PurposeTo analyze the effect of medication withdraw (MW) on long-term electroencephalogram (EEG) monitoring in children who need preoperative assessment for refractory epilepsy.MethodsRetrospective analysis was performed on the data of preoperative long-term EEG monitoring of children with refractory epilepsy who needed preoperative evaluation in the Pediatric Epilepsy Center of Peking University First Hospital from August 2018 to December 2019. Monitoring duration: at least three habitual seizures were detected, or the monitoring duration were as long as 10 days. MW protocol was according to the established plan.ResultsA total of 576 children (median age 4.4 years) required presurgical ictal EEGs, and 75 (75/576, 13.0%) needed MW for ictal EEGs. Among the 75 cases, 38 were male and 37 were female. The age range was from 15 months to 17 years (median age: 7.0 years). EEG and clinical data of with 65 children who strictly obey the MW protocol were analyzed. The total monitoring duration range was from 44.1 h (about 2 days) to 241.8 h (about 10 days)(median: 118.9 h (about 5 days)). Interictal EEG features before MW were including focal interictal epileptiform discharge (IED) in 39 cases (39/65, 60%), focal and generalized IED in 2 cases (2/65, 3.1%), multifocal IED in 20 cases (20/65, 30.7%), multifocal and generalized IED in 2 cases (2/65, 3.1%), and no IED in 2 cases (2/65, 3.1%). After MW, 18 cases (18/65, 27.7%) had no change in IED and the other 47 cases had changes of IED after MW. And IEDs in 46 cases (46/65, 70.8%) were aggravated, and IED was decreased in 1 case. The pattern of aggravated IED was original IED increasement, in 41 cases (41/46, 89.1%), and 5 cases (5 /46, 10.9%) had generalized IED which was not detected before MW. Of the 46 patients with IED exacerbations, 87.3% appeared within 3 days after MW. Habitual seizures were detected in 56 cases (86.2%, 56/65) after MW, and within 3 days of MW in 80.4% cases. Eight patients (14.3%) had secondary bilateral-tonic seizure (BTCS), of which only 1 patient had no BTCS in his habitual seizures. In 56 cases, 94.6% (53/56) had seizures after MW of two kinds of AEDs.Conclusions① In this group, thirteen percent children with intractable epilepsy needed MW to obtain ictal EEG; ② Most of them (86.2%) could obtain ictal EEG by MW. The IED and ictal EEG after MW were still helpful for localization of epileptogenic zone; ③ Most of the patients can obtain ictal EEG within 3 days after MW or after MW of two kinds of AEDs;4. The new secondary generalization was extremely rare.
In recent years, it has become a new direction in the field of neuroscience to explore the mode characteristics, functional significance and interaction mechanism of resting spontaneous electroencephalography (EEG) and task-evoked EEG. This paper introduced the basic characteristics of spontaneous EEG and task-evoked EEG, and summarized the core role of spontaneous EEG in shaping the adaptability of the nervous system. It focused on how the spontaneous EEG interacted with the task-evoked EEG in the process of task processing, and emphasized that the spontaneous EEG could significantly affect the performance of tasks such as perception, cognition and movement by regulating neural activities and predicting external stimuli. These studies provide an important theoretical basis for in-depth understanding of the principle and mechanism of brain information processing in resting and task states, and point out the direction for further exploring the complex relationship between them in the future.
Epileptic seizures and the interictal epileptiform discharges both have similar waveforms. And a method to effectively extract features that can be used to distinguish seizures is of crucial importance both in theory and clinical practice. We constructed state transfer networks by using visibility graphlet at multiple sampling intervals and analyzed network features. We found that the characteristics waveforms in ictal periods were more robust with various sampling intervals, and those feature network structures did not change easily in the range of the smaller sampling intervals. Inversely, the feature network structures of interictal epileptiform discharges were stable in range of relatively larger sampling intervals. Furthermore, the feature nodes in networks during ictal periods showed long-term correlation along the process, and played an important role in regulating system behavior. For stereo-electroencephalography at around 500 Hz, the greatest difference between ictal and the interictal epileptiform occurred at the sampling interval around 0.032 s. In conclusion, this study effectively reveals the correlation between the features of pathological changes in brain system and the multiple sampling intervals, which holds potential application value in clinical diagnosis for identifying, classifying, and predicting epilepsy.
Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.
ObjectiveTo investigate the application of stereoelectroencephalography (SEEG) in the refractory epilepsy related to periventricular nodular heterotopia (PNH). MethodsTen patients with drug-resistant epilepsy related to PNHs from Guangdong Sanjiu Brain Hospital and the First Affiliated Hospital of Jinan University from April 2017 to February 2021 were studied. Electrodes were implanted based on non-invasive preoperative evaluation. Then long-term monitoring of SEEG was carried out. The patterns of epileptogenic zone (EZ) were divided into four categories based on the ictal SEEG: A. only the nodules started; B. nodules and cortex synchronous initiation; C. the cortex initiation with early spreading to nodules; D. only cortex initiation. All patients underwent SEEG-guided radiofrequency thermocoagulation (RFTC), with a follow-up of at least 12 months. ResultsAll cases were multiple nodules. Four cases were unilateral and six bilateral. Eight cases were distributed in posterior pattern, and one in anterior pattern and one in diffused pattern, respectively. Seven patients had only PNH (pure PNH) and three patients were associated with other overlying cortex malformations (PNH plus). The EZ patterns of all cases were confirmed by the ictal SEEG: six patients were in pure type A, two patients were in pure type B, one patient in type A+B and one in type A+B+C, respectively. In eight patients SEEG-guided RF-TC was targeted only to PNHs; and in two patients RFTC was directed to both heterotopias and related cortical regions. The mean follow up was (33.4±14.0) months (12 ~ 58 months). Eight patients (in pure type A or type A included) were seizure free. Two patients were effective. None of the patients had significant postoperative complications or sequelae. ConclusionThe epileptic network of Epilepsy associated with nodular heterotopia may be individualized. Not all nodules are always epileptogenic, the role of each nodule in the epileptic network may be different. And multiple epileptic patterns may occur simultaneously in the same patient. SEEG can provide individualized diagnosis and treatment, be helpful to prognosis.
ObjectiveThe aim was to summarize the seizure and video electroencephalogram (VEEG) characteristics of Dyke-Davidoff-Masson syndrome (DDMS). Methods The case data of four patients with Dyke-Davidoff-Masson syndrome (DDMS) who attended the Epilepsy Center of Hunan Provincial Brain Hospital from March 2022 to March 2023 were retrospectively analyzed to summarize the clinical manifestations of their seizures and the characteristics of their video electroencephalogram (VEEG). Results One case of symptomatic epilepsy with focal seizures; VEEG showed poor background activity alpha rhythmic modulation, amplitude modulation, and increased distribution of slow wave activity in the left frontal and temporal regions; bilateral frontal-central and anterior-temporal regions (more so on the left side), with sharp and slow composite wave issuance.Two cases of symptomatic epilepsy with focal seizures progressing to generalized seizures; in one case, the VEEG showed: background activity α-rhythmic modulation, amplitude modulation is possible, the left frontal, central, anterior temporal region slow wave increase; the left frontal central, parietal anterior temporal region spike-like slow wave activity mixed with spike wave, spike-slow complex wave short-medium-range issuance; the other VEEG showed: background activity α-rhythmic modulation, amplitude modulation is possible, the right frontal central, anterior temporal region slow wave increase; right frontal, central, and anterior temporal region for the famous medium-extremely high-high-amplitude slow wave activity mixed with spike wave, spike-slow complex wave short-medium-range issuance. One case of symptomatic epilepsy with generalized seizures; VEEG showed bilateral occipital alpha rhythm asymmetry, right occipital region <50% of the left side, poor regulation and amplitude modulation; bilateral frontal pole, frontal region, anterior temporal region spike and spiking slow complex wave discharges (right side was prominent), and right pterionic electrodes, anterior temporal and mesial temporal spike and spiking slow wave discharges. Conclusions Epileptic seizures are one of the main clinical manifestations of DDMS and most of them are consulted after a seizure, and their seizure types tend to be focal seizures or progress to generalized seizures, and most of them are drug-refractory epilepsies. The results of VEEG monitoring tend to be characterized by abnormal background activity, increased slow-wave activity, and the site of epileptogenic wave-like discharges tends to be in line with the site of cerebral softening foci or the site of the atrophic side of the brain as shown by cranial MRI.
Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups ofⅣ_ⅢandⅣ_Ⅰ. The experimental results proved that the method proposed in this paper was feasible.
The aim of this study is to evaluate the effect of laparoscopic simulation training with different attention. Attention was appraised using the sample entropy and θ/β value, which were calculated according to electroencephalograph (EEG) signal collected with BrainLink. The effect of laparoscopic simulation training was evaluated using the completion time, error number and fixation number, which were calculated according to eye movement signal collected with Tobii eye tracker. Twenty volunteers were recruited in this study. Those with the sample entropy lower than 0.77 were classified into group A and those higher than 0.77 into group B. The results showed that the sample entropy of group A was lower than that of group B, and fluctuations of A were more steady. However, the sample entropy of group B showed steady fluctuations in the first five trainings, and then demonstrated relatively dramatic fluctuates in the later five trainings. Compared with that of group B, the θ/β value of group A was smaller and shows steady fluctuations. Group A has a shorter completion time, less errors and faster decrease of fixation number. Therefore, this study reached the following conclusion that the attention of the trainees would affect the training effect. Members in group A, who had a higher attention were more efficient and faster training. For those in group B, although their training skills have been improved, they needed a longer time to reach a plateau.
Objective To research clinical manifestations, electrophysiological characteristics of epileptic seizures arising from diagonal sulci (DS), to improve the level of the diagnosis and treatment of frontal epilepsy. MethodsWe reviewed all the patients underwent a detailed presurgical evaluation, including 5 patients with seizures to be proved originating from diagonal sulci by Stereo-electroencephalography (SEEG). All the 5 patients with detailed medical history, head Magnetic resonance (MRI), the Positron emission computered tomography (PET-CT) and psychological evaluation, habitual seizures were recorded by Video-electroencephalography (VEEG) and SEEG, we review the intermittent VEEG and ictal VEEG, analyzing the symptoms of seizures. Results 5 patients were divided into 2 groups by SEEG, group 1 including 3 patients with seizures arising from the bottom of DS, group 2 including 2 patients with seizures arising from the surface of DS, all the tow groups with seizures characterized by both having tonic and complex motors, tonic seizures were prominent in seizures from left DS, and tonic seizures may absent in seizures from right DS. Intermittent discharges with group1 were diffused, and intermittent discharges with group 2 were focal, but both brain areas of frontal and temporal were infected. Ictal EEG findings were consistent with the characteristics of neocortical seizures, the onset EEG shows voltage attenuation, seizures from bottom of DS with diffused EEG onset, and seizures from surface of DS with more focal EEG onset, but both frontal and anterior temporal regions were involved. Conclusionthe symptom of seizures arising from DS characterized by tonic and complex motor, can be divided into seizures arising from the bottom of DS and seizures from the surface of DS, with different electrophysiological characters.
The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-invasive nature and ease of use. However, motor imagery EEG signals have low signal-to-noise ratios and spatiotemporal resolutions, leading to low decoding recognition rates with traditional neural networks. To address this, this paper proposed a three-dimensional (3D) convolutional neural network (CNN) method that learns spatial-frequency feature maps, using Welch method to calculate the power spectrum of EEG frequency bands, converted time-series EEG into a brain topographical map with spatial-frequency information. A 3D network with one-dimensional and two-dimensional convolutional layers was designed to effectively learn these features. Comparative experiments demonstrated that the average decoding recognition rate reached 86.89%, outperforming traditional methods and validating the effectiveness of this approach in motor imagery EEG decoding.
