Cognitive neurophysiology: Beyond averaging

Averaging of repeated responses to sensory stimuli is the standard approach in cognitive electrophysiology. This procedure can give rise to inappropriate interpretations in some situations, because two factors contribute to the average ERP responses: the amplitude of the responses during the individual experimental trials, and the concentration of the phases (phase-locking) across responses. Larger poststimulus single-trial amplitudes compared to prestimulus baseline are thought to correspond to a stimulus-related increase of postsynaptic potentials or/and activation of an increased amount of neural assemblies. But the functional interpretation of an enhanced inter-trial phase-locking is unclear. BOLD responses are probably related to single-trial EEG amplitudes, but not to the phase concentration across trials. Therefore, separation of amplitude and phase contributions is indispensable to avoid misinterpretations and to gain a deeper understanding of the relation between event-related EEG and fMRI.

Correcting for the sampling bias problem in spike train information measures

Information Theory enables the quantification of how much information a neuronal response carries about external stimuli and is hence a natural analytic framework for studying neural coding. The main difficulty in its practical application to spike train analysis is that estimates of neuronal information from experimental data are prone to a systematic error (called "bias"). This bias is an inevitable consequence of the limited number of stimulus-response samples that it is possible to record in a real experiment. In this paper, we first explain the origin and the implications of the bias problem in spike train analysis. We then review and evaluate some recent general-purpose methods to correct for sampling bias: the Panzeri-Treves, Quadratic Extrapolation, Best Universal Bound, Nemenman-Shafee-Bialek procedures, and a recently proposed shuffling bias reduction procedure. Finally, we make practical recommendations for the accurate computation of information from spike trains. Our main recommendation is to estimate information using the shuffling bias reduction procedure in combination with one of the other four general purpose bias reduction procedures mentioned in the preceding text. This provides information estimates with acceptable variance and which are unbiased even when the number of trials per stimulus is as small as the number of possible discrete neuronal responses.

Single-trial multiwavelet coherence in application to neurophysiological time series

A method of single-trial coherence analysis is presented, through the application of continuous multiwavelets. Multiwavelets allow the construction of spectra and bivariate statistics such as coherence within single trials. Spectral estimates are made consistent through optimal time-frequency localization and smoothing. The use of multiwavelets is considered along with an alternative single-trial method prevalent in the literature, with the focus being on statistical, interpretive and computational aspects. The multiwavelet approach is shown to possess many desirable properties, including optimal conditioning, statistical descriptions and computational efficiency. The methods are then applied to bivariate surrogate and neurophysiological data for calibration and comparative study. Neurophysiological data were recorded intracellularly from two spinal motoneurones innervating the posterior biceps muscle during fictive locomotion in the decerebrated cat.

Gene therapy of metachromatic leukodystrophy reverses neurological damage and deficits in mice

Metachromatic leukodystrophy (MLD) is a demyelinating lysosomal storage disorder for which new treatments are urgently needed. We previously showed that transplantation of gene-corrected hematopoietic stem progenitor cells (HSPCs) in presymptomatic myeloablated MLD mice prevented disease manifestations. Here we show that HSC gene therapy can reverse neurological deficits and neuropathological damage in affected mice, thus correcting an overt neurological disease. The efficacy of gene therapy was dependent on and proportional to arylsulfatase A (ARSA) overexpression in the microglia progeny of transplanted HSPCs. We demonstrate a widespread enzyme distribution from these cells through the CNS and a robust cross-correction of neurons and glia in vivo. Conversely, a peripheral source of enzyme, established by transplanting ARSA-overexpressing hepatocytes from transgenic donors, failed to effectively deliver the enzyme to the CNS. These results indicate that the recruitment of gene-modified, enzyme-overexpressing microglia makes the enzyme bioavailable to the brain and makes therapeutic efficacy and disease correction attainable. Overall, our data provide a strong rationale for implementing HSPC gene therapy in MLD patients.

A distributed computing system for multivariate time series analyses of multichannel neurophysiological data

We present a client-server application for the distributed multivariate analysis of time series using standard PCs. We here concentrate on analyses of multichannel EEG/MEG data, but our method can easily be adapted to other time series. Due to the rapid development of new analysis techniques, the focus in the design of our application was not only on computational performance, but also on high flexibility and expandability of both the client and the server programs. For this purpose, the communication between the server and the clients as well as the building of the computational tasks has been realized via the Extensible Markup Language (XML). Running our newly developed method in an asynchronous distributed environment with random availability of remote and heterogeneous resources, we tested the system's performance for a number of different univariate and bivariate analysis techniques. Results indicate that for most of the currently available analysis techniques, calculations can be performed in real time, which, in principle, allows on-line analyses at relatively low cost.

Testing for directed influences among neural signals using partial directed coherence

One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity. When applying multivariate time series analysis techniques to neural signals, detection of directed relationships, which can be described in terms of Granger-causality, is of particular interest. Partial directed coherence has been introduced for a frequency domain analysis of linear Granger-causality based on modeling the underlying dynamics by vector autoregressive processes. We discuss the statistical properties of estimates for partial directed coherence and propose a significance level for testing for nonzero partial directed coherence at a given frequency. The performance of this test is illustrated by means of linear and non-linear model systems and in an application to electroencephalography and electromyography data recorded from a patient suffering from essential tremor.

Testing equality of two functions using BARS

This article presents two methods of testing the hypothesis of equality of two functions H(0):f(1)(t)=f(2)(t) for all t, in a generalized non-parametric regression framework using a recently developed generalized non-parametric regression method called Bayesian adaptive regression splines (BARS). Of particular interest is the special case of testing equality of two Poisson process intensity functions lambda(1) (t)=lambda(2) (t), which arises frequently in neurophysiological applications. The first method uses Bayes factors, and the second method uses a modified Hotelling T(2) test. Both methods are applied to the analysis of 347 motor cortical neurons and, for certain choices of test criteria, the two methods lead to the same conclusions for all but 7 neurons. A small simulation study of power indicates that the Bayes factor can be somewhat more powerful in small samples. The T(2)-type test should be useful in screening large number of neurons for condition-related activity, while the Bayes factor will be especially helpful in assessing evidence in favour of H(0).

Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements

Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of "tetrode" (4-wire electrode)-recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of cellular synchrony. The lowest possible separation error rates were estimated by a search for the best ellipsoidal cluster shape. Human operator performance was significantly below the estimated optimum. Investigation of error distributions indicated that suboptimal performance was caused by inability of the operators to mark cluster boundaries accurately in a high-dimensional feature space. We therefore hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates. Implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0-8%.

Methods for spherical data analysis and visualization

A systematic analysis of the localization of objects in extra-personal space requires a three-dimensional method of documenting location. In auditory localization studies the location of a sound source is often reduced to a directional vector with constant magnitude with respect to the observer, data being plotted on a unit sphere with the observer at the origin. This is an attractive form of data representation as the relevant spherical statistical and graphical methods are well described. In this paper we collect together a set of spherical plotting and statistical procedures to visualize and summarize these data. We describe methods for visualizing auditory localization data without assuming that the principal components of the data are aligned with the coordinate system. As a means of comparing experimental techniques and having a common set of data for the verification of spherical statistics, the software (implemented in MATLAB) and database described in this paper have been placed in the public domain. Although originally intended for the visualization and summarization of auditory psychophysical data, these routines are sufficiently general to be applied in other situations involving spherical data.

Change-point analysis of neuron spike train data

In many medical experiments, data are collected across time, over a number of similar trials, or over a number of experimental units. As is the case of neuron spike train studies, these data may be in the form of counts of events per unit of time. These counts may be correlated within each trial. It is often of interest to know if the introduction of an intervention, such as the application of a stimulus, affects the distribution of the counts over the course of the experiment. In such investigations, each trial generates a sequence of data that may or may not contain a change in distribution at some point in time. Each sequence of integer counts can be viewed as arising from a Poisson process and are therefore independently distributed or as an integer-valued time series that allows for correlations between these counts. The main aim of this paper is to show how the ensemble of sample paths may be used to make inference about the distribution of the instantaneous times of change in a given population. This will be accomplished using a Bayesian hierarchical model for these change-points in time. A bonus of these models is they also allow for inference about the probability of a change in each unit and the magnitude of the effects, if any. The use of such change-point models on integer-valued time series is illustrated on neuron spike train data, although the methods can be applied to other situations where integer-valued processes arise.

An advanced demultiplexing system for physiological stimulation

A CMOS very large scale integration (VLSI) chip has been designed and built to implement a scheme developed for multiplexing/demultiplexing the signals required to operate an intracortical stimulating electrode array. Because the use of radio telemetry in a proposed system utilizing this chip may impose limits upon the rate of data transmission to the chip, the scheme described herein was used to reduce the amount of digital information which must be sent to control a large quantity (up to several hundred) of stimulating electrodes. By incorporating multiple current sources on chip, many channels may be stimulated simultaneously. By incorporating on-chip timers, control over pulse timing is assigned to the chip, reducing by up to fourfold the amount of control data which must be sent. By incorporating on-chip RAM, information associated with the desired stimulus amplitude and pulse timing can be stored on chip. In this manner, it is necessary to send control information to the chip only when the information changes, rather than at the stimulus repeat rate for each channel. This further reduces the data rate by a factor of five to ten times or more. The architecture described here, implemented as an eight-channel stimulator, is scalable to a 625-channel stimulator while keeping data transmission rates under 2 Mbps.

SWEEPS: a program for the acquisition and analysis of neurophysiological data

I describe SWEEPS, a program for the acquisition and analysis of neurophysiological data written with LabWindows/CVI. SWEEPS includes many features of general interest to neurobiologists, such as digital filtering, window discrimination, and the construction of peristimulus time histograms. As the program is written using LabWindows/CVI, a C-programming system which includes routines for many data-processing and display tasks, it can be easily modified to accommodate new analysis needs as they arise.

A fast graphics printing program for neurophysiological data

A program was written in assembly language for fast graphics screen dump of neurophysiological data during and after experiments. This program takes approximately 3 s to plot a 1024 x 768 graphics image. A resident program has also been produced, which allows a screen graphics image to be printed by using a keyboard command. A Pascal-compatible object file is available to interface the program with Turbo-Pascal programs.