Design and application of neural electrical stimulation system with time-varying parameters / 生物医学工程学杂志

Currently, commercial devices for electrical neural stimulations can only provide fixed stimulation paradigms with preset constant parameters, while the development of new stimulation paradigms with time-varying parameters has emerged as one of the important research directions for expanding clinical applications. To facilitate the performance of electrical stimulation paradigms with time-varying parameters in animal experiments, the present study developed a well-integrated stimulation system to output various pulse sequences by designing a LabVIEW software to control a general data acquisition card and an electrical stimulus isolator. The system was able to generate pulse sequences with inter-pulse-intervals (IPI) randomly varying in real time with specific distributions such as uniform distribution, normal distribution, gamma distribution and Poisson distribution. It was also able to generate pulse sequences with arbitrary time-varying IPIs. In addition, the pulse parameters, including pulse amplitude, pulse width, interphase delay of biphasic pulse and duration of pulse sequence, were adjustable. The results of performance tests of the stimulation system showed that the errors of the parameters of pulse sequences output by the system were all less than 1%. By utilizing the stimulation system, pulse sequences with IPI randomly varying in the range of 5~10 ms were generated and applied in rat hippocampal regions for animal experiments. The experimental results showed that, even with a same mean pulse frequency of ~130 Hz, for neuronal populations, the excitatory effect of stimulations with randomly varying IPIs was significantly greater than the effect of stimulations with fixed IPIs. In conclusion, the stimulation system designed here may provide a useful tool for the researches and the development of new paradigms of neural electrical stimulations.

A design of raster plot for illustrating dynamic neuronal activity during deep brain stimulation / 生物医学工程学杂志

Deep brain stimulation (DBS), which usually utilizes high frequency stimulation (HFS) of electrical pulses, is effective for treating many brain disorders in clinic. Studying the dynamic response of downstream neurons to HFS and its time relationship with stimulus pulses can reveal important mechanisms of DBS and advance the development of new stimulation modes (e.g., closed-loop DBS). To exhibit the dynamic neuronal firing and its relationship with stimuli, we designed a two-dimensional raster plot to visualize neuronal activity during HFS (especially in the initial stage of HFS). Additionally, the influence of plot resolution on the visualization effect was investigated. The method was then validated by investigating the neuronal responses to the axonal HFS in the hippocampal CA1 region of rats. Results show that the new design of raster plot is able to illustrate the dynamics of indexes (such as phase-locked relationship and latency) of single unit activity (i.e., spikes) during periodic pulse stimulations. Furthermore, the plots can intuitively show changes of neuronal firing from the baseline before stimulation to the onset dynamics during stimulation, as well as other information including the silent period of spikes immediately following the end of HFS. In addition, by adjusting resolution, the raster plot can be adapted to a large range of firing rates for clear illustration of neuronal activity. The new raster plot can illustrate more information with a clearer image than a regular raster plot, and thereby provides a useful tool for studying neuronal behaviors during high-frequency stimulations in brain.

High frequency stimulations change the phase-locking relationship between neuronal firing and the rhythms of field potentials / 生物医学工程学杂志

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 ( < 0.001, paired -test, = 8). The phase-locking values of interneuron spikes were also decreased significantly from 0.27 ± 0.08 to 0.09 ± 0.05 ( < 0.01, paired -test, = 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.

Design of a system for real-time seizure detection and closed-loop electrical stimulation / 生物医学工程学杂志

In order to investigate the effect of deep brain stimulation on diseases such as epilepsy, we developed a closed-loop electrical stimulation system using LabVIEW virtual instrument environment and NI data acquisition card. The system was used to detect electrical signals of epileptic seizures automatically and to generate electrical stimuli. We designed a novel automatic detection algorithm of epileptic seizures by combining three features of field potentials: the amplitude, slope and coastline index. Experimental results of rat epileptic model in the hippocampal region showed that the system was able to detect epileptic seizures with an accuracy rate 91.3% and false rate 8.0%. Furthermore, the on-line high frequency electrical stimuli showed a suppression effect on seizures. In addition, the system was adaptive and flexible with multiple work modes, such as automatic and manual modes. Moreover, the simple time-domain algorithm of seizure detection guaranteed the real-time feature of the system and provided an easy-to-use equipment for the experiment researches of epilepsy control by electrical stimulation.

Simulation of extracellular action potential for hippocampal pyramidal neurons / 生物医学工程学杂志

In order to extract more information from extracellular action potential (EAP) of neurons recorded deep in the brain tissue, we established simulation models of various pyramidal neurons in the hippocampal CA1 region and investigated the effects of dendrite currents, cell morphology and ion mechanisms on the formation of EAP waveforms. The results show that dendrite currents have significant effects on the EAP at the locations far from cell body, but not on those near cell body. The differences of shape of various pyramidal neurons result in large changes in the EAP amplitudes. However, the shapes of these different EAP are very similar. Ion mechanisms, such as calcium channels, have little effect on EAP waveforms. These results provide important information for experimental EAP recordings, EAP data analysis, and developing new methods to extract more neuronal data from EAP.

A Novel Method for Multi-channel Neuronal Spike Detection and Classification / 生物化学与生物物理进展

The detection and classification of extracellular action potentials(i.e.spike) of various single neurons from extracellular recordings are crucial for extracting neuronal spike sequences and thereby for investigating the mechanisms of neural information processing in the central nervous system.In order to increase the correctness of spike detecting and sorting,a new analysis algorithm for processing multi-channel spike signals recorded from rat hippocampi with silicon microelectrode arrays is presented.Four recording contacts on the electrode array are arranged close enough to simultaneously record spikes emitted from same neurons.Firstly,the algorithm extracts all spikes in the four channel recordings by using a multi-channel threshold detection method.Secondly,the algorithm classifies the spikes based on a principle component analysis for a specifically designed type of compound spike waveforms.The compound spike waveform is formed by linking four spike waveforms of a same neuronal firing in the four recording channels one by one in series.The test results with both synthetic datasets and experimental recordings reveal that compared with corresponding traditional single-channel algorithm,the multi-channel algorithm can significantly enhance both the number of extracted spikes and the correctness of spike classifications.The algorithm can also increase the number of isolated neurons from a single experimental preparation.These results indicate that the novel method is efficient for the automatic detection and classification of neuronal spikes.

Recordings of The Hippocampal Field Potentials and Unit Activity by Using Linear Silicon Electrode Array / 生物化学与生物物理进展

The rapid development of silicon microelectrode arrays provides an ideal means for the study of spatio-temporal features of neuronal activity in the brain. The stability of the linear silicon electrode array (LSEA) in recording neuronal potentials and its validity in recording unit activity are investigated. The experimental results showed that during the recording of field potentials in the hippocampal CA1 region of anesthetized rats, upward and downward movements of the recording probe for a distance of 200 ?m did not affect the orthordromic and antidromic evoked potentials significantly. The data indicated that the probe movements caused very small damage to the neurons, and the recording was stable. The contact sites that located in the pyramidal cell layer acquired CA1 neuronal unit activity validly. Different types of unit activity from independent neurons were easily distinguished in epochs of recording from a same recording site. These results demonstrated the features of the LSEA, including the facility of probe manipulation, the stability of recording and the abundance of data acquirement. The data will be helpful to the researchers involved in the application of microelectrode array for neuroscience researches.

Using the histogram analysis method to assess the time-frequency features of rat EEG under different vigilance states / 生物医学工程学杂志

To investigate the non-stationary time-frequency features in rat Electroencephalogram (EEG) under different vigilance states, the methods of multi-resolution wavelet transform (WT) and statistical histogram analysis were used. EEGs of the freely moving rats were recorded with implanted electrodes under the vigilance states of waking, slow wave sleep (SWS) and rapid eye movement sleep (REM). The EEGs were firstly decomposed into four frequency components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the parameters of mean value, standard deviation, skewness and kurtosis of the logarithm power histograms and the power percentage histograms of each of the frequency components were calculated. The results showed that the distributions of the logarithm power histograms were not quite different from the normal distribution. However, most of the power percentage histograms were significantly different from the normal distribution. The results of one-way ANOVA indicated that there were significant differences in the parameter values of the histograms both among different states and among different frequency components. Moreover, Skewness and kurtosis of the logarithm power histograms of some characteristic waves in EEG, such as delta wave during SWS and theta wave during waking and REM, obtained high values. Thus, the histogram parameters of EEG WT components might become as quantitative measures to describe the dynamic time-frequency features of EEG.

Effects of sampling parameter variation on the complexity analysis of EEG / 生物医学工程学杂志

The algorithmic complexity and the approximate entropy of EEG were calculated and analyzed with different data points, different sample frequencies and different sample time duration. The results showed that under fixed sample frequency, the longer the data was, the more stable the complexity values were. With fixed sample time duration or fixed data point, lower sample frequency would be better both for EEG distinguishing and for computing time saving.