Comparison of 2 Robotic Systems for Pediatric Stereoelectroencephalography Implantation.

BACKGROUND: Stereoelectroencephalography (SEEG) remains critical in guiding epilepsy surgery. Robot-assisted techniques have shown promise in improving SEEG implantation outcomes but have not been directly compared. In this single-institution series, we compared ROSA and Stealth AutoGuide robots in pediatric SEEG implantation. METHODS: We retrospectively reviewed 21 sequential pediatric SEEG implantations consisting of 6 ROSA and 15 AutoGuide procedures. We determined mean operative time, time per electrode, root mean square (RMS) registration error, and surgical complications. Three-dimensional radial distances were calculated between each electrode's measured entry and target points with respective errors from the planned trajectory line. RESULTS: Mean overall/per electrode operating time was 73.5/7.5 minutes for ROSA and 126.1/10.9 minutes for AutoGuide (P = 0.030 overall, P = 0.082 per electrode). Mean RMS registration error was 0.77 mm (0.55-0.93 mm) for ROSA and 0.6 mm (0.2-1.0 mm) for AutoGuide (P = 0.26). No procedures experienced complications. The mean radial (entry point error was 1.23 ± 0.11 mm for ROSA and 2.65 ± 0.12 mm for AutoGuide (P < 0.001), while the mean radial target point error was 1.86 ± 0.15 mm for ROSA and 3.25 ± 0.16 mm for AutoGuide (P < 0.001). CONCLUSIONS: Overall operative time was greater for AutoGuide procedures, although there was no statistically significant difference in time per electrode. Both systems are highly accurate with no significant RMS error difference. While the ROSA robot yielded significantly lower entry and target point errors, both robots are safe and reliable for deep electrode insertion in pediatric epilepsy.

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.

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.

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data.

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform >70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients.

Indications for Inpatient Magnetoencephalography in Children - An Institution's Experience.

Magnetoencephalography (MEG) is recognized as a valuable non-invasive clinical method for localization of the epileptogenic zone and critical functional areas, as part of a pre-surgical evaluation for patients with pharmaco-resistant epilepsy. MEG is also useful in localizing functional areas as part of pre-surgical planning for tumor resection. MEG is usually performed in an outpatient setting, as one part of an evaluation that can include a variety of other testing modalities including 3-Tesla MRI and inpatient video-electroencephalography monitoring. In some clinical circumstances, however, completion of the MEG as an inpatient can provide crucial ictal or interictal localization data during an ongoing inpatient evaluation, in order to expedite medical or surgical planning. Despite well-established clinical indications for performing MEG in general, there are no current reports that discuss indications or considerations for completion of MEG on an inpatient basis. We conducted a retrospective institutional review of all pediatric MEGs performed between January 2012 and December 2020, and identified 34 cases where MEG was completed as an inpatient. We then reviewed all relevant medical records to determine clinical history, all associated diagnostic procedures, and subsequent treatment plans including epilepsy surgery and post-surgical outcomes. In doing so, we were able to identify five indications for completing the MEG on an inpatient basis: (1) super-refractory status epilepticus (SRSE), (2) intractable epilepsy with frequent electroclinical seizures, and/or frequent or repeated episodes of status epilepticus, (3) intractable epilepsy with infrequent epileptiform discharges on EEG or outpatient MEG, or other special circumstances necessitating inpatient monitoring for successful and safe MEG data acquisition, (4) MEG mapping of eloquent cortex or interictal spike localization in the setting of tumor resection or other urgent neurosurgical intervention, and (5) international or long-distance patients, where outpatient MEG is not possible or practical. MEG contributed to surgical decision-making in the majority of our cases (32 of 34). Our clinical experience suggests that MEG should be considered on an inpatient basis in certain clinical circumstances, where MEG data can provide essential information regarding the localization of epileptogenic activity or eloquent cortex, and be used to develop a treatment plan for surgical management of children with complicated or intractable epilepsy.

Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging.

OBJECTIVE: This study aims to evaluate the performance of convolutional neural networks (CNNs) trained with resting-state functional magnetic resonance imaging (rfMRI) latency data in the classification of patients with pediatric epilepsy from healthy controls. METHODS: Preoperative rfMRI and anatomic magnetic resonance imaging scans were obtained from 63 pediatric patients with refractory epilepsy and 259 pediatric healthy controls. Latency maps of the temporal difference between rfMRI and the global mean signal were calculated using voxel-wise cross-covariance. Healthy control and epilepsy latency z score maps were pseudorandomized and partitioned into training data (60%), validation data (20%), and test data (20%). Healthy control individuals and patients with epilepsy were labeled as negative and positive, respectively. CNN models were then trained with the designated training data. Model hyperparameters were evaluated with a grid-search method. The model with the highest sensitivity was evaluated using unseen test data. Accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve were used to evaluate the ability of the model to classify epilepsy in the test data set. RESULTS: The model with the highest validation sensitivity correctly classified 74% of unseen test patients with 85% sensitivity, 71% specificity, F1 score of 0.56, and an area under the receiver operating characteristic curve of 0.86. CONCLUSIONS: Using rfMRI latency data, we trained a CNN model to classify patients with pediatric epilepsy from healthy controls with good performance. CNN could serve as an adjunct in the diagnosis of pediatric epilepsy. Identification of pediatric epilepsy earlier in the disease course could decrease time to referral to specialized epilepsy centers and thus improve prognosis in this population.

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.

The 10 Common Evidence-Supported Indications for MEG in Epilepsy Surgery: An Illustrated Compendium.

Unfamiliarity with the indications for and benefits of magnetoencephalography (MEG) persists, even in the epilepsy community, and hinders its acceptance to clinical practice, despite the evidence. The wide treatment gap for patients with drug-resistant epilepsy and immense underutilization of epilepsy surgery had similar effects. Thus, educating referring physicians (epileptologists, neurologists, and neurosurgeons) both about the value of epilepsy surgery and about the potential benefits of MEG can achieve synergy and greatly improve the process of selecting surgical candidates. As a practical step toward a comprehensive educational process to benefit potential MEG users, current MEG referrers, and newcomers to MEG, the authors have elected to provide an illustrated guide to 10 everyday situations where MEG can help in the evaluation of people with drug-resistant epilepsy. They are as follows: (1) lacking or imprecise hypothesis regarding a seizure onset; (2) negative MRI with a mesial temporal onset suspected; (3) multiple lesions on MRI; (4) large lesion on MRI; (5) diagnostic or therapeutic reoperation; (6) ambiguous EEG findings suggestive of "bilateral" or "generalized" pattern; (7) intrasylvian onset suspected; (8) interhemispheric onset suspected; (9) insular onset suspected; and (10) negative (i.e., spikeless) EEG. Only their practical implementation and furtherance of personal and collective education will lead to the potentially impactful synergy of the two-MEG and epilepsy surgery. Thus, while fulfilling our mission as physicians, we must not forget that ignoring the wealth of evidence about the vast underutilization of epilepsy surgery - and about the usefulness and value of MEG in selecting surgical candidates - is far from benign neglect.

Magnetoencephalographic Infraslow Activity: A Feasibility Study.

PURPOSE: To explore if background infraslow activity (ISA) can be retrieved from archived magnetoencephalographic (MEG) recordings and its potential clinical relevance. METHODS: Archived recordings of 15 patients with epilepsy and 10 normal subjects were evaluated for MEG/EEG delta (0.5-3 Hz) and ISA (0.01-0.1 Hz). The data were obtained on a Neuromag/Elekta system with 204 planar gradiometers and 102 magnetometer sensors and also 60 EEG channels. To remove artifacts, all MEG files were temporal signal space separation filtered. The data were then analyzed with the BESA Research software. RESULTS: Infraslow activity was present in all files for MEG and EEG. Good concordance between EEG and MEG ISA was seen with delta for laterality and with clinical features. Delta frequencies were always less than 2 Hz. During sleep, an inverse relationship between delta and ISA occurred. With increasing depth of sleep, delta activity increased while ISA decreased and vice versa. Intermittent higher amplitude transients, arising from background, were also seen but their nature is at present unknown. Clinically relevant ictal onset baseline shifts were likewise observed. CONCLUSION: Infraslow activity is a normal segment of the cerebral electromagnetic frequency spectrum. It follows physiologic rules and can be related to areas of pathology. This is in accord with previously published EEG observations and further studies of this segment of the electromagnetic frequency spectrum for its origin and changes in health and disease are indicated.

The role of magnetoencephalography in "nonlesional" epilepsy.

The surgical management of neocortical epilepsy is challenging because many patients are without obvious structural lesions, or lesions are small and easily overlooked during routine clinical interpretation of magnetic resonance imaging (MRI) data. Even when functional imaging data suggest focal epileptiform pathology, in the absence of a concordant structural lesion, invasive monitoring is often required to confirm that an appropriate surgical target has been identified. This study sought to determine the extent to which knowledge of magnetoencephalography (MEG) data can augment the MRI-based detection of structural brain lesions. MRI and whole-head MEG data were obtained from 40 patients with neocortical epilepsy. As a result of MEG data, 29 cases were sent for MRI reevaluation. In seven of these cases, MEG-guided review led to specification of now clear, but previously unidentified, lesions. There were two additional cases for which follow-up high-resolution imaging did not confirm structural abnormalities. In patients with neocortical epilepsy, MEG is a useful adjunct to MRI for the identification of structural lesions.

Magnetoencephalographically directed review of high-spatial-resolution surface-coil MR images improves lesion detection in patients with extratemporal epilepsy.

PURPOSE: To determine whether (a) interictal magnetoencephalographic (MEG) epileptiform activity corresponds to anatomic abnormalities at magnetic resonance (MR) imaging, (b) high-spatial-resolution MR imaging depicts lesions in regions without MEG spike activity, (c) MEG-directed review of high-spatial-resolution MR images enables detection of abnormalities not apparent on conventional MR images, and (d) MEG information results in a greater number of diagnosed lesions at re-review of conventional MR images. MATERIALS AND METHODS: Twenty patients with neocortical epilepsy were evaluated with MEG, conventional brain MR imaging with a head coil, and high-spatial-resolution MR imaging with either a surface coil (n = 17) or a high-spatial-resolution birdcage coil (n = 3). Abnormal MEG foci were compared with corresponding anatomic areas on conventional and high-spatial-resolution MR images to determine the presence (concordance) or absence (discordance) of anatomic lesions corresponding to foci of abnormal MEG activity. RESULTS: Forty-four epileptiform MEG foci were identified. Twelve foci (27%) were concordant with an anatomic abnormality at high-spatial-resolution MR imaging, and 32 foci (73%) were discordant. Results of high-spatial-resolution MR imaging were normal in eight patients, and 23 lesions were detected in the remaining 12 patients. Twelve lesions (52%) were concordant with abnormal MEG epileptiform activity, and 11 (48%) were discordant (ie, there was normal MEG activity in the region of the anatomic abnormality). At retrospective reevaluation of conventional MR images with MEG guidance, four occult gray matter migration lesions that had initially been missed were observed. An additional patient with MEG-concordant postoperative gliosis was readily identified with high-spatial-resolution MR images but not with conventional MR images. CONCLUSION: Review of MEG-localized epileptiform areas on high-spatial-resolution MR images enables detection of epileptogenic neocortical lesions, some of which are occult on conventional MR images.