Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 μV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 μV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.
ObjectiveTo analyze the risk factors for electrical status epilepticus during sleep (ESES) in patients with self-limited epilepsy with centrotemporal spikes (SeLECTs) and to construct a nomogram model. MethodsThis study selected 174 children with SeLECTs who visited the Third Affiliated Hospital of Zhengzhou University from March 2017 to March 2024 and had complete case data as the research subjects. According to the results of video electroencephalogram monitoring during the course of the disease, the children were divided into non-ESES group (88 cases) and ESES group (86 cases). Multivariate logistic regression analysis was used to identify the risk factors for the occurrence of ESES in SeLECTs patients. ResultsThe multifactor Logistic regression analysis demonstrated that the EEG discharges in bilateral cerebral areas,types of seizure, epileptic seizures after initial treatment were the independent risk factors for the occurrence of ESES in SeLECTs. ConclusionBilateral distribution of electroencephalogram discharges before treatment, emergence of new seizure forms, and epileptic seizures after initial treatment are risk factors for the ESES in SeLECTs patients. The nomogram model constructed based on the above risk factors has a high degree of accuracy.
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.
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.
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.
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.
A novel coronavirus (SARS-CoV-2) that broke out at the end of 2019 is a newly discovered highly pathogenic human coronavirus and has some similarities with severe acute respiratory syndrome coronavirus (SARS-CoV). Angiotensin-converting enzyme 2 (ACE2) is the receptor for infected cells by SARS-CoV. SARS-CoV can invade cells by binding to ACE2 through the spike protein and SARS-CoV-2 may also infect cells through ACE2. Meanwhile, ACE2 also plays an important role in the course of pneumonia. Therefore the possible role of ACE2 in SARS and coronavirus disease 2019 (COVID-19) is worth discussing. This paper briefly summarized the role of ACE2 in SARS, and discussed the possible function of ACE2 in COVID-19 and potential risk of infection with other organs. At last, the function of ACE2 was explored for possible treatment strategies for SARS. It is hoped to provide ideas and theoretical support for clinical treatment of COVID-19.
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.
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.
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.