Clinical, genetic, and cognitive correlates of seizure occurrences in Phelan-McDermid syndrome.

BACKGROUND: Phelan-McDermid syndrome (PMS) is a genetic neurodevelopmental disorder caused by SHANK3 haploinsufficiency and is associated with an increased risk for seizures. Previous literature indicates that around one third of individuals with PMS also have epilepsy or seizures, with a wide range of types and ages of onset. Investigating the impact of seizures on intellectual and adaptive functioning for PMS is a primary concern for caregivers and is important to understanding the natural history of this syndrome. METHODS: We report on results from 98 individuals enrolled in a prospective, longitudinal study. We detailed seizure frequency, type, and age of onset, and we analyzed seizure occurrence with best estimate IQ, adaptive functioning, clinical features, and genotype. We conducted multiple linear regression analyses to assess the relationship between the presence of seizures and the Vineland Adaptive Behavior Scale, Second Edition (VABS-II) Adaptive Behavior Composite score and the best estimate full-scale IQ. We also performed Chi-square tests to explore associations between seizure prevalence and genetic groupings. Finally, we performed Chi-square tests and t-tests to explore the relationship between seizures and demographic features, features that manifest in infancy, and medical features. RESULTS: Seizures were present in 41% of the cohort, and age of onset was widely variable. The presence of seizures was associated with significantly lower adaptive and intellectual functioning. Genotype-phenotype analyses were discrepant, with no differences in seizure prevalence across genetic classes, but with more genes included in deletions of participants with 22q13 deletions and seizures compared to those with 22q13 deletions and no seizures. No clinical associations were found between the presence of seizures and sex, history of pre- or neonatal complications, early infancy, or medical features. In this cohort, generalized seizures were associated with developmental regression, which is a top concern for PMS caregivers. CONCLUSIONS: These results begin to eludicate correlates of seizures in individuals with PMS and highlight the importance of early seizure management. Importantly, presence of seizures was associated with adaptive and cognitive functioning. A larger cohort might be able to identify additional associations with medical features. Genetic findings suggest an increased capability to realize genotype-phenotype relationships when deletion size is taken into account.

Multiple back fractures as a result of the first seizure.

Non-traumatic fractures due to seizures are an overlooked diagnostic group. It is well known that patients with generalized tonic-clonic seizures have an increased trauma risk. However, the cause of fracture is rarely due to the violent forces of muscle contractions. Usually, the primary patient examination focuses on the aetiology of the seizure, which sometimes delays the diagnosis of fractures. This is a case report of a 19-year-old woman who sustained three compression fractures of the thoracic spine due to a generalized tonic-clonic seizure, and a discussion of the diagnostic challenges in such a rare case.

A woman in her seventies with fever and convulsions. / En kvinne i 70-årene med feber og krampeanfall.

Background: Chagas encephalitis is a rare but severe manifestation of reactivation in patients with chronic Chagas disease. Case presentation: A woman in her seventies who was immunosuppressed after a heart transplant due to Chagas disease was admitted with convulsions, headache and visual disturbances. She developed fever, confusion and repeated convulsions. Pleocytosis was found in spinal fluid. Wet-mount microscopy of spinal fluid revealed motile Trypanosoma cruzi trypomastigotes, and multiple trypomastigotes were seen on a Giemsa-stained smear, confirming reactivation of Chagas disease with meningoencephalitis. Despite benznidazole treatment, she deteriorated, exhibiting pharyngeal paralysis, aphasia and increasing somnolence. Brain CT showed pathology consistent with Chagas encephalitis. Nifurtimox was given as an adjunctive treatment. After a week of treatment, the patient began to improve. She completed 60 days of benznidazole and had regained normal cognitive and neurological function on subsequent follow-up. She had no signs of myocarditis reactivation. Interpretation: Chronic Chagas disease is common among Latin American immigrants in Europe. Reactivation with myocarditis after a heart transplant is well known, while encephalitis is a rare manifestation. We report on a case of Chagas encephalitis in an immunosuppressed patient. Microscopy of parasites in spinal fluid revealed the diagnosis. The WHO provided antiparasitic medications, and despite a severe prognosis, the patient made a full recovery.

[Risk of epilepsy after a first epileptic seizure with unknown etiology in elderly patients]. / Riesgo de epilepsia tras una primera crisis epiléptica de etiología desconocida en pacientes de edad avanzada.

AIM: Patients whose epilepsy begins with seizures with unknown etiology in old age have been studied to a limited extent. The aim is to clinically characterise these patients, and predict their risk of developing epilepsy in the long term. MATERIALS AND METHODS: This is a retrospective observational study of patients over 55 years old experiencing a first epileptic seizure with unknown etiology. The data were collected from their clinical history, including electroencephalogram (EEG) and brain magnetic resonance imaging (MRI) results. RESULTS: Eighty-seven patients (58.6% male; 71.5 ± 8.1 years) were included. The mean follow-up was 7.3 ± 4.9 years. The most common vascular risk factor was arterial hypertension (77%; n = 67). Focal seizures with altered consciousness were the most frequent type of seizure (44.8%; n = 39), followed by focal seizures evolving to bilateral tonic-clonic seizures (39.1%; n = 34). Brain MRI showed cortical atrophy (50%; n = 42) and signs of small-vessel vascular disease (SVVD) (67.8%; n = 57). Interictal epileptiform EEG abnormalities were observed in 43.7% (n = 38) of the patients, mostly with temporal localisations (94.7%; n = 36). 44.8% (n = 39) had mild cognitive impairment at baseline. Recurrence of seizures, which was observed in 49 patients (56.1%), occurred after a median of 12 months (interquartile range: 4.4-25.9). Finally, 71 patients (81.6%) developed epilepsy. CONCLUSION: The risk of epilepsy in the long term following a single seizure of unknown etiology in elderly patients is greater than 80%. Arterial hypertension and mild cognitive impairment at baseline are the most common clinical features. Cortical atrophy and the presence of SVVD are frequent in MRI, and routine EEGs do not usually show epileptiform alterations.

TITLE: Riesgo de epilepsia tras una primera crisis epiléptica de etiología desconocida en pacientes de edad avanzada.Objetivo. Los pacientes que comienzan con crisis de origen desconocido en la edad avanzada no están bien estudiados. El objetivo es caracterizar clínicamente a estos pacientes y predecir el riesgo de desarrollar epilepsia a largo plazo. Materiales y métodos. Es un estudio observacional retrospectivo en pacientes mayores de 55 años con una primera crisis epiléptica de causa desconocida. Se recogieron los datos desde la historia clínica, incluyendo electroencefalograma (EEG) y resonancia magnética (RM) cerebral. Resultados. Se incluyó a 87 pacientes (58,6% varones; 71,5 ± 8,1 años). El seguimiento medio fue de 7,3 ± 4,9 años. El factor de riesgo vascular más frecuente fue la hipertensión arterial (77%; n = 67). Las crisis focales con alteración de la conciencia fueron el tipo de crisis más frecuente (44,8%; n = 39), seguidas de las crisis focales con evolución a bilaterales tonicoclónicas (39,1%; n = 34). La RM cerebral mostró atrofia cortical (50%; n = 42) y signos de enfermedad vascular de pequeño vaso (EVPV) (67,8%; n = 57). Se observaron anomalías epileptiformes intercríticas en el EEG en un 43,7% (n = 38) de los pacientes, mayoritariamente con localización temporal (94,7%; n = 36). Hasta un 44,8% (n = 39) presentaba deterioro cognitivo leve basalmente. La recurrencia de crisis, observada en 49 pacientes (56,1%), sucedió con una mediana de 12 meses (rango intercuartílico: 4,4-25,9). Finalmente, 71 pacientes (81,6%) desarrollaron epilepsia. Conclusión. El riesgo de epilepsia a largo plazo tras una crisis única de etiología desconocida en pacientes de edad avanzada es superior al 80%. La hipertensión arterial y el deterioro cognitivo leve en el inicio son las características clínicas más frecuentes. En la RM, la atrofia cortical y la presencia de EVPV son frecuentes, y los EEG de rutina no suelen mostrar alteraciones epileptiformes.

Anticonvulsant effects of Paeonia daurica subsp. macrophylla root extracts in pentylenetetrazol-induced seizure models in mice / Efectos anticonvulsivos de Paeonia daurica subsp. macrophylla extractos de raíz en modelos de convulsiones inducidas por pentilentetrazol en ratones

Introduction: In the present study, anticonvulsant effects of aqueous extract (AE), hydro-alcoholic crude extract (HE), and its fractions (F-CHCl3, F-EtOAc, F-MeOH) of Paeonia daurica subsp. macrophylla (P. daurica ssp. macrophylla) root examined by using a pentylenetetrazol-induced model (PTZ) on mice. Methods: HE and its fractions as well as AE, in concentrations of (100, 200 and 400 mg/kg), valproate (Val) (100 and 200 mg/kg), and saline (negative control) (10 mg/kg) were injected intraperitoneally (i.p.) 30 min before PTZ (80 mg/kg, i.p.). The time taken before the onset of myoclonic convulsions (MC), MC duration, time taken before the onset of generalized tonic-clonic seizures (GTCS), the duration of GTCS, and the percentage of GTCS and mortality protection recorded. The plant's anticonvulsant mechanisms were assessed using flumazenil (5 mg/kg, i.p.) before AE (100, 200, and 400 mg/kg, i.p.) injection. GraphPad Prism software was used to compare the differences between various treatment groups with one-way analysis of variance (ANOVA) followed by Tukey–Krammer multiple comparison tests. Results: All the plant samples except F-EtOAc significantly delayed the onset and decreased the duration of PTZ-induced MCS and GTCS, and significantly reduced the GTCS and mortality rate. Pretreatment with flumazenil diminished the significant anticonvulsant effects of AE against PTZ-induced seizures. Conclusions: It can report that extract of P. daurica ssp. macrophylla might be a helpful guide for future studies in the treatment of epilepsy.(AU)

Introducción: Epilepsia es el término usado para un grupo de trastornos caracterizado por las convulsiones espontáneas recurrentes. Un estudio enfocado en los productos naturales de los recursos tradicionales ofrece ventajas significativas que se están utilizando de manera más amplia en modelos animales de epilepsia y candidatos a mayor desarrollo clínico y sus fracciones (F-CHCl3, F-EtOAc, F-MeOH) de Paeonia daurica subsp. macrophylla (P. daurica ssp. macrophylla) raíz examinada utilizando un modelo inducido por pentilentetrazol (PTZ) en ratones. Métodos: La maceración dinámica utilizada para extraer HE de la planta y técnica de cromatografía en columna de sílice utilizada para obtener F-CHCl3, F-EtOAc, así como fracciones de F-MeOH. La extracción de raíces secas se utilizó con agua destilada y se provocó AE. Las muestras de plantas (100, 200 y 400 mg/kg), valproato (Val) (100 y 200 mg/kg) y suero (control negativo) se inyectaron por vía intraperitoneal (ip) 30 min antes de PTZ (80 mg/kg, ip). El tiempo transcurrido antes del comienzo de convulsiones mioclónicas (MC), duración de las MC, tiempo transcurrido antes del comienzo de convulsiones tónico-clónicas generalizadas (GTCS), la duración de GTCS, así como el porcentaje de GTCS y protección contra la mortalidad registrada. Los mecanismos anticonvulsivos de planta fueron evaluados mediante el uso de flumazenil (5 mg/kg, ip) antes de AE (100, 200 y 400 mg/kg, ip) inyección. Se utilizaba el software GraphPad Prism® comparando las diferencias entre varios grupos de tratamiento con un análisis unilateral de variación (ANOVA) seguido por las pruebas de comparación múltiple de Tukey's Krammer. Resultados: Todas las muestras de plantas, excepto F-EtOAc, retrasaron de manera considerable el inicio, y disminuyeron la duración de PTZ inducidos por MCS y GTCS, y redujo significativamente el GTCS, así como la tasa de mortalidad...(AU)

Aneuploidy is Linked to Neurological Phenotypes Through Oxidative Stress.

Aneuploidy, having an aberrant genome, is gaining increasing attention in neurodegenerative diseases. It gives rise to proteotoxic stress as well as a stereotypical oxidative shift which makes these cells sensitive to internal and environmental stresses. A growing body of research from numerous laboratories suggests that many neurodegenerative disorders, especially Alzheimer's disease and frontotemporal dementia, are characterised by neuronal aneuploidy and the ensuing apoptosis, which may contribute to neuronal loss. Using Drosophila as a model, we investigated the effect of induced aneuploidy in GABAergic neurons. We found an increased proportion of aneuploidy due to Mad2 depletion in the third-instar larval brain and increased cell death. Depletion of Mad2 in GABAergic neurons also gave a defective climbing and seizure phenotype. Feeding animals an antioxidant rescued the climbing and seizure phenotype. These findings suggest that increased aneuploidy leads to higher oxidative stress in GABAergic neurons which causes cell death, climbing defects, and seizure phenotype. Antioxidant feeding represents a potential therapy to reduce the aneuploidy-driven neurological phenotype.

Unrecognized Focal Nonmotor Seizures in Adolescents Presenting to Emergency Departments.

BACKGROUND AND OBJECTIVES: Many adolescents with undiagnosed focal epilepsy seek evaluation in emergency departments (EDs). Accurate history-taking is essential to prompt diagnosis and treatment. In this study, we investigated ED recognition of motor vs nonmotor seizures and its effect on management and treatment of focal epilepsy in adolescents. METHODS: This was a retrospective analysis of enrollment data from the Human Epilepsy Project (HEP), an international multi-institutional study that collected data from 34 sites between 2012 and 2017. Participants were 12 years or older, neurotypical, and within 4 months of treatment initiation for focal epilepsy. We used HEP enrollment medical records to review participants' initial diagnosis and management. RESULTS: A total of 83 adolescents were enrolled between 12 and 18 years. Fifty-eight (70%) presented to an ED before diagnosis of epilepsy. Although most ED presentations were for motor seizures (n = 52; 90%), many patients had a history of nonmotor seizures (20/52 or 38%). Adolescents with initial nonmotor seizures were less likely to present to EDs (26/44 or 59% vs 32/39 or 82%, p = 0.02), and nonmotor seizures were less likely to be correctly identified (2/6 or 33% vs 42/52 or 81%, p = 0.008). A history of initial nonmotor seizures was not recognized in any adolescent who presented for a first-lifetime motor seizure. As a result, initiation of treatment and admission from the ED was not more likely for these adolescents who met the definition of epilepsy compared with those with no seizure history. This lack of nonmotor seizure history recognition in the ED was greater than that observed in the adult group (0% vs 23%, p = 0.03) and occurred in both pediatric and nonpediatric ED settings. DISCUSSION: Our study supports growing evidence that nonmotor seizures are often undiagnosed, with many individuals coming to attention only after conversion to motor seizures. We found this treatment gap is exacerbated in the adolescent population. Our study highlights a critical need for physicians to inquire about the symptoms of nonmotor seizures, even when the presenting seizure is motor. Future interventions should focus on improving nonmotor seizure recognition for this population in EDs.

Approach to Patients with Seizures and Epilepsy: A Guide for Primary Care Physicians.

Seizures and epilepsy are common neurologic conditions that are frequently encountered in the outpatient primary care setting. An accurate diagnosis relies on a thorough clinical history and evaluation. Understanding seizure semiology and classification is crucial in conducting the initial assessment. Knowledge of common seizure triggers and provoking factors can further guide diagnostic testing and initial management. The pharmacodynamic characteristics and side effect profiles of anti-seizure medications are important considerations when deciding treatment and counseling patients, particularly those with comorbidities and in special populations such as patient of childbearing potential.

Novel predictive approaches for drug-induced convulsions in non-human primates using machine learning and heart rate variability analysis.

Drug-induced convulsions are a major challenge to drug development because of the lack of reliable biomarkers. Using machine learning, our previous research indicated the potential use of an index derived from heart rate variability (HRV) analysis in non-human primates as a biomarker for convulsions induced by GABAA receptor antagonists. The present study aimed to explore the application of this methodology to other convulsants and evaluate its specificity by testing non-convulsants that affect the autonomic nervous system. Telemetry-implanted males were administered various convulsants (4-aminopyridine, bupropion, kainic acid, and ranolazine) at different doses. Electrocardiogram data gathered during the pre-dose period were employed as training data, and the convulsive potential was evaluated using HRV and multivariate statistical process control. Our findings show that the Q-statistic-derived convulsive index for 4-aminopyridine increased at doses lower than that of the convulsive dose. Increases were also observed for kainic acid and ranolazine at convulsive doses, whereas bupropion did not change the index up to the highest dose (1/3 of the convulsive dose). When the same analysis was applied to non-convulsants (atropine, atenolol, and clonidine), an increase in the index was noted. Thus, the index elevation appeared to correlate with or even predict alterations in autonomic nerve activity indices, implying that this method might be regarded as a sensitive index to fluctuations within the autonomic nervous system. Despite potential false positives, this methodology offers valuable insights into predicting drug-induced convulsions when the pharmacological profile is used to carefully choose a compound.

Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction.

BACKGROUND: The seizure prediction algorithms have demonstrated their potential in mitigating epilepsy risks by detecting the pre-ictal state using ongoing electroencephalogram (EEG) signals. However, most of them require high-density EEG, which is burdensome to the patients for daily monitoring. Moreover, prevailing seizure models require extensive training with significant labeled data which is very time-consuming and demanding for the epileptologists. METHOD: To address these challenges, here we propose an adaptive channel selection strategy and a semi-supervised deep learning model respectively to reduce the number of EEG channels and to limit the amount of labeled data required for accurate seizure prediction. Our channel selection module is centered on features from EEG power spectra parameterization that precisely characterize the epileptic activities to identify the seizure-associated channels for each patient. The semi-supervised model integrates generative adversarial networks and bidirectional long short-term memory networks to enhance seizure prediction. RESULTS: Our approach is evaluated on the CHB-MIT and Siena epilepsy datasets. With utilizing only 4 channels, the method demonstrates outstanding performance with an AUC of 93.15% on the CHB-MIT dataset and an AUC of 88.98% on the Siena dataset. Experimental results also demonstrate that our selection approach reduces the model parameters and training time. CONCLUSIONS: Adaptive channel selection coupled with semi-supervised learning can offer the possible bases for a light weight and computationally efficient seizure prediction system, making the daily monitoring practical to improve patients' quality of life.

Synthesis, In Vivo Anticonvulsant Activity Evaluation and In Silico Studies of Some Quinazolin-4(3H)-One Derivatives.

Two series, "a" and "b", each consisting of nine chemical compounds, with 2,3-disubstituted quinazolin-4(3H)-one scaffold, were synthesized and evaluated for their anticonvulsant activity. They were investigated as dual potential positive allosteric modulators of the GABAA receptor at the benzodiazepine binding site and inhibitors of carbonic anhydrase II. Quinazolin-4(3H)-one derivatives were evaluated in vivo (D1-3 = 50, 100, 150 mg/kg, administered intraperitoneally) using the pentylenetetrazole (PTZ)-induced seizure model in mice, with phenobarbital and diazepam, as reference anticonvulsant agents. The in silico studies suggested the compounds act as anticonvulsants by binding on the allosteric site of GABAA receptor and not by inhibiting the carbonic anhydrase II, because the ligands-carbonic anhydrase II predicted complexes were unstable in the molecular dynamics simulations. The mechanism targeting GABAA receptor was confirmed through the in vivo flumazenil antagonism assay. The pentylenetetrazole experimental anticonvulsant model indicated that the tested compounds, 1a-9a and 1b-9b, present a potential anticonvulsant activity. The evaluation, considering the percentage of protection against PTZ, latency until the onset of the first seizure, and reduction in the number of seizures, revealed more favorable results for the "b" series, particularly for compound 8b.

Early Seizure Prophylaxis in Mild and Moderate Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

Importance: Guidelines recommend seizure prophylaxis for early posttraumatic seizures (PTS) after severe traumatic brain injury (TBI). Use of antiseizure medications for early seizure prophylaxis after mild or moderate TBI remains controversial. Objective: To determine the association between seizure prophylaxis and risk reduction for early PTS in mild and moderate TBI. Data Sources: PubMed, Google Scholar, and Web of Science (January 1, 1991, to April 18, 2023) were systematically searched. Study Selection: Observational studies of adult patients presenting to trauma centers in high-income countries with mild (Glasgow Coma Scale [GCS], 13-15) and moderate (GCS, 9-12) TBI comparing rates of early PTS among patients with seizure prophylaxis with those without seizure prophylaxis. Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) reporting guidelines were used. Two authors independently reviewed all titles and abstracts, and 3 authors reviewed final studies for inclusion. A meta-analysis was performed using a random-effects model with absolute risk reduction. Main Outcome Measures: The main outcome was absolute risk reduction of early PTS, defined as seizures within 7 days of initial injury, in patients with mild or moderate TBI receiving seizure prophylaxis in the first week after injury. A secondary analysis was performed in patients with only mild TBI. Results: A total of 64 full articles were reviewed after screening; 8 studies (including 5637 patients) were included for the mild and moderate TBI analysis, and 5 studies (including 3803 patients) were included for the mild TBI analysis. The absolute risk reduction of seizure prophylaxis for early PTS in mild to moderate TBI (GCS, 9-15) was 0.6% (95% CI, 0.1%-1.2%; P = .02). The absolute risk reduction for mild TBI alone was similar 0.6% (95% CI, 0.01%-1.2%; P = .04). The number needed to treat to prevent 1 seizure was 167 patients. Conclusion and Relevance: Seizure prophylaxis after mild and moderate TBI was associated with a small but statistically significant reduced risk of early posttraumatic seizures after mild and moderate TBI. The small absolute risk reduction and low prevalence of early seizures should be weighed against potential acute risks of antiseizure medications as well as the risk of inappropriate continuation beyond 7 days.

Multi-omics technologies and molecular biomarkers in brain tumor-related epilepsy.

BACKGROUND: Brain tumors are one of the leading causes of epilepsy, and brain tumor-related epilepsy (BTRE) is recognized as the major cause of intractable epilepsy, resulting in huge treatment cost and burden to patients, their families, and society. Although optimal treatment regimens are available, the majority of patients with BTRE show poor resolution of symptoms. BTRE has a very complex and multifactorial etiology, which includes several influencing factors such as genetic and molecular biomarkers. Advances in multi-omics technologies have enabled to elucidate the pathophysiological mechanisms and related biomarkers of BTRE. Here, we reviewed multi-omics technology-based research studies on BTRE published in the last few decades and discussed the present status, development, opportunities, challenges, and prospects in treating BTRE. METHODS: First, we provided a general review of epilepsy, BTRE, and multi-omics techniques. Next, we described the specific multi-omics (including genomics, transcriptomics, epigenomics, proteomics, and metabolomics) techniques and related molecular biomarkers for BTRE. We then presented the associated pathogenetic mechanisms of BTRE. Finally, we discussed the development and application of novel omics techniques for diagnosing and treating BTRE. RESULTS: Genomics studies have shown that the BRAF gene plays a role in BTRE development. Furthermore, the BRAF V600E variant was found to induce epileptogenesis in the neuronal cell lineage and tumorigenesis in the glial cell lineage. Several genomics studies have linked IDH variants with glioma-related epilepsy, and the overproduction of D2HG is considered to play a role in neuronal excitation that leads to seizure occurrence. The high expression level of Forkhead Box O4 (FOXO4) was associated with a reduced risk of epilepsy occurrence. In transcriptomics studies, VLGR1 was noted as a biomarker of epileptic onset in patients. Several miRNAs such as miR-128 and miRNA-196b participate in BTRE development. miR-128 might be negatively associated with the possibility of tumor-related epilepsy development. The lncRNA UBE2R2-AS1 inhibits the growth and invasion of glioma cells and promotes apoptosis. Quantitative proteomics has been used to determine dynamic changes of protein acetylation in epileptic and non-epileptic gliomas. In another proteomics study, a high expression of AQP-4 was detected in the brain of GBM patients with seizures. By using quantitative RT-PCR and immunohistochemistry assay, a study revealed that patients with astrocytomas and oligoastrocytomas showed high BCL2A1 expression and poor seizure control. By performing immunohistochemistry, several studies have reported the relationship between D2HG overproduction and seizure occurrence. Ki-67 overexpression in WHO grade II gliomas was found to be associated with poor postoperative seizure control. According to metabolomics research, the PI3K/AKT/mTOR pathway is associated with the development of glioma-related epileptogenesis. Another metabolomics study found that SV2A, P-gb, and CAD65/67 have the potential to function as biomarkers for BTRE. CONCLUSIONS: Based on the synthesized information, this review provided new research perspectives and insights into the early diagnosis, etiological factors, and personalized treatment of BTRE.

Brain activation and connection across resting and motor-task states in patients with generalized tonic-clonic seizures.

AIMS: Motor abnormalities have been identified as one common symptom in patients with generalized tonic-clonic seizures (GTCS) inspiring us to explore the disease in a motor execution condition, which might provide novel insight into the pathomechanism. METHODS: Resting-state and motor-task fMRI data were collected from 50 patients with GTCS, including 18 patients newly diagnosed without antiepileptic drugs (ND_GTCS) and 32 patients receiving antiepileptic drugs (AEDs_GTCS). Motor activation and its association with head motion and cerebral gradients were assessed. Whole-brain network connectivity across resting and motor states was further calculated and compared between groups. RESULTS: All patients showed over-activation in the postcentral gyrus and the ND_GTCS showed decreased activation in putamen. Specifically, activation maps of ND_GTCS showed an abnormal correlation with head motion and cerebral gradient. Moreover, we detected altered functional network connectivity in patients within states and across resting and motor states by using repeated-measures analysis of variance. Patients did not show abnormal connectivity in the resting state, while distributed abnormal connectivity in the motor-task state. Decreased across-state network connectivity was also found in all patients. CONCLUSION: Convergent findings suggested the over-response of activation and connection of the brain to motor execution in GTCS, providing new clues to uncover motor susceptibility underlying the disease.