Stereo-electroencephalography (SEEG) is widely used to record the electrical activity of patients' brain in clinical. The SEEG-based epileptogenic network can better describe the origin and the spreading of seizures, which makes it an important measure to localize epileptogenic zone (EZ). SEEG data from six patients with refractory epilepsy are used in this study. Five of them are with temporal lobe epilepsy, and the other is with extratemporal lobe epilepsy. The node outflow (out-degree) and inflow (in-degree) of information are calculated in each node of epileptic network, and the overlay between selected nodes and resected nodes is analyzed. In this study, SEEG data is transformed to bipolar montage, and then the epileptic network is established by using independent effective coherence (iCoh) method. The SEEG segments at onset, middle and termination of seizures in Delta, Theta, Alpha, Beta, and Gamma rhythms are used respectively. Finally, the K-means clustering algorithm is applied on the node values of out-degree and in-degree respectively. The nodes in the cluster with high value are compared with the resected regions. The final results show that the accuracy of selected nodes in resected region in the Delta, Alpha and Beta rhythm are 0.90, 0.88 and 0.89 based on out-degree values in temporal lobe epilepsy patients respectively, while the in-degree values cannot differentiate them. In contrast, the out-degree values are higher outside the temporal lobe in the patient with extratemporal lobe epilepsy. Based on the out-degree feature in low-frequency epileptic network, this study provides a potential quantitative measure for identifying patients with temporal lobe epilepsy in clinical.
The early diagnosis of children with autism spectrum disorders (ASD) is essential. Electroencephalography (EEG) is one of most commonly used neuroimaging techniques as the most accessible and informative method. In this study, approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn) and wavelet entropy (WaEn) were extracted from EEGs of ASD child and a control group, and Student's t-test was used to analyze between-group differences. Support vector machine (SVM) algorithm was utilized to build classification models for each entropy measure derived from different regions. Permutation test was applied in search for optimize subset of features, with which the SVM model achieved best performance. The results showed that the complexity of EEGs in children with autism was lower than that of the normal control group. Among all four entropies, WaEn got a better classification performance than others. Classification results vary in different regions, and the frontal lobe showed the best performance. After feature selection, six features were filtered out and the accuracy rate was increased to 84.55%, which can be convincing for assisting early diagnosis of autism.
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.
ObjectiveTo evaluate the application of stereotactic electrode implantation on precise epileptogenic zone localization. MethodRetrospectively studied 140 patients with drug-resist epilepsy from March 2012 to June 2015, who undergone a procedure of intracranial stereotactic electrode for localized epileptogenic zone. ResultsIn 140 patients who underwent the ROSA navigated implantation of intracranial electrode, 109 are unilateral implantation, 31 are bilateral; 3 patients experienced an intracranial hematoma caused by the implantation. Preserved time of electrodes, on average, 8.4days (range 2~35 days); Obseved clinical seizures, on average, 10.8 times per pt (range 0~98 times); There were no cerebrospinal fluid leak, intracranial hematoma, electrodes fracture or patient death, except 2 pt's scalp infection (1.43%, scalp infection rate); 131 pts' seizure onset area was precisely localized; 71 pts underwent SEEG-guide resections and were followed up for more than 6 months. In the group of 71 resection pts, 56 pts were reached Engel I class, 2 were Engel Ⅱ, 3 was Engel Ⅲ and 10 were Engel IV class. ConclusionTo intractable epilepsy, when non-invasive assessments can't find the epileptogenic foci, intracranial electrode implantation combined with long-term VEEG is an effective method to localize the epileptogenic foci, especially the ROSA navigated stereotactic electrode implantation, which is a micro-invasive, short-time, less-complication, safe-guaranteed, and precise technique.
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.
Background music has been increasingly affecting people’s lives. The research on the influence of background music on working memory has become a hot topic in brain science. In this paper, an improved electroencephalography (EEG) experiment based on n-back paradigm was designed. Fifteen university students without musical training were randomly selected to participate in the experiment, and their behavioral data and the EEG data were collected synchronously in order to explore the influence of different types of background music on spatial positioning cognition working memory. The exact low-resolution brain tomography algorithm (eLORETA) was applied to localize the EEG sources and the cross-correlation method was used to construct the cortical brain function networks based on the EEG source signals. Then the characteristics of the networks under different conditions were analyzed and compared to study the effects of background music on people’s working memory. The results showed that the difference of peak periods after stimulated by different types of background music were mainly distributed in the signals of occipital lobe and temporal lobe (P < 0.05). The analysis results showed that the brain connectivity under the condition with background music were stronger than those under the condition without music. The connectivities in the right occipital and temporal lobes under the condition of rock music were significantly higher than those under the condition of classical music. The node degrees, the betweenness centrality and the clustering coefficients under the condition without music were lower than those under the condition with background music. The node degrees and clustering coefficients under the condition of classical music were lower than those under the condition of rock music. It indicates that music stimulation increases the brain activity and has an impact on the working memory, and the effect of rock music is more remarkable than that of classical music. The behavioral data showed that the response accuracy in the state of no music, classical music and rock music were 86.09% ± 0.090%, 80.96% ± 0.960% and 79.36% ± 0.360%, respectively. We conclude that background music has a negative impact on the working memory, for it takes up the cognitive resources and reduces the cognitive ability of spatial location.
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.
ObjectiveTo investigate characteristics of motor semiology of epileptic seizure originated from dorsolateral frontal lobe. MethodsRetrospectively analysis the clinical profiles of patients who were diagnosed dorsolateral frontal lobe epilepsy (FLE) based on stereoelectroencephalography (SEEG) and underwent respective surgeries subsequently. Component of motor semiology in a seizure can be divided into elementary motor (EM, include tonic, versive, clonic, and myoclonic seizures) and complex motor (CM, include automotor, hypermotor, and so on). A Talairach coordinate system was constructed in the sagittal series of MRI images in each case. From the cross point of VAC and the Sylvian Fissure, a line was drawn antero-superiorly, which made an angle of 60° with the AC-PC line, then the frontal lobe could be divided into anterior and posterior portion. The epileptogenic zone, which was defined as ictal onset and early spreading zone in SEEG, was classified into three types, according to the positional relationship of the responding electrodes contacts and the "60° line": the anterior, posterior, and intermediate FLE. The correlation of the components of motor semiology in seizures and the location of the epileptogenic zone was analyzed. ResultsFive cases (26.3%) were verified as anterior FLE, among which there were 2 of EM, one of CM, and 2 of EM+CM. In 7 cases (36.8%) of intermediate FLE, there were one of EM, none of CM, and 6 of EM+CM. In the rest 7 cases of posterior FLE, there were 6 of EM, none of CM, and one of EM+CM. Compared with the cases that the epileptogenic zone involved anterior portion, the posterior FLE is more likely to present EM seizures (85.7%), and less likely to show CM components (P < 0.05). And Compared with the anterior FLE and posterior FLE, the intermediate FLE is more likely to present EM+CM seizures (85.7%)(P < 0.05). ConclusionThe motor seizure semiology of dorsolateral FLE has significant correlation with the localization of the epileptogenic zone. Posterior FLE mainly present a pure elementary motor seizure, and once the epileptogenic zone involved anteriorly beyond the "60° line", the component of complex motor seizure would be seen. Intermediate FLE, as its specialty of transboundary, is more likely to show "comprised semiology" of EM and CM. Construction of the "60° line" with AC-PC coordinate system in the MRI images may play an useful role in semiology analysis in presurgical evaluation of FLE.
ObjectiveTo understand the relationship between the anatomy and the function of the insula lobe cortex based on the stereo-electro encephalography (SEEG) by direct electric stimulation of the insula cortex performed in the patients who suffered from the refractory epilepsy. MethodsRetrospective review was performed on 12 individuals with refractory epilepsy who were diagnosed in the Department of Functional neurosurgery of RenJi Hospital from December 2013 to September 2015. We studied all the SEEG electrodes implanted in the brain with contacts in the insula cortex. Direct electric stimulation was given to gain the brain mapping of the insula. Results12 consecutive patients with refractory epilepsy were implanted SEEG electrodes into the insula cortex. In all, 176 contacts were in the insula cortex, and 154 were included. The main clinical manifestations obtained by the stimulation were somatosensory abnormalities, laryngeal constriction, dyspnea, nausea, flustered. While somatosensory symptoms were located in the posterior insula, visceral sensory symptoms distribute relatively in the anterior insula, and other symptoms were mainly in the central and anterior part. ConclusionsThe symptoms of the insula present mainly according to the anatomy, but some of them are mixed. In addition, the manifestations of the insula are usually complex and individually.
The purpose of using brain-computer interface (BCI) is to build a bridge between brain and computer for the disable persons, in order to help them to communicate with the outside world. Electroencephalography (EEG) has low signal to noise ratio (SNR), and there exist some problems in the traditional methods for the feature extraction of EEG, such as low classification accuracy, lack of spatial information and huge amounts of features. To solve these problems, we proposed a new method based on time domain, frequency domain and space domain. In this study, independent component analysis (ICA) and wavelet transform were used to extract the temporal, spectral and spatial features from the original EEG signals, and then the extracted features were classified with the method combined support vector machine (SVM) with genetic algorithm (GA). The proposed method displayed a better classification performance, and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%. The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.