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PubMed2004

Explanatory limitations of the HKB model: incentives for a two-tiered model of rhythmic interlimb coordination.

The HKB model for rhythmic interlimb coordination has highlighted the importance of coordinative stability and loss of stability, and introduced, with this focus, a new set of explanatory constructs. However, the phenomenological character of both parts of this model (i.e., the potential and the associated system of coupled oscillators) precludes an understanding of how the observed stability characteristics are related to more specific (e.g., biomechanical and neurophysiological) aspects of the movement system. A two-tiered model (involving a distinction between 'neural' and 'effector' dynamics) is discussed that offers handles for addressing such underpinnings of the identified coordination dynamics. The promise of the model in this regard is illustrated by two recent studies showing how explicit accounts of the effector dynamics may help disclose why (and how) particular properties of the peripheral system affect the overall coordination dynamics.

Attention↗
PubMed2004

Internal models for bi-manual tasks.

Co-ordinated bi-manual actions form the basis for many everyday motor skills. In this review, the internal model approach to the problem of bi-manual co-ordination is presented. Bi-manual coordinative tasks are often regarded as a hallmark of complex action. They are often associated with object manipulation, whether the holding of a single object between the two hands or holding an object in each hand. However, the task of movement and control is deceptively difficult even when we execute an action with a single hand without holding an object. The simplest voluntary action requires the problems of co-ordination, timing and interaction between neural, muscular and skeletal structures to be overcome. When we are making a movement whilst holding an object, a further requirement is that an internal model is able to predict the dynamics of the object that is being held as well as the dynamics of the motor system. There has been extensive work examining the formation of internal models when acting in novel environments. The majority of studies examine uni-lateral learning of a task generally to the participant's dominant hand. However, many everyday motor tasks are bi-manual, and the existing findings regarding the learning of internal models in uni-manual tasks and their subsequent generalization highlights the complexities that must underlie the formation of bi-manual tasks. Our ability to perform bi-manual tasks raises interesting questions about how internal models are specified for co-ordinative actions, and also for how the motor system learns to represent the properties of objects.

Attention↗
PubMed2006

Delusions: A suitable case for imaging?

This review is intended to outline the need/opportunities for imaging research in the area of delusions. In particular, delusions of misidentification are offered as possible examples of how both spatial and temporal brain imaging may throw light upon the theoretical, parallel processes of identification and emotional arousal occurring when a familiar face is encountered. Other types of Delusional Misidentifications are also briefly explored. The review then turns to related phenomena, including the ways imaging may help elucidate different types of covert face recognition; and also further explain the distinctive (but not entirely independent) processes underlying face, voice and object recognition. Throughout the review the aim is to emphasise the potential value to cognitive neuropsychiatry of good imaging techniques.

Brain↗
PubMed2006

fMRI bold signal analysis using a novel nonparametric statistical method.

We present in this article a novel analytical method that enables the application of nonparametric rank-order statistics to fMRI data analysis, since it takes the omnipresent serial correlations (temporal autocorrelations) properly into account. Comparative simulations, using the common General Linear Model and the permutation test, confirm the validity and usefulness of our approach. Our simulations, which are performed with both synthetic and real fMRI data, show that our method requires significantly less computation time than permutation-based methods, while offering the same order of robustness and returning more information about the evoked response when combined with/compared to the results obtained with the common General Lineal Model approach.

Algorithms↗
PubMed2003

Variance-mean analysis: a simple and reliable approach for investigating synaptic transmission and modulation.

The mechanisms underlying synaptic plasticity can be investigated by analyzing synaptic amplitude fluctuations before and after a synaptic modulation. However, many older fluctuation analysis techniques rely on models of synaptic transmission that incorporate unrealistic simplifying assumptions or have too many free parameters. As a result, these techniques have sometimes produced counterintuitive or contradictory results. In contrast, the variance-mean (V-M) technique requires fewer assumptions and is more robust than previous approaches. It achieves these improvements by focusing on two key parameters of synaptic transmission, the average probability that a vesicle is released from a synaptic terminal following a presynaptic stimulus (Pav), and the average amplitude of the postsynaptic response to a vesicle of transmitter (Qav). To apply V-M analysis, a fluctuating postsynaptic current (PSC) is recorded at several different extracellular Ca2+ or Cd2+ concentrations. The variance of the PSC amplitude is plotted against the mean amplitude at each concentration, forming a parabola. The degree of parabolic curvature estimates Pav, and the limiting slope under low release conditions estimates Qav. The shape of the V-M parabola changes in characteristic ways following each of the three standard forms of synaptic modulation: a change in Qav (postsynaptic), a change in Pav (presynaptic), or a change in the number of terminals (N). The approach does not require specialized software, and can even be implemented as a purely graphical technique. V-M analysis has been used to investigate the site of expression of long-term potentiation and the mechanisms underlying paired-pulse depression. This report presents a detailed mathematical development of the technique, and explores the limiting conditions under which it can confidently be applied. V-M analysis requires fewer than 100 PSC amplitude measurements to accurately estimate Pav and Qav, and it can reliably identify whether a synaptic modulation occurs at a pre- or postsynaptic site. In contrast to other techniques, V-M analysis is largely insensitive to recording noise, nonuniform modulation and intrinsic variability of the unitary synaptic amplitude.

Algorithms↗
PubMed2003

Combining deconvolution and fluctuation analysis to determine quantal parameters and release rates.

Analysis methods are described which integrate information from fluctuation analysis with that from deconvolution. Together the two approaches allow to derive a consistent quantitative description of quantal release (both evoked, spontaneous and asynchronous) under conditions in which quantal parameters may change during a repetitively applied stimulation protocol. Specifically, our methods take into account the effects of accumulating transmitter in the synaptic cleft and postsynaptic receptor desensitization, which may develop during strong stimulation. Several ways to handle non-stationarities are described. Examples are provided for the Calyx of Held, a glutamatergic synapse, in which both the pre- and the postsynaptic compartments can be voltage-clamped.

Algorithms↗
PubMed2003

Quantal analysis based on density estimation.

When direct measurements of the quantal parameters for a synapse cannot be made, these parameters can be extracted from an analysis of the fluctuations in the evoked response at that synapse. In this article, a decision tree is described in which the ability of the data to match simple models of quantal transmission is rigorously compared with its ability to fit progressively more complex models. The Wilks statistic is the basis for this comparison. The procedure commences with optimal transformation of peak amplitude measurements into a probability density function (PDF). It then examines this PDF against various models of transmission, commencing with a multi-modal but non-quantal distribution, then to a multi-modal distribution with quantal peak separation with and without quantal variability, and, finally, the constraints of uniform and non-uniform release probabilities are imposed. These procedures are illustrated by example. A comparison is made between the relative sensitivities of the Wilks statistic and the chi2 goodness-of-fit criteria in rejecting inappropriate models at all stages in these procedures.

Analysis of Variance↗
PubMed2003

Combining principal component and spectral analyses with the method of moments in studies of quantal transmission.

This chapter considers methods for measurements of postsynaptic responses and simple approaches to the estimation of parameters of quantal release in synapses of the central nervous system of vertebrates. The use of these methods is illustrated by the analysis of single-fibre and "minimal" monosynaptic postsynaptic potentials (PSPs) or currents (PSCs) recorded from neurons of the frog spinal cord and rat hippocampus. First, we briefly discuss traditional methods of the response measurements using peak amplitudes or areas, further focusing on a novel method based on multivariate statistical techniques of the principal component analysis (PCA). This approach provides typically better signal-to-noise ratios and is able to separate two or more response components, which can arise due to activation of more than one presynaptic fibre, axon collaterals, receptor subtypes or spatially separated transmission sites. Second, spectral analysis is introduced as the method of choice to verify whether the amplitude fluctuations of the postsynaptic responses have a quantal nature and to obtain estimations of the "basic" quantal parameters, i.e. the quantal size (Q) and mean quantal content (m), without introducing assumptions on release statistics. Third, we show how the method of moments could be applied in the framework of the Poisson and binomial models to estimate the basic quantal parameters and parameters p and n, which reflect the release probability and maximum number of quanta released (or the number of effective release sites), respectively. Fourth, we show that the analysis of the moments can also be instrumental to reveal non-uniformity of release probabilities and compare how several competing models of neurotransmitter release fit to multiple experimental data sets.

Animals↗
PubMed2003

Estimation of nonuniform quantal parameters with multiple-probability fluctuation analysis: theory, application and limitations.

Synapses are a key determinant of information processing in the central nervous system. Investigation of the mechanisms underlying synaptic transmission at central synapses is complicated by the inaccessibility of synaptic contacts and the fact that their temporal dynamics are governed by multiple parameters. Multiple-probability fluctuation analysis (MPFA) is a recently developed method for estimating quantal parameters from the variance and mean amplitude of evoked steady-state synaptic responses recorded under a range of release probability conditions. This article describes the theoretical basis and the underlying assumptions of MPFA, illustrating how a simplified multinomial model can be used to estimate mean quantal parameters at synapses where quantal size and release probability are nonuniform. Interpretations of the quantal parameter estimates are discussed in relation to uniquantal and multiquantal models of transmission. Practical aspects of this method are illustrated including a new method for estimating quantal size and variability, approaches for optimising data collection, error analysis and a method for identifying multivesicular release. The advantages and limitations of investigating synaptic function with MPFA are explored and contrasted with those for traditional quantal analysis and more recent optical quantal analysis methods.

Algorithms↗
PubMed2004

Influence of high motor unit synchronization levels on non-linear and spectral variables of the surface EMG.

The aim of this study was to investigate the influence of high degrees of motor unit synchronization on surface EMG variables extracted by linear and non-linear analysis techniques. For this purpose, spectral and recurrent quantification analysis (RQA) were applied to both simulated and experimental EMG signals. Synthetic surface EMG signals were generated with a model of volume conductor comprising muscle, fat, and skin tissues. The synchronization was quantified by the percent of discharges of each motor unit synchronized with discharges of other motor units. The simulated signals presented degrees of synchronization in the range 0-80% (10% increments) and three mean values of motor unit conduction velocity distribution (3, 4 and 5 m/s). Experimental signals were collected from the first dorsal interosseous muscle of five patients with Parkinson disease during 10s of rest and 10s of isometric voluntary contraction at 50% of the maximal force. Mean power spectral frequency (MNF) and percent of determinism (%DET) of the surface EMG were computed from the simulated and experimental signals. In the simulated signals, %DET was linearly related to the level of synchronization in the entire range considered while MNF was sensitive to changes in synchronization in a smaller range (0-20%), outside which it levelled off. The experimental results indicated that %DET was significantly higher in the resting condition (with presence of tremor; mean +/- S.E., 85.4 +/- 0.8%) than during the voluntary contraction (which partly suppressed tremor; 60.0 +/- 2.3%; P < 0.01). On the contrary, MNF did not depend on the condition (114.3 +/- 1.5 Hz and 118.0 +/- 0.8 Hz for the resting and voluntary contraction, respectively), confirming the simulation results. Overall, these results indicated that linear and non-linear analyses of the surface EMG may have different sensitivities to the underlying physiological mechanisms in specific conditions, thus their joint use provides a more complete view of the muscle status than spectral analysis only.

Action Potentials↗
PubMed2004

Analysis of the time-varying energy of brain responses to an oddball paradigm using short-term smoothed Wigner-Ville distribution.

Cognitive brain responses to external stimuli, as measured by event related potentials (ERPs), have been analyzed from a variety of perspectives to investigate brain dynamics. Here, the brain responses of healthy subjects to auditory oddball paradigms, standard and deviant stimuli, recorded on an Fz electrode site were studied using a short-term version of the smoothed Wigner-Ville distribution (STSW) method. A smoothing kernel was designed to preserve the auto energy of the signal with maximum time and frequency resolutions. Analysis was conducted mainly on the time-frequency distributions (TFDs) of sweeps recorded during successive trials including the TFD of averaged single sweeps as the evoked time-frequency (ETF) brain response and the average of TFDs of single sweeps as the time-frequency (TF) brain response. Also the power entropy and the phase angles of the signal at frequency f and time t locked to the stimulus onset were studied across single trials as the TF power-locked and the TF phase-locked brain responses, respectively. TFDs represented in this way demonstrated the ERP spectro-temporal characteristics from multiple perspectives. The time-varying energy of the individual components manifested interesting TF structures in the form of amplitude modulated (AM) and frequency modulated (FM) energy bursts. The TF power-locked and phase-locked brain responses provoked ERP energies in a manner modulated by cognitive functions, an observation requiring further investigation. These results may lead to a better understanding of integrative brain dynamics.

Algorithms↗
PubMed2005

Entropy analysis of neuronal spike train synchrony.

Although vector strength (VS) and the Rayleigh tests are widely used to quantify neuronal firing synchrony to cyclic events, their use is valid only for singly peaked, unimodal distributions. In this report, we propose a new method to quantify synchrony, applicable to both unimodal and multimodal distributions. We also propose a statistical test to examine temporal structure under a null hypothesis of no synchrony.

Action Potentials↗
PubMed2005

Starting and stopping mechanisms of absence epileptic seizures are revealed by hazard functions.

We show that the hazard function provides useful information about the starting and the stopping mechanisms of absence epileptic seizures. The hazard function quantifies changes in the probability that an event (respectively, the starting and the stopping of a seizure) occurs in some small time interval given that it has not occurred yet. It informs us about changes in the concentration of endogenous substances that modulate the neuronal signalling properties of (parts of) the brain. In a pharmacological experiment, we used the hazard function to study the effect of a GABA-transaminase inhibitor (vigabatrin) on the starting and the stopping mechanisms of absence epileptic seizures in a genetic rat model of absence epilepsy (the WAG/Rij rat). This experiment showed that a high GABA level changed the stopping mechanism of the absence epileptic seizures, creating much better conditions for very long seizures to develop. With respect to the starting mechanism, it was found that both with a high and a low GABA level, there was evidence for a recovery mechanism that decreases the probability that a new seizure starts. Initially, this probability is larger with a high GABA level, but gradually it converges to the same constant baseline probability as in the condition with a low GABA level.

Algorithms↗
PubMed2005

Errors in the estimation of the variance: implications for multiple-probability fluctuation analysis.

Synapses play a crucial role in information processing in the brain. Amplitude fluctuations of synaptic responses can be used to extract information about the mechanisms underlying synaptic transmission and its modulation. In particular, multiple-probability fluctuation analysis can be used to estimate the number of functional release sites, the mean probability of release and the amplitude of the mean quantal response from fits of the relationship between the variance and mean amplitude of postsynaptic responses, recorded at different probabilities. To determine these quantal parameters, calculate their uncertainties and the goodness-of-fit of the model, it is important to weight the contribution of each data point in the fitting procedure. We therefore investigated the errors associated with measuring the variance by determining the best estimators of the variance of the variance and have used simulations of synaptic transmission to test their accuracy and reliability under different experimental conditions. For central synapses, which generally have a low number of release sites, the amplitude distribution of synaptic responses is not normal, thus the use of a theoretical variance of the variance based on the normal assumption is not a good approximation. However, appropriate estimators can be derived for the population and for limited sample sizes using a more general expression that involves higher moments and introducing unbiased estimators based on the h-statistics. Our results are likely to be relevant for various applications of fluctuation analysis when few channels or release sites are present.

Animals↗
PubMed2006

Statistically robust detection of spontaneous, non-stereotypical neural signals.

Neural signals of interest are often temporally spontaneous and non-stereotypical in waveform. Detecting such signals is difficult, since one cannot use time-locking or simple template-matching techniques. We have sought a statistical method for automatically estimating the baseline in these conditions, and subsequently detecting the occurrence of neural signals. One could consider the signals as outliers in the distribution of neural activity and thus separate them from the baseline with median-based techniques. However, we found that baseline estimators that rely on the median are problematic. They introduce progressively greater estimation errors as the neural signal's duration, amplitude or frequency increases. Therefore, we tested several mode-based algorithms, taking advantage of the most probable state of the neural activity being the baseline. We found that certain mode-based algorithms perform baseline estimation well, with low susceptibility to changes in event duration, amplitude or frequency. Once the baseline is properly established, its median absolute deviation (MAD) can be determined. One can then use it to detect spontaneous signals robustly as outliers from the noise distribution. We also demonstrate how the choice of detection threshold in terms of MADs can be used to bias against false positives, without creating too many false negatives or vice versa.

Action Potentials↗
PubMed2006

Current-source density estimation based on inversion of electrostatic forward solution: effects of finite extent of neuronal activity and conductivity discontinuities.

A new method for estimation of current-source density (CSD) from local field potentials is presented. This inverse CSD (iCSD) method is based on explicit inversion of the electrostatic forward solution and can be applied to data from multielectrode arrays with various geometries. Here, the method is applied to linear-array (laminar) electrode data. Three iCSD methods are considered: the CSD is assumed to have cylindrical symmetry and be (i) localized in infinitely thin discs, (ii) step-wise constant or (iii) continuous and smoothly varying (using cubic splines) in the vertical direction. For spatially confined CSD distributions the standard CSD method, involving a discrete double derivative, is seen in model calculations to give significant estimation errors when the lateral source dimension is comparable to the size of a cortical column (less than approximately 1 mm). Further, discontinuities in the extracellular conductivity are seen to potentially give sizable errors for even wider source distributions. The iCSD methods are seen to give excellent estimates when the correct lateral source dimension and spatial distribution of conductivity are incorporated. To illustrate the application to real data, iCSD estimates of stimulus-evoked responses measured with laminar electrodes in the rat somatosensory (barrel) cortex are compared to estimates from the standard CSD method.

Algorithms↗
PubMed2006

Measuring spike pattern reliability with the Lempel-Ziv-distance.

Spike train distance measures serve two purposes: to measure neuronal firing reliability, and to provide a metric with which spike trains can be classified. We introduce a novel spike train distance based on the Lempel-Ziv complexity that does not require the choice of arbitrary analysis parameters, is easy to implement, and computationally cheap. We determine firing reliability in vivo by calculating the deviation of the mean distance of spike trains obtained from multiple presentations of an identical stimulus from a Poisson reference. Using both the Lempel-Ziv-distance (LZ-distance) and a distance focussing on coincident firing, the pattern and timing reliability of neuronal firing is determined for spike data obtained along the visual information processing pathway of macaque monkey (LGN, simple and complex cells of V1, and area MT). In combination with the sequential superparamagnetic clustering algorithm, we show that the LZ-distance groups together spike trains with similar but not necessarily synchronized firing patterns. For both applications, we show how the LZ-distance gives additional insights, as it adds a new perspective on the problem of firing reliability determination and allows neuron classifications in cases, where other distance measures fail.

Algorithms↗
PubMed2006

A tool for synthesizing spike trains with realistic interference.

Spike detection and spike sorting techniques are often difficult to assess because of the lack of ground truth data (i.e., spike timings for each neuron). This is particularly important for in vitro recordings where the signal to noise ratio is poor (as is the case for multi-electrode arrays at the bottom of a cell culture dish). We present an analysis of the transmission of intracellular signals from neurons to an extracellular electrode, and a set of MATLAB functions based on this analysis. These produce realistic signals from neighboring neurons as well as interference from more distant neurons, and Gaussian noise. They thus generate realistic but controllable synthetic signals (for which the ground truth is known) for assessing spike detection and spike sorting techniques. They can also be used to generate realistic (non-Gaussian) background noise. We use signals generated in this way to compare two automated spike-sorting techniques. The software is available freely on the web.

Algorithms↗