogenic zone and guide the placement of intracranial electrodes, or even to determine these areas without additional invasive recordings. As a consequence of this improved detection, and given that interictal biomarkers are much more frequent and easy to record than ictal episodes, the presurgical evaluation process can be more comfortable for the patient and in a more economic way.La magnetoencefalografía (MEG) es una técnica no invasiva de adquisición de señales cerebrales que proporciona una excelente resolución temporal y una cobertura total de la cabeza, permitiendo el mapeo espacial de las fuentes cerebrales. Estas características hacen del MEG una técnica apropiada para localizar la zona epileptogénica (EZ) en la evaluación preoperatoria de la epilepsia refractaria. La evaluación prequirúrgica con MEG puede orientar la colocación del EEG intracraneal (iEEG), el actual modelo de referencia en la práctica clínica, e incluso suministrar información suficiente para una intervención quirúrgica sin registros invasivos; reduciendo la invasividad, la incomodidad y el costo del diagnóstico de la epilepsia prequirúrgica. Sin embargo, las señales MEG tienen baja relación señal ruido en comparación con el iEEG pudiendo imposibilitar la detección de descargas epileptiformes interictales (IEDs) y oscilaciones de alta frecuencia (HFOs), dos importantes biomarcadores utilizados en la evaluación preoperatoria de la epilepsia.En esta tesis, la reducción de dos tipos de interferencia está dirigida a mejorar la relación señal-ruido de la señal MEG: los artefactos metálicos que enmascaran la actividad de las IEDs; y el ruido de alta frecuencia, que enmascara la actividad de las HFOs. Debido al gran número de canales MEG y la larga duración de los registros, tanto reducir el ruido como seleccionar los biomarcadores manualmente es una tarea que consume mucho tiempo. Los algoritmos presentados en esta tesis aportan soluciones automáticas dirigidas a la reducción de interferencias y la detección de HFOs. En primer lugar, se presenta y valida un nuevo algoritmo automático basado en BSS para reducir interferencias metálicas mediante señales simuladas y reales. Se prueban tres métodos: AMUSE, una técnica BSS de segundo orden; y INFOMAX y FastICA, basados en estadísticos de orden superior. El algoritmo de detección automático utiliza las características conocidas de la señal producida por la interferencia metálica. Los resultados indican que AMUSE recupera mejor la actividad cerebral y permite una eliminación efectiva de componentes artefactuales.Posteriormente, se evalúa la influencia del filtrado de artefactos metálicos en la localización de IEDs en pacientes con epilepsia focal refractaria. Se realiza una comparación entre las posiciones resultantes de dipolos de corriente equivalentes (ECDs) producidos por IEDs: sin eliminar interferencias metálicas, rechazando solamente canales con elevados artefactos metálicos y, por último, después de una reducción utilizando el algoritmo BSS desarrollado. Los resultados muestran que se logra una reducción significativa en la dispersión utilizando el procedimiento de reducción basado en BSS, lo que produce ubicaciones factibles de los dipolos en contraste con los otros enfoques.En segundo lugar, se desarrolla un algoritmo para la detección automática ripples epilépticos en MEG utilizando sensores virtuales basados en la técnica de beamformer. La detección de ripples se realiza mediante un enfoque en dos etapas. Primero, se determina el área de interés usando beamformer. Posteriormente, se realiza la detección automática de ripples utilizando las características en tiempo-frecuencia. El rendimiento del algoritmo se evalúa utilizando registros iEEG simultáneos.Los nuevos enfoques desarrollados en esta tesis permiten una detección no invasiva mejor de los biomarcadores interictales, que pueden ayudar a delimitar la zona epileptogénica y guiar la colocación de electrodos intracraneales, o incluso determinar estas áreas sin este tipo de registros. Como consecuencia de esta mejora en la detección, y dado que los biomarcadores interictales son mucho más frecuentes y fáciles de registrar que los episodios ictales, la evaluación prequirúrgica puede ser más cómoda y menos costosa para el paciente.Postprint (published version

Scanning Reduction Strategy in MEG/EEG Beamformer Source Imaging

MEG/EEG beamformer source imaging is a promising approach which can easily address spatiotemporal multi-dipole problems without a priori information on the number of sources and is robust to noise. Despite such promise, beamformer generally has weakness which is degrading localization performance for correlated sources and is requiring of dense scanning for covering all possible interesting (entire) source areas. Wide source space scanning yields all interesting area images, and it results in lengthy computation time. Therefore, an efficient source space scanning strategy would be beneficial in achieving accelerated beamformer source imaging. We propose a new strategy in computing beamformer to reduce scanning points and still maintain effective accuracy (good spatial resolution). This new strategy uses the distribution of correlation values between measurements and lead-field vectors. Scanning source points are chosen yielding higher RMS correlations than the predetermined correlation thresholds. We discuss how correlation thresholds depend on SNR and verify the feasibility and efficacy of our proposed strategy to improve the beamformer through numerical and empirical experiments. Our proposed strategy could in time accelerate the conventional beamformer up to over 40% without sacrificing spatial accuracy

IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Peer reviewe

Cortical oscillatory dysrhythmias in visual snow syndrome: a magnetoencephalography study

Visual Snow refers to the persistent visual experience of static in the whole visual field of both eyes. It is often reported by patients with migraine and co-occurs with conditions like tinnitus and tremor. The underlying pathophysiology of the condition is poorly understood. Previously we hypothesised, that visual snow syndrome may be characterised by disruptions to rhythmical activity within the visual system. To test this, data from 18 patients diagnosed with visual snow syndrome, and 16 matched controls, were acquired using magnetoencephalography. Participants were presented with visual grating stimuli, known to elicit decreases in alpha-band (8-13Hz) power and increases in gamma-band power (40-70Hz). Data were mapped to source-space using a beamformer. Across both groups, decreased alpha power and increased gamma power localised to early visual cortex. Data from the primary visual cortex were compared between groups. No differences were found in either alpha or gamma peak frequency or the magnitude of alpha power, p>0.05. However, compared with controls, our visual snow syndrome cohort displayed significantly increased primary visual cortex gamma power, p=0.035. This new electromagnetic finding concurs with previous functional MRI and PET findings suggesting that in visual snow syndrome, the visual cortex is hyper-excitable. The coupling of alpha-phase to gamma amplitude within the primary visual cortex was also quantified. Compared with controls, the visual snow syndrome group had significantly reduced alpha-gamma phase-amplitude coupling, p<0.05, indicating a potential excitation-inhibition imbalance in visual snow syndrome, as well as a potential disruption to top-down “noise-cancellation” mechanisms. Overall, these results suggest that rhythmical brain activity in primary visual cortex is both hyperexcitable and disorganised in visual snow syndrome, consistent with this being a condition of thalamocortical dysrhythmia

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