ObjectiveThe risk factors of relapse in 133 epileptic children after withdrawal were analyzed retrospectively and provide reference for clinical withdrawal.MethodsFrom January 2017 to March 2019, 133 children with withdrawal epilepsy were selected as the study object. According to whether there was recurrence during the follow-up period, the children with epilepsy were divided into recurrence group (42 cases) and non recurrence group (91 cases). The gender, age of onset, history of trauma, frequency of seizure before treatment, EEG before drug reduction, imaging, type of medication, family history, time of reaching control, course of disease before treatment, comorbidity, multiple attack types, withdrawal speed and EEG before treatment were observed and compared between the two groups. ResultsThere were significant differences in EEG (χ2 =7.621), medication type (χ2=8.760), time to control (χ2=6.618), course before treatment (χ2=6.435), multiple seizure types (χ2=5.443) and epilepsy comorbidity (χ2=42.795) between the two groups (P < 0.05). The results of Logistic multiple regression analysis showed that the recurrence of epileptic children after drug reduction / withdrawal was correlated with abnormal EEG before drug reduction [OR=9.268, 95%CI (2.255, 38.092)], combined drug treatment [OR=3.205, 95%CI (1.159, 8.866)] and course of disease > 1 year before treatment [OR=5.363, 95%CI (1.781, 16.150)] (P < 0.05).ConclusionsIn order to reduce the possibility of recurrence of epileptic children, the treatment time of epileptic children with abnormal EEG, combined medication and long course before treatment should be prolonged properly.
Cerebral amyloid angiopathy (CAA) is an age-dependent disease affecting older subjects. CAA is characterized by lobar intracerebral hemorrhage (ICH), lobar cerebral microbleeds (CMBs), nontraumatic subarachnoid hemorrhage, and cortical superficial siderosis (cSS), which is the main causes of spontaneous intracranial hemorrhage in the elderly. If a patient had experienced dementia, psychiatric symptoms, recurrent or multiple lobar hemorrhage, the possibility of CAA should be considered. Epilepsy can be associated with CAA. Literature studies had found that CAA-related inflammation are predisposing factors for the development of epilepsy. It is a unique subtype of CAA, which is a form of inflammation and a rare clinical manifestation of sporadic CAA. CAA-ri is a special type of central nervous system vasculitis. Once CAA patients had exhibited atypical clinical manifestations, such as headache, epilepsy, behavioral changes, focal neurological signs, consciousness impairment combined with asymmetric T2 weighted magnetic resonance imaging high signal lesions, clinicians had to consider it maybe CAA-ri. Although CAA- ri is rare, timely diagnosis is important because once seizure had occured, which may indicated the inflammation in CAA patients may had reached a very serious level. Therefore, timely identification and treatment are particularly important. Literature shows that most patients responded well to immunosuppressants. Because of its uncommon, researches on epilepsy in CAA mainly focused on case reports currently, and there were many controversies about its pathological mechanism, treatment and prognosis. This article mainly reviews the incidence rate , pathological mechanism, treatment and prognosis of epilepsy in CAA.
Objectives To analyze the prevalence and clinical features of depression, anxiety, depression and anxiety in Tibetan patients with epilepsy and to improve the diagnosis and treatment. Methods 102 patients with epilepsy, who had been admitted to the Department of Neurology of the People's Hospital of Tibet Autonomous Region from January 2017 to December 2017, were diagnosed according to the Chinese Standard Classification and Diagnostic Criteria for Mental Disorders (3rd Edition) (CCMD-3). The Hamilton depression scale (HAMD 24 items) and the Hamilton anxiety scale (HAMA 14 items) were used to measure depression and anxiety. Different genders, ages, durations, frequency of attacks, and seizures types were analyzed for depression, anxiety, depression and anxiety. Univariate analysis was used to screen the factors that may cause depression, anxiety, depression and anxiety in patients with epilepsy. Logistic regression was used to analyze the risk factors of depression, anxiety, depression and anxiety in patients with epilepsy. Results Among the 102 patients with epilepsy, 35 (34.31%) comorbid depression, 10 (9.80%) comorbid anxiety, and 54 (52.94%) comorbid depression and anxiety. Univariate analysis showed that there was a significantly statistical difference in the duration of the disease and the frequency of seizures in local patients with epilepsy (P<0.05). There was a statistically significant difference in the frequency of epileptic seizures and anxiety (P<0.05). Multivariate logistic regression analysis showed that the probability of anxiety in patients with a disease duration of ≤2 years was only 10.1% of those with a course >2 years [OR=0.101, 95%CI (0.012, 0.915), P<0.05]; and the frequency of seizures was not an risk factors for epileptic comorbid with anxiety (P>0.05). The rate of depression and anxiety in patients with seizure frequency >2 times per month was 4.853 times higher than that of patients with seizure frequency ≤2 times per month [OR=4.853, 95%CI (2.024, 11.634), P<0.05]. Conclusions Tibetan patients with epilepsy have a high prevalence of depression, anxiety, depression and anxiety. In the diagnosis and treatment, we should strengthen the understanding and provide the appropriate prevention and treatment to improve the diagnosis and treatment level.
Currently, about one-third of patients with anti-epilepsy drug or resective surgery continue to have sezure, the mechanism remin unknown. Up to date, the main target for presurgical evaluation is to determene the EZ and SOZ. Since the early nineties of the last century network theory was introduct into neurology, provide new insights into understanding the onset, propagation and termination. Focal seizure can impact the function of whole brain, but the abnormal pattern is differet to generalized seizure. Brain network is a conception of mathematics. According to the epilepsy, network node and hub are related to the treatment. Graphy theory and connectivity are main algorithms. Understanding the mechanism of epilepsy deeply, since study the theory of epilepsy network, can improve the planning of surgery, resection epileptogenesis zone, seizure onset zone and abnormal node of hub simultaneously, increase the effect of resectiv surgery and predict the surgery outcome. Eventually, develop new drugs for correct the abnormal network and increase the effect. Nowadays, there are many algorithms for the brain network. Cooperative study by the clinicans and biophysicists instituted standard and extensively applied algorithms is the precondition of widely used clinically.
ObjectiveThe purpose of this study was to better delineate the clinical spectrum of periventricular nodular heterotopia (PNH) in a large patient population to better understand social support in people with PNH and epilepsy in west China. Specifically, this study aimed to relate PNH subtypes to clinical or epileptic outcomes and epileptic discharges by analyzing anatomical features. MethodsThe study included 70 patients with radiologically confirmed nodular heterotopias and epilepsy. We also recruited healthy controls from nearby urban and rural areas. People with PNH and epilepsy and healthy controls were gender-and age-matched. Two-sided Chi-square test and Fisher's exact t-test were used to assess associations between the distribution of PNHs and specific clinical features. ResultsBased on imaging data, patients were subdivided into three groups: (a) classical (bilateral frontal and body, n=25), (b) bilateral asymmetrical or posterior (n=9) and (c) unilateral heterotopia (n=36). Most patients with classical heterotopia were females, but were mostly seizure-free. Patients with unilateral heterotopia were prone to develop refractory epilepsy. ConclusionsEach group's distinctive genetic mutations, epileptic discharge patterns and overall clinical outcomes confirm that the proposed classification system is reliable. These findings could not only be an indicator of a more severe morphological and clinical phenotype, but could also have clinical implications with respect to the epilepsy management and optimization of therapeutic options.
ObjectiveThe purpose of this study was to find a new method for the treatment of drug-resistant epilepsy, and to study the efficacy and safety of Bacteroidesfragilis (BF839) in the adjunctive treatment of refractory epilepsy, as well as the improvement of comorbidity.MethodsA prospective, single-arm, open pilot clinical study was designed for the additive treatment of drug-resistant epilepsy using BacteroidesFragilis 839 (BF839). 47 patients with refractory epilepsy, who were admitted to the epilepsy outpatient clinic of the Second Affiliated Hospital of Guangzhou Medical University from April 2019 to October 2019, were enrolled and treated with BF839 adjunct treatment. The primary efficacy endpoint was median percent reduction from baseline in monthly (28-day) seizure frequency for the 16-week treatment period. Other efficacy analysis included response rate(proportion of patients with ≥ 50% seizure reduction) in the 16 weeks period, the proportion of patients seizure free and the retention rate after12 months intervention, and the observance of the side effects and comorbidities.ResultsThe median reduction percent of all seizure types was ?53.5% (P=0.002). The response rate was 61.1% (22/36). 8.5% (4/47) patients seizure free at 12 months. The retention rate at 12 months was 57.4% (27/47). The side effects were diarrhea 4.3% (2/47) and constipation 4.3% (2/47). 48.9% (23/47) of the patients reported improvement in comorbidities, with cognitive improvement of 21.2% (10/47).ConclusionBF839 can be used as an effective additive therapy to treat drug-resistant epilepsy. It is safe and beneficial to the improvement of comorbidities. This is the first time in the world that a single intestinal strain has been reported to be effective in treating drug-resistant epilepsy. This research has important implications.
Temporal lobe epilepsy is the most common type of epilepsy in clinic. In recent years, many studies have found that patients with temporal lobe epilepsy have different degrees of influence in executive function related fields. This influence may not only exist in a certain field of executive function, but may be affected in several fields, and may be related to the origin site of seizures. However, up to now, there is no unified standard for the composition of executive function, and it is widely accepted that the three core components of executive function are working memory, inhibitory control and cognitive flexibility/switching. In addition, the International League Against Epilepsy proposed a new definition in 2010, and epilepsy is a brain network disease. There is a close relationship between brain neural network and cognitive impairment. According to the cognitive field, the brain neural network can be divided into six types: default mode network, salience network, executive control network, dorsal attention network, somatic motor network and visual network. In recent years, there has been increasing evidence that four related internal brain networks are series in a range of cognitive processes. The executive dysfunction of temporal lobe epilepsy may be related to the changes of functional connectivity of neural network, and may be related to the left uncinate fasciculus. This article reviews the research progress related to executive function in temporal lobe epilepsy from working memory, inhibitory control and cognitive flexibility, and discusses the correlation between the changes of temporal lobe epilepsy neural network and executive function research.
External trigeminal nerve stimulation (eTNS) is a new non-invasive physical and electrical stimulation therapy based on the anatomical characteristics of the trigeminal nerve. It can control seizures by regulating epilepsia-related brainstem nuclei and part of forebrain structures, regulating neuroinflammation, improving synaptic plasticity and promoting neurogenesis, which has broad clinical application prospects. It has been approved by the European Union as an adjuvant treatment for drug-resistant epilepsy patients over the age of 9 years old. Therefore, this article mainly reviews the central nervous system regulatory mechanism of eTNS in improving epilepsy, eTNS stimulation mode and parameters.
Objective To investigate the changes of cognitive function of epileptic patients after antiepileptic drugs (AEDs) therapy. Methods Twenty eight cases of epileptic patients with new diagnosis and untreatment from March 2015 to February 2016 were collected. According to the seizure type, degree of attack and drug efficacy, patients were divided into three groups and treated with one of three AEDs, including Lamotrigine (LTG), Oxcarbazepine (OXC), and Sodium valproate (VPA). Among them, 11 were LTG group, 12 were OXC group and 5 were VPA group.Then the patients were followed up for 1 year. The clinical memory scale was used to analyze cognitive function of epileptic patients before and after therapy. Results Compared to 30 cases of healthy volunteers, the scores of memory quotient (P<0.01), directed memory (P<0.05), associative learning (P<0.05) and image free recall (P<0.01) of epileptic patients were obviously decreased before AEDs therapy.AEDs therapy reduced or controlled seizures in new diagnostic epileptic patients, and the total effective rate was 85.7%. In the clinical memory scale tests, the scores of memory quotient (P<0.01), directed memory (P<0.05), associative learning (P<0.05), portrait characteristics contact memory (P<0.05) were improved after therapy. The scores of image free recall and meaningless graphics recognition were also improved, but there was no statistical significance. Besides, there was a statistically significant improvement in the score of portrait characteristics contact memory after LTG treatment (P<0.05), and directed memory after VPA treatment (P<0.05). Conclusions Epileptic patients accompanied with cognitive deficits before drug intervention. Through standard AEDs treatment, seizures could be better controlled. The cognitive function of epileptic patients was not declined after short-term（within 1 year） intervention of LTG, OCX or VPA. Moreover some parts of the cognitive domain could be improved.
With the development of artificial intelligence (AI) technology, great progress has been made in the application of AI in the medical field. While foreign journals have published a large number of papers on the application of AI in epilepsy, there is a dearth of studies within domestic journals. In order to understand the global research progress and development trend of AI applications in epilepsy, a total of 895 papers on AI applications in epilepsy included in the Web of Science Core Collection and published before December 31, 2022 were selected as the research objects. The annual number of papers and their cited times, the most published authors, institutions and countries, and their cooperative relationships were analyzed, and the research hotspots and future trends in this field were explored by using bibliometrics and other methods. The results showed that before 2016, the annual number of papers on the application of AI in epilepsy increased slowly, and after 2017, the number of publications increased rapidly. The United States had the largest number of papers (n=273), followed by China (n=195). The institution with the largest number of papers was the University of London (n=36), and Capital Medical University in China had 23 papers. The author with the most published papers was Gregory Worrell (n=14), and the scholar with the most published articles in China was Guo Jiayan from Xiamen University (n=7). The application of machine learning in the diagnosis and treatment of epilepsy is an early research focus in this field, while the seizure prediction model based on EEG feature extraction, deep learning especially convolutional neural network application in epilepsy diagnosis, and cloud computing application in epilepsy healthcare, are the current research priorities in this field. AI-based EEG feature extraction, the application of deep learning in the diagnosis and treatment of epilepsy, and the Internet of things to solve epilepsy health-related problems are the research aims of this field in the future.