To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
ObjectiveAicardi and Goutières syndrome was first reported as a rare hereditary encephalopathy with white matter involvement in 1984. Typical clinical manifestations include severe mental motor development retardation or regression, pyramidal and extrapyramidal symptoms and signs, epilepsy, microcephaly and frostbite.MethodsTo collect a case of patient who presented with convulsions 14 days after birth without obvious inducement. The child was diagnosed as epilepsy in the local hospital and the symptoms improved after treatment with antiepileptic drugs. At 4 months, the child presented nods and clenched fists, and was diagnosed as infantile spasm. After Adrenocorticotrophic hormone and drug treatment, the symptoms gradually improved. Due to upper respiratory track infection, the child was aggravated at the age of 1 year and 2 months, and then diagnosed as Aicardi-Goutières syndrome by video EEG, skull MRI, fundus and gene screening.ResultsSurgery and treatment with antiepileptic drugs significantly improved the symptoms of the child, and the pathological biopsy of the brain tissue supported the previous diagnosis.ConclusionsThe report of this case will help to improve the clinician's diagnosis and treatment of Aicardi-Goutières syndrome.
Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.
Using the computer to imitate the neural oscillations of the brain is of great significance for the analysis of brain functions. Thalamocortical neural mass model (TNMM) reflects the mechanisms of neural activities by establishing the relationships between the thalamus and the cortex, which contributes to the understanding of some specific cognitive functions of the brain and the neural oscillations of electroencephalogram (EEG) rhythms. With the increasing complexity and scale of neural mass model, the performance of conventional computer system can not achieve rapid and large-scale model simulation. In order to solve this problem, we proposed a computing method based on Field Programmable Gate Array (FPGA) hardware in this study. The Altera's DSP Builder module combined with MATLAB/Simulink was used to achieve the construction of complex neural mass model algorithm, which is transplanted to the FPGA hardware platform. This method takes full advantage of the ability of parallel computing of FPGA to realize fast simulation of large-scale and complex neural mass models, which provides new solutions and ideas for computer implementation of neural mass models.
ObjectiveTo investigate the lateralization of ictal scalp EEG in different times in focal epilepsy.Methods356 surface ictal EEG of 41 patients were reviewed retrospectively in focal epilepsy arising from the mesial frontal, lateralfrontal, mesialtemporal, neocorticaltemporal, insular lobes and posterior cortex from July, 2010 to at, 2016. Each ictal scalp EEG was subdivided into ten epoches (E1-E10), then the lateralization of every epoch was analyzed. Ten epochs EEG were merged into three timesas E1-E3, E4-E6 and E7-E10. The ratio of lateralization, mislateralization and non-lateralization of each timeEEG were studied. Ictal onset zone (IOZ) were precise localized by intracranial EEG. The results of epileptogenic zone corresponded with surgical outcomes as seizure free or decreased.Results62% seizures were lateralized by surface ictal EEG in all epilepsies. Lateralized ictal scalp EEG were seen in nearly 80% of seizures in all times in temporal lobe epilepsy (TLE). The highest lateralization of 89% occurred inE4-E6 andfalse lateralization up to 30% in E1-E3 in mesial temporal lobe epilepsy (MTLE), whereas 95% lateralized seizures emerged in E1-E3 in neocortical temporal lobe epilepsy (NTLE). Apparent non-lateralization in all times were higher than lateralization in frontal lobe epilepsy (FLE), especially in mesial frontal lobe epilepsy (MFLE). Lateralization in E1-E3 was only 24% higher than other times. In addition, False lateralization never occurred in all times in lateral frontal lobe epilepsy (LFLE). There were maximum of 83%lateralized seizures in E1-E3 in LFLE and 93% in E1-E3 in posterior cortex epilepsy (PCE). Seizures arising from insular lobe epilepsy (ILE) tendedto predict less lateralization in all times.ConclusionsIctal scalp EEG of E1-E3 are valuable in the lateralization in all epilepsies particularly in LFLE, NTLE and PCE. Lateralized E4-E6 and E7-10 are very useful in MTLE.