Study on dynamic characteristics' change of hippocampal neuron reduced models caused by the Alzheimer's disease.

In the paper, based on the electrophysiological experimental data, the Hippocampal neuron reduced model under the pathology condition of Alzheimer's disease (AD) has been built by modifying parameters' values. The reduced neuron model's dynamic characteristics under effect of AD are comparatively studied. Under direct current stimulation, compared with the normal neuron model, the AD neuron model's dynamic characteristics have obviously been changed. The neuron model under the AD condition undergoes supercritical Andronov-Hopf bifurcation from the rest state to the continuous discharge state. It is different from the neuron model under the normal condition, which undergoes saddle-node bifurcation. So, the neuron model changes into a resonator with monostable state from an integrator with bistable state under AD's action. The research reveals the neuron model's dynamic characteristics' changing under effect of AD, and provides some theoretic basis for AD research by neurodynamics theory.

Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator.

Mental fatigue is a contributing factor to some serious transportation crashes. In this study, we measured mental fatigue in drivers using electroencephalogram (EEG) and electrocardiograph (ECG). Together, thirteen healthy subjects performed a continuous simulated driving task for 90 min with simultaneous ECG and multi-channel EEG recording of each subject. Several important physiological parameters were investigated using preprocessed ECG and EEG signals. The results show that the EEG alpha and beta, the relative power, the amplitude of P300 wave of event-related potential (ERP), the approximated entropy of the ECG, and the lower and upper bands of power of heart rate variability (HRV) are significantly different before and after finishing the driving task (p<0.05). These metrics are possible indices for measuring simulated driving mental fatigue.

A new approach for concealed information identification based on ERP assessment.

Recently, numerous concealed information test (CIT) studies have been done with event related potential (ERP) for its sufficient validity in applied use. In this study, a new approach based on wavelet coefficients (WCs) and kernel learning algorithm is proposed to identify concealed information. Totally 16 subjects went through the designed CIT paradigm and the multichannel electroencephalogram (EEG) signals were recorded. Then, the high-dimensional WCs of ERP in delta, theta, alpha and beta rhythms were extracted. For the analysis of the data, kernel principle component analysis (KPCA) and a support vector machines (SVM) classifier are implemented. The results show that WCs features are significant differences between concealed information and irrelevant information (P < 0.05). The KPCA is able to effectively reduce feature dimensionalities and increase generalization performance of SVM. A high accuracy (93.6%) in recognizing concealed information and irrelevant information is achieved, which indicates the combination KPCA and SVM may provide a useful tool for detecting the concealed information.

[Application of pulse-coupled neural network combined with genetic algorithm on MR images of hypoxic-ischemic encephalopathy].

This paper is to provide a basis for the establishment of an early diagnostic system for hypoxic-ischemic encephalopathy (HIE) by performing segmentation and feature extraction of lesions on the MR images of neonatal babies with HIE. The segmentation on MR images of HIE based on the genetic algorithm (GA) combined with a pulse-coupled neural network (PCNN) were carried out. There were better segmentation results by using PCNN segmentation based on GA than PCNN segmentation with fixed parameters. The data suggested that a PCNN based on GA could provide effective assistance for diagnosis and research.

[The study on the early diagnosis of hypoxic ischemic encephalopathy (HIE) in the newborns by fuzzy BP neural networks].

This paper is aimed to study a method and feasibility of early diagnostic system using hypoxic ischemic encephalopathy (HIE) in the newborns. Fifteen non-invasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE on the basis of related researches from the literature and the researches in our laboratory. The diagnostic test was done with 140 cases with the HIE, using the fussy BP neural network experiment system. The initial results showed that the accuracy rate was 100% for the training set and 95% for the testing set, and the error rate was 5%. The data suggested that the fuzzy back-propagation neural networks, with the clinical comprehensive indicators, exhibited a high accuracy for the early diagnosis of HIE. This method provides an objective and convenient new way for the early clinical diagnosis of the HIE.

Decreased delta event-related synchronization in patients with early vascular dementia.

Electroencephalogram (EEG) activity, recorded while performing an "odd ball" detection task, was compared between patients with early vascular dementia (VD), healthy young controls and healthy elderly controls performing the same task. The data were analyzed using the event-related synchronization/desynchronization (ERS/ERD) method. VD patients, compared with controls, showed decreased ERS effects in the delta frequency band (0.5-3.5Hz) of EEG after the target stimulus appeared in frontal, central and parietal regions. Similarly, elderly controls also showed a decreased ERS compared with young controls only in central and parietal regions. As part of this analysis, we introduce a novel quantitative index, the Event-related Energy Change Progression (EECP), which provides a reliable measure that distinguishes these groups and thereby provides a promising marker for early diagnosis of VD.

Denoised P300 and machine learning-based concealed information test method.

In this paper, a novel P300-based concealed information test (CIT) method was proposed to improve the efficiency of differentiating deception and truth-telling. Thirty subjects including the guilty and innocent performed the paradigm based on three types of stimuli. In order to reduce the influence from the occasional variability of cognitive states on the CIT, several single-trials from Pz in probe stimuli within each subject were first averaged. Then the three groups of features were extracted from these averaged single-trials. Finally, two classes of feature samples were used to train a support vector machine (SVM) classifier. Meanwhile, the optimal number of averaged Pz waveforms and some other parameter values in the classifiers were determined by the cross validation procedures. Results show that if choosing accuracy of 90% as a detecting standard of P3 component to classify a subject's status (guilty or innocent), our method can achieve individual diagnostic rate of 100%. The individual diagnostic rate of our method was higher than the results of the other related reports. The presented method improves efficiency of CIT, and is more practical, lower fatigue and less countermeasure behavior in comparison with previous report methods, which could extend the laboratory study to the practical application.

[Identification of mentality facticity based on multi-channel event-related potentials].

This paper explores the use of multi-channel event-related potentials (ERP) to identify mentality facticity or detect lie. Some identifiably meaning information, such as subjects' name and birthday, were selected as concealed information to be identified, 15 subjects were tested by concealed information test (CIT) paradigm and their electroencephalographs (EEG) were recorded from 30 electrodes. In virtue of analysis on the statistically significant difference between multi-channel ERPs evoked by probe information and that evoked by irrelevant information, the P300 amplitudes of 15 electrodes were selected as F-test samples. The significant difference of feature sample values between probe and irrelevant information was applied to identify mentality facticity. The results indicate that P300 amplitudes evoked in many electrodes are significantly different between probe and irrelevant information (P < 0.01). According to the significant difference of space sample values between probe and irrelevant information, the detection correctness to probe information reaches to 93.3% and is better than that of methods based single-channel ERP. The method proposed has the advantages of non-invasion and better accuracy, which could be used to identify mentality facticity effectively.

Estimation of the cortical functional connectivity by directed transfer function during mental fatigue.

In this paper, the directed transfer function (DTF) method is used to characterize changes in the functional coupling of EEG rhythms in different brain cortical areas due to the mental fatigue caused by long-term cognitive tasks. There is a parietal-to-frontal functional coupling of the total (0.5-30 Hz) EEG frequency band in the right and middle brain cortical areas during the pre-task period, and an inversion of that direction, even a significant prevalence of the frontal-to-parietal direction, after the completion of the task. When mental fatigue levels increase, the parietal-to-frontal functional coupling of the alpha (8-12 Hz) frequency band is weakened, and the beta (13-30 Hz) frequency band changes from a balanced directionality of the functional cortical coupling to frontal-to-parietal functional coupling, whereas the frontal-to-center functional coupling of the total frequency band is enhanced in the right hemisphere, and the frontal-to-center functional coupling of the beta frequency band is heightened in the left hemisphere. Meanwhile, in the central cortical area, the middle-to-left functional coupling of the total, beta and alpha frequency bands increases significantly and the middle-to-right functional coupling of the total and beta frequency bands increases significantly after the task as compared to the pre-task period. These findings suggest that the functional coupling of the frontal, central and parietal brain cortical areas is strongly correlated with a change in mental fatigue levels in the wake-fatigue transition. The experimental results indicate that the DTF method can effectively explore the change of the direction and strength of the information flow underlying cortical-to-cortical functional coupling when mental fatigue is increased by long-term cognitive work. The DTF method may open a promising way to study mental fatigue.

Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis.

The electroencephalogram (EEG) is often contaminated by electromyography (EMG). In this paper, a novel and robust technique is presented to eliminate EMG artifacts from EEG signals in real-time. First, the canonical correlation analysis (CCA) method is applied on the simulated EEG data contaminated by EMG and electrooculography (EOG) artifacts for separating EMG artifacts from EEG signals. The components responsible for EMG artifacts are distinguished from those responsible for brain activity based on the relative low autocorrelation. We demonstrate that the CCA method is more suitable to reconstruct the EMG-free EEG data than independent component analysis (ICA) methods. In addition, by applying CCA to analyze a number of EEG data contaminated by EMG artifacts, a correlation threshold is determined using an unbiased procedure. Hence, CCA can be used to remove EMG artifacts automatically. Finally, an example is given to verify that, after EMG artifacts were removed successfully from the EEG data contaminated by EMG and EOG simultaneously, not only the underlying brain activity signals but the EOG artifacts are preserved with little distortion.

Analysis of the distance between two classes for tuning SVM hyperparameters.

An important step in the construction of a support vector machine (SVM) is to select optimal hyperparameters. This paper proposes a novel method for tuning the hyperparameters by maximizing the distance between two classes (DBTC) in the feature space. With a normalized kernel function, we find that DBTC can be used as a class separability criterion since the between-class separation and the within-class data distribution are implicitly taken into account. Employing DBTC as an objective function, we develop a gradient-based algorithm to search the optimal kernel parameter. On the basis of the geometric analysis and simulation results, we find that the optimal algorithm and the initialization problem become very simple. Experimental results on the synthetic and real-world data show that the proposed method consistently outperforms other existing hyperparameter tuning methods.

Evaluation of urinary S100B protein level and lactate/creatinine ratio for early diagnosis and prognostic prediction of neonatal hypoxic-ischemic encephalopathy.

BACKGROUND: Early identification and prevention of hypoxic-ischemic encephalopathy (HIE) in newborns may reduce neonatal mortality and neurological dysfunction. OBJECTIVE: To analyze the diagnostic and prognostic values of urinary S100B level and lactate/creatinine ratio in newborns with HIE. METHODS: Seventy-eight full-term newborns with HIE and 25 normal newborns were enrolled. The Neonatal Behavioral Neurological Assessment (NBNA) and Developmental Screening Test were scored. The concentration of urinary S100B protein was determined using the S100B enzyme-linked immunosorbent assay and the levels of urinary lactate and creatinine were measured with the enzyme colorimetric method. RESULTS: Urinary S100B level on days 1-3 after birth and lactate/creatinine ratio on day 1 were significantly higher in newborns with HIE than those in the control group. Both indexes were positively correlated with the clinical grading of HIE. A cutoff value for the S100B level of 0.47 microg/l on day 3 after birth had a sensitivity of 90% and specificity of 92% for prediction of HIE. A lactate/creatinine ratio of more than 0.55 on day 1 showed the highest sensitivity (92%) and specificity (90%). A combination of both indexes improved the sensitivity and specificity to 99 and 97%, respectively. A negative correlation of both lactate/creatinine ratio on day 1 and S100B level on days 1-3 after birth with the NBNA score was identified on days 3, 7 and 14 after birth. The Developmental Screening Test score of 36 newborns with HIE within 6 months after birth showed that 65% of infants with moderate and high HIE had an abnormal developmental quotient. CONCLUSION: These data suggest that early measurement of both S100B level and lactate/creatinine ratio in the urine of newborns with HIE is a practical convenient and sensitive way to improve diagnosis on the third day of life and prognostic prediction of HIE.

[Amplitude coupling analysis of EEG using nonlinear regressive coefficients during mental fatigue].

Computing the Nonlinear regressive (NLR) coefficients of electroencephalogram (EEG) rhythms at different brain cortical areas for the mental fatigue caused by long-term cognitive task, the variations of NLR coefficients of EEG rhythms under different mental fatigue level are sought out. The experimental results show that the NLR coefficients of EEG rhythms can effectively characterize the changes of amplitude coupling at different brain cortical areas under different mental fatigue level. The NLR coefficient provides a powerful tool for the EEG functional coupling analysis of mental fatigue.

Risk factors and laser therapy for retinopathy of prematurity in neonatal intensive care unit.

BACKGROUND: With the increasing survival rates of preterm infants, the incidence of retinopathy of prematurity (ROP) is also increasing. This study aimed to investigate the risk factors for ROP in Neonatal Intensive Care Unit, and the effects of laser therapy. METHODS: Thirty-five premature infants with various other diseases were studied. Data were collected including gender, gestational age, birth weight, maternal gravidity, Apgar score, antenatal and postnatal hypoxia, anemia, jaundice, infection, pregnancy complications, and head ultrasound manifestations. All patients were subjected to fundus examinations at a postmenstrual age of 34 weeks using an indirect ophthalmoscope. The infants were divided into ROP group and non-ROP group. Infants with ROP received laser treatment. The data of the infants were analyzed using univariate analysis and the logistic regression model. RESULTS: Twenty-seven (77.1%) of the 35 infants had ROP and 21 of them also had plus diseases. The recovery rates were 74.1% and 92.6% at 2 weeks and 3 months after laser therapy, respectively. The birth weight and gestational age in the ROP group were significantly lower than those in the non-ROP group (P<0.01). The lower the gestational age and birth weight, the higher the incidence of ROP. There was a significant difference in the incidence of ROP with postnatal hypoxia (P=0.013), anemia (P=0.012) and gravidity two (P<0.05), analyzed with the Fisher's exact probability test (P<0.05). The gestational age was significantly lower in the plus disease group than in the non-plus disease group (P<0.05). CONCLUSIONS: The incidence of ROP is high in premature infants with various other diseases and is correlated with birth weight, gestational age, postnatal hypoxia, anemia and maternal gravidity. Laser therapy shows good outcomes.

[Power spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue].

The aim of this experiment is to find a feasible impersonal index for analyzing the physiological mental fatigue level. Three characteristic parameters, relative power in different rhythm, barycenter frequency and power spectral entropy, are extracted from two channels' electroencephalogram (EEG) under two physiological mental fatigue states. Then relationships between such three parameters and physiological mental fatigue are analyzed to explore whether they can be of use for detecting (or monitoring) the mental fatigue level. The experiment results show that the relative power, barycenter frequency and power spectral entropy of EEG exhibit strong correlation with physiological mental fatigue level. While physiological mental fatigue level increases, the relative power in theta, alpha and beta rhythms, barycenter frequency and power spectral entropy of EEG decrease, but the relative power in delta rhythm of EEG increases. The relative power in four rhythms, barycenter frequency and power spectral entropy of EEG reflect the change of physiological mental fatigue level sensitively, and may hopefully be used as indexes for detecting physiological mental fatigue level.

[Study of dopamine transporter imaging on the brain of children with autism].

This study was conducted to evaluate the applicability of 99mTc-2beta-[ N, N'-bis (2-mercaptoethyl) ethylenediamino]methyl,3beta(4-chlorophenyl)tropane(TRODAT-1) dopamine transporter(DAT) SPECT imaging in children with autism, and thus to provide an academic basis for the etiology, mechanism and clinical therapy of autism. Ten autistic children and ten healthy controls were examined with 99mTc-TRODAT-1 DAT SPECT imaging. Striatal specific uptake of 99mTc-TRODAT-1 was calculated with region of interest analysis according to the ratics between striatum and cerebellum [(STR-BKG)/BKG]. There was no statistically significant difference in semiquantitative dopamine transporter between the bilateral striata of autistic children (P=0.562), and between those of normal controls (p=0.573); Dopamine transporter in the brain of patients with autism increased significantly as compared with that in the brain of normal controls (P=0.017). Dopaminergic nervous system is dysfunctioning in the brain of children with autism, and DAT 99mTc-TRODAT-1 SPECT imaging on the brain will help the imaging diagnosis of childhcod autism.

Multi-channel linear descriptors for event-related EEG collected in brain computer interface.

By three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing event-related EEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.

[Progress of brain-neural function informatics].

Firstly the fundamental concept and research hotspots of Brain-Neural Function Informatics (BNFI) are described. Then the main study fields and progresses of BNFI are expounded. Finally the prospects of BNFI research are given. Studies on BNFI not only promote the "Brain Science" progress, but also boost the industry of a new kind of medical instruments - function rehabilitation equipment and artificial functional prostheses.

[Modeling for activating peripheral nerves by transverse electric field].

The classical cable function has been used to represent the response of peripheral nerves stimulated by external parallel electric field. It can not describe the excitation of peripheral nerves stimulated by perpendicular electric field. In this paper, responses of the nodes of Ranvier to transverse-field are deeply investigated by mathematic simulation and in-vitro experiments. The paper demonstrates that, under perpendicular electric field stimulation, the responses evoke a two-stage process including an initial polarization and the actual change of the transmembrane potential. It is the net inward current along a radial direction of the node of Ranvier that causes the peripheral nerve excitation. Based on the two-stage process, a novel model is introduced to describe peripheral nerves stimulated by transverse-field, and the classical cable function is modified. The new model and the improved cable function are verified by several in-vitro experiments. They can be used to represent peripheral nerves responses by arbitrary electric field stimulation.