Imaging and resection strategies for pediatric gangliogliomas (GG) and dysembryoplastic neuroepitheliomas (DNET) presenting with epilepsy were retrospectively analyzed in a consecutive institutional series of surgically treated patients.
Twenty-two children (median 8 years, 3–18 years) presented with seizures for 30 months median (14–55.2 months) due to a histologically verified GG/DNET.
There were 20 GG and 2 DNT, 68 % located temporal, 32 % extra-temporal. Seizure history was significantly longer in temporal cases (38 versus 14 months median, p < 0.01). MRI contrast enhancement was present in 50 % and methionine (MET) positron emission tomography (PET) uptake in 70 % (standard uptake values (SUVs) 2.92 mean, from 1.6 to 6.4). 27 % had glucose PET hypometabolism. Primarily, in temporal GG, ECoG (electrocorticography) -guided lesionectomies were performed in 87 % and antero-mesial temporal lobe resections (AMTLR) in 13 %, whereas in extra-temporal GG/DNETs, lesionectomies were performed in 100 %. ILAE Class 1 seizure outcome was primarily achieved in 73 % of the temporal cases, and was increased to 93 % by performing six repeat surgeries using AMTLR. Extratemporal patients experienced ILAE Class 1 seizure outcomes in 86 % without additional surgeries, although harboring significantly more residual tumor (p < 0.005, mean follow-up 28 months).
In children, MET PET imaging for suspected GG is proposed preoperatively showing a high diagnostic sensitivity and an option to delineate the lesions for navigated resection, whereas MRI contrast behavior was of no differential diagnostic use. As a surgical strategy we propose primarily lesionectomies for extratemporal but AMTLR for temporal GG respecting eloquent brain areas.
Epilepsy is a chronic neurological disorder that has complex relations with social, vocational and psychological functioning. Multiple studies showed that frequency of mood disorders in patients with epilepsy is increased and include depression, anxiety and psychosis. We present data from a neurobiological prospective having clinical relevance for epilepsy and comorbidities, including studies in people with late onset epilepsies. Better understanding of neurobiological mechanisms, anatomical, functional, neuroendocrine and molecular basis of psychiatric comorbidities in persons with epilepsy, can advance therapeutic responses. Epilepsy patients have a significantly higher prevalence of depressive symptoms. Many studies showed that depressive symptoms reduce their quality of life. Psychosis in epilepsy is a rare but severe disorder that usually occurs in patients with early onset of seizures, less localised ictal EEG recordings and seizure clustering. Suicide behavior presents an important problem in managing people with epilepsy. Suicidal ideation is not uncommon, and patients also have an increased risk for suicidal attempt or completed suicide. Psychiatric comorbidities present a significant problem and ask for a multidisciplinary approach to optimize treatment of people with epilepsy.
Epilepsy, a neurological disorder, is identified by the presence of recurrent seizures. We aimed to detect dietary fiber intake and its association with epilepsy prevalence in U.S. adults.
This cross-sectional study obtained data from the 2013–2018 National Health and Nutrition Examination Survey database. Univariate and multivariate logistic regression models were employed to estimate the association between dietary fiber intake and epilepsy prevalence. The restricted cubic spline (RCS) model was also applied to investigate the dose-response relationships between dietary fiber intake and epileptic seizure events(ESEs).
Our final sample included 13,277 NHANES participants, with the average prevalence of ESEs being 1.09 % (145/13277). After adjusting for all confounding factors, the third quartile of dietary fiber intake levels remained significantly associated with a decreased risk of ESEs[odds ratios (OR) 0.54,95 % confidence interval (CI) 0.33–0.88, P = 0.014)] compared to the first quartile. Higher fiber intake indicated a stable negative association with ESEs in the multivariate logistic regression analysis, weighted generalized additive model. A nonlinear dose-response relationship was observed between dietary fiber intake levels and decreased ESEs risk (P for overall=0.017, P for nonlinear=0.155). Interaction tests showed no significant effect of demographic and disease status on the association between dietary fiber intake and ESEs.
In this cross-sectional study, people with a high dietary fiber intake were at a reduced risk of ESEs. However, further prospective studies are needed to investigate the effect of dietary fiber intake in epilepsy events and to determine causality.
Dravet syndrome is an infantile-onset developmental and epileptic encephalopathy with limited data on the frequency of SCN1A in Indian children. The study aimed to identify and characterize the burden of SCN1A pathogenic variants associated with the Dravet syndrome phenotype through genetic testing in the North Indian population.
In this prospective, cross-sectional study from March 2015 to February 2019, we enrolled 52 children with Dravet syndrome phenotype who underwent genetic testing for SCN1A gene pathogenicity. We assessed variant effect using multiple algorithms, and genetic test results were reported based on recommendations from the American College of Medical Genetics and Genomics guidelines. Additionally, we performed multiplex-ligation dependent probe amplification (MLPA) to detect copy number variations of the SCN1A gene in children without identified genetic pathogenicity (n = 22) and analysed the results using Coffalyser.net.
Of the 52 probands studied, pathogenic variants in the SCN1A gene were identified in 30 children. Among these variants, 11 truncating variants (3 frame-shift variants, 3 intronic variants in splice site regions, and 5 nonsense variants) in 12 unrelated probands, and 17 missense variants in 18 unrelated probands were found. The genetic yield of SCN1A pathogenicity in our cohort (n = 52) was 58 %. Additionally, two of the identified variants were novel. Furthermore, MLPA analysis of the SCN1A gene in 22 children without pathogenic variants yielded no results.
This work represents a genetic analysis of a Dravet syndrome cohort, revealing a 58 % burden of SCN1A variants in children with the Dravet syndrome phenotype from the North Indian population.
The emergence of telemedicine and artificial intelligence (AI) has set the stage for a possible revolution in the future of medicine and neurology including the diagnosis and management of epilepsy. Telemedicine, with its proven efficacy during the COVID-19 pandemic, offers the advantage of bridging the gap between patients in resource-limited areas and specialized care, where in one study telemedicine reduced the epilepsy treatment gap from 43 % to 9 %. AI innovations promise a transformation in epilepsy care by possibly enhancing the accuracy of electroencephalogram (EEG) interpretation and seizure prediction through machine and deep learning. In one study, abnormal EEG recordings were classified into different categories using a convolutional neural networks (CNN) model showing a specificity of 90 % and an accuracy of 88.3 %. Other models constructed to predict seizures have also achieved a sensitivity of 96.8 % and specificity of 95.5 %. Various machine learning (ML) models highlight the potential AI holds in identifying interictal biomarkers and localizing seizure onset zones aiding in epilepsy treatment decision and outcome prediction. An ML model highlighted in this review localized seizure onset zone with an accuracy reaching 73 % and predicted surgical outcomes with an accuracy reaching 79 % compared to the 43 % accuracy of clinicians. However, limitations and challenges hinder the application of such technologies to reach their full potential in epilepsy care. Limitations include access to compatible devices, integration into clinical workflows, data bias, and availability of sufficient data. Extensive validated research is needed to guide future clinical practice with the implementation of technology-enhanced epilepsy care. This narrative review article will explore the use of AI and telemedicine in EEG and epilepsy care, examining their individual and combined impacts in shaping the future of epilepsy care and discussing the challenges and limitations faced in their usage.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：