多通道神经元锋电位检测和分类的新方法

大脑神经元胞外单细胞动作电位(即锋电位)的检测和分类是提取神经元脉冲序列、研究神经系统信息处理机制的关键．为了提高锋电位的检出率和分类的正确性,设计了一种处理多通道锋电位记录信号的算法,用于分析微电极阵列记录的大鼠海马神经元锋电位信号,电极阵列上的测量点排列紧密,4个通道可以同时记录到来自相同神经元的信号．该算法首先利用一种多通道阈值检测法检出四通道记录信号中的锋电位,然后利用一种基于复合锋电位的主成分特征参数分类法将锋电位分类．仿真数据和实验记录信号的检验结果表明：与相应的单通道算法相比,该算法的锋电位检出率和分类的正确性显著提高,并且可以增加单次实验测得的神经元数目．因此,该算法为实现神经元锋电位的自动检测提供了一种简单有效的新 方法．     

Optimal recognition of neuronal waveforms

Statistically optimal methods for identifying single unit activity in multiple unit recordings are discussed. These methods take into account both the nerve impulse waveforms and the firing patterns of the units. A generalized least-squares fit procedure is shown to be the optimal recognition scheme under some reasonable statistical assumptions, but the amount of computation becomes prohibitively large when the method is applied to the problem of sorting superimposed waveforms. A linear filter technique which relies on simultaneous recording from several electrodes in shown to give good separation of superimposed waveforms. An iterative recognition procedure can be applied to improve the results and reduce the number of recording electrodes required.     

Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes.

Neurons in auditory cortex show highly diverse responses to sounds. This study suggests that neuronal type inferred from baseline firing properties accounts for much of this diversity, with a subpopulation of bursting units being specialized for precise temporal encoding.     

Neuronal assembly detection and cell membership specification by principal component analysis

In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership.     

Stimulus dependent neural correlations in the auditory midbrain of the grassfrog (Rana temporaria L.)

Few-unit recordings were obtained using metal microelectrodes. Separation into single-unit spike trains was based on differences in spike amplitude and spike waveform. For that purpose a hardware microprocessor based spike waveform analyser was designed and built. Spikes are filtered by four matched filters and filter outputs at the moments of spike occurrence are read by a computer and used for off-line separation and spike waveform reconstruction. Thirty-one double unit recordings were obtained and correlation between the separated spike trains was determined. After stimulus correction correlation remained in only 8 of the double unit records. It appeared that in most cases this neural correlation was stimulus dependent. Continuous noise stimulation resulted in the strongest neural correlation remaining after correction for stimulus coupling, stimulation with 48 ms duration tonepips presented once per second generally did not result in a significant neural correlation after the correction procedure for stimulus lock. The usefulness of the additive model for neural correlation and the correction procedure based thereupon is discussed.     

Analysis of the middle latency evoked potentials to angular acceleration impulses in man

The middle latency vestibular evoked potential (ML-VsEP) recorded with scalp electrodes in man in response to impulses of angular acceleration is dominated by a forehead positive peak at about 15 ms and a negative peak at about 20 ms; the peak amplitude of this component is about 30 μV. This is followed by slower, smaller amplitude activity. The latency of this initial peak is similar to the latency of the vestibulo-ocular reflex (VOR) in monkeys. The present study was undertaken to elucidate the possible relation between the ML-VsEPs and VOR. This included recordings from forehead-mastoid electrodes (sites used to record VsEP) and other scalp electrodes and the recording of potentials due to eye movement: the electro-oculogram. Direct recording of eye movements was also conducted using an infra-red reflection device in those experiments in which the head was not moved. The recordings were conducted in man during vestibular stimulation eliciting VsEPs, during voluntary eye movements and during caloric and optokinetic stimulation. These experiments indicated that the 15–20 ms component of the ML-VsEP was not due to movements of the eye (corneoretinal dipole). The large amplitude 15–20 ms component of the ML-VsEP was similar in general magnitude, waveform, polarity, duration and rise time to the highly synchronous pre-saccadic spike (neural and/or myogenic) which precedes nystagnys and voluntary saccades. It therefore probably represents vestibular-initiated electrical activity in motor units of the extra-ocular muscles which then produce anti-compensatory saccades.     

Estimating the contribution of assembly activity to cortical dynamics from spike and population measures

The hypothesis that cortical networks employ the coordinated activity of groups of neurons, termed assemblies, to process information is debated. Results from multiple single-unit recordings are not conclusive because of the dramatic undersampling of the system. However, the local field potential (LFP) is a mesoscopic signal reflecting synchronized network activity. This raises the question whether the LFP can be employed to overcome the problem of undersampling. In a recent study in the motor cortex of the awake behaving monkey based on the locking of coincidences to the LFP we determined a lower bound for the fraction of spike coincidences originating from assembly activation. This quantity together with the locking of single spikes leads to a lower bound for the fraction of spikes originating from any assembly activity. Here we derive a statistical method to estimate the fraction of spike synchrony caused by assemblies—not its lower bound—from the spike data alone. A joint spike and LFP surrogate data model demonstrates consistency of results and the sensitivity of the method. Combining spike and LFP signals, we obtain an estimate of the fraction of spikes resulting from assemblies in the experimental data.     

Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat

Large scale electrophysiological recordings from neuronal ensembles offer the opportunity to investigate how the brain orchestrates the wide variety of behaviors from the spiking activity of its neurons. One of the most effective methods to monitor spiking activity from a large number of neurons in multiple local neuronal circuits simultaneously is by using silicon electrode arrays^1-3.Action potentials produce large transmembrane voltage changes in the vicinity of cell somata. These output signals can be measured by placing a conductor in close proximity of a neuron. If there are many active (spiking) neurons in the vicinity of the tip, the electrode records combined signal from all of them, where contribution of a single neuron is weighted by its ''electrical distance''. Silicon probes are ideal recording electrodes to monitor multiple neurons because of a large number of recording sites (+64) and a small volume. Furthermore, multiple sites can be arranged over a distance of millimeters, thus allowing for the simultaneous recordings of neuronal activity in the various cortical layers or in multiple cortical columns (Fig. 1). Importantly, the geometrically precise distribution of the recording sites also allows for the determination of the spatial relationship of the isolated single neurons⁴. Here, we describe an acute, large-scale neuronal recording from the left and right forelimb somatosensory cortex simultaneously in an anesthetized rat with silicon probes (Fig. 2).     

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.     

Oscillatory entrainment of striatal neurons in freely moving rats

Oscillations and synchrony in basal ganglia circuits may play a key role in the organization of voluntary actions and habits. We recorded single units and local field potentials from multiple striatal and cortical locations simultaneously, over a range of behavioral states. We observed opposite gradients of oscillatory entrainment, with dorsal/lateral striatal neurons entrained to high-voltage spindle oscillations ("spike wave discharges") and ventral/medial striatal neurons entrained to the hippocampal theta rhythm. While the majority of units were likely medium-spiny projection neurons, a second neuronal population showed characteristic features of fast-spiking GABAergic interneurons, including tonic activity, brief waveforms, and high-frequency bursts. These fired at an earlier spindle phase than the main neuronal population, and their density within striatum corresponded closely to the intensity of spindle oscillations. The orchestration of oscillatory activity by networks of striatal interneurons may be an important mechanism in the pathophysiology of neurological disorders such as Parkinson's disease.     

Employing ICA and SOM for spike sorting of multielectrode recordings from CNS

For classification of action potential shapes in multineuron recordings, we present a spike sorting system employing independent component analysis (ICA) and an unsupervised artificial neural network (Kohonen's self-organizing map, SOM). We focus on how ICA in the first stage of the spike sorting system can be used to address specific problems arising in recordings using multielectrode arrays in the CNS. Using real data recorded from the pontine nuclei in rats and simulated data, we evaluate the performance of several ICA algorithms to remove cross-talk between electrodes using data from continuous recording (or simulation). When using cut-out data, the standard format of extracellular spike recordings, new problems emerge and robust algorithms are needed. We demonstrate that several ICA algorithms show a good performance on cut-out data from multielectrode array recordings (simulated and real data). In tetrode recordings the same neuron is purposely recorded by several electrodes simultaneously and we show, how independent component analysis can be used in this case to identify redundant information and hence to compress relevant information, improving subsequent clustering of a SOM.     

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.     

The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity

To date, single neuron recordings remain the gold standard for monitoring the activity of neuronal populations. Since obtaining single neuron recordings is not always possible, high frequency or ‘multiunit activity’ (MUA) is often used as a surrogate. Although MUA recordings allow one to monitor the activity of a large number of neurons, they do not allow identification of specific neuronal subtypes, the knowledge of which is often critical for understanding electrophysiological processes. Here, we explored whether prior knowledge of the single unit waveform of specific neuron types is sufficient to permit the use of MUA to monitor and distinguish differential activity of individual neuron types. We used an experimental and modeling approach to determine if components of the MUA can monitor medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) in the mouse dorsal striatum. We demonstrate that when well-isolated spikes are recorded, the MUA at frequencies greater than 100Hz is correlated with single unit spiking, highly dependent on the waveform of each neuron type, and accurately reflects the timing and spectral signature of each neuron. However, in the absence of well-isolated spikes (the norm in most MUA recordings), the MUA did not typically contain sufficient information to permit accurate prediction of the respective population activity of MSNs and FSIs. Thus, even under ideal conditions for the MUA to reliably predict the moment-to-moment activity of specific local neuronal ensembles, knowledge of the spike waveform of the underlying neuronal populations is necessary, but not sufficient.     

Active stabilization of electrodes for intracellular recording in awake behaving animals

Intracellular recording is a powerful electrophysiology technique that has revealed much of what is known about the biophysical properties of neurons. However, neuronal properties are strongly affected by activity dependent and modulatory influences, making it essential, ultimately, to study these properties in behaving animals. Unfortunately, intracellular recording has only been widely applied in vitro, since cardiac and respiratory pulsations make intracellular recording difficult in vivo. In awake behaving animals, spontaneous movements make intracellular recording nearly impossible. Here I present a novel technique to dynamically stabilize the position of a recording electrode relative to the brain. Physiological signals that are predictive of brain motion at the recording site, such as the electrocardiogram (EKG), respiratory pressure, or cranial motion, are used to control a piezoelectric manipulator, making possible stable intracellular recordings in awake active animals.     

Flexible polyimide microelectrode array for in vivo recordings and current source density analysis

This work presents implantable, flexible polymer-based probes with embedded microelectrodes for acute and chronic neural recordings in vivo, as tested on rodents. Acute recordings using this array were done in mice under urethane anesthesia and compared to those made using silicon-based probes manufactured at the Center for Neural Communication Technology, University of Michigan. The two electrode arrays yielded similar results. Recordings with chronically implanted polymer-based electrodes were performed for 60 days post-surgically in awake, behaving rats. The microelectrodes were used to monitor local field potentials and capture laminar differences in function of cortex and hippocampus, and produced response waveforms of undiminished amplitude and signal-to-noise ratios 8 weeks after chronic implantation. The polymer-based electrodes could also be connected to a lesion current to mark specific locations in the tissue. Current source density (CSD) analysis from the recordings depicted a source - sink-composition. Tissue response was assessed 8 weeks after insertion by immunochemical labeling with glial fibrillary acidic protein (GFAP) to identify astrocytes, and histological analysis showed minimal tissue reaction to the implanted structures.     

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.     

Microarray Noninvasive Neuronal Seizure Recordings from Intact Larval Zebrafish

Zebrafish epilepsy models are emerging tools in experimental epilepsy. Zebrafish larvae, in particular, are advantageous because they can be easily genetically altered and used for developmental and drug studies since agents applied to the bath penetrate the organism easily. Methods for electrophysiological recordings in zebrafish are new and evolving. We present a novel multi-electrode array method to non-invasively record electrical activity from up to 61 locations of an intact larval zebrafish head. This method enables transcranial noninvasive recording of extracellular field potentials (which include multi-unit activity and EEG) to identify epileptic seizures. To record from the brains of zebrafish larvae, the dorsum of the head of an intact larva was secured onto a multi-electrode array. We recorded from individual electrodes for at least three hours and quantified neuronal firing frequency, spike patterns (continuous or bursting), and synchrony of neuronal firing. Following 15 mM potassium chloride- or pentylenetetrazole-infusion into the bath, spike and burst rate increased significantly. Additionally, synchrony of neuronal firing across channels, a hallmark of epileptic seizures, also increased. Notably, the fish survived the experiment. This non-invasive method complements present invasive zebrafish neurophysiological techniques: it affords the advantages of high spatial and temporal resolution, a capacity to measure multiregional activity and neuronal synchrony in seizures, and fish survival for future experiments, such as studies of epileptogenesis and development.     

Characterisation of the feeding behaviour of western flower thrips in terms of electrical penetration graph (EPG) waveforms

Western flower thrips, Frankliniella occidentalis (Pergande) causes damage to plants when they are feeding. Also, this thrips species transmits Tomato spotted wilt virus (TSWV) during stylet penetration. We investigated the penetration behaviour (probing) of thrips on pepper leaves and on liquid diet by electrical penetration graph (EPG, DC-system) recording. In addition, we used high-magnification video observations to correlate EPG waveforms with the insect's posture, head movements, and muscle contractions. Also, EPGs were correlated with probing on liquid diets containing radio-active tracers to distinguish and quantify ingestion waveforms. The previously described waveforms P, Q, and R were distinguished and additionally, a new waveform 'S' was distinguished. Waveform P could be linked with mandibular leaf penetration, waveform Q presumably with insertion of the maxillary stylets, and waveform R with ingestion of cell contents, whereas waveform S could not be correlated with any behavioural activity. Histology of the feeding damage in pepper leaves shows that thrips ingests the contents of multiple cells per probe.     

Spontaneous activity of olfactory bulb neurons in awake rabbits, with some observations on the effects of pentobarbital anaesthesia

A specially adapted microelectrode driver device has been used to record the spontaneous activity of neurons in the olfactory bulb of awake rabbits. Several parameters of this activity were studied in 78 neurons of conscious animals. A second experiment was performed to investigate anaesthetic-induced modifications of the spike discharge initially recorded in awake animals. 1. In unanaesthetized animals, the interspike interval distribution of all cells was found to be stable over short as well as long periods of time. 2. A periodical change in firing probability, correlated with respiratory activity, was observed in a high percentage of neurons. During inspiration, the discharge was markedly increased ("well synchronized" neurons, n = 2), slightly increased ("poorly synchronized" neurons, n = 15); or unchanged ("not synchronized" neurons, n = 8). 3. The passage from the awake to the anaesthetized state resulted in more regular cell activity with sudden changes from one steady firing level to another, without affecting the cell classification. As anaesthesia wore off, the cell units recovered the characteristic discharge pattern initially observed.     

Multiday Recordings from Olfactory Bulb Neurons in Awake Freely Moving Rats: Spatially and Temporally Organized Variability in Odorant Response Properties

Chronic single-unit recordings were obtained from the mitral celllayer of the olfactory bulbs of awake freely moving rats placed in anodorant stream. Over periods up to five days, 618 recordings from 186single neurons were obtained. Responses of individual neurons werefound to be quite variable over time, although this variability wasbelow chance and was not incremental. The responses of nearbyneurons were more similar than expected by chance but less similarthan individual neurons recorded at different times. However,responses of spatially well-separated neurons were more differentthan chance over short time periods. During rapid sniffing,single-unit responses became more variable, and the spatialorganization of responses became less apparent. These results suggestthat neuronal responses in the olfactory bulb are generally quitevariable over time, with this variability increasing during periodsof rapid sniffing. These results are interpreted in the context of adistributed, centrally modulated model of olfactoryprocessing.