Novel use of matched filtering for synaptic event detection and extraction

Efficient and dependable methods for detection and measurement of synaptic events are important for studies of synaptic physiology and neuronal circuit connectivity. As the published methods with detection algorithms based upon amplitude thresholding and fixed or scaled template comparisons are of limited utility for detection of signals with variable amplitudes and superimposed events that have complex waveforms, previous techniques are not applicable for detection of evoked synaptic events in photostimulation and other similar experimental situations. Here we report on a novel technique that combines the design of a bank of approximate matched filters with the detection and estimation theory to automatically detect and extract photostimluation-evoked excitatory postsynaptic currents (EPSCs) from individually recorded neurons in cortical circuit mapping experiments. The sensitivity and specificity of the method were evaluated on both simulated and experimental data, with its performance comparable to that of visual event detection performed by human operators. This new technique was applied to quantify and compare the EPSCs obtained from excitatory pyramidal cells and fast-spiking interneurons. In addition, our technique has been further applied to the detection and analysis of inhibitory postsynaptic current (IPSC) responses. Given the general purpose of our matched filtering and signal recognition algorithms, we expect that our technique can be appropriately modified and applied to detect and extract other types of electrophysiological and optical imaging signals.     

A simple exploratory algorithm for the accurate and fast detection of spontaneous synaptic events

We have developed a program for the fast and accurate detection of spontaneous synaptic events. The algorithm identifies each event of which the slope and amplitude which meet criteria. The significant feature of this algorithm is its stepwise and exploratory search for the onset and the peak points. During the first step, the program employing the algorithm makes a rough estimate of the candidate for a synaptic event, and determines a 'temporary' onset data point. The next step is the detection of the true onset data point and 'temporary' peak data point, which probably exist several points after the temporary onset data point. The third step is a backward search to detect the true peak data point. The final step is to check whether the amplitude of the detected event exceeds the threshold. This stepwise and shuttlewise search allows for the accurate detection of the peak points. Using this program, we succeeded in detecting an increased frequency and amplitude of spontaneous excitatory postsynaptic currents in chick cerebral neurons following the application of 12-O-tetradecanoyl-phorbol-13-acetate (TPA). In addition, we demonstrated that the program employing the algorithm was able to be used for the detection of extracellular action potentials.     

A Deconvolution-Based Method with High Sensitivity and Temporal Resolution for Detection of Spontaneous Synaptic Currents In?Vitro and?In?Vivo

Spontaneous postsynaptic currents (PSCs) provide key information about the mechanisms of synaptic transmission and the activity modes of neuronal networks. However, detecting spontaneous PSCs in vitro and in vivo has been challenging, because of the small amplitude, the variable kinetics, and the undefined time of generation of these events. Here, we describe a, to our knowledge, new method for detecting spontaneous synaptic events by deconvolution, using a template that approximates the average time course of spontaneous PSCs. A recorded PSC trace is deconvolved from the template, resulting in a series of delta-like functions. The maxima of these delta-like events are reliably detected, revealing the precise onset times of the spontaneous PSCs. Among all detection methods, the deconvolution-based method has a unique temporal resolution, allowing the detection of individual events in high-frequency bursts. Furthermore, the deconvolution-based method has a high amplitude resolution, because deconvolution can substantially increase the signal/noise ratio. When tested against previously published methods using experimental data, the deconvolution-based method was superior for spontaneous PSCs recorded in vivo. Using the high-resolution deconvolution-based detection algorithm, we show that the frequency of spontaneous excitatory postsynaptic currents in dentate gyrus granule cells is 4.5 times higher in vivo than in vitro.     

The effects of synaptic noise on measurements of evoked excitatory postsynaptic response amplitudes.

Spontaneously occurring synaptic events (synaptic noise) recorded intracellularly are usually assumed to be independent of evoked postsynaptic responses and to contaminate measures of postsynaptic response amplitude in a roughly Gaussian manner. Here we derive analytically the expected noise distribution for excitatory synaptic noise and investigate its effects on amplitude histograms. We propose that some fraction of this excitatory noise is initiated at the same release sites that contribute to the evoked synaptic event and develop an analytical model of the interaction between this fraction of the noise and the evoked postsynaptic response amplitude. Recording intracellularly with sharp microelectrodes in the in vitro hippocampal slice preparation, we find that excitatory synaptic noise accounts for up to 70% of the intracellular recording noise, when inhibition is blocked pharmacologically. Up to 20% of this noise shows a significant correlation with the evoked event amplitude, and the behavior of this component of the noise is consistent with a model which assumes that each release site experiences a refractory period of approximately 60 ms after release. In contrast with classical models of quantal variance, our models predict that excitatory synaptic noise can cause the apparent variance of successive peaks in an excitatory synaptic amplitude histogram to decrease from left to right, and in some cases to be less than the variance of the measured noise.     

应用小波变换检测细胞显微荧光图像中的囊泡

文章提出了一种用小波变换来检测生物荧光图像中囊泡的方法。作者用à trous小波对图像进行小波变换,然后求出每层系数的中值绝对偏差σ,并用t=kσ/0.67作为阈值对每层系数进行门限滤波,然后通过提取小波变换系数来重构图像。通过设计实验与常用的“rolling ball”算法对比,发现小波变换算法在低信噪比的情况下,具有更好的灵敏度;对于形状大小不同的信号,具有更好的稳定性;而且对于信号的细节信息具有更好的保真性。     

Waveform Similarity Analysis: A Simple Template Comparing Approach for Detecting and Quantifying Noisy Evoked Compound Action Potentials

Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-to-noise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies.     

Automated detection of elementary calcium release events using the á trous wavelet transform

We developed an algorithm for the automated detection and analysis of elementary Ca2+ release events (ECRE) based on the two-dimensional nondecimated wavelet transform. The transform is computed with the "à trous" algorithm using the cubic B-spline as the basis function and yields a multiresolution analysis of the image. This transform allows for highly efficient noise reduction while preserving signal amplitudes. ECRE detection is performed at the wavelet levels, thus using the whole spectral information contained in the image. The algorithm was tested on synthetic data at different noise levels as well as on experimental data of ECRE. The noise dependence of the statistical properties of the algorithm (detection sensitivity and reliability) was determined from synthetic data and detection parameters were selected to optimize the detection of experimental ECRE. The wavelet-based method shows considerably higher detection sensitivity and less false-positive counts than previously employed methods. It allows a more efficient detection of elementary Ca2+ release events than conventional methods, in particular in the presence of elevated background noise levels. The subsequent analysis of the morphological parameters of ECRE is reliably reproduced by the analysis procedure that is applied to the median filtered raw data. Testing the algorithm more rigorously showed that event parameter histograms (amplitude, rise time, full duration at half-maximum, and full width at half-maximum) were faithfully extracted from synthetic, "in-focus" and "out-of-focus" line scan sparks. Most importantly, ECRE obtained with laser scanning confocal microscopy of chemically skinned mammalian skeletal muscle fibers could be analyzed automatically to reproducibly establish event parameter histograms. In summary, our method provides a new valuable tool for highly reliable automated detection of ECRE in muscle but can also be adapted to other preparations.     

Properties of synaptic transmission at the neuromuscular junction of the squid, Loligo opalescens

1. Spontaneous and evoked synaptic activity were recorded from the muscles of squid fin and mantle. These spontaneous synaptic potentials were large (up to 30 mV) and pleomorphic. Their amplitudes were not normally distributed, nor did they appear to be clustered in integral multiples of some "unit" event size. 2. Electrical stimulation of the nerve resulted in muscle twitches when the bath calcium concentration was a third normal or higher. The frequency of spontaneous synaptic events was unaffected by low calcium. 3. The large size of spontaneous events may mean that the synchronized release of only a few such "quanta" are sufficient to cause muscle action potentials and contraction. 4. The shapes of spontaneous events correlated poorly with their amplitudes, which is consistent with release from multiple synaptic sites with distinct properties.     

The power of two-dimensional dwell-time analysis for model discrimination, temporal resolution, multichannel analysis and level detection

Two-dimensional (2D) dwell-time analysis of time series of single-channel patch-clamp current was improved by employing a Hinkley detector for jump detection, introducing a genetic fit algorithm, replacing maximum likelihood by a least square criterion, averaging over a field of 9 or 25 bins in the 2D plane and normalizing per measuring time, not per events. Using simulated time series for the generation of the "theoretical" 2D histograms from assumed Markov models enabled the incorporation of the measured filter response and noise. The effects of these improvements were tested with respect to the temporal resolution, accuracy of the determination of the rate constants of the Markov model, sensitivity to noise and requirement of open time and length of the time series. The 2D fit was better than the classical hidden Markov model (HMM) fit in all tested fields. The temporal resolution of the two most efficient algorithms, the 2D fit and the subsequent HMM/beta fit, enabled the determination of rate constants 10 times faster than the corner frequency of the low-pass filter. The 2D fit was much less sensitive to noise. The requirement of computing time is a problem of the 2D fit (100 times that of the HMM fit) but can now be handled by personal computers. The studies revealed a fringe benefit of 2D analysis: it can reveal the "true" single-channel current when the filter has reduced the apparent current level by averaging over undetected fast gating.     

Statistical models of synaptic transmission evaluated using the expectation-maximization algorithm.

Amplitude fluctuations of evoked synaptic responses can be used to extract information on the probabilities of release at the active sites, and on the amplitudes of the synaptic responses generated by transmission at each active site. The parameters that describe this process must be obtained from an incomplete data set represented by the probability density of the evoked synaptic response. In this paper, the equations required to calculate these parameters using the Expectation-Maximization algorithm and the maximum likelihood criterion have been derived for a variety of statistical models of synaptic transmission. These models are ones where the probabilities associated with the different discrete amplitudes in the evoked responses are a) unconstrained, b) binomial, and c) compound binomial. The discrete amplitudes may be separated by equal (quantal) or unequal amounts, with or without quantal variance. Alternative models have been considered where the variance associated with the discrete amplitudes is sufficiently large such that no quantal amplitudes can be detected. These models involve the sum of a normal distribution (to represent failures) and a unimodal distribution (to represent the evoked responses). The implementation of the algorithm is described in each case, and its accuracy and convergence have been demonstrated.     

Models of retinal signal processing at high luminances

Summary In psychophysics the relation between the threshold luminance increment and the background luminance for visual increment detection, is known to become linear for high luminances. This so called Weber-law means that at high luminances the threshold is caused by the detection apparatus, and not by the quantum noise of the stimulus itself. A simple machine implementing the Weber-law is discussed. This machine obviates the need for an internal noise source (or dark light). It is the combined result of an apparatus that reduces the output event rate by causing the quantum to spike ratio to follow the input intensity, and a detection criterion that needs a constant number of extra output events to generate a positive response. The dynamic properties of the machine are described by the solutions of a simple though nonlinear differential equation. The supra threshold properties prove to mimic several of the responses known to occur at ganglion cell level in the cat's retina. A tentative model of an on-center receptive field is presented, and results obtained by simulating this model on a digital cumputer are compared with some recent electrophysiological findings. Finally a possible extension of the model to a full on-center/off-surround receptive field model is discussed.The authors are indebted to the Netherlands Organisation for the Advancement of Pure Research (Z.W.O.) for financial support of a part of this investigation.We thank W. A. Aarnink for invaluable help in debugging and running the programs involved in this work.     

The Power of Two-Dimensional Dwell-Time Analysis for Model Discrimination, Temporal Resolution, Multichannel Analysis and Level Detection

Two-dimensional (2D) dwell-time analysis of time series of single-channel patch-clamp current was improved by employing a Hinkley detector for jump detection, introducing a genetic fit algorithm, replacing maximum likelihood by a least square criterion, averaging over a field of 9 or 25 bins in the 2D plane and normalizing per measuring time, not per events. Using simulated time series for the generation of the “theoretical” 2D histograms from assumed Markov models enabled the incorporation of the measured filter response and noise. The effects of these improvements were tested with respect to the temporal resolution, accuracy of the determination of the rate constants of the Markov model, sensitivity to noise and requirement of open time and length of the time series. The 2D fit was better than the classical hidden Markov model (HMM) fit in all tested fields. The temporal resolution of the two most efficient algorithms, the 2D fit and the subsequent HMM/beta fit, enabled the determination of rate constants 10 times faster than the corner frequency of the low-pass filter. The 2D fit was much less sensitive to noise. The requirement of computing time is a problem of the 2D fit (100 times that of the HMM fit) but can now be handled by personal computers. The studies revealed a fringe benefit of 2D analysis: it can reveal the “true” single-channel current when the filter has reduced the apparent current level by averaging over undetected fast gating.     

Parameter-free binarization and skeletonization of fiber networks from confocal image stacks

We present a method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from three-dimensional (3D) image stacks recorded with a confocal microscope. The method is based on a template matching algorithm that simultaneously performs a binarization and skeletonization of the network. The size and intensity pattern of the template is automatically adapted to the input data so that the method is scale invariant and generic. Furthermore, the template matching threshold is iteratively optimized to ensure that the final skeletonized network obeys a universal property of voxelized random line networks, namely, solid-phase voxels have most likely three solid-phase neighbors in a 3 x 3 x 3 neighborhood. This optimization criterion makes our method free of user-defined parameters and the output exceptionally robust against imaging noise.     

A multistate model for bivariate interval-censored failure time data

SUMMARY: Interval-censored life-history data arise when the events of interest are only detectable at periodic assessments. When interest lies in the occurrence of two such events, bivariate-interval censored event time data are obtained. We describe how to fit a four-state Markov model useful for characterizing the association between two interval-censored event times when the assessment times for the two events may be generated by different inspection processes. The approach treats the two events symmetrically and enables one to fit multiplicative intensity models that give estimates of covariate effects as well as relative risks characterizing the association between the two events. An expectation-maximization (EM) algorithm is described for estimation in which the maximization step can be carried out with standard software. The method is illustrated by application to data from a trial of HIV patients where the events are the onset of viral shedding in the blood and urine among individuals infected with cytomegalovirus.     

Voltage-sensitive dye recording of action potentials and synaptic potentials from sympathetic microcultures.

Given the appropriate multicell electrophysiological techniques, small networks of cultured neurons (microcultures) are well suited to long-term studies of synaptic plasticity. To this end, we have developed an apparatus for optical recording from cultured vertebrate neurons using voltage-sensitive fluorescent dyes (Chien, C.-B., and J. Pine. 1991. J. Neurosci. Methods. 38:93-105). We evaluate here the usefulness of this technique for recording action potentials and synaptic potentials in microcultures of neurons from the rat superior cervical ganglion (SCG). After extensive dye screening and optimization of conditions, we chose the styryl dye RH423, which gave fast linear fluorescence changes of approximately 1%/100 mV for typical recordings. The root mean square noise of the apparatus (limited by shot noise) was typically 0.03%, equivalent to 3 mV of membrane potential. Illumination for at least 100 flashes of 100 ms each caused no noticeable photodynamic damage. Our results show that voltage-sensitive dyes can be used to record from microcultures of vertebrate neurons with high sensitivity. Dye signals were detected from both cell bodies and neurites. Signals from presumptive dendrites showed hyperpolarizations and action potentials simultaneous with those in the cell body, while those from presumptive axons showed delayed propagating action potentials. Subthreshold synaptic potentials in the cell body were occasionally detectable optically; however, they were usually masked by signals from axons passing through the same pixel. This is due to the complex anatomy of SCG microcultures, which have many crisscrossing neurites that often pass over cell bodies. Given a simpler microculture system with fewer neurites, it should be possible to use dye recording to routinely measure subthreshold synaptic strengths.     

Aptamer-Based Sensitive Detection of Target Molecules via RT-PCR Signal Amplification

In the efforts to explore an aptamer-based approach for target sensing and detection with higher sensitivity and specificity, instead of directly labeling aptamer with fluorophores, we proposed a new strategy by attaching a polymerase chain reaction (PCR) template to an oligonucleotide aptamer selected by systematic evolution of ligands by exponential enrichment (SELEX), so that after aptamer target binding, the template moiety serves as the PCR template in real-time quantitative PCR (RT-PCR), and therefore, the binding event can be reported by the following RT-PCR signals. Using the subtractive SELEX method, the oligonucleotide aptamers specific for the Fc fragment of mouse IgG were selected and subjected to coupling with the PCR dsDNA template by using overlap and the asymmetric extension PCR method. The target binding affinity of the PCR template tethered aptamer has been proven by electrophoretic mobility shift assay (EMSA), and further template tethered aptamer mediated real-time quantitative PCR (A-PCR) was conducted to validate the application for such a template tethered aptamer to be a sensitive probe for IgG detection. The results show that the protocols of A-PCR can detect 10-fold serial dilutions of the target, demonstrating a new mechanism to convert aptamer target binding events to amplified RT-PCR signal, and the feasibility of the PCR template tethered aptamer as a facile, specific, and sensitive target probing and detection is established. This new approach also has potential applications in multiple parallel target detection and analysis in a wide range of research fields.     

An influence of afterhyperpolarization on the pattern of motoneuronal rhythmic activity.

Motoneuronal spike trains were generated according to a simple model assuming algebraical summation of the afterhyperpolarization (AHP) curve and noisy synaptic inflow. An influence of various model parameters on the relationship between standard deviation (SD), sigma, of interspike intervals (ISIs) and their mean value, Tm, was studied. A typical sigma(Tm) relationship resembled those obtained experimentally and consisted of two parts: a short-interval part with constant sigma and a long-interval part where sigma increased linearly with increasing ISI. It is concluded that the placement of the range of transition between short- and long-interval parts of the plot depends on the properties of the motoneuron and not on those of the synaptic inflow. The break-point interval of the plot is correlated with the AHP duration but is shorter than it. Further, not only are the curves for shorter AHP durations shifted towards shorter ISIs, but they also have lower SDs at their short-interval parts.     

Hippocampal synaptic plasticity: role in spatial learning or the automatic recording of attended experience?

Allocentric spatial learning can sometimes occur in one trial. The incorporation of information into a spatial representation may, therefore, obey a one-trial correlational learning rule rather than a multi-trial error-correcting rule. It has been suggested that physiological implementation of such a rule could be mediated by N-methyl-D-aspartate (NMDA) receptor-dependent long-term potentiation (LTP) in the hippocampus, as its induction obeys a correlational type of synaptic learning rule. Support for this idea came originally from the finding that intracerebral infusion of the NMDA antagonist AP5 impairs spatial learning, but studies summarized in the first part of this paper have called it into question. First, rats previously given experience of spatial learning in a watermaze can learn a new spatial reference memory task at a normal rate despite an appreciable NMDA receptor blockade. Second, the classical phenomenon of ''blocking'' occurs in spatial learning. The latter finding implies that spatial learning can also be sensitive to an animal''s expectations about reward and so depend on more than the detection of simple spatial correlations. In this paper a new hypothesis is proposed about the function of hippocampal LTP. This hypothesis retains the idea that LTP subserves rapid one-trial memory, but abandons the notion that it serves any specific role in the geometric aspects of spatial learning. It is suggested that LTP participates in the automatic recording of attended experience'': a subsystem of episodic memory in which events are temporarily remembered in association with the contexts in which they occur. An automatic correlational form of synaptic plasticity is ideally suited to the online registration of context event associations. In support, it is reported that the ability of rats to remember the most recent place they have visited in a familiar environment is exquisitely sensitive to AP5 in a delay-dependent manner. Moreover, new studies of the lasting persistence of NMDA-dependent LTP, known to require protein synthesis, point to intracellular mechanisms that enable transient synaptic changes to be stabilized if they occur in close temporal proximity to important events. This new property of hippocampal LTP is a desirable characteristic of an event memory system.     

Site frequency spectra from genomic SNP surveys

Genomic survey data now permit an unprecedented level of sensitivity in the detection of departures from canonical evolutionary models, including expansions in population size and selective sweeps. Here, we examine the effects of seemingly subtle differences among sampling distributions on goodness of fit analyses of site frequency spectra constructed from single nucleotide polymorphisms. Conditioning on the observation of exactly two alleles in a random sample results in a site frequency spectrum that is independent of the scaled rate of neutral substitution (θ). Other sampling distributions, including conditioning on a single mutational event in the sample genealogy or randomly selecting a single mutation from a genealogy with multiple mutations, have distinct site frequency spectra that show highly significant departures from the predictions of the biallelic model. Some aspects of data filtering may contribute to significant departures of site frequency spectra from expectation, apart from any violation of the standard neutral model.