高频电刺激改变神经元锋电位的波形

为了正确检测和研究高频电刺激(high frequencystimulation,HFS)期间神经元的动作电位发放活动,进而深入揭示深部脑刺激治疗神经系统疾病的机制,本课题研究HFS期间锋电位波形的变化.在麻醉大鼠海马CA1区的输入神经通路Schaffer侧支上,施加1~2 min时长的100或者200 Hz顺向高频刺激(orthodromic-HFS,O-HFS),利用微电极阵列采集刺激下游神经元的多通道锋电位信号,并获得由O-HFS经过单突触传导激活的中间神经元的单元锋电位波形及其特征参数.结果表明,O-HFS使得锋电位的幅值明显减小而半高宽明显增加,以基线记录为基准计算百分比值,O-HFS期间锋电位的降支幅值和升支幅值分别可减小20%和40%左右,半高宽则增加10%以上.并且,在大量神经元同时产生动作电位期间,或者在比200 Hz具有更大兴奋作用的100 Hz刺激期间,锋电位波形的改变更多,幅值的减小可达50%,宽度的增加可达20%.可以推测,高频电刺激对于神经元的兴奋作用可能升高细胞膜电位,从而改变细胞膜离子通道的活动特性,导致动作电位波形的改变.这些结果支持深部脑刺激具有兴奋性调节作用的假说,对于正确分析高频电刺激期间神经元锋电位活动具有指导意义,也为进一步研究深部脑刺激(DBS)治疗脑神经系统疾病的机制提供了重要线索.     

微丝电极阵列的参考电极内置与外置的对比研究*

目的: 通过对比内置和外置参考电极的微丝电极阵列在记录大鼠脑神经元放电过程中的优缺点,优化微丝电极阵列的制作与埋置,为多通道电生理实时记录系统提供更加实惠、优异的媒介工具。方法: 采用镍铬合金丝、电路板、电极引脚和地线(银线)制作16通道的微丝电极阵列,通过内置(参考电极与电极阵列并列排布)或外置(参考电极与地线分别焊接在电极一侧的两端)微丝电极阵列的参考电极,观察对比两种电极在记录大鼠ACC脑区神经元放电中的区别。实验大鼠分为内置组(8只)和外置组(9只),检测指标有信噪比(n=8)、放电幅度(n=380)和放电频率(n=54)。结果: 内置与外置参考电极的微丝电极阵列均可顺利记录出大鼠ACC脑区神经元的电信号;与外置组相比,内置组的神经元电信号具有信噪比高(P＜0.05)、背景信号幅度小、受噪音干扰小,和放电幅度大(P＜0.05)的优点;锋电位放电频率没有显著差异(P＞0.05)。结论: 在记录大鼠ACC脑区神经元电活动时,内置参考电极的微丝电极阵列记录到更高信噪比、更大放电幅度的电信号,为多通道电生理技术提供更加可靠的工具。     

基于最大锋电位间隔的爆发检测自适应算法

在各种类型的培养神经元网络、哺乳动物中枢神经系统和切片中,都可以观察到爆发。爆发是空间-时间放电模式的重要特征,它由一系列高频率发放的连续动作电位组成,由于在时间尺度上的复杂性,使其辨识和探测存在许多困难。自适应算法利用爆发外部锋电位间隔超过爆发内部锋电位间隔的累加和识别爆发本身。基于该算法原理,以爆发内部最大锋电位间隔参数作为确定爆发的约束条件,改进爆发检测自适应算法。实验结果表明,改进算法可以有效地避免爆发的漏检和错检,较准确地检测出神经元的爆发活动,确定爆发活动的数目和持续时间等,爆发检测的平均准确率为93.8%,比原自适应算法提高了35.3%。     

中枢神经元放电的在体多通道同步记录技术

中枢神经元放电的在体多通道同步记录技术是采用细胞外记录的方法来监测神经元群的同步电活动。该系统包括微电极阵列(microarray)、数据采集和分析系统。应用这一技术可以同步记录多个脑区的大量神经元的电活动,研究不同脑区的神经元放电在时间和空间上的联系,进而通过分析神经元的放电模式来研究大脑对外部事件的编码机制。     

大鼠多通道在体记录

多通道在体记录技术,能在自由活动的动物脑内,观察和记录局部脑区群体神经元的活动状况,是分析大脑神经信息编码的有力工具。要开展多通道在体记录研究,多电极阵列驱动器的设计非常关键,也是实现该技术的一大难点。根据转动螺杆推动螺帽移动的机械驱动原理,作者设计了适合大鼠多通道在体记录的、独立可调式16道电极阵列驱动装置。通过该装置,可对16道记录电极中的任意一道进行独立驱动,从而控制每根记录电极在大鼠大脑中的垂直记录位置。运用该多电极阵列驱动装置,对大鼠单侧海马脑区的多通道在体记录表明,在大鼠海马CA1区,存在不同放电波形和放电模式的神经元,它们分别与海马CA1区的锥体神经元和中间神经元相对应。一般锥体神经元动作电位的放电波形较宽,放电频率则较低。在海马CA1区还存在编码空间环境中特定位置信息的神经元,被称为位置细胞。这些位置细胞在某一空间环境中有各自对应的反应区域,在该区域内位置细胞的放电频率增加,在区域外则基本维持在一较低的活动水平。     

多通道神经信号分析方法及其应用

在神经科学研究中,多通道记录方法被普遍应用在对神经元群体活动特性的研究中.通过分析多个神经元的活动,可以了解神经系统协同编码外界信息的规则以及大脑实现各项功能的机制.为了挖掘出多通道神经信号中携带的信息及其潜在的相关性,需要合适的计算方法辅助对神经元放电活动进行解码.本文回顾了多通道神经信号分析中的一些常见方法,以及它...     

大鼠运动皮层神经元集群锋电位时空模式解析

在大鼠前肢压杆任务中,同步记录初级运动皮层神经元集群活动信号与压杆的压力信号,分析神经元锋电位发放的时空模式,并用于大鼠前肢运动的解析和预测.数据分析显示在压杆阶段与非压杆阶段大鼠运动皮层神经元锋电位发放模式存在着显著差别,且神经元活动变化先于前肢运动的发生约300~400ms,并可通过与行为的相关性将神经元的发放模式分为4类.研究结果同时显示,两层Elman神经网络可用于神经元集群活动的解码,解码所得到的压力值与系统所采集的压杆压力信号有较好的拟合度,二者间的相关系数可达0.8766.研究表明了运动相关的神经信息处理和表征依赖于初级运动皮层神经元的相互作用和整合,揭示了神经元集群活动在运动信息编码中的重要作用.实验结果也揭示神经元集群活动信号解析后有望用于对外部器械进行直接控制,推动植入式脑-机接口及运动重建等康复技术的发展.     

多通道在体记录技术——动作电位与场电位信号处理

多通道在体记录可以同时记录到多个神经元的胞外放电信号以及对应的局部场电位的活动信号。如何对记录到的这两种电信号进行合适的处理,以确保实验结果的准确性,是运用好多通道在体记录技术的关键之一。本文旨在针对多通道在体记录的原始数据,介绍动作电位及场电位信号的常用数据处理方法。动作电位信号属于高频信号,一般用40 kHz的高速采样频率进行采集和记录。根据记录到的神经元胞外动作电位波形,运用主成分分析技术,再结合四电极记录技术的优势,可对来自记录电极周围不同空间位置的神经元放电信号进行良好的甄别,从而获得较精确的单神经元放电时间序列。而局部场电位信号属低频信号(300 Hz),一般用1 kHz的采样频率进行采集和记录。记录到的场电位原始信号需要进行数字滤波,从而分离出场电位信号中不同频率段的节律性振荡。啮齿类动物海马结构中常见的节律性振荡有动物清醒活动及快速眼动睡眠时的theta节律(4~12 Hz);清醒认知活动过程中,伴随着theta节律一起出现的gamma节律(30~80 Hz);以及清醒静止及慢波睡眠时的ripple高频振荡(100~250 Hz)。针对以上处理获得的数据,常用的后续数据分析方法有:神经元放电间隔分析、神经元放电自相关与互相关分析、以及信号的频谱分析等。     

一种基于支持向量机的自适应肿瘤分类检测算法

根据肿瘤分类检测模型的特点,提出了一种新的算法,该算法结合使用了基因选择和数据抽取的有效方法,并在此基础上使用支持向量机对基因表达数据进行分类或者检测。其中乳腺癌的分类交叉验证结果由88．46％提高到100．0％,急性白血病的也由71．05％提高至100．0％。实验结果说明了这一方法的有效性,为在大量的基因表达数据中提高检测癌症的准确性提出了一种比较通用的方法。     

应用线性硅电极阵列检测海马场电位和单细胞动作电位

近年来,硅材料微电极阵列发展迅速,μ为研究大脑神经细胞活动的时空特性提供了理想的手段.考察了线性硅材料微电极阵列在神经细胞电位检测中的稳定性,以及对于单细胞动作电位检测的有效性.实验结果表明,在麻醉大鼠海马CA1区场电位记录中,上下移动记录微电极200μm,对于正向和反向诱发电位的记录几乎没有影响,说明,线性微电极阵列对于神经细胞的损伤很小,检测性能稳定.电极阵列上处于细胞胞体层的测量点可以有效地记录到CA1神经细胞的动作电位发放,同一记录点上可以清楚地分辨出数个不同神经细胞的发放电位.实验结果显示了硅电极阵列操作简便、检测信号稳定和获取信息多等特点,对于开展微电极阵列应用研究的工作人员具有借鉴作用.     

Action potential waveform variability limits multi-unit separation in freely behaving rats

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or multiwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥ 4) and low neuronal density (≈ 20,000/ mm(3)). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution.     

Estimating Extracellular Spike Waveforms from CA1 Pyramidal Cells with Multichannel Electrodes

Extracellular (EC) recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the “local field potential” (LFP). In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe). We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike.     

An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.     

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs)¹. Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue². Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (>97% spike detection efficiency), has a low rate of false positive spike detection (< 0.003 spikes/sec), and a high temporal precision (about 3 msec) ³. This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo^3,4.     

An unsupervised automatic method for sorting neuronal spike waveforms in awake and freely moving animals

The present study introduces an approach to automatic classification of extracellularly recorded action potentials of neurons. The classification of spike waveform is considered a pattern recognition problem of special segments of signal that correspond to the appearance of spikes. The spikes generated by one neuron should be recognized as members of the same class. The spike waveforms are described by the nonlinear oscillating model as an ordinary differential equation with perturbation, thus characterizing the signal distortions in both amplitude and phase. It is shown that the use of local variables reduces the problem of spike recognition to the separation of a mixture of normal distributions in the transformed feature space. We have developed an unsupervised iteration-learning algorithm that estimates the number of classes and their centers according to the distance between spike trajectories in phase space. This algorithm scans the learning set to evaluate spike trajectories with maximal probability density in their neighborhood. Following the learning, the procedure of minimal distance is used to perform spike recognition. Estimation of trajectories in phase space requires calculation of the first- and second-order derivatives, and integral operators with piecewise polynomial kernels were used. This provided the computational efficiency of the developed approach for real-time application as required by recordings in behaving animals and in human neurosurgical operations. The new method of spike sorting was tested on simulated and real data and performed better than other approaches currently used in neurophysiology.     

Quantifying Spike Train Oscillations: Biases,Distortions and Solutions

Estimation of the power spectrum is a common method for identifying oscillatory changes in neuronal activity. However, the stochastic nature of neuronal activity leads to severe biases in the estimation of these oscillations in single unit spike trains. Different biological and experimental factors cause the spike train to differentially reflect its underlying oscillatory rate function. We analyzed the effect of factors, such as the mean firing rate and the recording duration, on the detectability of oscillations and their significance, and tested these theoretical results on experimental data recorded in Parkinsonian non-human primates. The effect of these factors is dramatic, such that in some conditions, the detection of existing oscillations is impossible. Moreover, these biases impede the comparison of oscillations across brain regions, neuronal types, behavioral states and separate recordings with different underlying parameters, and lead inevitably to a gross misinterpretation of experimental results. We introduce a novel objective measure, the "modulation index", which overcomes these biases, and enables reliable detection of oscillations from spike trains and a direct estimation of the oscillation magnitude. The modulation index detects a high percentage of oscillations over a wide range of parameters, compared to classical spectral analysis methods, and enables an unbiased comparison between spike trains recorded from different neurons and using different experimental protocols.     

Extracellular potentials from single spinal motoneurons

Extracellular action potentials found close to the surface of motoneurons are related to the intracellular spikes. Evidence is cited to support the assumption that the extracellular spikes have the same time course as the membrane current at the site of recording. Simultaneously recorded intracellular and extracellular spikes are compared. Intracellular spikes are transformed, by means of a circuit which is equivalent to the extracellular recording situation, into transients that are like those appearing extracellularly. Evidence is given that the recordings are from the cell bodies of motoneurons. The results show that the membrane at the extracellular recording site does not produce a spike since the time course of the extracellular potentials is determined by the passive properties of the membrane.     

Coding with spike shapes and graded potentials in cortical networks

In cortical neurones, analogue dendritic potentials are thought to be encoded into patterns of digital spikes. According to this view, neuronal codes and computations are based on the temporal patterns of spikes: spike times, bursts or spike rates. Recently, we proposed an 'action potential waveform code' for cortical pyramidal neurones in which the spike shape carries information. Broader somatic action potentials are reliably produced in response to higher conductance input, allowing for four times more information transfer than spike times alone. This information is preserved during synaptic integration in a single neurone, as back-propagating action potentials of diverse shapes differentially shunt incoming postsynaptic potentials and so participate in the next round of spike generation. An open question has been whether the information in action potential waveforms can also survive axonal conduction and directly influence synaptic transmission to neighbouring neurones. Several new findings have now brought new light to this subject, showing cortical information processing that transcends the classical models.     

Electrical properties of neurons recorded from the rat supraoptic nucleus in vitro

The electrical properties of neurons in the supraoptic nucleus (so.n.) have been studied in the hypothalamic slice preparation by intracellular and extracellular recording techniques, with Lucifer Yellow CH dye injection to mark the recording site as being the so.n. Intracellular recordings from so.n. neurons revealed them to have an average membrane potential of -67 +/- 0.8 mV (mean +/- s.e.m.), membrane resistance of 145 +/- 9 M omega with linear current-voltage relations from 40 mV in the hyperpolarizing direction to the level of spike threshold in the depolarizing direction. Average cell time constant was 14 +/- 2.2 ms. So.n. action potentials ranged in amplitude from 55 to 95 mV, with a mean of 76 +/- 2 mV, and a spike width of 2.6 +/- 0.5 ms at 30% of maximal spike height. Both single spikes and trains of spikes were followed by a strong, long-lasting hyperpolarization with a decay fitted by a single exponential having a time constant of 8.6 +/- 1.8 ms. Action potentials could be blocked by 10(-6) M tetrodotoxin. Spontaneously active so.n. neurons were characterized by synaptic input in the form of excitatory and inhibitory postsynaptic potentials, the latter being apparently blocked when 4 M KCl electrodes were used. Both forms of synaptic activity were blocked by application of divalent cations such as Mg2+, Mn2+ or Co2+. 74% of so.n. neurons fired spontaneously at rates exceeding 0.1 spikes per second, with a mean for all cells of 2.9 +/- 0.2 s-1. Of these cells, 21% fired slowly and continuously at 0.1 - 1.0 s-1, 45% fired continuously at greater than 1 Hz, and the remaining 34% fired phasically in bursts of activity followed by silence or low frequency firing. Spontaneously firing phasic cells showed a mean burst length of 16.7 +/- 4.5 s and a silent period of 28.2 +/- 4.2 s. Intracellular recordings revealed the presence of slow variations in membrane potential which modified the neuron's proximity to spike threshold, and controlled phasic firing. Variations in synaptic input were not observed to influence firing in phasic cells.     

Strategies for High-Performance Resource-Efficient Compression of Neural Spike Recordings

Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based basis is used.