Axonal speeding: shaping synaptic potentials in small neurons by the axonal membrane compartment.

The role of the axonal membrane compartment in synaptic integration is usually neglected. We show here that in interneurons of the cerebellar molecular layer, where dendrites are so short that the somatodendritic domain can be considered isopotential, the axonal membrane contributes a significant part of the cell input capacitance. We examine the impact of axonal membrane on synaptic integration by cutting the axon with two-photon illumination. We find that the axonal compartment acts as a sink for signals generated at fast conductance synapses, thus increasing the initial decay rate of corresponding synaptic potentials over the value predicted from the resistance-capacitance (RC) product of the cell membrane; signals generated at slower synapses are much less affected. This mechanism sharpens the spike firing precision of fast glutamatergic inputs without resorting to multisynaptic pathways.

A simple method for getting standard error on the ratiometric calcium estimator.

The ratiometric fluorescent calcium indicator Fura-2 plays a fundamental role in the investigation of cellular calcium dynamics. Despite of its widespread use in the last 30 years, only one publication (Joucla et al., 2010)) proposed a way of obtaining confidence intervals on fitted calcium dynamic model parameters from single 'calcium transients'. Shortcomings of this approach are its requirement for a '3 wavelengths' protocol (excitation at 340 and 380 nm as usual plus at 360 nm, the isosbestic point) as well as the need for an autofluorence / background fluorescence model at each wavelength. Here, we propose a simpler method that eliminates both shortcommings:1.a precise estimation of the standard errors of the raw data is obtained first,2.the standard error of the ratiometric calcium estimator (a function of the raw data values) is derived using both the propagation of uncertainty and a Monte-Carlo method.Once meaningful standard errors for calcium estimates are available, standard errors on fitted model parameters follow directly from the use of nonlinear least-squares optimization algorithms.

Datasets for calcium dynamics comparison between the whole-cell and a ß-escin based perforated patch configuration in brain slices from adult mice.

Multiple processes shape calcium signals in neurons. The spatial and temporal dynamics of these signals are determined by various cellular parameters, including the calcium influx, calcium buffering, and calcium extrusion. The different Ca2+ handling properties can be estimated using the 'added buffer approach' [1], which is based on a single compartment model of Ca2+ buffering. To use this approach, the cell has to be loaded with a Ca2+ sensitive dye (e.g., fura-2) via the patch pipette, which is usually done in the whole-cell patch clamp configuration. However, determining Ca2+ handling properties can be complex and frequently unsuccessful due to the wash-out of intracellular components (e.g., mobile Ca2+ buffers) during whole-cell patch clamp recordings. We present two Ca2+ imaging datasets from adult substantia nigra dopamine neurons where the 'added buffer approach' was either combined with the 'conventional' whole-cell configuration or with a ß-escin based perforated patch clamp configuration. These data can be used to compare the two methods or to draw comparisons with the Ca2+ handling properties of other neuron types. Further details and an in-depth analysis of the new combination of the 'added buffer approach' with the ß-escin based perforated patch clamp configuration can be found in our companion manuscripts "Analysis of neuronal Ca2+ handling properties by combining perforated patch clamp recordings and the added buffer approach" [2] and "A Simple Method for Getting Standard Error on the Ratiometric Calcium Estimator" [3].

Analysis of neuronal Ca2+ handling properties by combining perforated patch clamp recordings and the added buffer approach.

Ca2+ functions as an important intracellular signal for a wide range of cellular processes. These processes are selectively activated by controlled spatiotemporal dynamics of the free cytosolic Ca2+. Intracellular Ca2+ dynamics are regulated by numerous cellular parameters. Here, we established a new way to determine neuronal Ca2+ handling properties by combining the 'added buffer' approach [1] with perforated patch-clamp recordings [2]. Since the added buffer approach typically employs the standard whole-cell configuration for concentration-controlled Ca2+ indicator loading, it only allows for the reliable estimation of the immobile fraction of intracellular Ca2+ buffers. Furthermore, crucial components of intracellular signaling pathways are being washed out during prolonged whole-cell recordings, leading to cellular deterioration. By combining the added buffer approach with perforated patch-clamp recordings, these issues are circumvented, allowing the precise quantification of the cellular Ca2+ handling properties, including immobile as well as mobile Ca2+ buffers.

Face Processing in Developmental Prosopagnosia: Altered Neural Representations in the Fusiform Face Area.

Rationale: Face expertise is a pivotal social skill. Developmental prosopagnosia (DP), i.e., the inability to recognize faces without a history of brain damage, affects about 2% of the general population, and is a renowned model system of the face-processing network. Within this network, the right Fusiform Face Area (FFA), is particularly involved in face identity processing and may therefore be a key element in DP. Neural representations within the FFA have been examined with Representational Similarity Analysis (RSA), a data-analytical framework in which multi-unit measures of brain activity are assessed with correlation analysis. Objectives: Our study intended to scrutinize modifications of FFA-activation during face encoding and maintenance based on RSA. Methods: Thirteen participants with DP (23-70 years) and 12 healthy control subjects (19-62 years) participated in a functional MRI study, including morphological MRI, a functional FFA-localizer and a modified Sternberg paradigm probing face memory encoding and maintenance. Memory maintenance of one, two, or four faces represented low, medium, and high memory load. We examined conventional activation differences in response to working memory load and applied RSA to compute individual correlation-matrices on the voxel level. Group correlation-matrices were compared via Donsker's random walk analysis. Results: On the functional level, increased memory load entailed both a higher absolute FFA-activation level and a higher degree of correlation between activated voxels. Both aspects were deficient in DP. Interestingly, control participants showed a homogeneous degree of correlation for successful trials during the experiment. In DP-participants, correlation levels between FFA-voxels were significantly lower and were less sustained during the experiment. In behavioral terms, DP-participants performed poorer and had longer reaction times in relation to DP-severity. Furthermore, correlation levels were negatively correlated with reaction times for the most demanding high load condition. Conclusion: We suggest that participants with DP fail to generate robust and maintained neural representations in the FFA during face encoding and maintenance, in line with poorer task performance and prolonged reaction times. In DP, alterations of neural coding in the FFA might therefore explain curtailing in working memory and contribute to impaired long-term memory and mental imagery.

Quantitative estimation of calcium dynamics from ratiometric measurements: a direct, nonratioing method.

Measuring variations of intracellular free calcium concentration through the changes in fluorescence of a calcium-sensitive dye is a ubiquitous technique in neuroscience. Despite its popularity, confidence intervals (CIs) on the estimated parameters of calcium dynamics models are seldom given. To address this issue, we have developed a two-stage model for ratiometric measurements obtained with a charge-coupled device (CCD) camera. Its first element embeds a parametric calcium dynamics model into a fluorescence intensity model and its second element probabilistically describes the fluorescence measurements by a CCD camera. Using Monte Carlo simulations, we first show that the classical ratiometric transformation gives reliable CIs for time constants only and not baseline calcium concentration nor influx. We then introduce a direct method, which consists of fitting directly and simultaneously the fluorescence transients at both wavelengths, without any data ratioing. This approach uses a probabilistic description of the camera, leading to the construction of meaningful CIs for the calcium parameters. Moreover, using approaches inspired by constrained linear regression, we can take into account the finite precision on calibrated parameters (such as the dye dissociation constant in the cell). These key features are illustrated on simulated data using Monte Carlo simulations. Moreover, we illustrate the strength of the direct method on experimental recordings from insect olfactory interneurons. In particular, we show how to handle a time-dependent buffer concentration, thereby considerably improving our goodness of fit. The direct method was implemented in the open-source software R and is freely distributed in the CalciOMatic package.

Transfer functions linking neural calcium to single voxel functional ultrasound signal.

Functional ultrasound imaging (fUS) is an emerging technique that detects changes of cerebral blood volume triggered by brain activation. Here, we investigate the extent to which fUS faithfully reports local neuronal activation by combining fUS and two-photon microscopy (2PM) in a co-registered single voxel brain volume. Using a machine-learning approach, we compute and validate transfer functions between dendritic calcium signals of specific neurons and vascular signals measured at both microscopic (2PM) and mesoscopic (fUS) levels. We find that transfer functions are robust across a wide range of stimulation paradigms and animals, and reveal a second vascular component of neurovascular coupling upon very strong stimulation. We propose that transfer functions can be considered as reliable quantitative reporters to follow neurovascular coupling dynamics.

SIMONE: a realistic neural network simulator to reproduce MEA-based recordings.

Contemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution. We validated our tool by simulating data recorded from the first olfactory relay of an insect. Different key aspects make this tool suitable for testing the robustness and accuracy of neural signal processing algorithms (such as the detection, alignment, and classification of spikes). For instance, most of the parameters can be defined by a probabilistic distribution, then tens of simulations may be obtained from the same scenario. This is especially useful when validating the robustness of the processing algorithm. Moreover, the number of active cells and the exact firing activity of each one of them is perfectly known, which provides an easy way to test accuracy.

Energy imbalance alters Ca2+ handling and excitability of POMC neurons.

Satiety-signaling, pro-opiomelanocortin (POMC)-expressing neurons in the arcuate nucleus of the hypothalamus play a pivotal role in the regulation of energy homeostasis. Recent studies reported altered mitochondrial dynamics and decreased mitochondria- endoplasmic reticulum contacts in POMC neurons during diet-induced obesity. Since mitochondria play a crucial role in Ca2+ signaling, we investigated whether obesity alters Ca2+ handling of these neurons in mice. In diet-induced obesity, cellular Ca2+ handling properties including mitochondrial Ca2+ uptake capacity are impaired, and an increased resting level of free intracellular Ca2+ is accompanied by a marked decrease in neuronal excitability. Experimentally increasing or decreasing intracellular Ca2+ concentrations reproduced electrophysiological properties observed in diet-induced obesity. Taken together, we provide the first direct evidence for a diet-dependent deterioration of Ca2+ homeostasis in POMC neurons during obesity development resulting in impaired function of these critical energy homeostasis-regulating neurons.

Efficient spike-sorting of multi-state neurons using inter-spike intervals information.

We demonstrate the efficacy of a new spike-sorting method based on a Markov chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique in its capability to estimate and make use of the firing statistics as well as the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple discharge states, giving rise to multi-modal inter-spike interval (ISI) histograms and to correlations between successive ISIs. The amplitude of the spikes generated by a PC in an "active" state decreases, a feature typical of many neurons from both vertebrates and invertebrates. These two features constitute a major and recurrent problem for all the presently available spike-sorting methods. We first show that a hidden Markov model with three log-normal states provides a flexible and satisfying description of the complex firing of single PCs. We then incorporate this model into our previous MCMC based spike-sorting algorithm [Pouzat C, Delescluse M, Viot P, Diebolt J. Improved spike-sorting by modeling firing statistics and burst-dependent spike amplitude attenuation: a Markov chain Monte Carlo approach. J Neurophysiol 2004;91:2910-28] and test this new algorithm on multi-unit recordings of bursting PCs. We show that our method successfully classifies the bursty spike trains fired by PCs by using an independent single unit recording from a patch-clamp pipette.

Using noise signature to optimize spike-sorting and to assess neuronal classification quality.

We have developed a simple and expandable procedure for classification and validation of extracellular data based on a probabilistic model of data generation. This approach relies on an empirical characterization of the recording noise. We first use this noise characterization to optimize the clustering of recorded events into putative neurons. As a second step, we use the noise model again to assess the quality of each cluster by comparing the within-cluster variability to that of the noise. This second step can be performed independently of the clustering algorithm used, and it provides the user with quantitative as well as visual tests of the quality of the classification.

Estimating background-subtracted fluorescence transients in calcium imaging experiments: a quantitative approach.

Calcium imaging has become a routine technique in neuroscience for subcellular to network level investigations. The fast progresses in the development of new indicators and imaging techniques call for dedicated reliable analysis methods. In particular, efficient and quantitative background fluorescence subtraction routines would be beneficial to most of the calcium imaging research field. A background-subtracted fluorescence transients estimation method that does not require any independent background measurement is therefore developed. This method is based on a fluorescence model fitted to single-trial data using a classical nonlinear regression approach. The model includes an appropriate probabilistic description of the acquisition system's noise leading to accurate confidence intervals on all quantities of interest (background fluorescence, normalized background-subtracted fluorescence time course) when background fluorescence is homogeneous. An automatic procedure detecting background inhomogeneities inside the region of interest is also developed and is shown to be efficient on simulated data. The implementation and performances of the proposed method on experimental recordings from the mouse hypothalamus are presented in details. This method, which applies to both single-cell and bulk-stained tissues recordings, should help improving the statistical comparison of fluorescence calcium signals between experiments and studies.

Making neurophysiological data analysis reproducible: why and how?

Reproducible data analysis is an approach aiming at complementing classical printed scientific articles with everything required to independently reproduce the results they present. "Everything" covers here: the data, the computer codes and a precise description of how the code was applied to the data. A brief history of this approach is presented first, starting with what economists have been calling replication since the early eighties to end with what is now called reproducible research in computational data analysis oriented fields like statistics and signal processing. Since efficient tools are instrumental for a routine implementation of these approaches, a description of some of the available ones is presented next. A toy example demonstrates then the use of two open source software programs for reproducible data analysis: the "Sweave family" and the org-mode of emacs. The former is bound to R while the latter can be used with R, Matlab, Python and many more "generalist" data processing software. Both solutions can be used with Unix-like, Windows and Mac families of operating systems. It is argued that neuroscientists could communicate much more efficiently their results by adopting the reproducible research paradigm from their lab books all the way to their articles, thesis and books.

Towards reliable spike-train recordings from thousands of neurons with multielectrodes.

The new generation of silicon-based multielectrodes comprising hundreds or more electrode contacts offers unprecedented possibilities for simultaneous recordings of spike trains from thousands of neurons. Such data will not only be invaluable for finding out how neural networks in the brain work, but will likely be important also for neural prosthesis applications. This opportunity can only be realized if efficient, accurate and validated methods for automatic spike sorting are provided. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-sorting algorithms.

Automatic spike train analysis and report generation. An implementation with R, R2HTML and STAR.

Multi-electrode arrays (MEA) allow experimentalists to record extracellularly from many neurons simultaneously for long durations. They therefore often require that the data analyst spends a considerable amount of time first sorting the spikes, then doing again and again the same basic analysis on the different spike trains isolated from the raw data. This spike train analysis also often generates a considerable amount of figures, mainly diagnostic plots, that need to be stored (and/or printed) and organized for efficient subsequent use. The analysis of our data recorded from the first olfactory relay of an insect, the cockroach Periplaneta americana, has led us to settle on such "routine" spike train analysis procedures: one applied to spontaneous activity recordings, the other used with recordings where an olfactory stimulation was repetitively applied. We have developed a group of functions implementing a mixture of common and original procedures and producing graphical or numerical outputs. These functions can be run in batch mode and do moreover produce an organized report of their results in an HTML file. A R package: Spike Train Analysis with R (STAR) makes these functions readily available to the neurophysiologists community. Like R, STAR is open source and free. We believe that our basic analysis procedures are of general interest but they can also be very easily modified to suit user specific needs.

Differences of Ca2+ handling properties in identified central olfactory neurons of the antennal lobe.

Information processing in neurons depends on highly localized Ca2+ signals. The spatial and temporal dynamics of these signals are determined by a variety of cellular parameters including the calcium influx, calcium buffering and calcium extrusion. Our long-term goal is to better understand how intracellular Ca2+ dynamics are controlled and contribute to information processing in defined interneurons of the insect olfactory system. The latter has served as an excellent model to study general mechanisms of olfaction. Using patch-clamp recordings and fast optical imaging in combination with the 'added buffer approach', we analyzed the Ca2+ handling properties of different identified neuron types in Periplaneta americana's olfactory system. Our focus was on two types of local interneurons (LNs) with significant differences in intrinsic electrophysiological properties: (1) spiking LNs that generate 'normal' Na+ driven action potentials and (2) non-spiking LNs that do not express voltage-activated Na+ channels. We found that the distinct electrophysiological properties from different types of central olfactory interneurons are strongly correlated with their cell specific calcium handling properties: non-spiking LNs, in which Ca2+ is the only cation that enters the cell to contribute to membrane depolarization, had the highest endogenous Ca2+ binding ratio and Ca2+ extrusion rate.

Improved spike-sorting by modeling firing statistics and burst-dependent spike amplitude attenuation: a Markov chain Monte Carlo approach.

Spike-sorting techniques attempt to classify a series of noisy electrical waveforms according to the identity of the neurons that generated them. Existing techniques perform this classification ignoring several properties of actual neurons that can ultimately improve classification performance. In this study, we propose a more realistic spike train generation model. It incorporates both a description of "nontrivial" (i.e., non-Poisson) neuronal discharge statistics and a description of spike waveform dynamics (e.g., the events amplitude decays for short interspike intervals). We show that this spike train generation model is analogous to a one-dimensional Potts spin-glass model. We can therefore tailor to our particular case the computational methods that have been developed in fields where Potts models are extensively used, including statistical physics and image restoration. These methods are based on the construction of a Markov chain in the space of model parameters and spike train configurations, where a configuration is defined by specifying a neuron of origin for each spike. This Markov chain is built such that its unique stationary density is the posterior density of model parameters and configurations given the observed data. A Monte Carlo simulation of the Markov chain is then used to estimate the posterior density. We illustrate the way to build the transition matrix of the Markov chain with a simple, but realistic, model for data generation. We use simulated data to illustrate the performance of the method and to show that this approach can easily cope with neurons firing doublets of spikes and/or generating spikes with highly dynamic waveforms. The method cannot automatically find the "correct" number of neurons in the data. User input is required for this important problem and we illustrate how this can be done. We finally discuss further developments of the method.