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.     

Annotated regions of significance of SELDI-TOF-MS spectra for detecting protein biomarkers

Peak detection is a key step in the analysis of SELDI-TOF-MS spectra, but the current default method has low specificity and poor peak annotation. To improve data quality, scientists still have to validate the identified peaks visually, a tedious and time-consuming process, especially for large data sets. Hence, there is a genuine need for methods that minimize manual validation. We have previously reported a multi-spectral signal detection method, called RS for 'region of significance', with improved specificity. Here we extend it to include a peak quantification algorithm based on annotated regions of significance (ARS). For each spectral region flagged as significant by RS, we first identify a dominant spectrum for determining the number of peaks and the m/z region of these peaks. From each m/z region of peaks, a peak template is extracted from all spectra via the principal component analysis. Finally, with the template, we estimate the amplitude and location of the peak in each spectrum with the least-squares method and refine the estimation of the amplitude via the mixture model.We have evaluated the ARS algorithm on patient samples from a clinical study. Comparison with the standard method shows that ARS (i) inherits the superior specificity of RS, and (ii) gives more accurate peak annotations than the standard method. In conclusion, we find that ARS alleviates the main problems in the preprocessing of SELDI-TOF spectra. The R-package ProSpect that implements ARS is freely available for academic use at http://www.meb.ki.se/ yudpaw.     

Automatic step-detection algorithm for analysis of sarcomere dynamics

Motile systems exhibit a stepwise nature, seen most prominently in muscle contraction. A novel algorithm has been developed that detects steps automatically in sarcomere-length change data and computes their size. The method is based on a nonlinear filter and a step detection protocol that detects local slope values in comparison to a threshold. The algorithm was first evaluated using artificial data with various degrees of Gaussian noise. Steps were faithfully detected even with significant noise. With actual data, the algorithm detected 2.7 nm steps and integer multiples thereof. The results confirm a quantal 2.7 nm step-size reported earlier. As stepwise phenomena become increasingly evident, automatic step-detection algorithms become increasingly useful since the limiting factor is almost always noise. The algorithm presented here offers a versatile and accurate tool that should be useful not only within muscle contraction and motility fields, but in fields which quantal phenomena play a role.     

Automatic step-detection algorithm for analysis of sarcomere dynamics

Motile systems exhibit a stepwise nature, seen most prominently in muscle contraction. A novel algorithm has been developed that detects steps automatically in sarcomere-length change data and computes their size. The method is based on a nonlinear filter and a step detection protocol that detects local slope values in comparison to a threshold. The algorithm was first evaluated using artificial data with various degrees of Gaussian noise. Steps were faithfully detected even with significant noise. With actual data, the algorithm detected 2.7 nm steps and integer multiples thereof. The results confirm a quantal 2.7 nm step-size reported earlier. As stepwise phenomena become increasingly evident, automatic step-detection algorithms become increasingly useful since the limiting factor is almost always noise. The algorithm presented here offers a versatile and accurate tool that should be useful not only within muscle contraction and motility fields, but in fields which quantal phenomena play a role.     

Detection of spontaneous synaptic events with an optimally scaled template.

Spontaneous synaptic events can be difficult to detect when their amplitudes are close to the background noise level. Here we report a sensitive new technique for automatic detection of small asynchronous events. A waveform with the time course of a typical synaptic event (a template) is slid along the current or voltage trace and optimally scaled to fit the data at each position. A detection criterion is calculated based on the optimum scaling factor and the quality of the fit. An event is detected when this criterion crosses a threshold level. The algorithm automatically compensates for changes in recording noise. The sensitivity and selectivity of the method were tested using real and simulated data, and the influence of the template parameter settings was investigated. Its performance was comparable to that obtained by visual event detection, and it was more sensitive than previously described threshold detection techniques. Under typical recording conditions, all fast synaptic events with amplitudes of at least three times the noise standard deviation (3 sigma) could be detected, as could 75% of events with amplitudes of 2 sigma. The scaled template technique is implemented within a commercial data analysis application and can be applied to many standard electrophysiological data file formats.     

A stepwise algorithm for finding minimum evolution trees

A stepwise algorithm for reconstructing minimum evolution (ME) trees from evolutionary distance data is proposed. In each step, a taxon that potentially has a neighbor (another taxon connected to it with a single interior node) is first chosen and then its true neighbor searched iteratively. For m taxa, at most (m-1)!/2 trees are examined and the tree with the minimum sum of branch lengths (S) is chosen as the final tree. This algorithm provides simple strategies for restricting the tree space searched and allows us to implement efficient ways of dynamically computing the ordinary least squares estimates of S for the topologies examined. Using computer simulation, we found that the efficiency of the ME method in recovering the correct tree is similar to that of the neighbor-joining method (Saitou and Nei 1987). A more exhaustive search is unlikely to improve the efficiency of the ME method in finding the correct tree because the correct tree is almost always included in the tree space searched with this stepwise algorithm. The new algorithm finds trees for which S values may not be significantly different from that of the ME tree if the correct tree contains very small interior branches or if the pairwise distance estimates have large sampling errors. These topologies form a set of plausible alternatives to the ME tree and can be compared with each other using statistical tests based on the minimum evolution principle. The new algorithm makes it possible to use the ME method for large data sets.      

Computer analysis of rapid eye movements

Simulation of the saccadic eye movement mechanism and, more recently, diagnosis of neuromuscular disorders associated with saccades rely on accurate recording and analysis of saccade characteristics. Traditionally, eye movements are monitored objectively by registering a transduced voltage correlate of eye position on a pen or cathode ray oscillograph. Analysis of the record obtained is tedious and often inaccurate. The advent of small digital computers with analog-to-digital capability permits more efficient recording. However, computer programs reviewed are limited to the analysis of specific saccade parameters or partly depend on manual operations. The computer program described stores the entire saccadic event of each eye between pre-defined limits including the pre- and post-saccade intervals. Preliminary operations including artifact identification, location of onset, elimination of RC decay, DC offset and amplitude scaling prepare the data for display and subsequent analysis. The program also includes a subroutine to derive the mean and standard deviation of successive saccades.     

A method for determining minimal sets of markers for the estimation of center of mass,linear and angular momentum

A new method is proposed for finding small sets of points on the body giving sufficient information for estimating the whole body center of mass (CoM), as well as the linear momenta (LM) and angular momenta (AM). In the underlying model each point (whose trajectory is tracked by a marker) is a point mass: Hence the body is represented by a simple system of point masses. The first step is to determine the appropriate set of points and the mass of each point, which is assumed to be specific for the movement performed. The distribution of the mass to each marker is determined from training data for which the true (or reference) trajectories of the CoM, LM or AM are known. This leads to a quadratic optimization problem with inequality constraints. The use of the method is demonstrated on data from discus throw. Results indicate reasonably small errors, considering the magnitude of other error sources, in CoM position (average magnitude of estimation error 1–2 cm), and moderate errors in AM (13–20% of peak value).     

Statistical analysis of mediator release from the cyto-neural junction of the posterior canal of the labyrinth isolated from the frog]

The transmitter release mechanism was investigated at the cyto-neural junction of the frog labyrinth posterior canal. Low frequency (less than 100/s) non overlapping EPSPs were intracellularly recorded both at rest and during inhibitory mechanical stimulation of the canal (2-8 deg/s2). Recordings were obtained: in control solution; in the presence of increased external Ca2+ (9 mM); in Ca-free EGTA (5 mM) solution and during electrical activation at 50 Hz of the posterior canal inhibitory efferent system. Individual synaptic potentials were digitized and their peak amplitudes, their time integrals as well as the time intervals between them were evaluated. The time intervals proved to be exponentially distributed, suggesting a random EPSP occurrence. The analytical reconstruction of the EPSP waveform indicated that a gamma- function fitted reasonably well both the single and averaged events. As regards the averaged event, despite the scatter in the values of the gamma-function exponential factor (range 1.1-2.2), in the EPSP time-to-peak (0.6-1.2 ms) and peak amplitude (0.9-2.7 mV) displayed by the units, no significant differences were observed in the same fibre between control and test conditions. Moreover, the event peak amplitude distribution represented by cumulative plots or amplitude histograms was fitted by a lognormal function. The distributions obtained for the same unit in control solution proved to be not significantly different from those successively obtained under test conditions. The unimodal and continuous EPSP distributions, together with the unvarying characteristics of the single events, strongly suggest that the observed potentials are true mEPSPs due to the release of single quanta of transmitter.(ABSTRACT TRUNCATED AT 250 WORDS)     

Identifying mutation regions for closely related individuals without a known pedigree

ABSTRACT: BACKGROUND: Linkage analysis is the rst step in the search for a disease gene. Linkage studies have facilitated the identication of several hundred human genes that can harbor mutations leading to a disease phenotype. In this paper, we study a very important case, where the sampled individuals are closely related, but the pedigree is not given. This situation happens very often when the individuals share a common ancestor 6 or more generations ago. To our knowledge, no algorithm can give good results for this case. RESULTS: To solve this problem, we rst developed some heuristic algorithms for haplotype inference without any given pedigree. We propose a model using the parsimony principle that can be viewed as an extension of the model rst proposed by Dan Guseld. Our heuristic algorithm uses Clark's inference rule to infer haplotype segments. CONCLUSIONS: We ran our program both on the simulated data and a set of real data from the phase II HapMap database. Experiments show that our program performs well. The recall value is from 90% to 99% in various cases. This implies that the program can report more than 90% of the true mutation regions. The value of precision varies from 29% to 90%. When the precision is 29%, the size of the reported regions is three times that of the true mutation region. This is still very useful for narrowing down the range of the disease gene location. Our program can complete the computation for all the tested cases, where there are about 110,000 SNPs on a chromosome, within 20 seconds.     

Posttetanic changes in hippocampal minimal evoked potentials

Changes in the EEG induced by a single spike were recorded in the hippocampus of an unanesthetized rabbit. Summation of focal electrical activity synchronous with spontaneous single unit discharges at the symmetrical point of contralateral hemisphere revealed no stable potentials which could reflect these changes. In two cases discharges identified as activity of Shaffer's collaterals were recorded in area CA₁. Summation of post-spike changes in evoked activity recorded by the same microelectrode showed stable negative waves with an amplitute of 40–60 µV, which could have been evoked by single spikes. The curve of amplitude of the averaged evoked potentials versus near-threshold current strength stimulating the intrahippocampal pathways was not smooth in most experiments but stepwise in character. It is suggested that the minimal evoked potential corresponding to the first step (amplitude 40–80 µV) reflects a response to stimulation of one fiber. After above-threshold tetanization prolonged posttetanic potentiation of the minimal evoked potentials did not arise in CA₁ in response to stimulation of Shaffer's collaterals. Minimal evoked potentials recorded in area CA₃ in response to stimulation of the dentate fascia showed clear potentiation. The results are in agreement with the hypothesis of the synaptic localization of the mechanisms responsible for prolonged posttetanic potentiation.Brain Institute, Academy of Medical Sciences of the USSR, Moscow. Translated from Neirofiziologiya, Vol. 9, No. 2, pp. 124–134, March–April, 1977.     

Exogenous synapsin I promotes functional maturation of developing neuromuscular synapses.

We have investigated the possible role of synapsin I, a nerve terminal-specific protein, in the maturation of neuromuscular synapses in Xenopus cell cultures. Purified synapsin I was loaded into embryonic spinal neurons by injection of the protein into one of the early blastomeres of a Xenopus embryo. At synapses made by synapsin I-loaded neurons, spontaneous synaptic currents occurred with higher frequency and amplitude, and the amplitude exhibited an earlier appearance of a bell-shaped distribution. These characteristics are indicative of more mature quantal secretion. Impulse-evoked synaptic currents also showed a significant increase in amplitude. Using cell manipulation techniques, enhanced transmitter release from synapsin I-loaded neurons was shown to occur at the onset of synaptogenesis, suggesting a presynaptic developmental action of synapsin I prior to synaptic contact. Taken together, these results suggest that endogenous synapsin I may participate in the functional maturation of synapses.     

Wavelet Transform-Based De-Noising for Two-Photon Imaging of Synaptic Ca2+ Transients

Postsynaptic Ca²⁺ transients triggered by neurotransmission at excitatory synapses are a key signaling step for the induction of synaptic plasticity and are typically recorded in tissue slices using two-photon fluorescence imaging with Ca²⁺-sensitive dyes. The signals generated are small with very low peak signal/noise ratios (pSNRs) that make detailed analysis problematic. Here, we implement a wavelet-based de-noising algorithm (PURE-LET) to enhance signal/noise ratio for Ca²⁺ fluorescence transients evoked by single synaptic events under physiological conditions. Using simulated Ca²⁺ transients with defined noise levels, we analyzed the ability of the PURE-LET algorithm to retrieve the underlying signal. Fitting single Ca²⁺ transients with an exponential rise and decay model revealed a distortion of τ_rise but improved accuracy and reliability of τ_decay and peak amplitude after PURE-LET de-noising compared to raw signals. The PURE-LET de-noising algorithm also provided a ∼30-dB gain in pSNR compared to ∼16-dB pSNR gain after an optimized binomial filter. The higher pSNR provided by PURE-LET de-noising increased discrimination accuracy between successes and failures of synaptic transmission as measured by the occurrence of synaptic Ca²⁺ transients by ∼20% relative to an optimized binomial filter. Furthermore, in comparison to binomial filter, no optimization of PURE-LET de-noising was required for reducing arbitrary bias. In conclusion, the de-noising of fluorescent Ca²⁺ transients using PURE-LET enhances detection and characterization of Ca²⁺ responses at central excitatory synapses.     

The Transient Precision of Integrate and Fire Neurons: Effect of Background Activity and Noise

We study the response of an integrate and fire neuron to a randomly timed step stimulus. We calculate the latency to the first spike after stimulus onset and its jitter. Background activity, seen in most neurons, reduces latency but causes substantial jitter in the response, indicating a tradeoff between timing precision and latency. The effect of intrinsic noise and synaptic noise on this tradeoff is studied. For synaptic noise we find that, unexpectedly, jitter does not increase for larger synaptic amplitudes, instead, jitter is practically independent of synaptic amplitude. Constant intrinsic noise interacts counterintuitively with latency and jitter, and depending on the stimulus strength, noise shifts the tradeoff in either direction.     

Real-time analysis of hippocampal neural activity in the intervals between interictal spikes

Previously the electroencephalogram (EEG) was modeled as consisting of faster, smaller waves superimposed on larger, slower waves. The intent of this study is to modify the program to sample neural activity over specific intervals of time following detection of a distinct wave pattern. The use of conditional sampling is illustrated by considering wave detection following epileptic interictal spikes in the rabbit hippocampus. To create an epileptic focus, small pellets of sodium penicillin suspended in agar were placed on the rabbit hippocampus. This produced regularly recurring, spontaneous, large amplitude discharges, or interictal spikes, at the site of application. Following detection of an interictal spike, the program delayed the onset of a sample period for either 1.0 s or 6.0 s. The neural activity was then sampled for 5.1 s, and fast and slow waves were detected over the sample period. The frequency distribution of waves in four of these 5.1 s intervals was calculated. Comparison of the frequency distributions following the 1.0 s and 6.0 s delays showed no discernible differences. The data illustrate that not all types of neural activity are markedly modified by interictal spikes and suggest that hippocampal cellular populations generate similar waves 1.0 s and 6.0 s after such a spike. Moreover, this experiment illustrates adaptation of the program to sample activity over a limited period of time following detection of a specific cortical waveform.     

Analysis of fetal breathing in real time using a microprocessor

A system has been designed using an inexpensive microprocessor to analyze fetal breathing movements on a breath-by-breath basis in real time. An algorithm was developed which would recognize fetal breathing from the changes in tracheal pressure and which was capable of rejecting the artifactual changes caused by fetal or maternal movements. The tracheal pressure signal was digitized (at 51 samples/s), differentiated, and the start and peak of each breath was recognized from the zero-crossing points of the differentiated signal. Each breath was validated for size and shape according to a set of criteria incorporated into the breath recognition algorithm. On acceptance of the pressure change as a valid breath, the inspiratory time, breath amplitude, breath-to-breath interval, and inspiratory effort were calculated and stored in memory. The program was structured so that the microprocessor was able to accept new data and output summarizes of previous data concurrently. More than 95% of breaths in records contaminated with movement artifacts were recognized.     

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.     

An adaptive, object oriented strategy for base calling in DNA sequence analysis.

An algorithm has been developed for the determination of nucleotide sequence from data produced in fluorescence-based automated DNA sequencing instruments employing the four-color strategy. This algorithm takes advantage of object oriented programming techniques for modularity and extensibility. The algorithm is adaptive in that data sets from a wide variety of instruments and sequencing conditions can be used with good results. Confidence values are provided on the base calls as an estimate of accuracy. The algorithm iteratively employs confidence determinations from several different modules, each of which examines a different feature of the data for accurate peak identification. Modules within this system can be added or removed for increased performance or for application to a different task. In comparisons with commercial software, the algorithm performed well.