Atypical EEG abnormalities in genetic generalized epilepsies

EEG in focal and generalized epilepsies: Pearls and perils

Correctly diagnosing and classifying seizures and epilepsies is vital to ensure a tailored approach to patients with epilepsy. The ILAE seizure classification consists of two main groups: focal and generalized. Establishing if a seizure is focal or generalized is essential to classify the epilepsy type and the epilepsy syndrome, providing more personalized treatment and counseling about prognosis. EEG is one of the most essential tools for this classification process and further localization of the epileptogenic focus. However, some EEG findings are misleading and may postpone the correct diagnosis and proper treatment. Knowing the most common EEG pitfalls in focal and generalized epilepsies is valuable for clinical practice, avoiding misinterpretations. Some atypical features can be challenging in focal epilepsies, such as secondary bilateral synchrony, focal epileptiform activity induced by hyperventilation and photic stimulation, and non-focal slowing. On the other hand, more than 60 % of persons with idiopathic generalized epilepsies have at least one type of atypical abnormality. In this manuscript, we describe and illustrate some of the most common EEG findings that can make even experienced epileptologists question not only where the epileptogenic focus is but also if the patient has focal or generalized epilepsy. This review summarizes the perils and provide some pearls to assist EEG readers.

Idiopathic generalized epilepsies in the epilepsy monitoring unit: Systematic quantification of focal EEG and semiological signs

OBJECTIVE

Focal seizure symptoms (FSS) and focal interictal epileptiform discharges (IEDs) are common in patients with idiopathic generalized epilepsies (IGEs), but dedicated studies systematically quantifying them both are lacking. We used automatic IED detection and localization algorithms and correlated these EEG findings with clinical FSS for the first time in IGE patients.

METHODS

32 patients with IGEs undergoing long-term video EEG monitoring were systematically analyzed regarding focal vs. generalized IEDs using automatic IED detection and localization algorithms. Quantitative EEG findings were correlated with FSS.

RESULTS

We observed FSS in 75% of patients, without significant differences between IGE subgroups. Mostly varying/shifting lateralizations of FSS across successive recorded seizures were seen. We detected a total of 81,949 IEDs, whereof 19,513 IEDs were focal (23.8%). Focal IEDs occurred in all patients (median 13% focal IEDs per patient, range 1.1 - 51.1%). Focal IED lateralization and localization predominance had no significant effect on FSS.

CONCLUSIONS

All included patients with IGE showed focal IEDs and three-quarter had focal seizure symptoms irrespective of the specific IGE subgroup. Focal IED localization had no significant effect on lateralization and localization of FSS.

SIGNIFICANCE

Our findings may facilitate diagnostic and treatment decisions in patients with suspected IGE and focal signs.

Response to photic stimulation as a measure of cortical excitability in epilepsy patients

Studying states and state transitions in the brain is challenging due to nonlinear, complex dynamics. In this research, we analyze the brain's response to non-invasive perturbations. Perturbation techniques offer a powerful method for studying complex dynamics, though their translation to human brain data is under-explored. This method involves applying small inputs, in this case via photic stimulation, to a system and measuring its response. Sensitivity to perturbations can forewarn a state transition. Therefore, biomarkers of the brain's perturbation response or "cortical excitability" could be used to indicate seizure transitions. However, perturbing the brain often involves invasive intracranial surgeries or expensive equipment such as transcranial magnetic stimulation (TMS) which is only accessible to a minority of patient groups, or animal model studies. Photic stimulation is a widely used diagnostic technique in epilepsy that can be used as a non-invasive perturbation paradigm to probe brain dynamics during routine electroencephalography (EEG) studies in humans. This involves changing the frequency of strobing light, sometimes triggering a photo-paroxysmal response (PPR), which is an electrographic event that can be studied as a state transition to a seizure state. We investigate alterations in the response to these perturbations in patients with genetic generalized epilepsy (GGE), with (n = 10) and without (n = 10) PPR, and patients with psychogenic non-epileptic seizures (PNES; n = 10), compared to resting controls (n = 10). Metrics of EEG time-series data were evaluated as biomarkers of the perturbation response including variance, autocorrelation, and phase-based synchrony measures. We observed considerable differences in all group biomarker distributions during stimulation compared to controls. In particular, variance and autocorrelation demonstrated greater changes in epochs close to PPR transitions compared to earlier stimulation epochs. Comparison of PPR and spontaneous seizure morphology found them indistinguishable, suggesting PPR is a valid proxy for seizure dynamics. Also, as expected, posterior channels demonstrated the greatest change in synchrony measures, possibly reflecting underlying PPR pathophysiologic mechanisms. We clearly demonstrate observable changes at a group level in cortical excitability in epilepsy patients as a response to perturbation in EEG data. Our work re-frames photic stimulation as a non-invasive perturbation paradigm capable of inducing measurable changes to brain dynamics.

Circadian distribution of epileptiform discharges in epilepsy: Candidate mechanisms of variability

Epilepsy is a serious neurological disorder characterised by a tendency to have recurrent, spontaneous, seizures. Classically, seizures are assumed to occur at random. However, recent research has uncovered underlying rhythms both in seizures and in key signatures of epilepsy-so-called interictal epileptiform activity-with timescales that vary from hours and days through to months. Understanding the physiological mechanisms that determine these rhythmic patterns of epileptiform discharges remains an open question. Many people with epilepsy identify precipitants of their seizures, the most common of which include stress, sleep deprivation and fatigue. To quantify the impact of these physiological factors, we analysed 24-hour EEG recordings from a cohort of 107 people with idiopathic generalized epilepsy. We found two subgroups with distinct distributions of epileptiform discharges: one with highest incidence during sleep and the other during day-time. We interrogated these data using a mathematical model that describes the transitions between background and epileptiform activity in large-scale brain networks. This model was extended to include a time-dependent forcing term, where the excitability of nodes within the network could be modulated by other factors. We calibrated this forcing term using independently-collected human cortisol (the primary stress-responsive hormone characterised by circadian and ultradian patterns of secretion) data and sleep-staged EEG from healthy human participants. We found that either the dynamics of cortisol or sleep stage transition, or a combination of both, could explain most of the observed distributions of epileptiform discharges. Our findings provide conceptual evidence for the existence of underlying physiological drivers of rhythms of epileptiform discharges. These findings should motivate future research to explore these mechanisms in carefully designed experiments using animal models or people with epilepsy.

Responsive Neurostimulation of the Anterior Thalamic Nuclei in Refractory Genetic Generalized Epilepsy: A Case Series

Genetic generalized epilepsies (GGEs) are thought to represent disorders of thalamocortical networks. There are currently no well-established non-pharmacologic treatment options for patients with drug-resistant GGE. NeuroPace's Responsive Neurostimulation (RNS) System was approved by the United States Food and Drug Administration to treat focal seizures with up to two ictal foci. We report on three adults with drug-resistant GGE who were treated with thalamic RNS. Given the severity of their epilepsies and the potential ictogenic role of the thalamus in the pathophysiology of GGE, the RNS System was palliatively implanted with leads in the bilateral anterior thalamic nuclei (ANT) of these patients. The ANT was selected because it was demonstrated to be a safe target. We retrospectively evaluated metrics including seizure frequency over 18-32 months. One patient required explantation due to infection. The other two patients were clinical responders. By the end of the observation period reported here, one patient was seizure-free for over 9 months. All three self-reported an improved quality of life. The clinical response observed in these patients provides 'proof-of-principle' that GGE may be treatable with responsive thalamic stimulation. Our results support proceeding to a larger study investigating the efficacy and safety of thalamic RNS in drug-resistant GGE.

Paroxysmal fast activity is a biomarker of treatment response in deep brain stimulation for Lennox-Gastaut syndrome

OBJECTIVE

Epilepsy treatment trials typically rely on seizure diaries to determine seizure frequency, but these are time-consuming and difficult to maintain accurately. Fast, reliable, and objective biomarkers of treatment response are needed, particularly in Lennox-Gastaut syndrome (LGS), where high seizure frequency and comorbid cognitive and behavioral issues are additional obstacles to accurate diary-keeping. Here, we measured generalized paroxysmal fast activity (GPFA), a key interictal electrographic feature of LGS, and correlated GPFA burden with seizure diaries during a thalamic deep brain stimulation (DBS) treatment trial (Electrical Stimulation of the Thalamus in Epilepsy of Lennox-Gastaut Phenotype [ESTEL]).

METHODS

GPFA and electrographic seizure counts from intermittent, 24-h electroencephalograms (EEGs) were compared to 3-month diary-recorded seizure counts in 17 young adults with LGS (mean age ± SD = 24.9 ± 6.6) in the ESTEL study, a randomized clinical trial of DBS lasting 12 months (comprising a 3-month baseline and 9 months of postimplantation follow-up).

RESULTS

Baseline median seizures measured by diaries numbered 2.6 (interquartile range [IQR] = 1.4-5) per day, compared to 284 (IQR = 120.5-360) electrographic seizures per day, confirming that diaries capture only a small fraction of seizure burden. Across all patient EEGs, the average number of GPFA discharges per hour of sleep was 138 (IQR =72-258). GPFA duration and frequency, quantified over 2-h windows of sleep EEG, were significantly associated with diary-recorded seizure counts over 3-month intervals (p < .001, η2 p  = .30-.48). For every GPFA discharge, there were 20-25 diary seizures witnessed over 3 months. There was high between-patient variability in the ratio between diary seizure burden and GPFA burden; however, within individual patients, the ratio was similar over time, such that the percentage change from pre-DBS baseline in seizure diaries strongly correlated with the percentage change in GPFA.

SIGNIFICANCE

When seeking to optimize treatment in patients with LGS, monitoring changes in GPFA may allow rapid titration of treatment parameters, rather than waiting for feedback from seizure diaries.

EEG Markers of Treatment Resistance in Idiopathic Generalized Epilepsy: From Standard EEG Findings to Advanced Signal Analysis

Idiopathic generalized epilepsy (IGE) represents a common form of epilepsy in both adult and pediatric epilepsy units. Although IGE has been long considered a relatively benign epilepsy syndrome, a remarkable proportion of patients could be refractory to treatment. While some clinical prognostic factors have been largely validated among IGE patients, the impact of routine electroencephalography (EEG) findings in predicting drug resistance is still controversial and a growing number of authors highlighted the potential importance of capturing the sleep state in this setting. In addition, the development of advanced computational techniques to analyze EEG data has opened new opportunities in the identification of reliable and reproducible biomarkers of drug resistance in IGE patients. In this manuscript, we summarize the EEG findings associated with treatment resistance in IGE by reviewing the results of studies considering standard EEGs, 24-h EEG recordings, and resting-state protocols. We discuss the role of 24-h EEG recordings in assessing seizure recurrence in light of the potential prognostic relevance of generalized fast discharges occurring during sleep. In addition, we highlight new and promising biomarkers as identified by advanced EEG analysis, including hypothesis-driven functional connectivity measures of background activity and data-driven quantitative findings revealed by machine learning approaches. Finally, we thoroughly discuss the methodological limitations observed in existing studies and briefly outline future directions to identify reliable and replicable EEG biomarkers in IGE patients.

Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning—clinical application perspectives

The application of deep learning approaches for the detection of interictal epileptiform discharges is a nascent field, with most studies published in the past 5 years. Although many recent models have been published demonstrating promising results, deficiencies in descriptions of data sets, unstandardized methods, variation in performance evaluation and lack of demonstrable generalizability have made it difficult for these algorithms to be compared and progress to clinical validity. A few recent publications have provided a detailed breakdown of data sets and relevant performance metrics to exemplify the potential of deep learning in epileptiform discharge detection. This review provides an overview of the field and equips computer and data scientists with a synopsis of EEG data sets, background and epileptiform variation, model evaluation parameters and an awareness of the performance metrics of high impact and interest to the trained clinical and neuroscientist EEG end user. The gold standard and inter-rater disagreements in defining epileptiform abnormalities remain a challenge in the field, and a hierarchical proposal for epileptiform discharge labelling options is recommended. Standardized descriptions of data sets and reporting metrics are a priority. Source code-sharing and accessibility to public EEG data sets will increase the rigour, quality and progress in the field and allow validation and real-world clinical translation.

Pretreatment electroencephalographic features in patients with childhood absence epilepsy

OBJECTIVE

To analyze the ictal and interictal electroencephalographic (EEG) features in newly diagnosed childhood absence epilepsy (CAE) and determine the association between seizure onset topography, interictal focal spike-wave discharges (FSWDs) and accompanying clinical features of absence seizures.

METHODS

The authors searched the EEG database for a definite diagnosis of CAE according to ILAE 2017 criteria. Video-EEGs of untreated pediatric patients during sleep and wakefulness were evaluated retrospectively.

RESULTS

The study included 47 patients (25 males, 22 females). Interictal FSWDs were observed in 49% of patients with CAE during wakefulness and in 85.1% during sleep (p = 0.001). Interictal FSWDs were most frequently observed in the frontal regions (awake: 34%; asleep: 74.5%), followed by the posterior temporoparietooccipital region (awake: 21.2%; asleep: 36.1%), and the centrotemporal region (awake: 6.4%; asleep: 8.5%). Eleven patients (23.4%) had polyspikes during sleep. Both bilateral symmetric and asymmetric seizure onset were noted in 32%, whereas focal seizure onset was observed in 14.9% of the patients. Absence seizures with and without motor components were seen in 72.3% and 61.7% of patients, respectively, and in 33% of patients both occurred. There were no associations between the existence of interictal FSWDs, focal/asymmetric seizure onset, and absence seizures with and/or without motor components.

CONCLUSION

Asymmetric and/or focal seizure onset, interictal FSWDs, and absence seizures with motor components are commonly observed in drug-naive CAE. This study found no association between seizure onset topography, interictal FSWDs, and semiological features of absence seizures.

Generalized Fast Discharges Along the Genetic Generalized Epilepsy Spectrum: Clinical and Prognostic Significance

Objective

To investigate the electroclinical characteristics and the prognostic impact of generalized fast discharges in a large cohort of genetic generalized epilepsy (GGE) patients studied with 24-h prolonged ambulatory electroencephalography (paEEG).

Methods

This retrospective multicenter cohort study included 202 GGE patients. The occurrence of generalized paroxysmal fast activity (GPFA) and generalized polyspike train (GPT) was reviewed. GGE patients were classified as having idiopathic generalized epilepsy (IGE) or another GGE syndrome (namely perioral myoclonia with absences, eyelid myoclonia with absences, epilepsy with myoclonic absences, generalized epilepsy with febrile seizures plus, or GGE without a specific epilepsy syndrome) according to recent classification proposals.

Results

GPFA/GPT was found in overall 25 (12.4%) patients, though it was significantly less frequent in IGE compared with other GGE syndromes (9.3 vs. 25%, p = 0.007). GPFA/GPT was found independently of seizure type experienced during history, the presence of mild intellectual disability/borderline intellectual functioning, or EEG features. At multivariable analysis, GPFA/GPT was significantly associated with drug resistance (p = 0.04) and with a higher number of antiseizure medications (ASMs) at the time of paEEG (p < 0.001) and at the last medical observation (p < 0.001). Similarly, GPFA/GPT, frequent/abundant generalized spike-wave discharges during sleep, and a higher number of seizure types during history were the only factors independently associated with a lower chance of achieving 2-year seizure remission at the last medical observation. Additionally, a greater number of GPFA/GPT discharges significantly discriminated between patients who achieved 2-year seizure remission at the last medical observation and those who did not (area under the curve = 0.77, 95% confidence interval 0.57–0.97, p = 0.02)

Conclusion

We found that generalized fast discharges were more common than expected in GGE patients when considering the entire GGE spectrum. In addition, our study highlighted that GPFA/GPT could be found along the entire GGE continuum, though their occurrence was more common in less benign GGE syndromes. Finally, we confirmed that GPFA/GPT was associated with difficult-to-treat GGE, as evidenced by the multivariable analysis and the higher ASM load during history.

Brainwaves beyond diagnosis: Wider applications of electroencephalography in idiopathic generalized epilepsy

Electroencephalography (EEG) has long been used as a versatile and noninvasive diagnostic tool in epilepsy. With the advent of digital EEG, more advanced applications of EEG have emerged. Compared with technologically advanced practice in focal epilepsies, the utilization of EEG in idiopathic generalized epilepsy (IGE) has been lagging, often restricted to a simple diagnostic tool. In this narrative review, we provide an overview of broader applications of EEG beyond this narrow scope, discussing how the current clinical and research applications of EEG may potentially be extended to IGE. The current literature, although limited, suggests that EEG can be used in syndromic classification, guiding antiseizure medication therapy, predicting prognosis, unraveling biorhythms, and investigating functional brain connectivity of IGE. We emphasize the need for longer recordings, particularly 24-h ambulatory EEG, to capture discharges reflecting circadian and sleep-wake cycle-associated variations for wider EEG applications in IGE. Finally, we highlight the challenges and limitations of the current body of literature and suggest future directions to encourage and enhance more extensive applications of this potent tool.

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-electroencephalography (EEG) monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially heralding a more quantitative approach to therapeutic outcomes. There is substantial research into the automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic, and despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG. This study reports on a deep learning algorithm for computer-assisted EEG review. Deep convolutional neural networks were trained to detect epileptic discharges using a preexisting dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data were curated and confirmed independently by two epilepsy specialists (Seneviratne et al., 2016). The resulting automated detection algorithm was then used to review diagnostic scalp EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting. The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes. This article is part of the Special Issue "NEWroscience 2018".

Spectrum of Electroencephalography Findings in Newly Diagnosed Epilepsy

Background Epilepsy is a neurological disorder that presents with recurrent seizures associated with erratic brain activity which can be measured through EEG in addition to other neurological investigations. However, EEG may show abnormal patterns and waveforms while the patient is having a seizure which is crucial for making an accurate diagnosis. Objective This study aims to evaluate the spectrum of EEG findings in newly diagnosed epileptic patients as part of a neurological investigation. Material and methods This cross-sectional study was carried out at the Department of Paediatric Neurology, the Children's Hospital, and the Institute of Child Health, Lahore for six months. A sample of 122 patients was enrolled in this study with an age range of >1 month and <18 years, with a diagnosis of epilepsy based upon ≥2 unprovoked seizures that occurred ≥ 24 hours apart. After obtaining informed consent from the patients, a one-time EEG was carried out and details were noted such as type and frequency of the discharge, site of maximum amplitude, paroxysm morphology, and onset and offset (focal/generalized) of the discharges. The data was analyzed using SPSS v.25 (IBM SPSS Statistics for Windows, Armonk, NY). Results The mean age of children enrolled in this study was 5.58 ± 3.46 years. There were 70 (57.4%) males and 52 (42.6%) females. The mean age at the onset of seizures was 4.85 ± 3.16 years. Out of 122 children, focal onset aware epilepsy type was noted in 8 cases, focal onset impaired awareness was noted in 19 cases and generalized onset motor type of epilepsy was noted in 95 cases. Furthermore, EEG findings were normal in 41 (33.61%) patients; however, 81 (66.39%) EEG findings of the patients place them in the abnormal range. On EEG, paroxysm morphology was typical in 78 (96.3%) patients while atypical in 3 (3.7%) patients. Discharge spectrum was generalized in 46 (56.8%) patients, localized in 19 (23.5%) patients, bilateral independent in 1 (1.2%) patient and multifocal in 15 (18.5%) patients. Discharge pattern was periodic in seven (8.6%) cases, rhythmic delta activity was noted in 4 (4.9%) cases, spike and wave pattern was noted in 68 (84.0%) cases and sharp and wave pattern was observed in 36 (44.4%) patients. Conclusion Our study concluded that EEG findings were abnormal in 81 (66.39%) patients. Thus to make the recommendations locally and nationally, we observed that EEG can highlight the abnormal pattern and discharges in newly diagnosed individuals with epilepsy. Our findings could be instrumental to identify the type of EEG discharges in a timely fashion while making diagnoses and treatment plan protocols accordingly. This study finding recommends the early application of EEG after the presentation of epileptic symptoms by the patient. We further recommend that further similar studies be conducted in multiple tertiary care settings to reach a firm and valuable conclusion.

Our reasons for converting to valproic acid treatment in female patients with genetic generalized epilepsy: a retrospective, single-centre study

INTRODUCTION AND AIM

Valproic acid (Na valproate) is a broad-spectrum anti-seizure medication used in children and adolescents. It is thought to have fewer adverse effects; however, recent studies have restricted its use in women of reproductive age due to the teratogenic impacts on cognition. Although alternative drugs have been used to treat patients in clinical follow-up, some patients have to return to using valproic acid. Our study aimed to determine the rate of return to valproic acid treatment in female patients with follow-up in our centre and the reasons for the return.

MATERIALS AND METHODS

Female patients with genetic generalized epilepsy who were followed up in our centre were included in the study. Patient data were retrospectively obtained from file records. The patients were grouped by seizure subgroups, antiepileptic treatment used, electroencephalography characteristics, and seizure treatment response.

RESULTS

Sixty-three (31.7%) of the 199 patients had to return to VPA treatment. When the reasons for the discontinuation of other drugs were examined, non-response to treatment was found in 80.0% of patients, adverse medication effects in 18.3%, and 1.7% continued voluntarily. Patients who are JAE subtypes were more likely to return to VPA treatment than GTCS alone subtypes. A total of 7.4% of patients converted to VPA therapy had continued myoclonic seizures compared with 20.4% of patients treated with alternative drugs.

CONCLUSION

VPA treatment is not used as the first choice in females of reproductive age; however, some patients will only achieve seizure control with valproate, especially those with myoclonic seizures and JAE.

Brief potentially ictal rhythmic discharges and paroxysmal fast activity as scalp electroencephalographic biomarkers of seizure activity and seizure onset zone

OBJECTIVE

The electroencephalographic (EEG) terms "brief potentially ictal rhythmic discharges" (BIRDs) and "paroxysmal fast activity" (PFA) are considered distinct entities; however, their definitions overlap, and they may have similar clinical significance. We investigated their clinical significance and their association with seizures and the seizure onset zone (SOZ).

METHODS

We retrospectively identified an adult cohort (July 2015 to March 2018) whose long-term (>12 h) EEGs in any setting reported BIRDs (>4 Hz, lasting .5-10 s) and/or PFA. Different frequency cutoffs for PFA (>13 Hz or ≥8 Hz) were tested to compare their clinical significance. Patient demographics, clinical history, and EEG features were recorded.

RESULTS

We identified 94 patients with BIRDs/PFA out of 3520 patients (3%); 36 were critically ill (12 with epilepsy), and 58 were noncritically ill (all with epilepsy). The frequency of BIRDs/PFA was largely dependent on EEG background: it tended to be slower (theta) in the absence of a posterior dominant rhythm or in the presence of continuous focal slowing in the same region (p = .01). Sixty-two of 94 patients (66%; 32/36 [89%] critically ill, 30/58 [52%] noncritically ill) had electrographic seizures during the recording. The scalp EEG SOZ colocalized with BIRDs/PFA in all cases. BIRDs with faster frequency (also qualifying as PFA by definition) had similar seizure risk to that of slower BIRDs (62%-71%), regardless of frequency cutoff used to define PFA. In addition, 30 of 30 (100%) patients with evolving BIRDs/PFA (which lasted a median of 6 s, range = 2-9.5 s) had electrographic seizures (>10 s), compared to 32 of 64 (50%) with nonevolving BIRDs (median = 1 s, range = .5-3.5 s; p < .01).

SIGNIFICANCE

A high proportion of patients with BIRDs/PFA had seizures on EEG, regardless of their frequency (i.e., whether they also qualified as PFA), and their location colocalized with scalp SOZ in all cases. BIRDs appear to be a scalp EEG biomarker of uncontrolled seizure activity and a reliable localizing sign of the SOZ.

Domino-like transient dynamics at seizure onset in epilepsy

The International League Against Epilepsy (ILAE) groups seizures into "focal", "generalized" and "unknown" based on whether the seizure onset is confined to a brain region in one hemisphere, arises in several brain region simultaneously, or is not known, respectively. This separation fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset and even less so for the properties of the brain networks generating them. We consider three different patterns of domino-like seizure onset in Idiopathic Generalized Epilepsy (IGE) and present a novel approach to classification of seizures. To understand how these patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which currently is unknown. We investigate this interplay here in the framework of domino-like recruitment across a network. In particular, we use a phenomenological model of seizure onset with heterogeneous coupling and node properties, and show that in combination they generate a range of domino-like onset patterns observed in the IGE seizures. We further explore the individual contribution of heterogeneous node dynamics and coupling by interpreting in-vitro experimental data in which the speed of onset can be chemically modulated. This work contributes to a better understanding of possible drivers for the spatiotemporal patterns observed at seizure onset and may ultimately contribute to a more personalized approach to classification of seizure types in clinical practice.

Genetic generalized epilepsies with frontal lesions mimicking migratory disorders on the epilepsy monitoring unit

Objective

Some patients with genetic generalized epilepsy (GGE) may present with ambiguous and atypical findings and even focal brain abnormalities. Correct diagnosis may therefore be difficult.

Methods

We retrospectively collected six patients investigated on the epilepsy monitoring unit with MRI abnormalities mimicking focal cortical dysplasia (FCD-like) or heterotopias, but with semiology and EEG features of GGE. We compared them to four additional patients with GGE and nonmigratory abnormalities.

Results

All six patients presented with frontal MRI lesions: radial ("transmantle," n = 4), cortical-subcortical (n = 1), and periventricular heterotopia (n = 1). Five had positive family histories. Semiologic lateralizing signs compatible with the lesion were seen in four. Five patients had 3/s spike-wave complexes, with an asymmetric appearance in three. Regional EEG changes matched with the side of the abnormality in three patients. Invasive EEG (n = 2) or postoperative outcomes (n = 3) argued against an ictogenic role of the MRI abnormalities. Histology showed mild malformation of cortical development, but no focal cortical dysplasia. The six patients were finally diagnosed with juvenile myoclonic epilepsy (n = 2), juvenile absence epilepsy (n = 2), or GGE not further specified (nfs, n = 2). Compared to these patients, the other four (final diagnoses: childhood absence epilepsy, n = 1; perioral myoclonia with absences, n = 1; and GGE nfs, n = 2) had no lateralizing EEG findings.

Significance

Patients with GGE may have coincidental MRI abnormalities. These cases are challenging as frontal epilepsy and GGE can present with similar semiologies. GGE with coincidental FCD-like lesions/heterotopias is in particular difficult to diagnose as patients have more lateralizing features (in semiology and EEG) than those with tumors. A detailed noninvasive presurgical evaluation may be justified. We point out red flags that may help to distinguish GGE from frontal epilepsy, even in the presence of brain abnormalities: 3/s spike waves (even if asymmetric), changing lateralizing signs at different times, and a positive family history hinting at GGE.

Misleading Focal Clinical, Neurophysiologic, and Imaging Features in 2 Children With Generalized Epilepsy Who Underwent Invasive Electroencephalographic (EEG) Monitoring

Children and adults with genetic generalized epilepsy may have focal clinical seizure symptoms as well as electroencephalographic (EEG) findings. This may pose a diagnostic challenge to clinicians, especially when concomitant focal neuroimaging findings exist and the epilepsy is medically refractory. We sought to highlight the challenges that clinicians may face through the description of 2 children with suspected genetic generalized epilepsy who had both focal seizure symptoms and EEG/neuroimaging findings and underwent invasive EEG monitoring. Ultimately, invasive monitoring failed to demonstrate a focal origin for the seizures in both cases, and instead confirmed the presence of genetic generalized epilepsy. We demonstrate that ≥3-Hz generalized monomorphic spike and waves are less likely to represent secondary bilateral synchrony, that focal neuroimaging findings may not always be causal and that repeated hyperventilation is an essential activation procedure for genetic generalized epilepsy.

The Spatio-Temporal Equalization for Evoked or Event-Related Potential Detection in Multichannel EEG Data

GOAL

Evoked or Event-Related Potential (EP/ERP) detection is a major area of interest within the domain of EEG (electroencephalography) signal processing. While traditional methods of EEG processing have mostly focused on enhancing signal components, few have explored background noise suppression techniques. Optimizing the suppression of background noise can play a critical role in improving the performance of EP/ERP detection.

METHODS

In this study, a spatio-temporal equalization (STE) method was proposed based on the Multivariate Autoregressive (MVAR) model, which has been shown to suppress the spatio-temporal correlation of background noise and improve the EEG signal detection performance.

RESULTS

For practical applications, two optimization schemes based on the spatio-temporal equalization method were designed to solve two common challenges in EEG signal detection: P300 and steady state visual evoked potentials. Our results demonstrated that the STE method effectively improves recognition performance of evoked or event-related potential detection. Additionally, the STE method offers fewer parameters, lower computational complexity, and easier implementation.

CONCLUSION

These attributes allow the STE approach to be extended as a preprocessing method which can be used in combination with existing techniques.

Focal EEG abnormalities and focal ictal semiology in generalized epilepsy

In clinical practice, the diagnosis of focal vs generalized epilepsy dictates the management of the patient. The distinction between generalized and focal epilepsy is at times imperfect and some epilepsies have features that fall in between these two extremes. An example is the occurrence of focal interictal and focal ictal abnormalities in generalized epilepsies. As a part of the special issue on "The epileptogenic zone in pediatric epilepsy surgery", this focused narrative review will discuss different focal abnormalities seen in generalized epilepsy. An overlap of focal and generalized epileptiform abnormalities may support a continuum between focal and generalized epilepsy. When evaluating patients in the "gray zone", other factors such as ictal semiology, neuroimaging, genetic testing and functional deficits may need to be considered to reach an accurate diagnosis. Being aware of possible occurrence of focal clinical and EEG features in generalized epilepsy will help clinicians select more preferred AED (s), avoiding potential iatrogenic side effects and inappropriate consideration for epilepsy surgery.

Adult EEG

After more than 85 years of development and use in clinical practice, the electroencephalogram (EEG) remains a dependable, inexpensive, and useful diagnostic tool for the investigation of the electrophysiologic activity of the brain. The advent of digital technology has led to greater sophistication and multiple software applications to extend the utility of EEG beyond the confines of the laboratory. Despite the discovery of new waveforms, basic neurophysiologic principles remain essential to the clinical care of patients. Patterns in the interictal EEG make it possible to clarify the differential diagnosis of paroxysmal neurological events, classify seizure type and epilepsy syndromes, and characterize and quantify seizures when ictal recordings are obtained. EEG can also demonstrate cerebral dysfunction when structural imaging is normal to detect focal or lateralized abnormalities in patients with encephalopathy. High-density EEG with electrical source imaging has improved localization in candidates for epilepsy surgery. Quantitative EEG and broadband EEG are advancing our understanding of the functional processes of the brain itself.

Generalized polyspike train: An EEG biomarker of drug-resistant idiopathic generalized epilepsy

OBJECTIVE

To identify clinical and EEG biomarkers of drug resistance in adults with idiopathic generalized epilepsy.

METHODS

We conducted a case-control study consisting of a discovery cohort and a replication cohort independently assessed at 2 different centers. In each center, patients with the idiopathic generalized epilepsy phenotype and generalized spike-wave discharges on EEG were classified as drug-resistant or drug-responsive. EEG changes were classified into predefined patterns and compared between the 2 groups in the discovery cohort. Factors associated with drug resistance in multivariable analysis were tested in the replication cohort.

RESULTS

The discovery cohort included 85 patients (29% drug-resistant and 71% drug-responsive). Their median age at assessment was 32 years and 50.6% were female. Multivariable analysis showed that higher number of seizure types ever experienced (3 vs 1: odds ratio [OR] = 31.1, 95% confidence interval [CI]: 4.5-214, p < 0.001; 3 vs 2: OR = 14.6, 95% CI: 2.3-93.1, p = 0.004) and generalized polyspike train (burst of generalized rhythmic spikes lasting less than 1 second) during sleep were associated with drug resistance (OR = 10.8, 95% CI: 2.4-49.4, p = 0.002). When these factors were tested in the replication cohort of 80 patients (27.5% drug-resistant and 72.5% drug-responsive; 71.3% female; median age 27.5 years), the proportion of patients with generalized polyspike train during sleep was also higher in the drug-resistant group (OR = 4.0, 95% CI: 1.35-11.8, p = 0.012).

CONCLUSION

Generalized polyspike train during sleep may be an EEG biomarker for drug resistance in adults with idiopathic generalized epilepsy.

Can EEG Differentiate Among Syndromes in Genetic Generalized Epilepsy?

PURPOSE

To evaluate EEG differences among syndromes in genetic generalized epilepsy based on quantified data.

METHODS

Twenty-four-hour ambulatory EEGs were recorded in consecutive patients diagnosed with genetic generalized epilepsy. All epileptiform EEG abnormalities were quantified into density scores (total duration of epileptiform discharges per hour). One-way analysis of variance was conducted to find out differences in EEG density scores among the syndromes. Generalized linear mixed models were also fitted to explore the association between the proportion of "pure" generalized spike-wave paroxysms and fragments (without intervening polyspikes/polyspike-waves) and the syndromes.

RESULTS

In total, 6,923 epileptiform discharges were analyzed from 105 abnormal EEGs. In the analysis of variance, six EEG variables were significantly different among syndromes: total spike density (P = 0.001), total polyspike and polyspike-wave density (P = 0.049), generalized spike-wave-only density (P < 0.001), generalized paroxysm density (P < 0.001), generalized paroxysm duration mean (P = 0.018), and generalized paroxysm duration maximum (P = 0.009). The density of epileptiform discharges and the paroxysm durations were the highest in juvenile absence epilepsy followed by juvenile myoclonic epilepsy, childhood absence epilepsy, and generalized epilepsy with tonic-clonic seizures only. Generalized linear mixed models revealed that "pure" generalized spike-wave discharges (without intervening polyspikes/polyspike waves) tended to be more frequent in absence epilepsies, although the difference was not statistically significant (P = 0.21).

CONCLUSIONS

The findings of this study suggest that the density and duration of epileptiform discharges can help differentiate among genetic generalized epilepsy syndromes.

Characteristics of Epileptiform Discharge Duration and Interdischarge Interval in Genetic Generalized Epilepsies

We sought to investigate (1) the characteristics of epileptiform discharge (ED) duration and interdischarge interval (IDI) and (2) the influence of vigilance state on the ED duration and IDI in genetic generalized epilepsy (GGE). In a cohort of patients diagnosed with GGE, 24-h ambulatory EEG recordings were performed prospectively. We then tabulated durations, IDI, and vigilance state in relation to all EDs captured on EEGs. We used K-means cluster analysis and finite mixture modeling to quantify and characterize the groups of ED duration and IDI. To investigate the influence of sleep, we calculated the mean, median, and SEM in each population from all subjects for sleep state and wakefulness separately, followed by the Kruskal–Wallis test to compare the groups. We analyzed 4,679 EDs and corresponding IDI from 23 abnormal 24-h ambulatory EEGs. Our analysis defined two populations of ED durations and IDI: short and long. In all populations, both ED durations and IDI were significantly longer in wakefulness. Our results highlight different characteristics of ED populations in GGE and the influence by the sleep–wake cycle.

Generalized paroxysmal fast activity in EEG: An unrecognized finding in genetic generalized epilepsy

OBJECTIVE

To study generalized paroxysmal fast activity (GPFA) in patients with genetic generalized epilepsy (GGE).

INTRODUCTION

GPFA is an electroencephalographic (EEG) finding in patients with symptomatic generalized epilepsy consisting of 15-25Hz bifrontally predominant generalized fast activity seen predominantly in sleep. Historically GPFA is linked to epileptic encephalopathy with drug resistant epilepsy and intellectual disability. However, GPFA has been rarely described as an atypical finding in patients with GGE without negative prognostic implication. We report cognitive profile and seizure characteristics in seven patients with GGE and GPFA.

METHODS

The Vanderbilt EMU and EEG reports were searched for the keywords "idiopathic generalized epilepsy", "GPFA"and "generalized spike and wave discharges (GSWD)". We reviewed the EEG tracings and the electronic medical records of patients thus identified. The seizure type, frequency, neurological work-up, clinical profile and imaging data were recorded.

RESULTS

Awake and sleep states were captured on EEGs of all patients. On EEG tracing review six patients were confirmed to have GSWD and GPFA; one patient had GPFA but no GSWD. All patients had normal cognitive function. Four had a normal brain MRI and one a normal head CT (two were never imaged). None of the patients had tonic seizures. The main seizure type was generalized tonic-clonic seizures (GTCS) in five patients, absence in two. Age at onset of epilepsy ranged from 4 to 24years. The mean GTC seizure frequency at the time of EEG was 3; two patients were seizure free on two antiepileptic drugs (AEDs).

CONCLUSIONS

GPFA can be an unrecognized electrographic finding in patients with genetic generalized epilepsy. While GPFA remains an important diagnostic EEG feature for epileptic encephalopathy (Lennox-Gastaut syndrome) it is not specific for this diagnosis. Thus, GPFA may have a spectrum of variable phenotypic expression. The finding of GPFA is not necessarily indicative of unfavorable outcome.

Electroencephalography in the Diagnosis of Genetic Generalized Epilepsy Syndromes

Genetic generalized epilepsy (GGE) consists of several syndromes diagnosed and classified on the basis of clinical features and electroencephalographic (EEG) abnormalities. The main EEG feature of GGE is bilateral, synchronous, symmetric, and generalized spike-wave complex. Other classic EEG abnormalities are polyspikes, epileptiform K-complexes and sleep spindles, polyspike-wave discharges, occipital intermittent rhythmic delta activity, eye-closure sensitivity, fixation-off sensitivity, and photoparoxysmal response. However, admixed with typical changes, atypical epileptiform discharges are also commonly seen in GGE. There are circadian variations of generalized epileptiform discharges. Sleep, sleep deprivation, hyperventilation, intermittent photic stimulation, eye closure, and fixation-off are often used as activation techniques to increase the diagnostic yield of EEG recordings. Reflex seizure-related EEG abnormalities can be elicited by the use of triggers such as cognitive tasks and pattern stimulation during the EEG recording in selected patients. Distinct electrographic abnormalities to help classification can be identified among different electroclinical syndromes.

Self and informant report ratings of psychopathology in genetic generalized epilepsy

The psychological sequelae of genetic generalized epilepsies (GGE) is of growing research interest, with up to a third of all adults with GGE experiencing significant psychiatric comorbidity according to a recent systematic review. A number of unexplored questions remain. Firstly, there is insufficient evidence to determine relative prevalence of psychopathology between GGE syndromes. Secondly, the degree to which self-report and informant-report questionnaires accord in adults with epilepsy is unknown. Finally, while epilepsy severity is one likely predictor of worse psychopathology in GGE, evidence regarding other possible contributing factors such as epilepsy duration and antiepileptic drugs (AEDs) has been equivocal. The potential impact of subclinical epileptiform discharges remains unexplored. Self-report psychopathology symptoms across six DSM-Oriented Subscales were prospectively measured in 60 adults with GGE, with informant-report provided for a subset of 47. We assessed the burden of symptoms from both self- and informant-report, and the relationship between clinical epilepsy variables and self-reported symptoms. Results showed elevated symptoms in almost half of the sample overall. Depression and anxiety were the most commonly reported types of symptoms. There was a trend towards greater symptoms endorsement by self-report, and relatively modest interrater agreement. Symptoms of ADHD were significantly positively associated with number of AEDs currently prescribed. Other psychopathology symptoms were not significantly predicted by epilepsy duration, seizure-free duration or total duration of epileptiform discharges over a 24-hour period. The high prevalence of psychological needs suggests that routine screening of psychopathology and provision of psychoeducation may be essential to improving patient care and outcomes. Further investigation is required to better understand predictive and causal factors for psychopathology in GGE.

EEG correlates of seizure freedom in genetic generalized epilepsies

Background

We investigated the association between epileptiform EEG abnormalities and the preceding duration of seizure freedom in genetic generalized epilepsies (GGE).

Methods

We analyzed 24-hour ambulatory EEG recordings of patients with GGE diagnosed and classified according to the International League Against Epilepsy criteria. We quantified epileptiform EEG abnormalities into density scores (total duration of epileptiform discharges per hour) and estimated the preceding seizure-free duration at the time of EEG recording based on the last self-reported seizure. We then employed regression analysis to quantitate the relationship between the duration of seizure freedom and EEG variables.

Results

We analyzed 6,923 epileptiform discharges from 105 patients with abnormal 24-hour EEGs. In the regression analysis exploring the crude associations, we found significant correlations between 6 EEG variables and the duration of seizure freedom indicating that shorter duration of seizure freedom was associated with higher spike densities and longer paroxysms. These associations were not affected by confounders such as syndrome, age at EEG, age at epilepsy onset, sex, duration of epilepsy, or number of antiepileptic drugs.

Conclusions

Higher densities and longer durations of epileptiform discharges may be retrospectively associated with a shorter duration of self-reported seizure freedom. Hence, EEG can potentially be used as a biomarker of prognosis in GGE. These findings need to be validated in a prospective study in order to define EEG markers of future seizure freedom.

Temporal patterns of epileptiform discharges in genetic generalized epilepsies

OBJECTIVE

We sought to investigate the temporal patterns and sleep-wake cycle-related epileptiform discharges (EDs) in genetic generalized epilepsies (GGEs).

METHODS

We studied 24-hour ambulatory electroencephalography (EEG) recordings of patients with GGE, diagnosed and classified according to the International League against Epilepsy criteria. We manually coded the type of discharge, time of occurrence, duration, and arousal state of each ED. We employed mixed effects Poisson regression modeling to study the temporal distribution of epileptiform discharges. Additionally, we used multinomial regression analysis to explore the significance of the relationship between different states of arousal and types of epileptiform discharges.

RESULTS

We analyzed 6923 EDs from 105 abnormal 24-hour EEGs. Mixed effects Poisson regression analysis demonstrated significant changes in ED counts across time blocks. This distribution was largely influenced by the state of arousal. Generalized fragments (duration<2s) and focal discharges were more frequent during non-REM sleep while paroxysms (duration≥2s) were more frequent in wakefulness. Overall, 67% of epileptiform discharges occurred in non-REM sleep and only 33% occurred in wakefulness. Twenty-four patients (23%) had ED exclusively in sleep. Epileptiform discharges peaked from 23:00 through 07:00h.

SIGNIFICANCE

There is a time-of-day dependency of ED with a significant influence exerted by the state of arousal. Our observations suggest that the generation of epileptiform discharges is not a random process but is the result of complex interactions among biological rhythms such as the sleep-wake cycle and the intrinsic circadian pacemaker. High density of ED in sleep suggests that 24-hour EEG recording with the capture of natural sleep may be more useful than routine EEG to diagnose GGE.

Epilepsy beyond seizures: Predicting enduring cognitive dysfunction in genetic generalized epilepsies

Reduced cognitive functioning has been documented in the genetic generalized epilepsies (GGE). Among a number of hypothesized causal mechanisms, some evidence from other epilepsy syndromes suggests the impact of epileptiform discharges. This study investigates the relationship between cognitive function in GGE and burden of epileptiform discharges within a 24-hour EEG recording, controlling for variables relevant to cognitive function in epilepsy. As part of a larger prospective cohort study, 69 patients with EEG-confirmed GGE (11-58years) underwent 24-hour EEG and detailed neuropsychological assessment using the Woodcock Johnson III Tests. Ten-second pages of the EEG were marked manually page-by-page on longitudinal bipolar montage with 0.5 to 70Hz bandwidth by an experienced EEG reader. Multiple regression analyses were conducted. Epileptiform discharges were detected in 90% of patients. Less than 0.01% of electrophysiological events of two or more seconds were recognized by patients. Regression analysis demonstrated that the cumulative duration of epileptiform discharges over a 24-hour period predicted overall cognitive ability and memory function, accounting for 9.6% and 11.8% of adjusted variance, respectively. None of the epilepsy covariates included in multiple regression analysis added significantly to the model. Duration of epileptiform discharges negatively predicts overall cognitive ability and memory function, even after accounting for other known determinants of cognition. Prolonged epileptiform discharges are common and remain unreported by patients, raising important questions regarding the management of GGE syndromes and their associated comorbidities. Further research is required to investigate causal mechanisms if we are to improve cognitive outcomes in this common group of epilepsies.