Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

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.

Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.

Changes in interhemispheric coherence after total corpus callosotomy: a scalp EEG study in children with non-lesional generalized epilepsy.

PURPOSE: Coherence analysis in electroencephalography (EEG) allows measurement of the degree of consistency of amplitude between pairs of electrodes. Theoretically, disconnective epilepsy surgery should decrease coherence between corresponding areas. The study aimed to evaluate postoperative changes in interhemispheric coherence values after corpus callosotomy (CC). METHODS: Non-lesional, drug-resistant, generalized epilepsy patients who underwent total CC were retrospectively collected. To evaluate coherence, we divided the scalp interictal EEG into "baseline" and "discharge" states after excluding periods with artifacts. Interhemispheric coherence values were obtained between eight pairs of symmetrically opposite scalp electrodes in six different frequency bands. We analyzed both pre- and postoperative EEG sessions and calculated the percentage of difference (POD) in coherence values. RESULTS: We collected 13 patients and analyzed 2496 interhemispheric coherence values. Preoperative coherence values differed significantly between baseline and discharge states (p = 0.0003), but postoperative values did not (p = 0.11). For baseline state, coherence values were decreased after CC and median POD was - 22.3% (p < 0.0001). Delta frequency showed the most decreased POD (-44.3%, p = 0.0009). Median POD was lowest in the Fp1-Fp2 pair of electrodes. For discharge state, coherence values were decreased after CC and median POD was - 24.7% (p < 0.0001). Delta frequency again showed the most decreased POD (-55.9%, p = 0.0016). Median POD was lowest in the F7-F8 pair. CONCLUSION: After total CC, interhemispheric coherence decreased significantly in both baseline and discharge states. The most decreased frequency band was the delta band, which may be used as a representative frequency band in future studies.

Drug-resistant generalized epilepsies: Revisiting the frontiers of idiopathic generalized epilepsies.

The 2017 International League Against Epilepsy (ILAE) classification suggested that the term "genetic generalized epilepsies" (GGEs) should be used for the broad group of epilepsies with so-called "generalized" seizure types and "generalized" spike-wave activity on EEG, based on a presumed genetic etiology. Within this framework, idiopathic generalized epilepsies (IGEs) are described as a subset of GGEs and include only four epileptic syndromes: childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy, and epilepsy with generalized tonic-clonic seizures alone. The recent 2022 ILAE definition of IGEs is based on the current state of knowledge and reflects a community consensus and is designed to evolve as knowledge advances. The term "frontiers of IGEs" refers to the actual limits of our understanding of these four syndromes. Indeed, among patients presenting with a syndrome compatible with the 2022 definition of IGEs, we still observe a significant proportion of patients presenting with specific clinical features, refractory seizures, or drug-resistant epilepsies. This leads to the discussion of the boundaries of IGEs and GGEs, or what is accepted within a clinical spectrum of a definite IGE. Here, we discuss several entities that have been described in the literature for many years and that may either constitute rare features of IGEs or a distinct differential diagnosis. Their recognition by clinicians may allow a more individualized approach and improve the management of patients presenting with such entities.

Impaired State-Dependent Potentiation of GABAergic Synaptic Currents Triggers Seizures in a Genetic Generalized Epilepsy Model.

Epileptic activity in genetic generalized epilepsy (GGE) patients preferentially appears during sleep and its mechanism remains unknown. Here, we found that sleep-like slow-wave oscillations (0.5 Hz SWOs) potentiated excitatory and inhibitory synaptic currents in layer V cortical pyramidal neurons from wild-type (wt) mouse brain slices. In contrast, SWOs potentiated excitatory, but not inhibitory, currents in cortical neurons from a heterozygous (het) knock-in (KI) Gabrg2+Q/390X model of Dravet epilepsy syndrome. This created an imbalance between evoked excitatory and inhibitory currents to effectively prompt neuronal action potential firings. Similarly, physiologically similar up-/down-state induction (present during slow-wave sleep) in cortical neurons also potentiated excitatory synaptic currents within brain slices from wt and het KI mice. Moreover, this state-dependent potentiation of excitatory synaptic currents entailed some signaling pathways of homeostatic synaptic plasticity. Consequently, in het KI mice, in vivo SWO induction (using optogenetic methods) triggered generalized epileptic spike-wave discharges (SWDs), being accompanied by sudden immobility, facial myoclonus, and vibrissa twitching. In contrast, in wt littermates, SWO induction did not cause epileptic SWDs and motor behaviors. To our knowledge, this is the first mechanism to explain why epileptic SWDs preferentially happen during non rapid eye-movement sleep and quiet-wakefulness in human GGE patients.

Systematically disrupted functional gradient of the cortical connectome in generalized epilepsy: Initial discovery and independent sample replication.

Genetic generalized epilepsy is a network disorder typically involving distributed areas identified by classical neuroanatomy. However, the finer topological relationships in terms of continuous spatial arrangement between these systems are still ambiguous. Connectome gradients provide the topological representations of human macroscale hierarchy in an abstract low-dimensional space by embedding the functional connectome into a set of axes. Leveraging connectome gradients, we systematically scrutinized abnormalities of functional connectome gradient in patients with genetic generalized epilepsy with tonic-clonic seizure (GGE-GTCS, n = 78) compared to healthy controls (HC, n = 85), and further examined the reproducibility across multiple processing configurations and in an independent validation sample (patients with GGE-GTCS, n = 28; HC, n = 31). Our findings demonstrated an extended principal gradient at different spatial scales, network-level and vertex-level, in patients with GGE-GTCS. We found consistent results across processing parameters and in validation sample. The extended principal gradient revealed the excessive functional segregation between unimodal and transmodal systems associated with duration of epilepsy and age at seizure onset in patients. Furthermore, the connectivity profile of regions with abnormal principal gradients verified the disrupted functional hierarchy revealed by gradients. Together, our findings provided a novel view of functional system hierarchy alterations, which facilitated a continuous spatial arrangement of macroscale networks, to increase our understanding of the functional connectome hierarchy in generalized epilepsy.

Distinct effects of the basal ganglia and cerebellum on the thalamocortical pathway in idiopathic generalized epilepsy.

The aberrant thalamocortical pathways of epilepsy have been detected recently, while its underlying effects on epilepsy are still not well understood. Exploring pathoglytic changes in two important thalamocortical pathways, that is, the basal ganglia (BG)-thalamocortical and the cerebellum-thalamocortical pathways, in people with idiopathic generalized epilepsy (IGE), could deepen our understanding on the pathological mechanism of this disease. These two pathways were reconstructed and investigated in this study by combining diffusion and functional MRI. Both pathways showed connectivity changes with the perception and cognition systems in patients. Consistent functional connectivity (FC) changes were observed mainly in perception regions, revealing the aberrant integration of sensorimotor and visual information in IGE. The pathway-specific FC alterations in high-order regions give neuroimaging evidence of the neural mechanisms of cognitive impairment and epileptic activities in IGE. Abnormal functional and structural integration of cerebellum, basal ganglia and thalamus could result in an imbalance of inhibition and excitability in brain systems of IGE. This study located the regulated cortical regions of BG and cerebellum which been affected in IGE, established possible links between the neuroimaging findings and epileptic symptoms, and enriched the understanding of the regulatory effects of BG and cerebellum on epilepsy.

Probabilistic mapping of thalamic nuclei and thalamocortical functional connectivity in idiopathic generalised epilepsy.

It is well established that abnormal thalamocortical systems play an important role in the generation and maintenance of primary generalised seizures. However, it is currently unknown which thalamic nuclei and how nuclear-specific thalamocortical functional connectivity are differentially impacted in patients with medically refractory and non-refractory idiopathic generalised epilepsy (IGE). In the present study, we performed structural and resting-state functional magnetic resonance imaging (MRI) in patients with refractory and non-refractory IGE, segmented the thalamus into constituent nuclear regions using a probabilistic MRI segmentation method and determined thalamocortical functional connectivity using seed-to-voxel connectivity analyses. We report significant volume reduction of the left and right anterior thalamic nuclei only in patients with refractory IGE. Compared to healthy controls, patients with refractory and non-refractory IGE had significant alterations of functional connectivity between the centromedian nucleus and cortex, but only patients with refractory IGE had altered cortical connectivity with the ventral lateral nuclear group. Patients with refractory IGE had significantly increased functional connectivity between the left and right ventral lateral posterior nuclei and cortical regions compared to patients with non-refractory IGE. Cortical effects were predominantly located in the frontal lobe. Atrophy of the anterior thalamic nuclei and resting-state functional hyperconnectivity between ventral lateral nuclei and cerebral cortex may be imaging markers of pharmacoresistance in patients with IGE. These structural and functional abnormalities fit well with the known importance of thalamocortical systems in the generation and maintenance of primary generalised seizures, and the increasing recognition of the importance of limbic pathways in IGE.

Shared genetic basis between genetic generalized epilepsy and background electroencephalographic oscillations.

OBJECTIVE: Paroxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations. METHODS: Confounding factors, including the heterogeneous etiology of epilepsies and medication effects, hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a genome-wide association study (GWAS) on epilepsy (n = 12 803 people with epilepsy and 24 218 controls) with that from a GWAS on background EEG (n = 8425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta, and theta. We replicated our findings in an independent epilepsy replication dataset (n = 4851 people with epilepsy and 20 428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores, and Mendelian randomization analyses. RESULTS: Our analyses show strong genetic correlations of genetic generalized epilepsy (GGE) with background EEG oscillations, primarily in the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations. SIGNIFICANCE: Our results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic workup of epilepsy.

Interictal electroencephalographic functional network topology in drug-resistant and well-controlled idiopathic generalized epilepsy.

OBJECTIVE: The study aim was to compare interictal encephalographic (EEG) functional network topology between people with well-controlled idiopathic generalized epilepsy (WC-IGE) and drug-resistant IGE (DR-IGE). METHODS: Nineteen participants with WC-IGE, 18 with DR-IGE, and 20 controls underwent a resting state, 64-channel EEG. An artifact-free epoch was bandpass filtered into the frequency range of high and low extended alpha. Weighted functional connectivity matrices were calculated. Mean degree, degree distribution variance, characteristic path length (L), clustering coefficient, small world index (SWI), and betweenness centrality were measured. A Kruskal-Wallis H-test assessed effects across groups. Where significant differences were found, Bonferroni-corrected Mann-Whitney pairwise comparisons were calculated. RESULTS: In the low alpha band (6-9 Hz), there was a significant difference in L at the three-group level (p < .0001). This was lower in controls than both WC-IGE and DR-IGE (p < .0001 for both), with no difference in L between WC-IGE and DR-IGE. Mean degree (p = .031), degree distribution variance (p = .032), and SWI (p = .023) differed across the three groups in the high alpha band (10-12 Hz). Mean degree and degree distribution variance were lower in WC-IGE than controls (p = .029 for both), and SWI was higher in WC-IGE compared with controls (p = .038), with no differences in other pairwise comparisons. SIGNIFICANCE: IGE network topology is more regular in the low alpha frequency band, potentially reflecting a more vulnerable structure. WC-IGE network topology is different from controls in the high alpha band. This may reflect drug-induced network changes that have stabilized the WC-IGE network by rendering it less likely to synchronize. These results are of potential importance in advancing the understanding of mechanisms of epilepsy drug resistance and as a possible basis for a biomarker of DR-IGE.

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable.

OBJECTIVE: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics. METHODS: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort's data were provided in a different format and harmonized using the two versions of the HPO. RESULTS: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals' seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures. SIGNIFICANCE: We have generated a detailed contemporary conceptual map for harmonization of clinical seizure data, implemented in the official 2020-12-07 HPO release and freely available at hpo.jax.org. This will help to overcome the phenotypic bottleneck in genomics, facilitate reuse of valuable data, and ultimately improve diagnostics and precision treatment of the epilepsies.

Neural diffusivity and pre-emptive epileptic seizure intervention.

The propagation of epileptic seizure activity in the brain is a widespread pathophysiology that, in principle, should yield to intervention techniques guided by mathematical models of neuronal ensemble dynamics. During a seizure, neural activity will deviate from its current dynamical regime to one in which there are significant signal fluctuations. In silico treatments of neural activity are an important tool for the understanding of how the healthy brain can maintain stability, as well as of how pathology can lead to seizures. The hope is that, contained within the mathematical foundations of such treatments, there lie potential strategies for mitigating instabilities, e.g. via external stimulation. Here, we demonstrate that the dynamic causal modelling neuronal state equation generalises to a Fokker-Planck formalism if one extends the framework to model the ways in which activity propagates along the structural connections of neural systems. Using the Jacobian of this generalised state equation, we show that an initially unstable system can be rendered stable via a reduction in diffusivity-i.e., by lowering the rate at which neuronal fluctuations disperse to neighbouring regions. We show, for neural systems prone to epileptic seizures, that such a reduction in diffusivity can be achieved via external stimulation. Specifically, we show that this stimulation should be applied in such a way as to temporarily mirror the activity profile of a pathological region in its functionally connected areas. This counter-intuitive method is intended to be used pre-emptively-i.e., in order to mitigate the effects of the seizure, or ideally even prevent it from occurring in the first place. We offer proof of principle using simulations based on functional neuroimaging data collected from patients with idiopathic generalised epilepsy, in which we successfully suppress pathological activity in a distinct sub-network prior to seizure onset. Our hope is that this technique can form the basis for future real-time monitoring and intervention devices that are capable of treating epilepsy in a non-invasive manner.

CHD2-Related CNS Pathologies.

Epileptic encephalopathies (EE) are severe epilepsy syndromes characterized by multiple seizure types, developmental delay and even regression. This class of disorders are increasingly being identified as resulting from de novo genetic mutations including many identified mutations in the family of chromodomain helicase DNA binding (CHD) proteins. In particular, several de novo pathogenic mutations have been identified in the gene encoding chromodomain helicase DNA binding protein 2 (CHD2), a member of the sucrose nonfermenting (SNF-2) protein family of epigenetic regulators. These mutations in the CHD2 gene are causative of early onset epileptic encephalopathy, abnormal brain function, and intellectual disability. Our understanding of the mechanisms by which modification or loss of CHD2 cause this condition remains poorly understood. Here, we review what is known and still to be elucidated as regards the structure and function of CHD2 and how its dysregulation leads to a highly variable range of phenotypic presentations.

Photoplethysmographic evaluation of generalized tonic-clonic seizures.

OBJECTIVE: Photoplethysmography (PPG) is an optical technique measuring variations of blood perfusion in peripheral tissues. We evaluated alterations in PPG signals in relationship to the occurrence of generalized tonic-clonic seizures (GTCSs) in patients with epilepsy to evaluate the feasibility of seizure detection. METHODS: During electroencephalographic (EEG) long-term monitoring, patients wore portable wristband sensor(s) on their wrists or ankles recording PPG signals. We analyzed PPG signals during three time periods, which were defined with respect to seizures detected on EEG: (1) baseline (>30 minutes prior to seizure), (2) preseizure period, and (3) postseizure period. Furthermore, we selected five random control segments during seizure-free periods. PPG features, including frequency, amplitude, duration, slope, smoothness, and area under the curve, were automatically calculated. We used a linear mixed-effect model to evaluate changes in PPG features between different time periods in an attempt to identify signal changes that detect seizures. RESULTS: We prospectively enrolled 174 patients from the epilepsy monitoring unit at Boston Children's Hospital. Twenty-five GTCSs were recorded from 13 patients. Data from the first recorded GTCS of each patient were included in the analysis. We observed an increase in PPG frequency during pre- and postseizure periods that was higher than the changes during seizure-free periods (frequency increase: preseizure = 0.22 Hz, postseizure = 0.58 Hz vs changes during seizure-free period = 0.05 Hz). The PPG slope decreased significantly by 56.71 nW/s during preseizure periods compared to seizure-free periods. Additionally, the smoothness increased significantly by 0.22 nW/s during the postseizure period compared to seizure-free periods. SIGNIFICANCE: Monitoring of PPG signals may assist in the detection of GTCSs in patients with epilepsy. PPG may serve as a promising biomarker for future seizure detection systems and may contribute to future seizure prediction systems.

Audiogenic seizure as a model of sudden death in epilepsy: A comparative study between four inbred mouse strains from early life to adulthood.

OBJECTIVE: Mouse models of sudden unexpected death in epileptic patients (SUDEP) using audiogenic seizures (AGS) are valuable because death can occur following a sound-induced seizure in the absence of any pharmacologic or electric component. However, only a few strains of mice are AGS prone, and the vast majority of studies involve DBA/2 or DBA/1 inbred strains. With the goal of characterizing the variation of AGS susceptibility with age, and of offering a larger panel of mice available for AGS studies, we performed a comparative study of the variability in AGS responses. METHODS: The variation of AGS with age was determined in two classically used inbred strains of mice, DBA/2 and DBA/1, and two additional strains, BALB/c and 129/SvTer. As AGS-stimulated tonic seizures can be lethal or nonlethal, even in the same inbred strain, in a second experiment, we addressed whether there is an innate capacity to reproduce the same response after a tonic AGS, referred to as "determinism," in the DBA/2J, DBA/1J, and 129/SvTer mouse strains. RESULTS: Results show that the 129/SvTer mouse is a more versatile model of SUDEP due to its wider age range of susceptibility compared to the DBA/2J and DBA/1J mouse strains. In addition, we show that determinism is not consistently evident in DBA/2J and 129/SvTer strains after AGS. Hence, one cannot be certain that a lethal AGS will always be lethal in successive testing after resuscitation and vice versa in these two mouse strains. SIGNIFICANCE: These studies highlight the phenotypic variability of AGS in different mouse strains, show the value of an additional mouse strain, 129/SvTer, for studies using AGS, and thus provide valuable information for future studies of AGS and SUDEP.

Persistent treatment resistance in genetic generalized epilepsy: A long-term outcome study in a tertiary epilepsy center.

OBJECTIVE: To assess prognostic patterns and investigate clinical and electroencephalography (EEG) variables associated with persistent treatment resistance in a population of genetic generalized epilepsy (GGE) patients with a long-term follow-up. METHODS: Data from GGE patients followed from 1975 to 2019 were reviewed retrospectively. Subjects with a follow-up >10 years, starting from epilepsy diagnosis, were included. Persistent treatment resistance was defined as the absence of any period of remission ≥1 year despite treatment with two appropriate and adequate antiepileptic drugs (AEDs). RESULTS: One hundred ninety-nine patients were included. The median age was 39.5 years (interquartile range [IQR] 30-49) and the median follow-up was 27 years (IQR 18-35). The most common syndrome was juvenile myoclonic epilepsy (JME), diagnosed in 44.2% of patients. During follow-up, 163 subjects (81.9%) experienced 3-year remission from any seizure type, whereas 5- and 10-year remission occurred in 141 (70.8%) and 92 (46.2%) cases, respectively. The most common prognostic pattern was a relapsing-remitting course, observed in 80 patients (40.2%), whereas 29 (14.6%) displayed persistent treatment resistance. According to multivariable logistic regression analysis, febrile seizures (FS), specific EEG patterns (namely generalized paroxysmal fast activity, GPFA) and valproate (VPA) resistance were the only variables significantly associated with persistent treatment resistance. JME was the only epilepsy syndrome statistically associated with persistent treatment resistance in univariable logistic regression analysis. SIGNIFICANCE: Persistent treatment resistance was observed in almost 15% of GGE patients followed in a tertiary epilepsy center. A worse outcome was associated with specific clinical variables (JME, FS) and EEG patterns (GPFA).

Cortex leads the thalamic centromedian nucleus in generalized epileptic discharges in Lennox-Gastaut syndrome.

OBJECTIVE: We aimed to assess the roles of the cortex and thalamus (centromedian nucleus [CM]) during epileptic activity in Lennox-Gastaut syndrome (LGS) patients undergoing deep brain stimulation (DBS) surgery as part of the ESTEL (Electrical Stimulation of the Thalamus for Epilepsy of Lennox-Gastaut Phenotype) trial. METHODS: Twelve LGS patients (mean age = 26.8 years) underwent bilateral CM-DBS implantation. Intraoperatively, simultaneous electroencephalogram (EEG) was recorded (range = 10-34 minutes) from scalp electrodes and bilateral thalamic DBS electrodes. Temporal onsets of epileptic discharges (generalized paroxysmal fast activity [GPFA] and slow spike-and-wave [SSW]) were manually marked on recordings from scalp (ie, "cortex") and thalamus (ie, CM-DBS electrodes). Phase transfer entropy (PTE) analysis quantified the degree of information transfer from cortex to thalamus within different frequency bands around GPFA events. RESULTS: GPFA was captured in eight of 12 patients (total event number across patients = 168, cumulative duration = 358 seconds). Eighty-six percent of GPFA events were seen in both scalp and thalamic recordings. In most events (83%), onset occurred first at scalp, with thalamic onset lagging by a median of 98 milliseconds (interquartile range = 78.5 milliseconds). Results for SSW were more variable and seen in 11 of 12 patients; 25.4% of discharges were noted in both scalp and thalamus. Of these, 74.5% occurred first at scalp, with a median lag of 75 milliseconds (interquartile range = 228 milliseconds). One to 0.5 seconds and 0.5-0 seconds before GPFA onset, PTE analysis showed significant energy transfer from scalp to thalamus in the delta (1-3 Hz) frequency band. For alpha (8-12 Hz) and beta (13-30 Hz) frequencies, PTE was greatest 1-0.5 seconds before GPFA onset. SIGNIFICANCE: Epileptic activity is detectable in CM of thalamus, confirming that this nucleus participates in the epileptic network of LGS. Temporal onset of GPFA mostly occurs earlier at the scalp than in the thalamus. This supports our prior EEG-functional magnetic resonance imaging results and provides further evidence for a cortically driven process underlying GPFA in LGS.

Uni-hemispheric hyperperfusion in the early postictal state: case report.

BACKGROUND: In the emergency setting of acute ischemic stroke, seizures have been reported in up to 4% of patients. In the absence of arterial occlusion, seizures may also cause abnormalities in CT perfusion in 78% of cases when the time window from onset to imaging is short. Both hyperperfusion and hypoperfusion in the postictal state have been described. Also, though rarely reported, postictal perfusion changes can be uni-hemispheric. In these cases, perfusion maps should be analyzed thoroughly, since perfusion reconstruction software relies heavily on a "normal" contralateral perfusion status. CASE PRESENTATION: A 39-year-old man was found on the ground with a minor head injury. On admission, his reactions were generally slow, but there were no other neurological symptoms, and blood pressure was low. The patient had a history of primary generalized epilepsy and admitted to dropping off his anti-epileptic medication. He was transferred to the radiological department for imaging but shortly before began to experience generalized onset tonic-clonic seizures which were brought under control by intravenous therapy with 10 mg diazepam. After approximately 15 min, a multimodal CT scan was performed, revealing marked changes in the perfusion of the brain hemispheres and posterior fossa, with sharp delimitation at the midline. Blood gas analysis was congruent with respiratory acidosis. Clinically, the patient remained awake without developing any new symptoms. He gradually recovered over the following 3 h and, against our medical recommendation, discharged himself from the hospital. CONCLUSIONS: To the authors' knowledge, this is the first report of an early postictal state describing sharply delimited uni-hemispheric hyperperfusion and hemispheric alteration of the cerebellum with an equally split rhombencephalon. Surprisingly, these changes were not associated with any focal neurological signs. To prevent misdiagnosis of perfusion alterations in seizures, radiologists and neurologists should be aware of the limitations of CT perfusion maps and software reconstructions. Novel use of CT perfusion reconstruction using peak enhancement helped in identifying the cerebral pathology.

Tonic-clonic seizures in idiopathic generalized epilepsies: Prevalence, risk factors, and outcome.

PURPOSE: We investigated the prevalence of generalized tonic-clonic seizures (GTCSs) in patients with idiopathic generalized epilepsy (IGE) and the risk factors associated with them. We also studied the seizure outcome in patients with IGEs. METHODS: In this retrospective study, all patients with a diagnosis of IGE were recruited at the epilepsy clinic at Shiraz University of Medical Sciences, from 2008 through 2019. Age, gender, age at seizure onset, seizure type(s), EEG findings, and seizure outcome of all patients were registered. RESULTS: A total of 601 patients with IGE were studied; 516 patients (86%) had GTCSs. The ROC curve showed that reporting GTCSs was significantly associated with the time since the start of the disease (P = .0001; area under the curve = 0.71 [95% CI: 0.66-0.76]; a cutoff point of 4 years [sensitivity = 61% and specificity = 76%]). Age at onset was 3.3 years later in patients with GTCSs compared with that in patients without GTCSs. Generalized spike-wave complexes during interictal EEG recording were more frequently observed among patients without GTCSs. Generalized tonic-clonic seizures were significantly associated with experiencing seizure-related injuries. Valproate reduced the risk of experiencing GTCSs significantly (OR: 0.58; 95% CI: 0.34-0.99; P = .04). CONCLUSION: Generalized tonic-clonic seizures do not affect the seizure outcome in patients with IGEs per se, but how we manage them significantly affects the seizure outcome in these patients. Failure to prescribe valproate for women with IGE, particularly when another first-line treatment has failed, may not be in a patient's best interests.