Correlation of invasive EEG and scalp EEG

Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings

BACKGROUND

Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined.

NEW METHOD

We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared.

RESULTS

Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters.

COMPARISON WITH EXISTING METHODS

Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation.

CONCLUSIONS

Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.

Clinical Utility of Sleep Recordings During Presurgical Epilepsy Evaluation With Stereo-Electroencephalography: A Systematic Review

SUMMARY

Although the role of sleep in modulating epileptic activity is well established, many epileptologists overlook the significance of considering sleep during presurgical epilepsy evaluations in cases of drug-resistant epilepsy. Here, we conducted a comprehensive literature review from January 2000 to May 2023 using the PubMed electronic database and compiled evidence to highlight the need to revise the current clinical approach. All articles were assessed for eligibility by two independent reviewers. Our aim was to shed light on the clinical value of incorporating sleep monitoring into presurgical evaluations with stereo-electroencephalography. We present the latest developments on the important bidirectional interactions between sleep and various forms of epileptic activity observed in stereo-electroencephalography recordings. Specifically, epileptic activity is modulated by different sleep stages, peaking in non-rapid eye movement sleep, while being suppressed in rapid eye movement sleep. However, this modulation can vary across different brain regions, underlining the need to account for sleep to accurately pinpoint the epileptogenic zone during presurgical assessments. Finally, we offer practical solutions, such as automated sleep scoring algorithms using stereo-electroencephalography data alone, to seamlessly integrate sleep monitoring into routine clinical practice. It is hoped that this review will provide clinicians with a readily accessible roadmap to the latest evidence concerning the clinical utility of sleep monitoring in the context of stereo-electroencephalography and aid the development of therapeutic and diagnostic strategies to improve patient surgical outcomes.

Beyond Stereo-EEG: Is It Worth Combining Stereo-EEG With Other Diagnostic Methods?

SUMMARY

Stereo-EEG is a widely used method to improve the diagnostic precision of presurgical workup in patients with refractory epilepsy. Its ability to detect epileptic activity and identify epileptic networks largely depends on the chosen implantation strategy. Even in an ideal situation, electrodes record activity generated in <10% of the brain and contacts only record from brain tissue in their immediate proximity. In this article, the authors discuss how recording stereo-EEG simultaneously with other diagnostic methods can improve its diagnostic value in clinical and research settings. It can help overcome the limited spatial coverage of intracranial recording and better understand the sources of epileptic activity. Simultaneous scalp EEG is the most widely available method, often used to understand large epileptic networks, seizure propagation, and EEG activity occurring on the contralateral hemisphere. Simultaneous magnetoencephalography allows for more precise source localization and identification of deep sources outside the stereo-EEG coverage. Finally, simultaneous functional MRI can highlight metabolic changes following epileptic activity and help understand the widespread network changes associated with interictal activity. This overview highlights advantages and methodological challenges for all these methods. Clinical use and research applications are presented for each approach.

EEG Ictal Power Dynamics, Function-Structure Associations, and Epilepsy Surgical Outcomes

BACKGROUND AND OBJECTIVES

Postoperative seizure control in drug-resistant temporal lobe epilepsy (TLE) remains variable, and the causes for this variability are not well understood. One contributing factor could be the extensive spread of synchronized ictal activity across networks. Our study used novel quantifiable assessments from intracranial EEG (iEEG) to test this hypothesis and investigated how the spread of seizures is determined by underlying structural network topological properties.

METHODS

We evaluated iEEG data from 157 seizures in 27 patients with TLE: 100 seizures from 17 patients with postoperative seizure control (Engel score I) vs 57 seizures from 10 patients with unfavorable surgical outcomes (Engel score II-IV). We introduced a quantifiable method to measure seizure power dynamics within anatomical regions, refining existing seizure imaging frameworks and minimizing reliance on subjective human decision-making. Time-frequency power representations were obtained in 6 frequency bands ranging from theta to gamma. Ictal power spectrums were normalized against a baseline clip taken at least 6 hours away from ictal events. Electrodes' time-frequency power spectrums were then mapped onto individual T1-weighted MRIs and grouped based on a standard brain atlas. We compared spatiotemporal dynamics for seizures between groups with favorable and unfavorable surgical outcomes. This comparison included examining the range of activated brain regions and the spreading rate of ictal activities. We then evaluated whether regional iEEG power values were a function of fractional anisotropy (FA) from diffusion tensor imaging across regions over time.

RESULTS

Seizures from patients with unfavorable outcomes exhibited significantly higher maximum activation sizes in various frequency bands. Notably, we provided quantifiable evidence that in seizures associated with unfavorable surgical outcomes, the spread of beta-band power across brain regions is significantly faster, detectable as early as the first second after seizure onset. There was a significant correlation between beta power during seizures and FA in the corresponding areas, particularly in the unfavorable outcome group. Our findings further suggest that integrating structural and functional features could improve the prediction of epilepsy surgical outcomes.

DISCUSSION

Our findings suggest that ictal iEEG power dynamics and the structural-functional relationship are mechanistic factors associated with surgical outcomes in TLE.

A personalized earbud for non-invasive long-term EEG monitoring

Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities.Approach.The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial electroencephalography recordings in epilepsy patients.Main results.The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording.Significance.This offers significant reductions in burden to patients and their families. Furthermore, the device's utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.

Deep EEG source localization via EMD-based fMRI high spatial frequency

Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the head's conductivity, deep regions' sources with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRI's high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRI's high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels' intensity and their distribution. Our experimental results also support this conclusion.

Deep Learning for Generalized EEG Seizure Detection after Hypoxia-Ischemia-Preclinical Validation

Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia-ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI-normothermia term (n = 7), HI-hypothermia term (n = 14), sham-normothermia term (n = 5), and HI-normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI-normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI-normothermia preterm data were used in training, the performance on HI-normothermia term and HI-hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI-normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia.

Technical Considerations in EEG Source Imaging

SUMMARY

EEG source imaging is an established technique for identifying the origin of interictal and ictal epileptiform discharges in patients with epilepsy, and it is an important tool in neurophysiology research. Accurate and reliable EEG source imaging requires appropriate choices of how the head, skull, and scalp are modeled, and understanding of the different approaches to modeling is important to guide these choices. Similarly, numerous different approaches to modeling the electrical sources within the brain exist, and appropriate understanding of the strengths and limitations of each are essential to obtaining accurate, reliable, and interpretable solutions. This review aims to describe the essential theoretical basis for these head and source models while also discussing the practical implications of each in clinical or research applications.

Rolling in the Deep: Surface EEG Seizures Viewed Through the Lens of Stereo EEG

Intracerebral Correlates of Scalp EEG Ictal Discharges Based on Simultaneous Stereo-EEG Recordings Ferrand M, Baumann C, Aron O, Vignal JP, Jonas J, Tyvaert L, Colnat-Coulbois S, Koessler L, Maillard L. Neurology . 2023 Mar 24:10.1212/WNL.0000000000207135 . doi:10.1212/WNL.0000000000207135 . Epub ahead of print. PMID: 36963841. Background and objectives: It remains unknown to what extent ictal scalp EEG can accurately predict the localization of the intra-cerebral seizure onset in pre-surgical evaluation of drug resistant epilepsies. In this study, we aimed to define homogeneous ictal scalp EEG profiles (based on their first ictal abnormality) and assess their localizing value using simultaneously recorded scalp EEG and Stereo-EEG. Methods: We retrospectively included consecutive patients with drug-resistant focal epilepsy who had simultaneous stereo-EEG and scalp EEG recordings of at least one seizure, in the epileptology unit in Nancy, France. We analyzed one seizure per patient and used hierarchical cluster analysis to group similar seizure profiles on scalp EEG and then performed a descriptive analysis of their intra-cerebral correlates. Results: We enrolled 129 patients in this study. The hierarchical cluster analysis showed six profiles on scalp EEG first modification. None was specific to a single intra-cerebral localization. The “normal EEG” and “blurred EEG” clusters (early muscle artifacts) comprised only five patients each and corresponded to no preferential intra-cerebral localization. The “temporal discharge” cluster (n = 46) was characterized by theta or delta discharges on ipsilateral anterior temporal scalp electrodes and corresponded to a preferential mesial temporal intra-cerebral localization. The “posterior discharge” cluster (n = 42) was characterized by posterior ipsilateral or contralateral rhythmic alpha discharges or slow waves on scalp and corresponded to a preferential temporal localization. However, this profile was the statistically most frequent scalp EEG correlate of occipital and parietal seizures. The “diffuse suppression” cluster (n = 9) was characterized by a bilateral and diffuse background activity suppression on scalp and corresponded to mesial, and particularly insulo-opercular, localization. Finally, the “frontal discharge” cluster (n = 22) was characterized by bilateral frontal rhythmic fast activity or pre-ictal spike on scalp and corresponded to preferential ventrodorsal frontal intra-cerebral localizations. Discussion: Hierarchical cluster analysis identified six seizure profiles regarding the first abnormality on scalp EEG. None of them was specific of a single intra-cerebral localization. Nevertheless, the strong relationships between the “temporal”, “frontal”, “diffuse suppression” and “posterior” profiles and intra-cerebral discharges localizations may contribute to hierarchize hypotheses derived from ictal scalp EEG analysis regarding intra-cerebral seizure onset.

Ambulatory video EEG extended to 10 days: A retrospective review of a large database of ictal events

OBJECTIVE

This work aims to determine the ambulatory video electroencephalography monitoring (AVEM) duration and number of captured seizures required to resolve different clinical questions, using a retrospective review of ictal recordings.

METHODS

Patients who underwent home-based AVEM had event data analyzed retrospectively. Studies were grouped by clinical indication: differential diagnosis, seizure type classification, or treatment assessment. The proportion of studies where the conclusion was changed after the first seizure was determined, as was the AVEM duration needed for at least 99% of studies to reach a diagnostic conclusion.

RESULTS

The referring clinical question was not answered entirely by the first event in 29.6% (n = 227) of studies. Diagnostic and classification indications required a minimum of 7 days for at least 99% of studies to be answered, whilst treatment-assessment required at least 6 days.

CONCLUSIONS

At least 7 days of monitoring, and potentially multiple events, are required to adequately answer these clinical questions in at least 99% of patients. The widely applied 72 h or single event recording cut-offs may be inadequate to adequately answer these three indications in a substantial proportion of patients.

SIGNIFICANCE

Extended duration of monitoring and capturing multiple events should be considered when attempting to capture seizures on video-EEG.

Advances in Electrode Materials for Scalp, Forehead, and Ear EEG: A Mini-Review

Electroencephalogram (EEG) records the electrical activity of neurons in the cerebral cortex and is used extensively to diagnose, treat, and monitor psychiatric and neurological conditions. Reliable contact between the skin and the electrodes is essential for achieving consistency and for obtaining electroencephalographic information. There has been an increasing demand for effective equipment and electrodes to overcome the time-consuming and cumbersome application of traditional systems. Recently, ear-centered EEG has met with growing interest since it can provide good signal quality due to the proximity of the ear to the brain. In addition, it can facilitate mobile and unobtrusive usage due to its smaller size and ease of use, since it can be used without interfering with the patient's daily activities. The purpose of this mini-review is to first introduce the broad range of electrodes used in conventional (scalp) EEG and subsequently discuss the state-of-the-art literature about around- and in-the-ear EEG.

Spatiotemporal phase slip patterns for visual evoked potentials, covert object naming tasks, and insight moments extracted from 256 channel EEG recordings

Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4-7 Hz), alpha (7-12 Hz), beta (12-30 Hz), and low gamma (30-49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the 'Eureka' moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.

Spike count and morphology in the classification of epileptiform discharges

Purpose

The purpose of this study is to investigate the impact of Bergen Epileptiform Morphology Score (BEMS) and interictal epileptiform discharge (IED) candidate count in EEG classification.

Methods

We included 400 consecutive patients from a clinical SCORE EEG database during 2013-2017 who had focal sharp discharges in their EEG, but no previous diagnosis of epilepsy. Three blinded EEG readers marked all IED candidates. BEMS and IED candidate counts were combined to classify EEGs as epileptiform or non-epileptiform. Diagnostic performance was assessed and then validated in an external dataset.

Results

Interictal epileptiform discharge (IED) candidate count and BEMS were moderately correlated. The optimal criteria to classify an EEG as epileptiform were either one spike at BEMS > = 58, two at > = 47, or seven at > = 36. These criteria had almost perfect inter-rater reliability (Gwet's AC1 0.96), reasonable sensitivity of 56-64%, and high specificity of 98-99%. The sensitivity was 27-37%, and the specificity was 93-97% for a follow-up diagnosis of epilepsy. In the external dataset, the sensitivity for an epileptiform EEG was 60-70%, and the specificity was 90-93%.

Conclusion

Quantified EEG spike morphology (BEMS) and IED candidate count can be combined to classify an EEG as epileptiform with high reliability but with lower sensitivity than regular visual EEG review.

MindReader: Unsupervised Classification of Electroencephalographic Data

Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader's predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.

Extracting the Invisible: Mesial Temporal Source Detection in Simultaneous EEG and SEEG Recordings

Epileptic source detection relies mainly on visual expertise of scalp EEG signals, but it is recognised that epileptic discharges can escape to this expertise due to a deep localization of the brain sources that induce a very low, even negative, signal to noise ratio. In this methodological study, we aimed to investigate the feasibility of extracting deep mesial temporal sources that were invisible in scalp EEG signals using blind source separation (BSS) methods (infomax ICA, extended infomax ICA, and JADE) combined with a statistical measure (kurtosis). We estimated the effect of different methodological and physiological parameters that could alter or improve the extraction. Using nine well-defined mesial epileptic networks (1949 spikes) obtained from seven patients and simultaneous EEG-SEEG recordings, the first independent component extracted from the scalp EEG signals was validated in mean from 46 to 80% according to the different parameters. The three BSS methods equally performed (no significant difference) and no influence of the number of scalp electrodes used was found. At the opposite, the number and amplitude of spikes included in the averaging before the extraction modified the performance. Anyway, despite their invisibility in scalp EEG signals, this study demonstrates that deep source extraction is feasible under certain conditions and with the use of common signal analysis toolboxes. This finding confirms the crucial need to continue the signal analysis of scalp EEG recordings which contains subcortical signals that escape to expert visual analysis but could be found by signal processing.

Reflection of the Ictal Electrocorticographic Discharges Confined to the Medial Temporal Lobe to the Scalp-Recorded Electroencephalogram

Objective: Previous reports on the simultaneous recording of electroencephalography (EEG) and electrocorticography (ECoG) have demonstrated that, in patients with temporal lobe epilepsy (TLE), ictal ECoG discharges with an amplitude as high as 1000 μV originating from the medial temporal lobe could not be recorded on EEG. In contrast, ictal EEG discharges were recorded after ictal ECoG discharges propagated to the lateral temporal lobe. Here, we report a case of TLE in which the ictal EEG discharges, corresponding to ictal ECoG discharges confined to the medial temporal lobe, were recorded. Case report: In the present case, ictal EEG discharges were hardly recognized when the amplitude of the ECoG discharges was less than 1500 μV. During the evolution and burst suppression phase, corresponding to highly synchronized ECoG discharges with amplitudes greater than 1500 to 2000 μV, rhythmic negative waves with the same frequency were clearly recorded both on the lateral temporal lobe and scalp. The amplitude of the lateral temporal ECoG was approximately one-tenth of that of the medial temporal ECoG. The amplitude of the scalp EEG was approximately one-tenth of that of the lateral temporal ECoG. Conclusions: Highly synchronized ictal ECoG discharges with high amplitude of greater than 1500 to 2000 μV in the medial temporal lobe could be recorded on the scalp as ictal EEG discharges via volume conduction.

Spike-microstates correlate with interictal epileptogenic discharges: a marker for hidden epileptic activity

Objectively estimating disease severity and treatment success is a main problem in outpatient managing of epilepsy. Self-reported seizures diaries are well-known to underestimate the actual seizure count, and repeated EEGs might not show interictal epileptiform discharges (IEDs), although patients suffer from seizures. In this prospective study, we investigate the potential of microstate analysis to monitor epilepsy patients independently of their IED count. From our databank of candidates for epilepsy surgery, we included 18 patients who underwent controlled resting EEG sessions (with eyes closed, 30 min), at around the same time of the day, during at least four days (range: 4-8 days; mean: 5). Nine patients with temporal foci, six with extratemporal foci, and three with generalized epilepsy were included. Each patient's IEDs were marked and the topographic voltage maps of the IED peaks were averaged, and an individual average spike topography (AST) was created. The AST was then backfitted to each timepoint of the whole EEG resulting in the Spike-Microstate (SMS). The presence of the SMS in the residual EEG outside of the short IEDs epochs was determined for each recording session in each patient and correlated with the occurrence of the IEDs across all recording session, as well as with the drug charge of each day. Overall, SMS was much more represented in the routine EEG than the IEDs: they were identified 262 times more often than IEDs. The SMS time coverage correlated significantly with the IED occurrence rate (rho = 0.56; P < 0.001). If only patients with focal epilepsy were considered, this correlation was even higher rho = 0.69 (P < 0.001). Drug charge per day did not correlate with SMS. In this proof-of-concept study, the time coverage of SMS correlated strongly with the occurrence rate of the IEDs, they can be retrieved in the scalp EEG at a much higher occurrence rate. We conclude that SMS, once obtained for a given patient, are a more abundant marker of hidden epileptic activity than IEDs, in particular in focal epilepsy, and can be used also in absence of IEDs. Future larger studies are needed to verify its potential as monitoring tool and to determine cut-off values when drug protection becomes imperfect.

Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research

Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.

E2SGAN: EEG-to-SEEG translation with generative adversarial networks

High-quality brain signal data recorded by Stereoelectroencephalography (SEEG) electrodes provide clinicians with clear guidance for presurgical assessments for epilepsy surgeries. SEEG, however, is limited to selected patients with epilepsy due to its invasive procedure. In this work, a brain signal synthesis framework is presented to synthesize SEEG signals from non-invasive EEG signals. First, a strategy to determine the matching relation between EEG and SEEG channels is presented by considering both signal correlation and spatial distance. Second, the EEG-to-SEEG generative adversarial network (E2SGAN) is proposed to precisely synthesize SEEG data from the simultaneous EEG data. Although the widely adopted magnitude spectra has proved to be informative in EEG tasks, it leaves much to be desired in the setting of signal synthesis. To this end, instantaneous frequency spectra is introduced to further represent the alignment of the signal. Correlative spectral attention (CSA) is proposed to enhance the discriminator of E2SGAN by capturing the correlation between each pair of EEG and SEEG frequencies. The weighted patch prediction (WPP) technique is devised to ensure robust temporal results. Comparison experiments on real-patient data demonstrate that E2SGAN outperforms baseline methods in both temporal and frequency domains. The perturbation experiment reveals that the synthesized results have the potential to capture abnormal discharges in epileptic patients before seizures.

Intercorrelation of physiological seizure parameters and hormonal changes in electroconvulsive therapy

OBJECTIVE

Physiological parameters that predict electroconvulsive therapy (ECT) effectiveness may reflect propagation of the induced epileptic seizure. As an indication of seizure propagation to the diencephalon, we here examined the correlation between prolactin increase after ECT and clinical seizure evaluation parameters, focusing on peak heart rate. As a proxy for peripheral endocrine stress response, we examined the correlation to postictal cortisol increase.

METHODS

Participants were consecutively recruited from clinical ECT patients (n = 131, age 18-85 years). The first ECT session in a series was examined. For each participant, blood serum concentrations of prolactin and cortisol were measured immediately before and within 30 min after the seizure. Physiological parameters were extracted from clinical records: peak heart rate (HR) during seizure, electroencephalography (EEG) seizure duration, and motor seizure duration. Correlations were calculated using non-parametric tests.

RESULTS

Serum prolactin increased after ECT and correlated with peak HR, EEG seizure duration, and motor seizure duration. Peak HR during seizure also correlated positively with both EEG seizure duration and motor seizure duration. Correlations were unaffected by age, sex, baseline prolactin levels, antipsychotics, or beta-blocking agents. Serum cortisol increased after ECT but did not correlate with the seizure evaluation parameters, nor with prolactin concentrations.

CONCLUSIONS

Our findings of a positive correlation between peak HR and prolactin that was independent from the peripheral endocrine stress response might be in line with the idea that tachycardia during ECT seizures reflects seizure propagation to the diencephalon. This supports the practice of monitoring cardiovascular response for ECT seizure evaluation.

Nonpharmacologic Interventions and Seizure Frequency in Patients With Psychogenic Nonepileptic Seizures: An Integrative Review

BACKGROUND

Psychogenic nonepileptic seizures (PNES) pose a heavy burden on patients' lives and the health care system. The symptoms of PNES are often debilitating and cause high rates of disability and poor quality of life. Many treatment options are available, but there is no clear consensus on best practices.

AIM

To critique and synthesize the current literature on nonpharmacologic interventions and effects on seizure frequency in patients with PNES.

METHODS

An integrative review guided by the Whittemore and Knafl approach.

RESULTS

The review included 24 studies published from 2010 to 2020. Interventions for PNES included individualized psychotherapies, group therapies, multimodal psychotherapies, self-help therapies, and complementary and alternative medicine therapies. Individual psychotherapies such as cognitive behavioral therapy and psychoeducation were the most used treatment modalities. The most effective treatments for seizure frequency reduction were those that included multiple psychotherapy sessions with a health care provider and covered multiple domains (e.g., understanding of diagnosis, identifying triggers, and developing effective coping strategies).

CONCLUSIONS

Seizure frequency can be reduced in patients with PNES with multiple nonpharmacologic interventions. However, seizure frequency is not considered a comprehensive outcome measure and provides little insight into other important life domains. Further research is needed on nonpharmacologic interventions for PNES and effects on other areas of life such as sleep, employment status, global functioning, and self-efficacy.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Dogs as a Natural Animal Model of Epilepsy

Epilepsy is a common neurological disease in both humans and domestic dogs, making dogs an ideal translational model of epilepsy. In both species, epilepsy is a complex brain disease characterized by an enduring predisposition to generate spontaneous recurrent epileptic seizures. Furthermore, as in humans, status epilepticus is one of the more common neurological emergencies in dogs with epilepsy. In both species, epilepsy is not a single disease but a group of disorders characterized by a broad array of clinical signs, age of onset, and underlying causes. Brain imaging suggests that the limbic system, including the hippocampus and cingulate gyrus, is often affected in canine epilepsy, which could explain the high incidence of comorbid behavioral problems such as anxiety and cognitive alterations. Resistance to antiseizure medications is a significant problem in both canine and human epilepsy, so dogs can be used to study mechanisms of drug resistance and develop novel therapeutic strategies to benefit both species. Importantly, dogs are large enough to accommodate intracranial EEG and responsive neurostimulation devices designed for humans. Studies in epileptic dogs with such devices have reported ictal and interictal events that are remarkably similar to those occurring in human epilepsy. Continuous (24/7) EEG recordings in a select group of epileptic dogs for >1 year have provided a rich dataset of unprecedented length for studying seizure periodicities and developing new methods for seizure forecasting. The data presented in this review substantiate that canine epilepsy is an excellent translational model for several facets of epilepsy research. Furthermore, several techniques of inducing seizures in laboratory dogs are discussed as related to therapeutic advances. Importantly, the development of vagus nerve stimulation as a novel therapy for drug-resistant epilepsy in people was based on a series of studies in dogs with induced seizures. Dogs with naturally occurring or induced seizures provide excellent large-animal models to bridge the translational gap between rodents and humans in the development of novel therapies. Furthermore, because the dog is not only a preclinical species for human medicine but also a potential patient and pet, research on this species serves both veterinary and human medicine.

Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task

Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG.Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads.Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16-3.84 Hz for the phase and 50-85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load.Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.

Lessons from the video-EEG telemetry unit

Epilepsy is a clinical diagnosis, based primarily on patient and witness histories. Where there is diagnostic uncertainty or when epilepsy surgery is being considered, long-term video-EEG monitoring in a telemetry unit remains the gold standard investigation for diagnostic clarification or presurgical localisation. We present six illustrative cases, highlighting important points that emerged during video-EEG review including potential pitfalls in video-EEG interpretation, and how the investigation helped with diagnosis and subsequent management. The diagnostic process strongly emphasises seizure semiology, more so than EEG.

Electroclinical Mismatch During EEG Acquisition: What It Might Mean, What We Might Need to Do

An electroclinical mismatch is present if the electroencephalogram (EEG) shows evidence of moderate to severe diffuse encephalopathy but the patient’s mental status is only mildly altered. We describe five cases in which seizure or status epilepticus was suspected due to electroclinical mismatch. In all five cases, EEG was ordered to rule out nonconvulsive status epilepticus as the cause of the altered mental status. EEG initially showed generalized delta activity (GDA), with variable degrees of rhythmicity, with or without superimposed theta activity, with or without sporadic epileptiform discharges. During EEG acquisition, all patients followed commands and answered questions. The mental status change was limited to mild inattention and temporal disorientation. Benzodiazepine challenge was performed by administering lorazepam 2-mg IV. Within 10 minutes of injection, GDA started to break up and subsequently disappeared. EEG showed prominent sleep spindles in three patients and background changes, indicating drowsiness in two patients. The assessment of clinical response to lorazepam was confounded by sleepiness in all patients. Serial EEG recording or continuous EEG monitoring revealed reemergence of GDA, at times appearing more rhythmic than the GDA in the baseline study. All patients received nonsedating antiseizure drugs. GDA completely resolved and mental status normalized two to five days after starting antiseizure medication. In cases of electroclinical mismatch, the absence of clear-cut epileptiform discharges does not exclude the possibility that cortical hyperexcitability is contributing to the encephalopathic process. A positive response to benzodiazepine challenge suggests the presence of cortical hyperexcitability and the need to start, or increase the dosage of, antiseizure drugs.

Unsupervised seizure identification on EEG

BACKGROUND AND OBJECTIVE

Epilepsy is one of the most common neurological disorders, whose development is typically detected via early seizures. Electroencephalogram (EEG) is prevalently employed for seizure identification due to its routine and low expense collection. The stochastic nature of EEG makes manual seizure inspections laborsome, motivating automated seizure identification. The relevant literature focuses mostly on supervised machine learning. Despite their success, supervised methods require expert labels indicating seizure segments, which are difficult to obtain on clinically-acquired EEG. Thus, we aim to devise an unsupervised method for seizure identification on EEG.

METHODS

We propose the first fully-unsupervised deep learning method for seizure identification on raw EEG, using a variational autoencoder (VAE). In doing so, we train the VAE on recordings without seizures. As training captures non-seizure activity, we identify seizures with respect to the reconstruction errors at inference time. Moreover, we extend the traditional VAE training loss to suppress EEG artifacts. Our method does not require ground-truth expert labels indicating seizure segments or manual feature extraction.

RESULTS

We implement our method using the PyTorch library and execute experiments on an NVIDIA V100 GPU. We evaluate our method on three benchmark EEG datasets: (i) intracranial recordings from the University of Pennsylvania and the Mayo Clinic, (ii) scalp recordings from the Temple University Hospital of Philadelphia, and (iii) scalp recordings from the Massachusetts Institute of Technology and the Boston Children's Hospital. To assess performance, we report accuracy, precision, recall, Area under the Receiver Operating Characteristics Curve (AUC), and p-value under the Welch t-test for distinguishing seizure vs. non-seizure EEG windows. Our approach can successfully distinguish seizures from non-seizure activity, with up to 0.83 AUC on intracranial recordings. Moreover, our algorithm has the potential for real-time inference, by processing at least 10 s of EEG in a second.

CONCLUSION

We take the first successful steps in deep learning-based unsupervised seizure identification on raw EEG. Our approach has the potential of alleviating the burden on clinical experts regarding laborsome EEG inspections for seizures. Furthermore, aiding the identification of early seizures via our method could facilitate successful detection of epilepsy development and initiate antiepileptogenic therapies.

Seizure Prediction in EEG Signals Using STFT and Domain Adaptation

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional approaches commonly collect training and testing samples from the same patient due to inter-individual variability. However, the challenging problem of domain shift between various subjects remains unsolved, resulting in a low conversion rate to the clinic. In this work, a domain adaptation (DA)-based model is proposed to circumvent this issue. The short-time Fourier transform (STFT) is employed to extract the time-frequency features from raw EEG data, and an autoencoder is developed to map these features into high-dimensional space. By minimizing the inter-domain distance in the embedding space, this model learns the domain-invariant information, such that the generalization ability is improved by distribution alignment. Besides, to increase the feasibility of its application, this work mimics the data distribution under the clinical sampling situation and tests the model under this condition, which is the first study that adopts the assessment strategy. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can minimize the domain gap effectively compared with previous approaches.

The Sensitivity of Scalp EEG at Detecting Seizures-A Simultaneous Scalp and Stereo EEG Study

PURPOSE

Compare the detection rate of seizures on scalp EEG with simultaneous intracranial stereo EEG (SEEG) recordings.

METHODS

Twenty-seven drug-resistant epilepsy patients undergoing SEEG with simultaneous scalp EEG as part of their surgical work-up were included. A total of 172 seizures were captured.

RESULTS

Of the 172 seizures detected on SEEG, 100 demonstrated scalp ictal patterns. Focal aware and subclinical seizures were less likely to be seen on scalp, with 33% of each observed when compared with focal impaired aware (97%) and focal to bilateral tonic-clonic seizures (100%) (P < 0.001). Of the 72 seizures without ictal scalp correlate, 32 demonstrated an abnormality during the SEEG seizure that was identical to an interictal abnormality. Seizures from patients with MRI lesions were statistically less likely to be seen on scalp than seizures from nonlesional patients (P = 0.0162). Stereo EEG seizures not seen on scalp were shorter in duration (49 seconds) compared with SEEG seizures seen on scalp (108.6 seconds) (P < 0.001).

CONCLUSIONS

Scalp EEG is not a sensitive tool for the detection of focal aware and subclinical seizures but is highly sensitive for the detection of focal impaired aware and focal to bilateral tonic-clonic seizures. Longer duration of seizure and seizures from patients without MRI lesions were more likely to be apparent on scalp. Abnormalities seen interictally may at times represent an underlying seizure. The cognitive, affective, and behavioral long-term effects of ongoing difficult-to-detect seizures are not known.

Simultaneous Electroencephalographic and Electocorticographic Recordings of Lateralized Periodic Discharges in Temporal Lobe Epilepsy

OBJECTIVE

Lateralized periodic discharges (LPDs), which constitute an abnormal electroencephalographic (EEG) pattern, are most often observed in critically ill patients with acute pathological conditions, and are less frequently observed in chronic conditions such as focal epilepsies, including temporal lobe epilepsy (TLE). Here we aim to explore the pathophysiological mechanism of LPD in TLE.

METHODS

We retrospectively selected 3 patients with drug-resistant TLE who simultaneously underwent EEG and electrocorticography (ECoG) and demonstrated LPDs. We analyzed the correlation between the EEG and ECoG findings.

RESULTS

In patients 1 and 2, LPDs were recorded in the temporal region of the scalp during the interictal periods, when repeated spikes followed by slow waves (spike-and-wave complexes; SWs) and periodic discharges (PDs) with amplitudes of >600 to 800 µV appeared in the lateral temporal lobe over a cortical area of >10 cm². In patient 3, when the ictal discharges persisted and were confined to the medial temporal lobe, repeated SWs were provoked on the lateral temporal lobe. When repeated SWs with amplitudes of >800 µV appeared in an area of the lateral temporal lobe of >10 cm², the corresponding EEG discharges appeared on the temporal scalp.

CONCLUSIONS

LPDs in patients with TLE originate from repeated SWs and PDs of the lateral temporal lobe, which might represent a highly irritable state of the lateral temporal cortex during both interictal and ictal periods.

EEG source imaging concordance with intracranial EEG and epileptologist review in focal epilepsy

EEG source imaging is becoming widely used for the evaluation of medically refractory focal epilepsy. The validity of EEG source imaging has been established in several studies comparing source imaging to the surgical resection cavity and subsequent seizure freedom. We present a cohort of 87 patients and compare EEG source imaging of both ictal and interictal scalp EEG to the seizure onset zone on intracranial EEG. Concordance of EEG source imaging with intracranial EEG was determined on a sublobar level and was quantified by measuring the distance between the source imaging result and the centroid of the active seizure onset zone electrodes. The EEG source imaging results of a subgroup of 26 patients with high density 76-channel EEG were compared with the localization of three experienced epileptologists. Of 87 patients, 95% had at least one analysis concordant with intracranial EEG and 74% had complete concordance. There was a higher rate of complete concordance in temporal lobe epilepsy compared to extratemporal (89.3 and 62.8%, respectively, P = 0.015). Of the total 282 analyses performed on this cohort, higher concordance was also seen in temporal discharges (95%) compared to extratemporal (77%) (P = 0.0012), but no difference was seen comparing high-density EEG with standard (32-channel) EEG. Subgroup analysis of ictal waveforms showed greater concordance for ictal spiking, compared with rhythmic activity, paroxysmal fast activity, or obscured onset. Median distances from the dipole and maximum distributed source to a centroid of seizure onset zone electrodes were 30.0 and 32.5 mm, respectively, and the median distances from dipole and maximum distributed source to nearest seizure onset zone electrode were 22.8 and 21.7, respectively. There were significantly shorter distances in ictal spiking. There were shorter distances in patients with Engel Class 1 outcome from surgical resection compared to patients with worse outcomes. For the subgroup of 26 high-density EEG patients, EEG source localization had a significantly higher concordance (92% versus 65%), sensitivity (57% versus 35%) and positive predictive value (60% versus 36%) compared with epileptologist localization. Our study demonstrates good concordance between ictal and interictal source imaging and intracranial EEG. Temporal lobe discharges have higher concordance rates than extratemporal discharges. Importantly, this study shows that source imaging has greater agreement with intracranial EEG than visual review alone, supporting its role in surgical planning.

Automated electrical source imaging with scalp EEG to define the insular irritative zone: Comparison with simultaneous intracranial EEG

OBJECTIVE

To evaluate the accuracy of automatedinterictallow-density electrical source imaging (LD-ESI) to define the insular irritative zone (IZ) by comparing the simultaneous interictal ESI localization with the SEEG interictal activity.

METHODS

Long-term simultaneous scalp electroencephalography (EEG) and stereo-EEG (SEEG) with at least one depth electrode exploring the operculo-insular region(s) were analyzed. Automated interictal ESI was performed on the scalp EEG using standardized low-resolution brain electromagnetic tomography (sLORETA) and individual head models. A two-step analysis was performed: i) sublobar concordance betweencluster-based ESI localization and SEEG-based IZ; ii) time-locked ESI-/SEEG analysis. Diagnostic accuracy values were calculated using SEEG as reference standard. Subgroup analysis wascarried out, based onthe involvement of insular contacts in the seizure onset and patterns of insular interictal activity.

RESULTS

Thirty patients were included in the study. ESI showed an overall accuracy of 53% (C.I. 29-76%). Sensitivity and specificity were calculated as 53% (C.I. 29-76%), 55% (C.I. 23-83%) respectively. Higher accuracy was found in patients with frequent and dominant interictal insular spikes.

CONCLUSIONS

LD-ESI defines with good accuracy the insular implication in the IZ, which is not possible with classical interictalscalpEEG interpretation.

SIGNIFICANCE

Automated LD-ESI may be a valuable additional tool to characterize the epileptogenic zone in epilepsies with suspected insular involvement.

A Deep Fourier Neural Network for Seizure Prediction Using Convolutional Neural Network and Ratios of Spectral Power

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods usually adopt handcrafted features and manual parameter setting. The over-reliance on the expertise of specialists may lead to weak exploitation of features and low popularization of clinical application. This paper proposes a novel parameterless patient-specific method based on Fourier Neural Network (FNN), where the Fourier transform and backpropagation learning are synthesized to make the predictor more efficient and practical. The employment of FNN is the first attempt in the field of seizure prediction due to its automatic extraction of immanent spectra in epileptic signals. Despite the self-adaptive superiority of FNN, we introduce Convolutional Neural Network (CNN) to further improve its search capability in high-dimensional feature spaces. The study also develops a multi-layer module to estimate spectral power ratios of raw recordings, which optimizes the prediction by enhancing feature diversity. Based on these modules, this paper proposes a two-channel deep neural network: Fourier Ratio Convolutional Neural Network (FRCNN). To demonstrate the reliability of the model, we explain the mathematical meaning of hidden-layer neurons in FRCNN theoretically. This approach is evaluated on both intracranial and scalp EEG datasets. It shows that the predictor achieved a sensitivity of 91.2% and a false prediction rate (FPR) of 0.06[Formula: see text]h[Formula: see text] across intracranial subjects and a sensitivity of 85.4% and an FPR of 0.14[Formula: see text]h[Formula: see text] over scalp subjects. The results indicate that FRCNN enables the convenience of epilepsy treatments while preserving a high degree of precision. In the end, a detailed comparison with the previous methods demonstrates that FRCNN has achieved higher performance and generalization ability.

Encephalocele-Associated Drug-Resistant Epilepsy of Adult Onset: Diagnosis, Management, and Outcomes

Epileptogenic encephaloceles, most frequently located in the temporal lobe, are a known lesional cause of focal epilepsy. Data are limited regarding diagnosis, management, and outcomes of patients with epilepsy in the setting of an encephalocele, because the literature mostly comprises case reports, case series, and retrospective studies. We conducted a broad literature review for articles related to encephaloceles and epilepsy regardless of level of evidence. Hence, this review provides a summary of all available literature related to the topic. Thirty-six scientific reports that fulfilled our inclusion criteria were reviewed. Most reported patients presented with focal impaired awareness seizures and/or generalized tonic-clonic seizures. Although most of the encephaloceles were located in the temporal lobe, we found 5 cases of extratemporal encephaloceles causing epilepsy. More patients who underwent either lesionectomy or lobectomy were seizure free at time of follow-up. In the temporal lobe, there is no clear consensus on the appropriate management for epileptic encephaloceles and further studies are warranted to understand the associated factors and long-term outcomes associated with epilepsy secondary to encephaloceles. Reported data suggest that these patients could be manageable with surgical procedures including lesionectomy or lobectomy. In addition, because of data suggesting similar results between procedures, a more conservative surgery with lesionectomy and defect repair rather than a lobectomy may have lower surgical risks and similar seizure freedom.

Recognition of interictal and ictal discharges on EEG. Focal vs generalized epilepsy

INTRODUCTION

The differentiation between focal and generalized epilepsies based on clinical and electroencephalographic features is difficult and sometimes confusing.

OBJECTIVE

To review the EEG findings in patients with focal epilepsy.

METHODS

An extensive literature review was done. We used the following Pubmed and Medline descriptors alone and in different combinations for database searching: focal, partial, epilepsy, electroencephalographic findings, and EEG. Additional filters included review, original articles, and language limited to Spanish and English. Using the above criteria, a total of 69 articles showed the interictal and ictal EEG findings in focal epilepsy.

DEVELOPMENT

Focal epileptiform discharges and persistence of focal abnormalities, characterize the interictal EEG findings in focal epilepsies. To distinguish SBS from primary generalized spike waves are required to note: (a) a lead-in time of at least 2 s, (b) the morphology of the focal triggering spikes clearly differ from that of the bisynchronous epileptiform paroxysms, and (c) the morphology of triggering spikes resemble that of other focal spikes from the same region. Focal and Generalized Epilepsy can coexist. Delayed Lateralization on EEG with inconclusive onset and bizarre semiology confusing semiology should not be confused with generalized onset seizures with focal evolution.

CONCLUSIONS

A close attention to localization and morphology of epileptiform discharges, the correct interpretation of secondary bilateral synchrony, and provocative maneuvers help to correctly identify the EEG findings leading to diagnose focal epilepsies. The presence of generalized epileptiform activity does not rule out the existence of a focal epilepsy.

Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

Clinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

Extracting seizure onset from surface EEG with independent component analysis: Insights from simultaneous scalp and intracerebral EEG

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Independent component analysis (ICA) is able to identify seizure generators.

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Simultaneous long-term scalp-SEEG allows validation of the ICA results.

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Ability to record seizure onset patterns on scalp depends on generator depth.

The success of stereoelectroencephalographic (SEEG) investigations depends crucially on the hypotheses on the putative location of the seizure onset zone. This information is derived from non-invasive data either based on visual analysis or advanced source localization algorithms. While source localization applied to interictal spikes recorded on scalp is the classical method, it does not provide unequivocal information regarding the seizure onset zone. Raw ictal activity contains a mixture of signals originating from several regions of the brain as well as EMG artifacts, hampering direct input to the source localization algorithms. We therefore introduce a methodology that disentangles the various sources contributing to the scalp ictal activity using independent component analysis and uses equivalent current dipole localization as putative locus of ictal sources. We validated the results of our analysis pipeline by performing long-term simultaneous scalp – intracerebral (SEEG) recordings in 14 patients and analyzing the wavelet coherence between the independent component encoding the ictal discharge and the SEEG signals in 8 patients passing the inclusion criteria. Our results show that invasively recorded ictal onset patterns, including low-voltage fast activity, can be captured by the independent component analysis of scalp EEG. The visibility of the ictal activity strongly depends on the depth of the sources. The equivalent current dipole localization can point to the seizure onset zone (SOZ) with an accuracy that can be as high as 10 mm for superficially located sources, that gradually decreases for deeper seizure generators, averaging at 47 mm in the 8 analyzed patients. Independent component analysis is therefore shown to have a promising SOZ localizing value, indicating whether the seizure onset zone is neocortical, and its approximate location, or located in mesial structures. That may contribute to a better crafting of the hypotheses used as basis of the stereo-EEG implantations.

Acoustoelectric imaging of deep dipoles in a human head phantom for guiding treatment of epilepsy

OBJECTIVE

This study employs a human head model with real skull to demonstrate the feasibility of transcranial acoustoelectric brain imaging (tABI) as a new modality for electrical mapping of deep dipole sources during treatment of epilepsy with much better resolution and accuracy than conventional mapping methods.

APPROACH

This technique exploits an interaction between a focused ultrasound (US) beam and tissue resistivity to localize current source densities as deep as 63 mm at high spatial resolution (1 to 4 mm) and resolve fast time-varying currents with sub-ms precision.

MAIN RESULTS

Detection thresholds through a thick segment of the human skull at biologically safe US intensities was below 0.5 mA and within range of strong currents generated by the human brain.

SIGNIFICANCE

This work suggests that 4D tABI may emerge as a revolutionary modality for real-time high-resolution mapping of neuronal currents for the purpose of monitoring, staging, and guiding treatment of epilepsy and other brain disorders characterized by abnormal rhythms.

Wireless Programmable Recording and Stimulation of Deep Brain Activity in Freely Moving Humans

Uncovering the neural mechanisms underlying human natural ambulatory behavior is a major challenge for neuroscience. Current commercially available implantable devices that allow for recording and stimulation of deep brain activity in humans can provide invaluable intrinsic brain signals but are not inherently designed for research and thus lack flexible control and integration with wearable sensors. We developed a mobile deep brain recording and stimulation (Mo-DBRS) platform that enables wireless and programmable intracranial electroencephalographic recording and electrical stimulation integrated and synchronized with virtual reality/augmented reality (VR/AR) and wearables capable of external measurements (e.g., motion capture, heart rate, skin conductance, respiration, eye tracking, and scalp EEG). When used in freely moving humans with implanted neural devices, this platform is adaptable to ecologically valid environments conducive to elucidating the neural mechanisms underlying naturalistic behaviors and to the development of viable therapies for neurologic and psychiatric disorders.

Temporal and Spatial Dynamics of Different Interictal Epileptic Discharges: A Time-Frequency EEG Approach in Pediatric Focal Refractory Epilepsy

Objective: Characterization of the spatial and temporal dynamics of interictal epileptic discharges (IED) using time-frequency analysis (TFA) and electrical-source localization (ESL).

Methods: TFA was performed on IED (spikes, spike waves, and polyspike waves) recorded by high-density-EEG (HD-EEG) in 19 refractory focal epileptic children. Temporal modulations related to IEDs were analyzed in a time window around the IED peaks [−1,000 to 1,000 ms]. Spatial modulations were analyzed by ESL in the time-frequency and time domains.

Results: IED were associated with complex power spectral modulations. We observed increases in power spectrum (IPS) patterns specific to IED type. For spikes, the TFA pattern consisted of an IPS (−100 to +100 ms, 4–50 Hz). For spike waves, the IPS was followed by a second IPS (+100 to +400 ms, 4–10 Hz), corresponding to the slow wave. IPS patterns were preceded (−400 to −100 ms, 4–40 Hz), and followed (+100 to +400 ms) by a decrease in the power spectrum (DPS) (n = 8). For 14 out of 19 patients, at least one ESL method was concordant with the epileptogenic area. For the remaining five patients, all of them had temporal epilepsies. ESL in the time-frequency domain (DPS/IPS) provided concordant (n = 6) or complementary (n = 4) information to the ESL in the time domain concerning the epileptogenic zone. ESL in time-frequency domain (DPS/IPS) was the only method to provide concordant information concerning the epileptogenic zone in three patients.

Significance: TFA demonstrates complex time-frequency modulations of the neuronal networks around IED, suggesting that the pathological mechanisms are initiated well before onset of the classical hyper-synchronization of the IED. Combining time and time-frequency analysis of the ESL provides complementary information to define the epileptogenic zone in refractory focal epilepsy.

Localization of deep brain activity with scalp and subdural EEG

To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were ~70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around ~2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. SIGNIFICANCE STATEMENT: Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface.

Networks in Posterior Cortex Epilepsies

Posterior cortex epilepsies comprise all epilepsies with seizures generated from the occipital, parietal, and posterior temporal areas. Seizures usually occur early in life. Visual phenomena during seizures are the hallmark for occipital lobe seizures. Most patients show objective semiology mimicking seizures from other brain regions. Separation of symptomatogenic and epileptogenic zones complicates diagnosis. Understanding networks of propagation is crucial for planning surgery. An overview about typical clinical findings and prognostic value is presented. It explains ways to investigate the epileptogenic zone and propagation pathways to identify seizures from the posterior cortex and better categorize epilepsies for precise surgical treatment.

Epileptic seizure detection using EEG signals and extreme gradient boosting

The problem of automated seizure detection is treated using clinical electroencephalograms (EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus (TUSZ). Performances on this complex data set are still not encountering expectations. The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances. Two methods are explored: a standard partitioning on a recent and larger version of the TUSZ, and a leave-one-out approach used to increase the amount of data for the training set. XGBoost, a fast implementation of the gradient boosting classifier, is the ideal algorithm for these tasks. The performances obtained are in the range of what is reported until now in the literature with deep learning models. We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types. We show that generalized seizures tend to be far better predicted than focal ones. We also notice that some EEG channels and features are more important than others to distinguish seizure from background.

Fully Passive Flexible Wireless Neural Recorder for the Acquisition of Neuropotentials from a Rat Model

Wireless implantable neural interfaces can record high-resolution neuropotentials without constraining patient movement. Existing wireless systems often require intracranial wires to connect implanted electrodes to an external head stage or/and deploy an application-specific integrated circuit (ASIC), which is battery-powered or externally power-transferred, raising safety concerns such as infection, electronics failure, or heat-induced tissue damage. This work presents a biocompatible, flexible, implantable neural recorder capable of wireless acquisition of neuropotentials without wires, batteries, energy harvesting units, or active electronics. The recorder, fabricated on a thin polyimide substrate, features a small footprint of 9 mm × 8 mm × 0.3 mm and is composed of passive electronic components. The absence of active electronics on the device leads to near zero power consumption, inherently avoiding the catastrophic failure of active electronics. We performed both in vitro validation in a tissue-simulating phantom and in vivo validation in an epileptic rat. The fully passive wireless recorder was implanted under rat scalp to measure neuropotentials from its contact electrodes. The implanted wireless recorder demonstrated its capability to capture low voltage neuropotentials, including somatosensory evoked potentials (SSEPs), and interictal epileptiform discharges (IEDs). Wirelessly recorded SSEP and IED signals were directly compared to those from wired electrodes to demonstrate the efficacy of the wireless data. In addition, a convoluted neural network-based machine learning algorithm successfully achieved IED signal recognition accuracy as high as 100 and 91% in wired and wireless IED data, respectively. These results strongly support the fully passive wireless neural recorder's capability to measure neuropotentials as low as tens of microvolts. With further improvement, the recorder system presented in this work may find wide applications in future brain machine interface systems.

A Novel Modular Headmount Design for non-invasive Scalp EEG Recordings in Awake Animal Models

We have designed and developed a novel, noninvasive modular headmount to be used for awake animal scalp electroencephalography (EEG). The design is based on a developing rat that will accommodate rapid head growth. Desired characteristics include non-invasiveness, adjustable quantity and positioning, light weight, and tolerability by the animal. Axial Dependent Modular Electrode Mount (ADMEM), as designed here, addresses the aforementioned constraints by using light-weight and adjustable materials. The initial prototype of ADMEM has been tested in vivo with rat pups, using the open field test to assess for stress and anxiety at two post-installation time-points: one day after ADMEM installation (acute time-point) and four days after ADMEM installation (sub-acute time-point). There was no significant difference in normal developmental weight gain between Control and ADMEM rat groups. Although no significant difference was found in the level of anxiety between groups at the acute time-point, the ADMEM group spent significantly less time in the center of the open field test, suggesting higher anxiety. The test also showed no difference in the measured traveled distances between Control and ADMEM groups on either time-points.

Detection of epileptogenic focus using advanced dynamic statistical parametric mapping with magnetoencephalography in a patient with MRI-negative focal cortical dysplasia type IIB

Advanced dynamic statistical parametric mapping (AdSPM) with magnetoencephalography (MEG) was used to identify MRI-negative epileptogenic lesions in this report. A 15-year-old girl had MRI-negative and pharmacology-resistant focal-onset epilepsy. She experienced two types of seizures. Type I consisted of her arousal from sleep, staring, and a forced head-turning movement to the left, followed by secondary generalization. Type II began with an aura of dizziness followed by staring and postictal headache with fatigue. Scalp video-electroencephalography (EEG) captured two type I seizures originating from the right frontocentral region. MEG showed scattered dipoles over the right frontal region. AdSPM identified the spike source at the bottom of the right inferior frontal sulcus. Intracranial video-EEG captured one type I seizure, which originated from the depth electrode at the bottom of the sulcus and correlated with the AdSPM spike source. Accordingly, the patient underwent resection of the middle and inferior frontal gyri, including the AdSPM-identified spike source. Histopathological examination revealed that the patient had focal cortical dysplasia type IIB. To date, the patient has been seizure free for 2 years while receiving topiramate treatment. This is the first preliminary report to identify MRI-negative epilepsy using AdSPM. Further investigation of AdSPM would be valuable for cases of MRI-negative focal epilepsy.

New Approaches to Studying Silent Mesial Temporal Lobe Seizures in Alzheimer's Disease

Silent seizures were discovered in mouse models of Alzheimer's disease over 10 years ago, yet it remains unclear whether these seizures are a salient feature of Alzheimer's disease in humans. Seizures that arise early in the course of Alzheimer's disease most likely originate from the mesial temporal lobe, one of the first structures affected by Alzheimer's disease pathology and one of the most epileptogenic regions of the brain. Several factors greatly limit our ability to identify mesial temporal lobe seizures in patients with Alzheimer's disease, however. First, mesial temporal lobe seizures can be difficult to recognize clinically, as their accompanying symptoms are often subtle or even non-existent. Second, electrical activity arising from the mesial temporal lobe is largely invisible on the scalp electroencephalogram (EEG), the mainstay of diagnosis for epilepsy in this population. In this review, we will describe two new approaches being used to study silent mesial temporal lobe seizures in Alzheimer's disease. We will first describe the methodology and application of foramen ovale electrodes, which captured the first recordings of silent mesial temporal lobe seizures in humans with Alzheimer's disease. We will then describe machine learning approaches being developed to non-invasively identify silent mesial temporal lobe seizures on scalp EEG. Both of these tools have the potential to elucidate the role of silent seizures in humans with Alzheimer's disease, which could have important implications for early diagnosis, prognostication, and development of targeted therapies for this population.