Clinical Magnetoencephalography Research Articles

Magnetoencephalography for Epilepsy Presurgical Evaluation.

Magnetoencephalography (MEG) is a functional neuroimaging technique that records neurophysiology data with millisecond temporal resolution and localizes it with subcentimeter accuracy. Its capability to provide high resolution in both of these domains makes it a powerful tool both in basic neuroscience as well as clinical applications. In neurology, it has proven useful in its ability to record and localize epileptiform activity. Epilepsy workup typically begins with scalp electroencephalography (EEG), but in many situations, EEG-based localization of the epileptogenic zone is inadequate. The complementary sensitivity of MEG can be crucial in such cases, and MEG has been adopted at many centers as an important resource in building a surgical hypothesis. In this paper, we review recent work evaluating the extent of MEG influence of presurgical evaluations, novel analyses of MEG data employed in surgical workup, and new MEG instrumentation that will likely affect the field of clinical MEG. MEG consistently contributes to presurgical evaluation and these contributions often change the plan for epilepsy surgery. Extensive work has been done to develop new analytic methods for localizing the source of epileptiform activity with MEG. Systems using optically pumped magnetometry (OPM) have been successfully deployed to record and localize epileptiform activity. MEG remains an important noninvasive tool for epilepsy presurgical evaluation. Continued improvements in analytic methodology will likely increase the diagnostic yield of the test. Novel instrumentation with OPM may contribute to this as well, and may increase accessibility of MEG by decreasing cost.

Towards the automated detection of interictal epileptiform discharges with magnetoencephalography

BackgroundThe analysis of clinical magnetoencephalography (MEG) in patients with epilepsy traditionally relies on visual identification of interictal epileptiform discharges (IEDs), which is time consuming and dependent on subjective criteria. New MethodHere, we explore the ability of Independent Components Analysis (ICA) and Hidden Markov Modeling (HMM) to automatically detect and localize IEDs. We tested our pipelines on resting-state MEG recordings from 10 school-aged children with (multi)focal epilepsy. ResultsIn focal epilepsy patients, both pipelines successfully detected visually identified IEDs, but also revealed unidentified low-amplitude IEDs. Success was more mitigated in patients with multifocal epilepsy, as our automated pipeline missed IED activity associated with some foci—an issue that could be alleviated by post-hoc manual selection of epileptiform ICs or HMM states. Comparison with existing methodsWe compared our results with visual IED detection by an experienced clinical magnetoencephalographer, getting heightened sensitivity and requiring minimal input from clinical practitioners. ConclusionsIED detection based on ICA or HMM represents an efficient way to identify IED localization and timing. The development of these automatic IED detection algorithms provide a step forward in clinical MEG practice by decreasing the duration of MEG analysis and enhancing its sensitivity.

Role of optically pumped magnetometers in presurgical epilepsy evaluation: Commentary of the American Clinical Magnetoencephalography Society.

One of the major challenges of modern epileptology is the underutilization of epilepsy surgery for treatment of patients with focal, medication resistant epilepsy (MRE). Aggravating this distressing failure to deliver optimum care to these patients is the underuse of proven localizing tools, such as magnetoencephalography (MEG), a clinically validated, non-invasive, neurophysiological method used to directly measure and localize brain activity. A sizable mass of published evidence indicates that MEG can improve identification of surgical candidates and guide pre-surgical planning, increasing the yield of SEEG and improving operative outcomes. However, despite at least 10 common, evidence supported, clinical scenarios in MRE patients where MEG can offer non-redundant information and improve the pre-surgical evaluation, it is regularly used by only a minority of USA epilepsy centers. The current state of the art in MEG sensors employs SQUIDs, which require cooling with liquid helium to achieve superconductivity. This sensor technology has undergone significant generational improvement since whole head MEG scanners were introduced around in 1990s, but still has limitations. Further advances in sensor technology which may make ME G more easily accessible and affordable have been eagerly awaited, and development of new techniques should be encouraged. Of late, optically pumped magnetometers (OPMs) have received considerable attention, even prompting some potential acquisitions of new MEG systems to be put on hold, based on a hope that OPMs will usher in a new generation of MEG equipment and procedures. The development of any new clinical test used to guide intracranial EEG monitoring and/or surgical planning must address several specific issues. The goal of this commentary is to recognize the current state of OPM technology and to suggest a framework for it to advance in the clinical realm where it can eventually be deemed clinically valuable to physicians and patients. The American Clinical MEG Society (ACMEGS) strongly supports more advanced and less expensive technology and looks forward to continuing work with researchers to develop new sensors and clinical devices which will improve the experience and outcome for patients, and perhaps extend the role of MEG. However, currently, there are no OPM devices ready for practical clinical use. Based on the engineering obstacles and the clinical tradeoffs to be resolved, the assessment of experts suggests that there will most likely be another decade relying solely on "frozen SQUIDs" in the clinical MEG field.

Measurement of Frontal Midline Theta Oscillations using OPM-MEG

Optically pumped magnetometers (OPMs) are an emerging lightweight and compact sensor that can measure magnetic fields generated by the human brain. OPMs enable construction of wearable magnetoencephalography (MEG) systems, which offer advantages over conventional instrumentation. However, when trying to measure signals at low frequency, higher levels of inherent sensor noise, magnetic interference and movement artefact introduce a significant challenge. Accurate characterisation of low frequency brain signals is important for neuroscientific, clinical, and paediatric MEG applications and consequently, demonstrating the viability of OPMs in this area is critical. Here, we undertake measurement of theta band (4–8 Hz) neural oscillations and contrast a newly developed 174 channel triaxial wearable OPM-MEG system with conventional (cryogenic-MEG) instrumentation. Our results show that visual steady state responses at 4 Hz, 6 Hz and 8 Hz can be recorded using OPM-MEG with a signal-to-noise ratio (SNR) that is not significantly different to conventional MEG. Moreover, we measure frontal midline theta oscillations during a 2-back working memory task, again demonstrating comparable SNR for both systems. We show that individual differences in both the amplitude and spatial signature of induced frontal-midline theta responses are maintained across systems. Finally, we show that our OPM-MEG results could not have been achieved without a triaxial sensor array, or the use of postprocessing techniques. Our results demonstrate the viability of OPMs for characterising theta oscillations and add weight to the argument that OPMs can replace cryogenic sensors as the fundamental building block of MEG systems.

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists' skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was learned, and in the second stage, a segmentation network that extended the classification network was learned. The classification network had a mean AUC of 0.9868 (10-fold patient-wise cross-validation); the sensitivity and specificity were 0.7952 and 0.9971, respectively. The median distance between the ECDs estimated by the neurophysiologists and those using FAMED was 0.63 cm. Thus, the performance of FAMED is comparable to that of neurophysiologists, and it can contribute to the efficiency and consistency of MEG ECD analysis.

Magnetoencephalography: at the forefront of optimizing epilepsy surgery

Magnetoencephalography: at the forefront of optimizing epilepsy surgery

Gratifying Gizmos for Research and Clinical MEG.

Experimental designs are of utmost importance in neuroimaging. Experimental repertoire needs to be designed with the understanding of physiology, clinical feasibility, and constraints posed by a particular neuroimaging method. Innovations in introducing natural, ecologically-relevant stimuli, with successful collaboration across disciplines, correct timing, and a bit of luck may cultivate novel experiments, new discoveries, and open pathways to new clinical practices. Here I introduce some gizmos that I have initiated in magnetoencephalography (MEG) and applied with my collaborators in my home laboratory and in several other laboratories. These gizmos have been applied to address neuronal correlates of audiotactile interactions, tactile sense, active and passive movements, speech processing, and intermittent photic stimulation (IPS) in humans. This review also includes additional notes on the ideas behind the gizmos, their evolution, and results obtained.

Practical Fundamentals of Clinical MEG Interpretation in Epilepsy.

Magnetoencephalography (MEG) is a neurophysiologic test that offers a functional localization of epileptic sources in patients considered for epilepsy surgery. The understanding of clinical MEG concepts, and the interpretation of these clinical studies, are very involving processes that demand both clinical and procedural expertise. One of the major obstacles in acquiring necessary proficiency is the scarcity of fundamental clinical literature. To fill this knowledge gap, this review aims to explain the basic practical concepts of clinical MEG relevant to epilepsy with an emphasis on single equivalent dipole (sECD), which is one the most clinically validated and ubiquitously used source localization method, and illustrate and explain the regional topology and source dynamics relevant for clinical interpretation of MEG-EEG.

Clinical Validation of the Champagne Algorithm for Evoked Response Source Localization in Magnetoencephalography

Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.

Magnetoencephalography for epileptic focus localization based on Tucker decomposition with ripple window.

AimsTo improve the Magnetoencephalography (MEG) spatial localization precision of focal epileptic.Methods306‐channel simulated or real clinical MEG is estimated as a lower‐dimensional tensor by Tucker decomposition based on Higher‐order orthogonal iteration (HOOI) before the inverse problem using linearly constraint minimum variance (LCMV). For simulated MEG data, the proposed method is compared with dynamic imaging of coherent sources (DICS), multiple signal classification (MUSIC), and LCMV. For clinical real MEG of 31 epileptic patients, the ripples (80–250 Hz) were detected to compare the source location precision with spikes using the proposed method or the dipole‐fitting method.ResultsThe experimental results showed that the positional accuracy of the proposed method was higher than that of LCMV, DICS, and MUSIC for simulation data. For clinical real MEG data, the positional accuracy of the proposed method was higher than that of dipole‐fitting regardless of whether the time window was ripple window or spike window. Also, the positional accuracy of the ripple window was higher than that of the spike window regardless of whether the source location method was the proposed method or the dipole‐fitting method. For both shallow and deep sources, the proposed method provided effective performance.ConclusionTucker estimation of MEG for source imaging by ripple window is a promising approach toward the presurgical evaluation of epileptics.

Clinical Magnetoencephalography Practice in the United States Ten Years Later: A Survey-Based Reappraisal.

Clinical Magnetoencephalography Practice in the United States Ten Years Later: A Survey-Based Reappraisal.

The Value of Source Localization for Clinical Magnetoencephalography: Beyond the Equivalent Current Dipole.

Source localization for clinical magnetoencephalography recordings is challenging, and many methods have been developed to solve this inverse problem. The most well-studied and validated tool for localization of the epileptogenic zone is the equivalent current dipole. However, it is often difficult to summarize the richness of the magnetoencephalography data with one or a few point sources. A variety of source localization algorithms have been developed to more fully explain the complexity of clinical magnetoencephalography data used to define the epileptogenic network. In this review, various clinically available source localization methods are described and their individual strengths and limitations are discussed.

Visual Mapping With Magnetoencephalography: An Update on the Current State of Clinical Research and Practice With Considerations for Clinical Practice Guidelines.

Using visual evoked fields (VEFs) to differentiate healthy, normal brain function from dysfunctional cortex has been demonstrated to be both valid and reliable. Currently, VEFs are widely implemented to guide intracranial surgeries for epilepsy and brain tumors. There are several areas of possible future clinical use of VEFs, including early identification of disorders, such as multiple sclerosis, Parkinson's disease, stroke, and human immunodeficiency virus-associated neurocognitive disorders. These studies have suggested that VEFs could be used to study disease pathophysiology or as a biomarker for early identification of a disorder. The current clinical practice guidelines of the American Clinical Magnetoencephalography Society for VEFs are sufficient. At this time, VEFs should be used clinically to identify visual cortex and potentially tailor surgical resections.

The Wisdom and Vision From the ACMEGS Inaugural Decade.

Concise history of fascinating magnetoencephalography (MEG) technology and catalog of very selected milestone preclinical and clinical MEG studies are provided as the background. The focus is the societal context defining a journey of MEG to and through clinical practice and formation of the American Clinical MEG Society (ACMEGS). We aspired to provide an objective historic perspective and document contributions of many professionals while focusing on the role of ACMEGS in the growth and maturation of clinical MEG field. The ACMEGS was born (2006) out of inevitability to address two vital issues-fair reimbursement and proper clinical acceptance. A beacon of accountable MEG practice and utilization is now an expanding professional organization with the highest level of competence in practice of clinical MEG and clinical credibility. The ACMEGS facilitated a favorable disposition of insurances toward MEG in the United States by combining the national replication of the grassroots efforts and teaming up with the strategic partners-particularly the American Academy of Neurology (AAN), published two Position Statements (2009 and 2017), the world's only set of MEG Clinical Practice Guidelines (CPGs; 2011) and surveys of clinical MEG practice (2011 and 2020) and use (2020). In addition to the annual ACMEGS Course (2012), we directly engaged MEG practitioners through an Invitational Summit (2019). The Society remains focused on the improvements and expansion of clinical practice, education, clinical training, and constructive engagement of vendors in these issues and pivotal studies toward additional MEG indications. The ACMEGS not only had the critical role in the progress of Clinical MEG in the United States and beyond since 2006 but positioned itself as the field leader in the future.

Towards Best Practices in Clinical Magnetoencephalography: Patient Preparation and Data Acquisition.

A magnetoencephalography (MEG) recording for clinical purposes requires a different level of attention and detail than that for research. As contrasted with a research subject, the MEG technologist must work with a patient who may not fully cooperate with instructions. The patient is on a clinical schedule, with generally no opportunity to return due to an erroneous or poor acquisition. The data will generally be processed by separate MEG analysts, who require a consistent and high-quality recording to complete their analysis and clinical report. To assure a quality recording, (1) MEG technologists must immediately recheck their scalp measurement data during the patient preparation, to catch disturbances and ensure registration accuracy of the patient fiducials, electrodes, and head position indicator coils. During the recording, (2) the technologist must ensure that the patient remains quiet and as far as possible into the helmet. After the recording, (3) the technologist must consistently prepare the data for subsequent clinical analysis. This article aims to comprehensively address these matters for practitioners of clinical MEG in a helpful and practical way. Based on the authors' experiences in recording over three thousand patients between them, presented here are a collection of techniques for implementation into daily routines that ensure good operation and high data quality. The techniques address a gap in the clinical literature addressing the multitude of potential sources of error during patient preparation and data acquisition, and how to prevent, recognize, or correct those.

Clinical Magnetoencephalography Practice in the United States Ten Years Later: A Survey-Based Reappraisal.

Broader utilization of magnetoencephalography (MEG) and optimization of clinical practice remain strategic goals of the American Clinical Magnetoencephalography Society. Despite the implementation of the first MEG Clinical Practice Guidelines, clinical adoption has been less than expected, prompting a reassessment. Twenty-five clinical MEG centers were invited to participate anonymously in a survey of clinical practice. Centers (N = 18) mostly operated within an academic medical center (10/18), were owned by the "hospital" (10/18), associated with a level 4 National Association of Epilepsy center (15/18), and directed by neurologists (10/18). A total of 873 (median 59) epilepsy studies, 1,179 evoked fields (of all types), and 1,607 (median 30) research MEG studies were reported. Fourteen of 17 centers serve children (median 35%), but only 5 of 14 sedate children for MEG. All (N = 14) centers record EEG simultaneous with MEG, and 57% used dipole source localization. The median reporting time for epilepsy studies was 12 and 10 days for presurgical mapping studies. Most (12/14) were favorable toward the Clinical Practice Guidelines and "formalized certification" but were against mandating the latter. A plateau in MEG volumes suggests that MEG has not become a part of the standard of care, and correspondingly, the Clinical Practice Guidelines appeared to have had little impact on clinical practice. The American Clinical Magnetoencephalography Society must continue to engage magnetoencephalographers, potential referrers, and vendors.

Abnormality of visual neuromagnetic activation in female migraineurs without aura between attacks

ObjectiveThe present study aimed to preliminary explore the abnormal neuromagnetic activation in female migraine patients between attacks using magnetoencephalography (MEG) and pattern reversed visual evoked magnetic fields (PR-VEFs).MethodsA total of 17 female migraine subjects during the headache-free phase and 17 healthy controls (HC) were studied using a 275-channel magnetoencephalography (MEG) system. In this study, visual evoked magnetic fields (VEFs) were generated by a pattern-reversal check as the visual stimulus. The average of 100 VEFs was evolved by different half patterns were averaged and used to analyze waveform, spectrum, and source location within two frequency ranges (5–100 and 100–1000 Hz), respectively.ResultsIn migraine subjects, the latency of second peak of VEFs (VIIs) showed significant prolongations when compared with HC. On the sensor level, the cortical spectral power in migraine subjects was similar to that of HC in the 5–100 Hz range and was lower in the 1000–1000 Hz range. There was a decrement of source strength in the visual cortex in migraine patients when compared to HC in both the 5–100 and 100–1000 Hz frequency range. Moreover, there was a similar odds of activation in 5–100 and 100–1000 Hz frequency ranges in the area beyond the primary visual cortex between the two groups. In addition, no correlation was observed between clinical data (intensity of headache, headache-history duration, the frequency of headaches) and MEG results.ConclusionsThe findings presented in the current study, suggested that interictal cortical activation following a visual stimulus was low in female migraine patients. The low pre-activation was detected in the visual cortex using VEF and MEG in both low and high-frequency band. Our results add to the existing evidence that cortical interictal excitability change may be relative to the pain-module mechanism in migraine brains. Thus, our data improved the apprehension of the cortical disorder of migraine in the high-frequency domain.

Brain Network Analysis and Classification Based on Convolutional Neural Network.

Background: Convolution neural networks (CNN) is increasingly used in computer science and finds more and more applications in different fields. However, analyzing brain network with CNN is not trivial, due to the non-Euclidean characteristics of brain network built by graph theory.Method: To address this problem, we used a famous algorithm “word2vec” from the field of natural language processing (NLP), to represent the vertexes of graph in the node embedding space, and transform the brain network into images, which can bridge the gap between brain network and CNN. Using this model, we analyze and classify the brain network from Magnetoencephalography (MEG) data into two categories: normal controls and patients with migraine.Results: In the experiments, we applied our method on the clinical MEG dataset, and got the mean classification accuracy rate 81.25%.Conclusions: These results indicate that our method can feasibly analyze and classify the brain network, and all the abundant resources of CNN can be used on the analysis of brain network.

Evaluation of a dual signal subspace projection algorithm in magnetoencephalographic recordings from patients with intractable epilepsy and vagus nerve stimulators

Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG.

Relative Yield of MEG and EEG Spikes in Simultaneous Recordings.

Most clinical magnetoencephalography (MEG) centers record both MEG and EEG, but model only MEG sources. This may be related to the belief that MEG spikes are more prevalent, MEG is more sensitive, or to proprietary software limitations. Biophysics would contend, however, that EEG, being sensitive to radial and tangential source orientations, would provide complementary data for analysis. We recorded 306 channels of MEG and 25 channels of EEG simultaneously in 297 consecutive patients over 3 years. We inspected the MEG and EEG recordings separately, identified spikes in both, determined whether their voltage and/or magnetometer magnetic fields were dipolar and thus model-worthy, and segregated them into types based on similar and distinct field topography. We placed for each patient their spike types into categories, including those with both a recognizable MEG and EEG signal and those with only an MEG and only an EEG signal. Eighty-three percent of patients had spikes recorded, and these patients had an average of 2.7 spike types each. Fifty-six percent of spike types were present in both MEG and EEG. However, 36% of spike types were only evident in EEG, whereas 8% were noted in MEG alone. In 49% of patients with spikes, MEG review missed at least one spike type, whereas in 17% of patients, EEG review missed at least one spike type. To obtain an optimal yield of diagnostic information, EEG should also be subjected to source analysis in any clinical MEG study. EEG and MEG data are indeed complementary.