Absence status epilepticus (ASE) is a rare but treatable condition, and when present in older adults, it can be misinterpreted as encephalopathy or behavioral changes. Our case discusses a 63-year-old patient with myelofibrosis and allogeneic stem cell transplant with late-onset de novo status epilepticus. This case report adds to the rare body of literature discussing de novo ASE whose clinical presentation can be indistinguishable from other encephalopathic or behavioral conditions. Moreover, its occurrence during oncologic treatment warrants clinicians to be on the lookout for similar presentations and encourages future reports of this condition in association with similar therapies. This case report provides value to providers treating patients with similar oncologic therapies and highlights the need for ASE to be further studied as it is a possible rare complication of allogeneic transplantation of stem cells.

In patients suffering absence epilepsy, recurring seizures can significantly decrease their quality of life and lead to yet untreatable comorbidities. Absence seizures are characterized by spike-and-wave discharges on the electroencephalogram associated with a transient alteration of consciousness. However, it is still unknown how the brain responds to external stimuli during and outside of seizures. This study aimed to investigate responsiveness to visual and somatosensory stimulation in Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established rat model for absence epilepsy. Animals were imaged under non-curarized awake state using a quiet, zero echo time, functional magnetic resonance imaging (fMRI) sequence. Sensory stimulations were applied during interictal and ictal periods. Whole-brain hemodynamic responses were compared between these two states. Additionally, a mean-field simulation model was used to explain the changes of neural responsiveness to visual stimulation between states. During a seizure, whole-brain responses to both sensory stimulations were suppressed and spatially hindered. In the cortex, hemodynamic responses were negatively polarized during seizures, despite the application of a stimulus. The mean-field simulation revealed restricted propagation of activity due to stimulation and agreed well with fMRI findings. Results suggest that sensory processing is hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness during this absence epileptic process.

Advancing the mechanistic understanding of absence epilepsy is crucial for developing new therapeutics, especially for patients unresponsive to current treatments. Utilizing a recently developed mouse model of absence epilepsy carrying the BK gain-of-function channelopathy D434G, here we report that attenuating the burst firing of midline thalamus (MLT) neurons effectively prevents absence seizures. We found that enhanced BK channel activity in the BK-D434G MLT neurons promotes synchronized bursting during the ictal phase of absence seizures. Modulating MLT neurons through pharmacological reagents, optogenetic stimulation, or deep brain stimulation effectively attenuates burst firing, leading to reduced absence seizure frequency and increased vigilance. Additionally, enhancing vigilance by amphetamine, a stimulant medication, or physical perturbation also effectively suppresses MLT bursting and prevents absence seizures. These findings suggest that the MLT is a promising target for clinical interventions. Our diverse approaches offer valuable insights for developing next generation therapeutics to treat absence epilepsy.

High frequency oscillations are important novel biomarkers of epileptogenic tissue. The interaction of oscillations across different time scales is revealed as cross-frequency coupling (CFC) representing a high-order structure in the functional organization of brain rhythms. New artificial intelligence methods such as deep learning neural networks can provide powerful tools for automated analysis of EEG. Here we present a Stacked Sparse Autoencoder (SSAE) trained to recognize absence seizure activity based on the cross-frequency patterns within scalp EEG. We used EEG records from the Temple University Hospital database. Absence seizures (n = 94) from 12 patients were taken into analysis along with segments of background activity. Half of the records were selected randomly for network training and the second half were used for testing. Power-to-power coupling was calculated between all frequencies 2-120 Hz pairwise using the EEGLAB toolbox. The resulting CFC matrices were used as training or testing inputs to the autoencoder. The trained network was able to recognize background and seizure segments (not used in training) with a sensitivity of 96.3%, specificity of 99.8% and overall accuracy of 98.5%. Our results provide evidence that the SSAE neural networks can be used for automated detection of absence seizures within scalp EEG.

OBJECTIVE: To develop and validate a method for long-term (24-h) objective quantification of absence seizures in the EEG of patients with childhood absence epilepsy (CAE) in their real home environment using a wearable device (waEEG), comparing automatic detection methods with auditory recognition after seizure sonification.
METHODS: The waEEG recording was acquired with two scalp electrodes. Automatic analysis was performed using previously validated software (Persyst® 14) and then fully reviewed by an experienced clinical neurophysiologist. The EEG data were converted into an audio file in waveform format with a 60-fold time compression factor. The sonified EEG was listened to by three inexperienced observers and the number of seizures and the processing time required for each data set were recorded blind to other data. Quantification of seizures from the patient diary was also assessed.
RESULTS: Eleven waEEG recordings from seven CAE patients with an average age of 8.18 ± 1.60 years were included. No differences in the number of seizures were found between the recordings using automated methods and expert audio assessment, with significant correlations between methods (ρ > .89, p < .001) and between observers (ρ > .96, p < .001). For the entire data set, the audio assessment yielded a sensitivity of .830 and a precision of .841, resulting in an F1 score of .835.
CONCLUSIONS: Auditory waEEG seizure detection by lay medical personnel provided similar accuracy to post-processed automatic detection by an experienced clinical neurophysiologist, but in a less time-consuming procedure and without the need for specialized resources. Sonification of long-term EEG recordings in CAE provides a user-friendly and cost-effective clinical workflow for quantifying seizures in clinical practice, minimizing human and technical constraints.

BACKGROUND: Pathogenic variants of the GABRG2 gene, encoding a GABAA receptor subunit, have been associated with various epileptic syndromes and drug-resistant epilepsy. Vinpocetine has been previously reported efficacious in a patient harboring a GABRB3 pathogenic variant, encoding another GABAA receptor subunit.
METHODS: We describe a patient with GABRG2-related drug-resistant epilepsy who improved after vinpocetine treatment. An 8-year-old boy with a family history of epilepsy was diagnosed with early onset absence epilepsy at 6 months of age and was treated unsuccessfully with sodium valproate and ethosuximide. At 6 years of age, he developed generalized tonic-clonic seizures and increasing absences despite lamotrigine add-on as well as learning difficulties. Brain MRI was normal and video-EEG telemetry showed multiple myoclonic absences. An epilepsy gene panel analysis showed a GABRG2 pathogenic variant, c.254 T > A p.(Ile85Lys) (NM_198903.2), inherited from the proband\'s father. Seizures were resistant to several medications. After treatment with vinpocetine add-on, the patient showed a dramatic initial response, further reduction of seizures, and improvement of his cognitive functions.
CONCLUSIONS: This case illustrates that vinpocetine could be considered in drug-resistant epilepsies related to GABRG2 in accordance with the principles of precision medicine.

Absence seizures-generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE.

Generalised spike and wave discharges (SWDs) are pathognomonic EEG signatures for diagnosing absence seizures in patients with Genetic Generalized Epilepsy (GGE). The Genetic Absence Epilepsy Rats from Strasbourg (GAERS) is one of the best-validated animal models of GGE with absence seizures.
We developed an SWDs detector for both GAERS rodents and GGE patients with absence seizures using a neural network method. We included 192 24-hour EEG sessions recorded from 18 GAERS rats, and 24-hour scalp-EEG data collected from 11 GGE patients.
The SWDs detection performance on GAERS showed a sensitivity of 98.01% and a false positive (FP) rate of 0.96/hour. The performance on GGE patients showed 100% sensitivity in five patients, while the remaining patients obtained over 98.9% sensitivity. Moderate FP rates were seen in our patients with 2.21/hour average FP. The detector trained within our patient cohort was validated in an independent dataset, TUH EEG Seizure Corpus (TUSZ), that showed 100% sensitivity in 11 of 12 patients and 0.56/hour averaged FP.
We developed a robust SWDs detector that showed high sensitivity and specificity for both GAERS rats and GGE patients.
This detector can assist researchers and neurologists with the time-efficient and accurate quantification of SWDs.

The implementation of array comparative genomic hybridisation (array-CGH) allows us to describe new microdeletion/microduplication syndromes which were previously not identified. 9q21.13 microdeletion syndrome is a genetic condition due to the loss of a critical genomic region of approximately 750kb and includes several genes, such as RORB and TRPM6. Here, we report a case of a 7-year-old boy affected by 9q21.13 microdeletion syndrome. He presents with global developmental delay, intellectual disability, autistic behaviour, seizures and facial dysmorphism. Moreover, he has severe myopia, which was previously reported in only another patient with 9q21.13 deletion, and brain anomalies which were never described before in 9q21.13 microdeletion syndrome. We also collect 17 patients from a literature search and 10 cases from DECIPHER database with a total number of 28 patients (including our case). In order to better investigate the four candidate genes RORB, TRPM6, PCSK5, and PRUNE2 for neurological phenotype, we make, for the first time, a classification in four groups of all the collected 28 patients. This classification is based both on the genomic position of the deletions included in the 9q21.3 locus deleted in our patient and on the different involvement of the four-candidate gene. In this way, we compare the clinical problems, the radiological findings, and the dysmorphic features of each group and of all the 28 patients in our article. Moreover, we perform the genotype-phenotype correlation of the 28 patients to better define the syndromic spectrum of 9q21.13 microdeletion syndrome. Finally, we propose a baseline ophthalmological and neurological monitoring of this syndrome.

Absence seizures are a type of generalized onset seizure associated in humans with brief activity interruptions, unresponsiveness and staring. Absence seizures are infrequently reported in veterinary patients, visually indistinguishable from focal seizures, and so may be grouped as non-generalized tonic clonic seizures (non-GTCS). The objective of this retrospective study was to provide a preliminary understanding of the frequency of non-GTCS in dogs and estimate its prevalence by evaluating the distribution of seizure types presented to a referral hospital over 4 years (May 2017-April 2021), as determined from the medical record history and electroencephalography (EEG) diagnostic testing where available. A total of 528 cases were included via a medical record search for dogs with epilepsy and/or seizures presented to the neurology or emergency services. Cases were categorized into seizure types based on reported clinical signs. Each year, 53-63 % of seizure cases were described as generalized tonic clonic seizures (GTCS), 9-15 % GTCS with additional events and 29-35 % suspected non-GTCS. EEG confirmed absence seizures in 12 of 44 EEGs, 5 cases having a history of GTCS and seven without prior GTCS. This preliminary study suggests that non-GTCS may be relatively common as one third of seizure cases in the referral population presented with non-GTCS clinical signs. Prospective studies using EEG are merited to definitively determine the prevalence of these different seizure types in dogs. Acknowledging the impact of these seizures will improve awareness, aiding veterinarians in their recognition, diagnosis and potential treatment options.