MEG-based identification of the epileptogenic zone in occult peri-insular epilepsy

AbstractIntroductionPresurgical work-ups of patients with pharmacoresistant epileptic seizures can require multiple diagnostic methods if magnetic resonance imaging (MRI) combined with video-EEG monitoring fails to show an epileptogenic lesion. Yet, the added value of available methods is not clear. In particular, only a minority of epilepsy centres apply magnetoencephalography (MEG). This study explores the potential of MEG for patients whose previous sophisticated work-ups missed deep-seated, peri-insular epileptogenic lesions.Patients and methodsThree patients with well documented, frequent, stereotypical hypermotor seizures without clear focus hypotheses after repeated presurgical work-ups including video-EEG-monitoring, 3Tesla (3T) magnetic resonance imaging (MRI), morphometric MRI analysis, PET and SPECT were referred to MEG source localisation.ResultsIn two out of three patients, MEG source localisation identified very subtle morphological abnormalities formerly missed in MRI or classified as questionable pathology. In the third patient, MEG was not reliable due to insufficient detection of epileptic patterns. Here, a 1mm×1mm×1mm 3T fluid-attenuated inversion recovery (FLAIR) MRI revealed a potential epileptogenic lesion. A minimal invasive work-up via lesion-focused depth electrodes confirmed the intralesional seizure onset in all patients, and histology revealed dysplastic lesions. Seizure outcomes were Engel 1a in two patients, and Engel 1d in the third.DiscussionMEG can contribute to the identification of epileptogenic lesions even when multiple previous methods failed, and when the lesions are located in deep anatomical structures such as peri-insular cortex. For epilepsy centres without MEG capability, referral of patients with cryptogenic focal epilepsies to centres with MEG systems may be indicated

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Spectral bandwidth of interictal fast epileptic activity characterizes the seizure onset zone

The foremost aim of presurgical epilepsy evaluation is the delineation of the seizure onset zone (SOZ). There is increasing evidence that fast epileptic activity (FEA, 14–250 Hz) occurring interictally, i.e. between seizures, is predominantly localized within the SOZ. Currently it is unknown, which frequency band of FEA performs best in identifying the SOZ, although prior studies suggest highest concordance of spectral changes with the SOZ for high frequency changes. We suspected that FEA reflects dampened oscillations in local cortical excitatory-inhibitory neural networks, and that interictal FEA in the SOZ is a consequence of reduced oscillatory damping. We therefore predict a narrowing of the spectral bandwidth alongside increased amplitudes of spectral peaks during interictal FEA events. To test this hypothesis, we evaluated spectral changes during interictal FEA in invasive EEG (iEEG) recordings of 13 patients with focal epilepsy. In relative spectra of beta and gamma band changes (14–250 Hz) during FEA, we found that spectral peaks within the SOZ indeed were significantly more narrow-banded and their power changes were significantly higher than outside the SOZ. In contrast, the peak frequency did not differ within and outside the SOZ. Our results show that bandwidth and power changes of spectral modulations during FEA both help localizing the SOZ. We propose the spectral bandwidth as new source of information for the evaluation of EEG data

How to record high-frequency oscillations in epilepsy: A practical guideline

International audienceObjective: Technology for localizing epileptogenic brain regions plays a central role in surgical planning. Recent improvements in acquisition and electrode technology have revealed that high-frequency oscillations (HFOs) within the 80-500 Hz frequency range provide the neurophysiologist with new information about the extent of the epileptogenic tissue in addition to ictal and interictal lower frequency events. Nevertheless, two decades after their discovery there remain questions about HFOs as biomarkers of epileptogenic brain and there use in clinical practice.Methods: In this review, we provide practical, technical guidance for epileptologists and clinical researchers on recording, evaluation, and interpretation of ripples, fast ripples, and very high-frequency oscillations.Results: We emphasize the importance of low noise recording to minimize artifacts. HFO analysis, either visual or with automatic detection methods, of high fidelity recordings can still be challenging because of various artifacts including muscle, movement, and filtering. Magnetoencephalography and intracranial electroencephalography (iEEG) recordings are subject to the same artifacts.Significance: High-frequency oscillations are promising new biomarkers in epilepsy. This review provides interested researchers and clinicians with a review of current state of the art of recording and identification and potential challenges to clinical translation

How to record high-frequency oscillations in epilepsy : A practical guideline

Objective: Technology for localizing epileptogenic brain regions plays a central role in surgical planning. Recent improvements in acquisition and electrode technology have revealed that high-frequency oscillations (HFOs) within the 80–500 Hz frequency range provide the neurophysiologist with new information about the extent of the epileptogenic tissue in addition to ictal and interictal lower frequency events. Nevertheless, two decades after their discovery there remain questions about HFOs as biomarkers of epileptogenic brain and there use in clinical practice. Methods: In this review, we provide practical, technical guidance for epileptologists and clinical researchers on recording, evaluation, and interpretation of ripples, fast ripples, and very high-frequency oscillations. Results: We emphasize the importance of low noise recording to minimize artifacts. HFO analysis, either visual or with automatic detection methods, of high fidelity recordings can still be challenging because of various artifacts including muscle, movement, and filtering. Magnetoencephalography and intracranial electroencephalography (iEEG) recordings are subject to the same artifacts. Significance: High-frequency oscillations are promising new biomarkers in epilepsy. This review provides interested researchers and clinicians with a review of current state of the art of recording and identification and potential challenges to clinical translation

Combining EEG and MEG for the reconstruction of epileptic activity using a calibrated realistic volume conductor model.

To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data