ObjectiveThe aim of this study is to identify clinical and electroencephalographic features associated with refractoriness to the initial antiepileptic drug in typical benign childhood epilepsy with centrotemporal spikes (BECTS). MethodsA total of 87 children with typical BECTS were retrospectively reviewed in the analyses.The patients were subdivided into two groups:patients whose seizures were controlled with monotherapy, and those requiring two medications. 63 childrenachieved seizure-freedom with monotherapy, while 24 received two medications for seizure control. ResultsDiffusing foci at the follow-up EEG and delayed treatment (duration > 1 year) are two main risk factors associated with more refractory cases (P < 0.001). Delayed diagnosis (37.1%) and non-adherence to treatment (57.2%) contributed to delayed treatment. ConclusionsOur findings suggested that diffusing foci on EEG and delayed treatment are associated with more frequent seizures and refractoriness in BECTS. Diagnostic delays and non-adherence hindered timely care, which may represent opportunities for improved intervention.
Objective To identify clinical and electroencephalographic features of the variants of benign childhood epilespy with centrotemporal spikes (BECTs). Methods A total of 51 children with BECTs were restrospectively reviewed from July 2008 to December 2015 in the study, including the clinical data, electrophysiologic characteristics and effects of antiepileptic drugs. Results Age of the patients ranged from 2.5 to 11 years old, which were averged 7.03 years old. The duration of disease varied from 4 days to 6 years, and 2.36 years in average. Nearly continuous electric discharge were detected in slow sleeping, during which, the busting index was 90% in 19 patients’, 78% in 26, 52% in 6. the average busting index was 82.44%.47 patients (92.1%) had synptom of hand shaking; 8 patients(15.6%) had oropharyngeal automatism; 7 patients (13.7%) had language barrier; sample absence seizures or tumble occurred in 11 patients (21.5%); cognitive declined in 17 patients (33.3%). VPA monotherapy had good effect on 12 patients, 33 patients need combination of VPA and CBZ. However, there’s still 6 patients need adrenocortical hormone to control seizures. Conclusion The variants of BECT are companied with obvious deterioration of EEG. Lack of standard AED therapy may cause aggravations, so we need to monitor EEG closely. We use AED not merely in orde to control seizure but also inhibit abnormal EEG discharge.
The phase-locking relationship between the firings of neuronal action potentials (i.e., spikes) and the oscillations of local field potentials (LFP) reflects important neural coding information. However, the present analysis methods can only determine whether there has phase-locking, but not the different strengths among various types of phase-locking. In the present paper, we used spike-triggered average (STA) signals and the percentage ratio (named φ) of the STA power to the power of original LFP as an index to evaluate the strengths of phase-locking. Experimental recordings obtained from rat hippocampal CA1 region as well as simulation data were used to evaluate the method. The results showed that the index φ changed monotonically as a function of the strength of phase-locking, and it could provide an effective critical value to divide phase-locking from non-phase-locking. Because the calculation of the index does not need pre-filtering, it can avoid the unwanted influences caused by intentionally limiting the frequencies of LFP oscillations such as in the traditional bin statistical method. Therefore, the index φ provides a novel method to investigate the mechanisms underlying neuronal coding in brain.
To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.
Epilepsy is characterized by abnormally synchronized firing of neuronal populations, which is presented as epileptiform spikes in neural electrical signal recordings. In order to investigate the epileptiform spikes quantitatively, we designed a new window-based algorithm to automatically detect population spikes (PS) in acute epilepsy models in rat hippocampus CA1 region, and to calculate characteristic parameters of PS. Results show that the algorithm could recognize PS waveforms directly in wideband recording signals in epilepsy models induced by 4-aminopyridine (4-AP), a potassium channel blocker, or by picrotoxin (PTX), an antagonist of γ-aminobutyric acid A-type receptor. The PS detection ratios of the two epilepsy models were 94.2%±1.6% (n=11) and 95.9%±1.9% (n=12), respectively. The false positive ratios were 3.5%±2.3% (n=11) and 4.8%±2.3% (n=12), which were significantly lower than those of the conventional threshold method. Comparisons of the PS patterns between the 4-AP model and the PTX model showed that the PS of the 4-AP model had wider waveforms and fired more dispersedly with intervals mainly in the range of 100–700 ms. The PS of the PTX model fired as Burst with a higher firing rate and with intervals mainly in the range of 2–20 ms, resulting in a larger sum of spike amplitudes per second than the 4-AP model. Thus, the synchronous firing of neuronal populations in the PTX model was more intense than that in the 4-AP model. In conclusion, the new algorithm of PS detection can correctly detect and analyze epileptiform population spikes. It provides a useful tool of data analysis for investigating the underlying mechanism of seizure generation and for evaluating new therapeutics of epilepsy.
Transcranial magneto-acoustical stimulation (TMAS), utilizing focused ultrasound and a magnetostatic field to generate an electric current in tissue fluid to regulate the activities of neurons, has high spatial resolution and penetration depth. The neuronal spike-frequency adaptation plays an important role in the treatment of neural information. In this paper, we study the effects of ultrasonic intensity, magnetostatic field intensity and ultrasonic frequency on the neuronal spike-frequency adaptation based on the Ermentrout neuron model. The simulation results show that, the peak time interval becomes smaller, the interspike interval becomes shorter and the time of the firing of the neuron is shortened with the increasing of the magnetostatic field intensity. With the increase of the adaptive variables, the initial spike-frequency is shifted to the right with the magnetostatic field intensity, and the spike-frequency is linearly related to the increase of the magnetostatic field intensity in steady state. The simulation effect with change of the ultrasonic intensity is consistent with the change of magnetostatic field intensity. The change of the ultrasonic frequency has no effect on the neuronal spike-frequency adaptation. Under the different adaptive variables, with the increase of the adaptive variables, the initial spike-frequency amplitude decreased with the increasing of the ultrasonic frequency, and the spike-frequency is linearly related to the increase of the ultrasonic frequency in steady state. These results of the study can help us to reveal the mechanism of transcranial magneto-acoustical stimulation on the neuronal spike-frequency adaptation, and provide a theoretical basis for its application in the treatment of neurological disorders.
Deep brain stimulation (DBS) has been successfully used to treat a variety of brain diseases in clinic. Recent investigations have suggested that high frequency stimulation (HFS) of electrical pulses used by DBS might change pathological rhythms in action potential firing of neurons, which may be one of the important mechanisms of DBS therapy. However, experimental data are required to confirm the hypothesis. In the present study, 1 min of 100 Hz HFS was applied to the Schaffer collaterals of hippocampal CA1 region in anaesthetized rats. The changes of the rhythmic firing of action potentials from pyramidal cells and interneurons were investigated in the downstream CA1 region. The results showed that obvious θ rhythms were present in the field potential of CA1 region of the anesthetized rats. The θ rhythms were especially pronounced in the stratum radiatum. In addition, there was a phase-locking relationship between neuronal spikes and the θ rhythms. However, HFS trains significantly decreased the phase-locking values between the spikes of pyramidal cells and the θ rhythms in stratum radiatum from 0.36 ± 0.12 to 0.06 ± 0.04 (P < 0.001, paired t-test, N = 8). The phase-locking values of interneuron spikes were also decreased significantly from 0.27 ± 0.08 to 0.09 ± 0.05 (P < 0.01, paired t-test, N = 8). Similar changes were obtained in the phase-locking values between neuronal spikes and the θ rhythms in the pyramidal layer. These results suggested that axonal HFS could eliminate the phase-locking relationship between action potentials of neurons and θ rhythms thereby changing the rhythmic firing of downstream neurons. HFS induced conduction block in the axons might be one of the underlying mechanisms. The finding is important for further understanding the mechanisms of DBS.
The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.
The rapid development of artificial intelligence put forward higher requirements for the computational speed, resource consumption and the biological interpretation of computational neuroscience. Spiking neuron networks can carry a large amount of information, and realize the imitation of brain information processing. However, its hardware is an important way to realize its powerful computing ability, and it is also a challenging technical problem. The memristor currently is the electronic devices that functions closest to the neuron synapse, and able to respond to spike voltage in a highly similar spike timing dependent plasticity (STDP) mechanism with a biological brain, and has become a research hotspot to construct spiking neuron networks hardware circuit in recent years. Through consulting the relevant literature at home and abroad, this paper has made a thorough understanding and introduction to the research work of the spiking neuron networks based on the memristor in recent years.
Both spike and local field potential (LFP) signals are two of the most important candidate signals for neural decoding. At present there are numerous studies on their decoding performance in mammals, but the decoding performance in birds is still not clear. We analyzed the decoding performance of both signals recorded from nidopallium caudolaterale area in six pigeons during the goal-directed decision-making task using the decoding algorithm combining leave-one-out and k-nearest neighbor (LOO-kNN). And the influence of the parameters, include the number of channels, the position and size of decoding window, and the nearest neighbor k value, on the decoding performance was also studied. The results in this study have shown that the two signals can effectively decode the movement intention of pigeons during the this task, but in contrast, the decoding performance of LFP signal is higher than that of spike signal and it is less affected by the number of channels. The best decoding window is in the second half of the goal-directed decision-making process, and the optimal decoding window size of LFP signal (0.3 s) is shorter than that of spike signal (1 s). For the LOO-kNN algorithm, the accuracy is inversely proportional to the k value. The smaller the k value is, the larger the accuracy of decoding is. The results in this study will help to parse the neural information processing mechanism of brain and also have reference value for brain-computer interface.