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International audience; In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (

Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech. The articulatory-to-acoustic mapping is performed using a deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded on a reference speaker synchronously with the produced speech signal. This DNN is then used in both offline and online modes to map the position of sensors glued on different speech articulators into acoustic parameters that are further converted into an audio signal using a vocoder. In offline mode, highly intelligible speech could be obtained as assessed by perceptual evaluation performed by 12 listeners. Then, to anticipate future BCI applications, we further assessed the real-time control of the synthesizer by both the reference speaker and new speakers, in a closed-loop paradigm using EMA data recorded in real time. A short calibration period was used to compensate for differences in sensor positions and articulatory differences between new speakers and the reference speaker. We found that real-time synthesis of vowels and consonants was possible with good intelligibility. In conclusion, these results open to future speech BCI applications using such articulatory-based speech synthesizer., Author Summary Speech Brain-Computer-Interfaces may restore communication for people with severe paralysis by decoding cortical speech activity to control in real time a speech synthesizer. Recent data indicate that the speech motor cortex houses a detailed topographical representation of the different speech articulators of the vocal tract. Its activity could thus be used in a speech BCI paradigm to control an articulatory-based speech synthesizer. To date, there has been no report of an articulatory-based speech synthesizer producing fully intelligible speech, as well as no evidence that such device could be controlled in real time by a naive subject. Here, we present an articulatory-based speech synthesizer and show that it could be controlled in real time by several subjects articulating silently to produce intelligible speech.

Conference of 2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016 ; Conference Date: 22 May 2016 Through 25 May 2016; Conference Code:123235; International audience; An innovative approach for decoding of brain signals based on Spiking Neural Networks is presented in this paper. Synapses are implemented by BEOL compatible oxide resistive RAM (OxRAM) devices providing low programming voltages (

Penetrating neural probes are considered for neuroprosthetic devices to restore sensory or motor functions of the CNS using electrical neural microstimulation. These multielectrode systems require optimal electrode configurations to allow precise and focused tissue activation. Combining a finite element model of the spinal cord and compartmentalized models of both simple and complex neuron morphologies, we evaluated the use of the “ground surface” configuration, which consists in the integration of a conductive layer on the front side of electrode shanks, for the return of the stimulation current. Compared to the classical monopolar and bipolar configurations, this strategy resulted in a focalization of both the potential field and the threshold–distance curves. The improvement in focalization was highest for lowest impedance of the ground surface. Moreover, the gain in focality was highest on the side of the shank opposite to the electrode, so that only the neurons located in front of stimulation electrode were activated. This focalizing strategy will allow the design of new microstimulation paradigms aiming at precisely targeting the CNS with complex spatio-temporal stimulation patterns, which could benefit to future stimulation-based neuroprosthesis.

We report on the fabrication and characterization of an 8 × 8 multichannel Boron Doped Diamond (BDD) ultramicro-electrode array (UMEA). The device combines both the assets of microelectrodes, resulting from conditions in mass transport from the bulk solution toward the electrode, and of BDD’s remarkable intrinsic electrochemical properties. The UMEAs were fabricated using an original approach relying on the selective growth of diamond over pre-processed 4 inches silicon substrates. The prepared UMEAs were characterized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The results demonstrated that the electrodes have exhibited a very fast electrode transfer rate (k0) up to 0.05 cm∙s−1 (in a fast redox couple) and on average, a steady state limiting current (in a 0.5 M potassium chloride aqueous solution containing 1 mM Fe(CN)64− ion at 100 mV∙s−1) of 1.8 nA. The UMEAs are targeted for electrophysiological as well as analytical applications.

The increasing number of channels offered by new Microelectrode Array (MEA) systems raises the problem of data handling and analysis in real time. The goal of the RETINE project is to develop a new MEA system, called NeuroPXI, offering 256 channels with online features for MEA data recording, processing and stimulation. This system is a complete redesign of the ASIC-based BioMEA system developed previously. NeuroPXI is based on a front-end device featuring integrated electronics handling signal amplification and current-mode stimulation, and a back-end device handling real-time data processing and 2D spatiotemporal stimulus generation. Initial tests of this system have been performed on mouse spinal cord networks in which waves of activity were successfully recorded. Further tests of NeuroPXI, performed on rats, will consist first, in recording activity waves in the newborn retina and second, in patterned retinal stimulation combined with optical cortical recordings of the primary visual cortex. Such experiments have recently been conducted using the BioMEA system with standard pulse stimuli, and will be repeated with the NeuroPXI system to include more complex 2D-pattern stimuli.

Spontaneous rhythmic activity is a ubiquitous feature of developing neural structures that has been shown to be essential for the establishment of functional CNS connectivity. However, the primordial origin of these rhythms remains unknown. Here, we describe two types of rhythmic activity in distinct parts of the developing CNS isolated ex vivo on microelectrode arrays, the expression of which was found to be strictly dependent upon the movement of the artificial CSF (aCSF) flowing over the inner wall of the ventricles or over the outer surface of the CNS. First, whole embryonic mouse hindbrain-spinal cord preparations (stages E12.5-E15.5) rhythmically expressed waves of activity originating in the hindbrain and propagating in the spinal cord. Interestingly enough, the frequency of this rhythm was completely determined by the speed of the aCSF flow. In particular, at all stages considered, hindbrain activity was abolished when the perfusion was stopped. Immature rhythmic activity was also recorded in the isolated newborn (P0-P8) mouse cortex under normal aCSF perfusion. Again, this rhythm was abolished when the perfusion flow was stopped. In both structures, this phenomenon was not due to changes in temperature, oxygen level, or pH of the bath, but to the movement itself of the aCSF. These observations challenge the so-called "spontaneous" nature of rhythmic activity in immature neural networks and suggest that the movement of CSF in the ventricles and around the brain in vivo may mechanically drive rhythmogenesis in the developing CNS.

In order to understand the dynamics of large neuronal networks, one of the challenges of nowadays neuroscience is to reach the possibility to record large ensembles of neurons simultaneously. In this context, microelectrode arrays (MEA) are gaining a rapidly growing interest. However, among several major problems, increased thermal noise becomes a limiting factor when the size of the microelectrodes becomes small and weak electrical activity has to be measured. In this work, we try to overcome this limitation by using porous microelectrode arrays. In order to combine the advantages of an overall small geometric surface with an increased active surface, standard MEAs are covered with a porous metal overlayer, obtained via the colloidal crystal templating method. Using this approach, we were able to obtain first promising results with respect to a reduction of the noise level.

International audience; The MEDINAS project aims at the fabrication of Micro-Electrode Arrays (MEA) that take advantage of a novel electrode material, namely synthetic conducting diamond, as the bio-interface material. Diamond enables the fabrication of electrodes with high biocompatibility, high electrochemical interfacing performances and long-term stability. The project aims at the development of diamond based MEAs for recording and stimulating of neuron networks for both in vitro applications (rigid substrate) as well as for in vivo retinal prostheses (flexible biocompatible substrate). The performances of this new technology were assessed as compared to standard metallic solutions. (C) 2011 Elsevier Masson SAS. All rights reserved.

Microelectrode arrays (MEAs) offer a powerful tool to both record activity and deliver electrical microstimulations to neural networks either in vitro or in vivo. Microelectronics microfabrication technologies now allow building high-density MEAs containing several hundreds of microelectrodes. However, dense arrays of 3D micro-needle electrodes, providing closer contact with the neural tissue than planar electrodes, are not achievable using conventional isotropic etching processes. Moreover, increasing the number of electrodes using conventional electronics is difficult to achieve into compact devices addressing all channels independently for simultaneous recording and stimulation. Here, we present a full modular and versatile 256-channel MEA system based on integrated electronics. First, transparent high-density arrays of 3D-shaped microelectrodes were realized by deep reactive ion etching techniques of a silicon substrate reported on glass. This approach allowed achieving high electrode aspect ratios, and different shapes of tip electrodes. Next, we developed a dedicated analog 64-channel Application Specific Integrated Circuit (ASIC) including one amplification stage and one current generator per channel, and analog output multiplexing. A full modular system, called BIOMEA™, has been designed, allowing connecting different types of MEAs (64, 128, or 256 electrodes) to different numbers of ASICs for simultaneous recording and/or stimulation on all channels. Finally, this system has been validated experimentally by recording and electrically eliciting low-amplitude spontaneous rhythmic activity (both LFPs and spikes) in the developing mouse CNS. The availability of high-density MEA systems with integrated electronics will offer new possibilities for both in vitro and in vivo studies of large neural networks.

International audience; In order to understand the dynamics of large neural networks, where information is widely distributed over thousands of cells, one of today's challenges is to successfully record the simultaneous activities of as many neurons as possible. This is made possible by using microelectrodes arrays (MEA) positioned in contact with the neural tissue. Thanks to microelectronics' microfabrication technologies, it now becomes,possible to build high-density MEA containing several hundreds of microelectrodes. However, increasing the number of electrodes using conventional electronics is difficult to achieve. Moreover, high-density devices addressing all channels independently for simultaneous recording and stimulation are not readily available. Here, we present a 256-channel in vitro MEA data acquisition system with integrated electronics allowing simultaneous recording and stimulation of neural networks for in vitro and in vivo applications. This integration is a first step towards an implantable device for a neural prosthesis. (C) 2009 Elsevier Masson SAS. All rights reserved

International audience; The electrode material is a key element in the design of long-term neural implants and neuroprostheses. To date, the ideal electrode material offering high longevity, biocompatibility, low-noise recording and high stimulation capabilities remains to be found. We show that 3D-nanostructured boron doped diamond (BDD), an innovative material consisting in a chemically stable material with a high aspect ratio structure obtained by encapsulation of a carbon nanotube template within two BDD nanolayers, allows neural cell attachment, survival and neurite extension. Further, we developed arrays of 20-μm-diameter 3D-nanostructured BDD microelectrodes for neural interfacing. These microelectrodes exhibited low impedances and low intrinsic recording noise levels. In particular, they allowed the detection of low amplitude (10–20 μV) local-field potentials, single units and multiunit bursts neural activity in both acute whole embryonic hindbrain-spinal cord preparations and long-term hippocampal cell cultures. Also, cyclic voltammetry measurements showed a wide potential window of about 3 V and a charge storage capacity of 10 mC.cm−2, showing high potentiality of this material for neural stimulation. These results demonstrate the attractiveness of 3D-nanostructured BDD as a novel material for neural interfacing, with potential applications for the design of biocompatible neural implants for the exploration and rehabilitation of the nervous system.

International audience; TRPP2 is a cationic channel expressed in plasma membrane and in sarcoplasmic reticulum. In several cell lines, TRPP2 is described as a reticulum Ca2+ leak channel but it also interacts with ryanodine and inositol 1,4,5-trisphosphate (InsP3) receptors to inhibit and increase the release of Ca2+ stores, respectively. TRPP2 is known to be expressed in vascular smooth muscle cells, however its function in Ca2+ signals remains poorly described in native cells, principally because the pharmacology is not developed. TRPP2 was expressed in cerebral arteries. Triptolide evoked Ca2+ responses in a Ca2+-free solution as well as permeabilized arteries. This Ca2+ signal was inhibited in presence of antisense oligonucleotide and siRNA directed against TRPP2 and antibody directed against the first loop of TRPP2. The partial inhibition of TRPP2 expression increased both the caffeine-evoked Ca2+ responses and in vivo contraction. It also decreased the InsP3-evoked Ca2+ responses. Finally, aging affected the regulations in which TRPP2 is engaged, whereas the triptolide-evoked Ca2+ response was not modified. Taken together, our results have shown that TRPP2 is implicated in triptolide-induced Ca2+ release from intracellular Ca2+ stores. TRPP2 functionally interacts with both ryanodine and InsP3 receptors. These interactions were not similar in adult and old mice.

International audience; This article presents a pilot study on the real-time control of an articulatory synthesizer based on deep neural network (DNN), in the context of silent speech interface. The underlying hypothesis is that a silent speaker could benefit from real-time audio feedback to regulate his/her own production. In this study, we use 3D electromagnetic-articulography (EMA) to capture speech articulation, a DNN to convert EMA to spectral trajectories in real-time, and a standard vocoder excited by white noise for audio synthesis. As shown by recent literature on silent speech, adaptation of the articulo-acoustic modeling process is needed to account for possible inconsistencies between the initial training phase and practical usage conditions. In this study, we focus on different sensor setups across sessions (for the same speaker). Model adaptation is performed by cascading another neural network to the DNN used for articulatory-to-acoustic mapping. The intelligibility of the synthetic speech signal converted in real-time is evaluated using both objective and perceptual measurements.

While source localization methods are increasingly developed to identify brain areas underlying scalp electro/magnetoencephalographic data (EEG/MEG), these methods have not yet been used to identify the sources of intracerebral signals which offer highly detailed information. Here, we adapted the minimum current estimates method to intracranial data in order to localize supratemporal sources of intracerebral auditory 1-kHz-tone-evoked potentials occurring within 100 ms after stimulus onset. After an evaluation of localization method and despite inter-subject variability, we found a common spatiotemporal pattern of activities, which involved the first Heschl's gyrus (H1) and sulcus (HS), the Planum Temporale (PT), H2/H3 when present, and the superior temporal gyrus (STG). Four time periods of activity were distinguished, corresponding to the time range of the scalp components P0, Na, Pa/Pb, and N100. The sources of the earliest components P0 (16–19 ms) and Na (20–25 ms) could be identified in the postero-medial portion of HS or H1. Then, several areas became simultaneously active after 25 ms. The Pa/Pb time range (30–50 ms) was characterized by a medio-lateral and postero-anterior propagation of activity over the supratemporal plane involving successively H1/HS, the Planum Temporale, H2/H3 when present, and the STG. Finally, we found to a large extent that the N100 (55–100 ms) involved almost the same areas as those active during the Pa/Pb complex, with a similar propagation of activities. Reconstructing scalp data from these sources on fictive EEG/MEG channels reproduced classical auditory evoked waveforms and topographies. In conclusion, the spatiotemporal pattern of activation of supratemporal auditory areas could be identified on the individual anatomy using current estimates from intracerebral data. Such detailed localization approach could also be used prior to epilepsy surgery to help identify epileptogenic foci and preserve functional cortical areas.

Spontaneous rhythmic activity is a ubiquitous phenomenon in developing neural networks and is assumed to play an important role in the elaboration of mature circuitry. Here we describe the day-by-day evolution of spontaneous activity in the embryonic mouse spinal cord and show that, at a specific developmental stage, 2 distinct rhythms coexist. On embryonic days E12.5 and E13.5, we observed a single type of regularly recurring short spike-episodes synchronized across cervical, thoracic, and lumbar levels. By E14.5, in addition to this motor rhythm, another type of spontaneous synchronous activity appeared, characterized by much longer lasting episodes separated by longer time intervals. On E15.5, these long episodes disappeared. Short episodes were less numerous and more irregular except at the cervical level where a rhythm was occasionally observed. By E16.5, this cervical rhythm became more robust, whereas the lumbar level fell almost silent. Surprisingly, at E17.5, spontaneous activity resumed at caudal levels, now characterized by numerous erratic short episodes. A striking ontogenetic feature of spontaneous activity was the occurrence of long episodes only at E14.5. Although concomitant at all levels of the spinal cord, long episodes displayed different patterns along the spinal cord, with tonic firing at the thoracic level and rhythmic discharge with occasional sequences of left/right alternation at the lumbar level. Thus at E14.5, the originally synchronized network has started to segregate into more specialized subnetworks. In conclusion, this work suggests that ongoing spontaneous rhythms do not follow a smooth evolution during maturation, but rather undergo profound changes at very specific stages.

The first electrophysiological study of the human lateral geniculate nucleus (LGN), optic radiation, striate, and extrastriate visual areas is presented in the context of presurgical evaluation of three epileptic patients (Patients 1, 2, and 3). Visual-evoked potentials to pattern reversal and face presentation were recorded with depth intracranial electrodes implanted stereotactically. For Patient 1, electrode anatomical registration, structural magnetic resonance imaging, and electrophysiological responses confirmed the location of two contacts in the geniculate body and one in the optic radiation. The first responses peaked approximately 40 milliseconds in the LGN in Patient 1 and 60 milliseconds in the V1/V2 complex in Patients 2 and 3. Moreover, steady state visual-evoked potentials evoked by the unperceived but commonly experienced video-screen flicker were recorded in the LGN, optic radiation, and V1/V2 visual areas. This study provides topographic and temporal propagation characteristics of steady state visual-evoked potentials along human visual pathways. We discuss the possible relationship between the oscillating signal recorded in subcortical and cortical areas and the electroencephalogram abnormalities observed in patients suffering from photosensitive epilepsy, particularly video-game epilepsy. The consequences of high temporal frequency visual stimuli delivered by ubiquitous video screens on epilepsy, headaches, and eyestrain must be considered.

We describe documented simultaneous intracerebral auditory evoked potentials from the auditory cortex and medial geniculate body (MGB) of a human patient. The MGB response lasted > 300 ms, with an initial negativity at 13.5 ms (N13), two positive peaks P21 and P29, and two broader negativities N50 and N200. P21 and N50 amplitudes were strongest for lowest tone frequencies, suggesting possible MGB tonotopic organization. Thalamic peaks were strongly interlaced with cortical activities recorded in Heschl’s gyri before 30 ms: N13 preceded the first cortical component by 3.5 ms, then P21 and P29 preceded and lagged, respectively, the following two cortical polarity reversals by 1.5‐2 ms. This study provides new functional data on the human MGB, and supports a more complex than simply relay-like role of the thalamus in sound perception.

The design of neuro-stimulator has to respect tissue safety conditions. Especially, electrical charge-balancing of stimuli is of major concern, and electrode drivers have to ensure correct stimulation whatever the load. This paper describes a new front-end stimulation topology based on a charge metering closed-loop. The circuit was developed on a Field-ProgrammableAnalog-Array. Characterisation shows an equivalent leakage current under 1nA and the stimulator was successfully used on a rat’s spinal-cord. I. ELECTRICAL STIMULATION IN SAFE CONDITIONS Recent researches in neuroscience show that movement functions can be restored using a new generation of neural prostheses. In [1], locomotor-like activity is reproduced by stimulating the neural pattern generators of a rats spinal cord. In general, the development of autonomous systems for rehabilitation is based on both neural recording and stimulation interfaces. A typical biphasic current stimulation waveform is shown in Fig. 1(a). At first a cathodic pulse is delivered to induce firing or inhibition of targeted cells. Correct values are chosen by electro-physiologists depending on electrode geometry and tissue properties. Then, an anodic pulse is provided in order to avoid electrochemical reactions that can damage tissues. Safe protocols for electrical stimulation have been widely addressed in the literature [2], however electrical circuits for stimulation have to take care of their parasitics and limits to ensure tissue safety. As shown in Fig. 1(a) anodic current is turned off at a fixed time, so that any error on current value or additional charge injection is not taken into account. As a result, a residual charge can remain at the end of the stimulation pattern leading to a risk of damages by accumulation. In order to limit this residual charge several methods have been studied: a first solution focuses on matching between cathodic and anodic current sources. This can be performed before fabrication by minimizing the mismatch [3], or dynamically by adding current tuning circuits [4]. However, these solutions do not consider charge injection during switching or possible voltage saturation due to the evolution of the electrode impedance. A second technique was developed using integrators [5] for charge estimation, although additional circuits are in sp in al co rd istim Cblock typical stimulator amplifiers t t istim

Brain-Computer Interfaces (BCIs) usually propose typing strategies to restore communication for paralyzed and aphasic people. A more natural way would be to use speech BCI directly controlling a speech synthesizer. Toward this goal, a prerequisite is the development a synthesizer that should i) produce intelligible speech, ii) run in real time, iii) depend on as few parameters as possible, and iv) be robust to error fluctuations on the control parameters. In this context, we describe here an articulatory-to-acoustic mapping approach based on deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded synchronously with produced speech sounds. On this corpus, the DNN-based model provided a speech synthesis quality (as assessed by automatic speech recognition and behavioral testing) comparable to a state-of-the-art Gaussian mixture model (GMM), yet showing higher robustness when noise was added to the EMA coordinates. Moreover, to envision BCI applications, this robustness was also assessed when the space covered by the 12 original articulatory parameters was reduced to 7 parameters using deep auto-encoders (DAE). Given that this method can be implemented in real time, DNN-based articulatory speech synthesis seems a good candidate for speech BCI applications. Index Terms: articulatory speech synthesis, brain computer interface (BCI), deep neural networks, deep auto-encoder, EMA, noise robustness, dimensionality reduction

The practical use of realistic models in bioelectromagnetism is limited by the time‐consuming amount of numerical calculations. We propose a method leading to much higher speed than currently available, and compatible with any kind of numerical methods (boundary elements (BEM), finite elements, finite differences). Illustrated with the BEM for EEG and MEG, it applies to ECG and MCG as well. The principle is two‐fold. First, a Lead‐Field matrix is calculated (once for all) for a grid of dipoles covering the brain volume. Second, any forward solution is interpolated from the pre‐calculated Lead‐Fields corresponding to grid dipoles near the source. Extrapolation is used for shallow sources falling outside the grid. Three interpolation techniques were tested: trilinear, second‐order Bézier (Bernstein polynomials), and 3D spline. The trilinear interpolation yielded the highest speed gain, with factors better than ×10,000 for a 9,000‐triangle BEM model. More accurate results could be obtained with the Bézier interpolation (speed gain ∼1,000), which, combined with a 8‐mm step grid, lead to intrinsic localization and orientation errors of only 0.2 mm and 0.2 degrees. Further improvements in MEG could be obtained by interpolating only the contribution of secondary currents. Cropping grids by removing shallow points lead to a much better estimation of the dipole orientation in EEG than when solving the forward problem classically, providing an efficient alternative to locally refined models. This method would show special usefulness when combining realistic models with stochastic inverse procedures (simulated annealing, genetic algorithms) requiring many forward calculations. Hum. Brain Mapping 14:48–63, 2001. © 2001 Wiley‐Liss, Inc.

This article investigates the neural network system in the leech heartbeat and develops a real-time biomimetic digital hardware using few-resource implementation for hybrid experiments. The leech heartbeat neural network is one of the most simple central pattern generators (CPG). In biology, CPG provide for rhythmic bursts of spikes and is the basis for all muscles contractions orders (heartbeat) and locomotion (walking, running….). The leech neural network system was already investigated and this CPG has been already formalized with Hodgkin-Huxley neural model (HH) that is the most complex neuron model. However, the resources needed for a neural model is proportional to its complexity. To answer to this issue, this article describes a biomimetic implementation into FPGA (Field Programmable Gate Array) of a network of 240 CPGs using a simple model (Izhikevich model) and by proposing a new synapse model: activity dependent depression synapse. The architecture of the network implementation allows working on a single computation core. This digital system works in real-time, needs few resources and has the same bursting activity behavior than complex model. To validate our implementation of this CPG, we compare it firstly with a simulation of the complex model. Then we match its activity with the pharmacological data of the activity of the rat’s spinal cord. This digital system allows future hybrid experiments and will be a great step towards hybridation between biological tissue and artificial neural network. This network of CPG could be also useful for mimic the activity of a different animal locomotion or developing hybrid experiments for neuroprosthesis development.

MEG forward problem has been solved for about 2000 dipoles placed on the brain surface using a very fine 3-layer realistic model of the head and the boundary element method (BEM). For each dipole, spherical models, one-layer realistic BEM models and coarser 3-layer realistic BEM models, were used to reconstruct the dipole. It was found that the localization bias induced by using a spherical model of the head increased from 2.5 mm in the upper part of the head to 12 mm in the lower part, on average. It was also found that, for the same computing time, a 3-layer model of the head gave on average 2 mm better localization errors than a one-layer model of the head. Orientation errors of less than 20 degrees could only be retrieved with a 3-layer realistic model. Localization and orientation errors highly depended on the dipole position in the brain.

A method has been developed to match a standard digitised brain atlas onto MR images for identification of cerebral structures in anatomical images. This method uses, first, a three-dimensional crude registration based on the proportional system of Talairach. Then, a two-dimensional refined registration is performed using a deformation function based on a set of homologous landmarks on both images (MR and atlas). Displacements vectors are computed between each corresponding landmark. These vectors are interpolated by thin-plate splines, generating an unwarping function defined on the whole image. This function can then be applied on any structure of the atlas. An evaluation of the matching procedure has been performed. First, the influence of the choice of the landmarks has been evaluated for the fine registration method. The latter has been then compared to the crude registration method considered as a classical reference method. These results show the advantages of the fine registration approach.

International audience; In neuroprostheses, the focal stimulation of the neuronal tissues is today a real challenge. These questions are particularly important in retinal prostheses to improve the quality of the images mapped onto an electrode array. It has been demonstrated that to achieve a normal vision the future generation of retinal implant must have 600 pixels with a pitch between electrodes around 50 μm. The long term in-vivo stability of electrodes in contact with the tissues remains a general and important challenge. After several days of implantation several histological studies show an inflammatory of the tissue at the interface between the electrode and tissues. Here we proposed a new approach based on retinal implants fabricated from 3D shaped diamond electrodes exhibiting both an improvement of the spatial resolution of the stimulation and a long term bio-inertness.

International audience; We report on a new approach involving the use of Boron Doped Diamond (B-NCD) to fabricate Micro Electrode Arrays (MEAs). MEAs are common electrophysiology tools enabling to probe the neuronal activity distributed over large populations of neurons or from embryonic organs [1-3]. They allow recording neuronal network activities while delivering specific electrical stimulations. Specific MEAs can also be used to build neural prosthesis or implants in order to compensate for function losses due to lesions or degeneration of part of the Central Nervous System (CNS). Here the main advantage of diamond is its wide electrochemical window in aqueous electrolytes as well as its bio-inertness.

A systematic evaluation of dipole localization accuracy using the boundary element method is presented. EEG simulations are carried out with dipoles located in the right parietal and temporal regions of the head. Uniformly meshed and locally refined head models are considered in both spherical and realistic geometries. An initial study determines the influence upon the localization accuracy of the dipole depth below the brain surface, of its orientation (radial and tangential), and of the global and local mesh densities. Simulated potential data are computed analytically in the spherical case, and numerically using a very fine (locally refined) model in the realistic case. Results in both geometries show that in order to get localization errors of about 2-4 mm, uniformly meshed models may be used for dipoles located at depths greater than 20 mm, whereas locally refined models should be used for shallower dipoles. Two other studies show how the localization accuracy depends upon the size of the local refinement area and upon the number of electrodes (19, 32, 63). Results show that a large number of electrodes brings significant improvements, especially for shallow and tangential dipoles.

A method for semi-automatically constructing realistic surface meshes of 3 head structures--scalp, skull and brain--from a stack of MR images is described. Then an evaluation is given for both spherical and realistic dipolar models, using the boundary element method (BEM). In both cases, locally refined models were considered. Two characteristic mesh parameters were defined: the global and the local mesh densities (in triangles per cm2). In spherical geometries, numerical and analytical solutions were compared, and in the realistic case, all models were compared to a highly refined one, considered as a reference. Both geometries gave comparable results. It was found that for "deep dipoles" located at more than 20-30 mm under the brain surface, meshes with a global density of 0.5 tri/cm2 gave "acceptable" results, whereas for more superficial dipoles (2-3 mmdepth20-30 mm), it was necessary to locally refine meshes near the source location up to a local density of about 5-8 tri/cm2, to get comparable results.

En France, 300 000 personnes souffrent d’un trouble de la parole et plus de 5 millions de par le monde. Ces troubles surviennent le plus souvent suite a un accident vasculaire cerebral ayant endommage le reseau cortical du langage. Cependant, des aphasies severes sont egalement observees chez des personnes ou ce reseau n’a pas ete endommage, notamment dans le cas d’un syndrome d’enfermement (« locked-in »), de maladies neurodegeneratives telles que la sclerose laterale amyotrophique ou la maladie de Parkinson, de myopathies, ou encore de comas. Dans ce cas, il est possible d’envisager de restaurer la parole par un paradigme d’interface cerveau–machine (BCI) ou les activites corticales sont decodees pour predire ce que souhaite dire la personne [3] . Dans cette perspective, nous developpons une strategie visant a enregistrer les aires motrices de la parole controlant les articulateurs du conduit vocal, et a decoder leurs activites pour controler un synthetiseur vocal articulatoire. Les differentes etapes de ce projet seront discutees, ainsi que les premiers resultats deja obtenus en termes de synthese vocale artificielle en temps reel [1] et de decodage des activites corticales des aires de la parole. Nous soulignerons egalement certaines questions ethiques associees au projet et plus largement au developpement de neuroprotheses et d’interfaces cerveau-machine en general [2] .

Microelectrode arrays (MEAs) are appealing tools to probe large neural ensembles and build neural prostheses. Microelectronics microfabrication technologies now allow building high-density MEAs containing several hundreds of microelectrodes. However, several major problems become limiting factors when the size of the microelectrodes decreases. In particular, regarding recording of neural activity, the intrinsic noise level of a microelectrode dramatically increases when the size becomes small (typically below 20-μm diameter). Here, we propose to overcome this limitation using a template-based, single-scale meso- or two-scale macro-/mesoporous modification of the microelectrodes, combining the advantages of an overall small geometric surface and an active surface increased by several orders of magnitude. For this purpose, standard platinum MEAs were covered with a highly porous platinum overlayer obtained by lyotropic liquid crystal templating possibly in combination with a microsphere templating approach. These porous coatings were mechanically more robust than Pt-black coating and avoid potential toxicity issues. They had a highly increased active surface, resulting in a noise level ∼3 times smaller than that of conventional flat electrodes. This approach can thus be used to build highly dense arrays of small-size microelectrodes for sensitive neural signal detection.

Three-dimensional electrode geometries were proposed to increase the spatial resolution in retinal prostheses aiming at restoring vision in blind patients. We report here the results from a study in which finite-element modeling was used to design and optimize three-dimensional electrode geometries. Proposed implants exhibit an array of well-like shapes containing stimulating electrodes at their bottom, while the common return grid electrode surrounds each well on the implant top surface. Extending stimulating electrodes and/or the grid return electrode on the walls of the cavities was also considered. The goal of the optimization was to find model parameters that maximize the focalization of electrical stimulation, and therefore the spatial resolution of the electrode array. The results showed that electrode geometries with a well depth of 30 µm yield a tenfold increase in selectivity compared to the planar structures of similar electrode dimensions. Electrode array prototypes were microfabricated and implanted in dystrophic rats to determine if the tissue would behave as hypothesized in the model. Histological examination showed that retinal bipolar cells integrate the electrode well, creating isolated cell clusters. The modeling analysis showed that the stimulation current is confounded within the electrode well, leading to selective electrical stimulation of the individual bipolar cell clusters and thereby to electrode arrays with higher spatial resolution.

Microelectrode arrays (MEAs) are widely used tools for recording and stimulating extracellular neuronal activity. Major limitations when decreasing electrode size in dense arrays are increased noise level and low charge injection capability. Nanostructuration of the electrode sites on MEAs presents an efficient way to overcome these problems by decreasing the impedance of the electrode/solution interface. Here, we review different techniques used to achieve this goal including template assisted electrodeposition for generating macro- and mesoporous films, immobilization of carbon nanotubes (CNTs) and deposition of conducting polymers onto microelectrodes. When tested during in vitro and in vivo measurements, nanostructured MEAs display improved sensitivity during recording of neuronal activity together with a higher efficiency in the stimulation process compared to conventional microelectrodes.

Extracellular electrical stimulation of neural networks has been widely used empirically for decades with individual electrodes. Since recently, microtechnology provides advanced systems with high-density microelectrode arrays (MEAs). Taking the most of these devices for fundamental goals or developing neural prosthesis requires a good knowledge of the mechanisms underlying electrical stimulation. Here, we review modeling approaches used to determine (1) the electric potential field created by a stimulation and (2) the response of an excitable cell to an applied field. Computation of the potential field requires solving the Poisson equation. While this can be performed analytically in simple electrode-neuron configurations, numerical models are required for realistic geometries. In these models, special care must be taken to model the potential drop at the electrode/tissue interface using appropriate boundary conditions. The neural response to the field can then be calculated using compartmentalized cell models, by solving a cable equation, the source term of which (called activating function) is proportional to the second derivative of the extracellular field along the neural arborization. Analytical and numerical solutions to this equation are first presented. Then, we discuss the use of approximated solutions to intuitively predict the neuronal response: Either the “activating function” or the “mirror estimate”, depending on the pulse duration and the cell space constant. Finally, we address the design of optimal electrode configurations allowing the selective activation of neurons near each stimulation site. This can be achieved using either multipolar configurations, or the “ground surface” configuration, which can be easily integrated in high-density MEAs. Overall, models highlighting the mechanisms of electrical microstimulation and improving stimulating devices should help understanding the influence of extracellular fields on neural elements and developing optimized neural prostheses for rehabilitation.

A major characteristic of neural networks is the complexity of their organization at various spatial scales, from microscopic local circuits to macroscopic brain-scale areas. Understanding how neural information is processed thus entails the ability to study them at multiple scales simultaneously. This is made possible using microelectrodes array (MEA) technology. Indeed, high-density MEAs provide large-scale coverage (several square millimeters) of whole neural structures combined with microscopic resolution (about 50 μm) of unit activity. Yet, current options for spatiotemporal representation of MEA-collected data remain limited. Here we present NeuroMap, a new interactive Matlab-based software for spatiotemporal mapping of MEA data. NeuroMap uses thin plate spline interpolation, which provides several assets with respect to conventional mapping methods used currently. First, any MEA design can be considered, including 2D or 3D, regular or irregular, arrangements of electrodes. Second, spline interpolation allows the estimation of activity across the tissue with local extrema not necessarily at recording sites. Finally, this interpolation approach provides a straightforward analytical estimation of the spatial Laplacian for better current sources localization. In this software, coregistration of 2D MEA data on the anatomy of the neural tissue is made possible by fine matching of anatomical data with electrode positions using rigid-deformation-based correction of anatomical pictures. Overall, NeuroMap provides substantial material for detailed spatiotemporal analysis of MEA data. The package is distributed under GNU General Public License and available at http://sites.google.com/site/neuromapsoftware.

In order to understand the dynamics of large neural networks, where information is widely distributed over thousands of cells, one of today's challenges is to successfully monitor the simultaneous activity of as many neurons as possible. This is made possible by using the Micro-Electrode Array (MEA) technology allowing neural cell culture and/or tissue slice experimentation in vitro. Thanks to development of microelectronics' technologies, a novel data acquisition system based on MEA technology has been developed, the BioMEA™. It combines the most advanced MEA biochips with integrated electronics, and a novel user-friendly software interface. To move from prototype (result of the RMNT research project NEUROCOM) to manufactured product, a number of changes have been made. Here, we present a 256-channel MEA data acquisition system with integrated electronics (BioMEA™) allowing simultaneous recording and stimulation of neural networks for in vitro and in vivo applications. This integration is a first step towards an implantable device for BCI (Brain Computer Interface) studies and neural prosthesis.

Extracellular electrical stimulation (EES) of the central nervous system (CNS) has been used empirically for decades, with both fundamental and clinical goals. Currently, microelectrode arrays (MEAs) offer new possibilities for CNS microstimulation. However, although focal CNS activation is of critical importance to achieve efficient stimulation strategies, the precise spatial extent of EES remains poorly understood. The aim of the present work is twofold. First, we validate a finite element model to compute accurately the electrical potential field generated throughout the extracellular medium by an EES delivered with MEAs. This model uses Robin boundary conditions that take into account the surface conductance of electrode/medium interfaces. Using this model, we determine how the potential field is influenced by the stimulation and ground electrode impedances, and by the electrical conductivity of the neural tissue. We confirm that current-controlled stimulations should be preferred to voltage-controlled stimulations in order to control the amplitude of the potential field. Second, we evaluate the focality of the potential field and threshold-distance curves for different electrode configurations. We propose a new configuration to improve the focality, using a ground surface surrounding all the electrodes of the array. We show that the lower the impedance of this surface, the more focal the stimulation. In conclusion, this study proposes new boundary conditions for the design of precise computational models of extracellular stimulation, and a new electrode configuration that can be easily incorporated into future MEA devices, either in vitro or in vivo, for a better spatial control of CNS microstimulation.

Achieving controlled extracellular microstimulation of the central nervous system requires understanding the membrane response of a neuron to an applied electric field. The "activating function" has been proposed as an intuitive predictor of membrane polarization during stimulation, but subsequent literature raised several limitations of this estimate. In this study, we show that, depending on the space constant lambda, the steady-state solution to the passive cable equation is theoretically well approximated by either the activating function when lambda is small, or the "mirror" image of the extracellular potential when lambda is large. Using simulations, we then explore the respective domain of both estimates as a function of lambda, stimulus duration, fiber length, and electrode-fiber distance. For realistic lambda (50-100 microm), the mirror estimate is the best predictor for either long electrode-fiber distances or short distances (20-30 microm) when stimulus durations exceed a few tens of microseconds. For intermediate distances, the mirror estimate is all the more valid that the stimulus duration is long and the fiber is short. We also illustrate that this estimate correctly predicts the steady-state membrane polarization of complex central nervous system arborizations. In conclusion, the mirror estimate can often be preferred to the activating function to intuitively predict membrane polarization during extracellular stimulation.

International audience; 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.

A transient finite-element model has been developed to simulate an extracellular action potential recording in a tissue slice by a planar microelectrode array. The thin-film approximation of the active neuron membrane allows the simulation within single finite-element software of the intracellular and extracellular potential fields. In comparison with a compartmental neuron model, it is shown that the thin-film approximation-based model is able to properly represent the neuron bioelectrical behavior in terms of transmembrane current and potential. Moreover, the model is able to simulate extracellular action potential recordings with properties similar to those observed in biological experiments. It is demonstrated that an ideal measurement system model can be used to represent the recording microelectrode, provided that the electronic recording system adapts to the electrode-tissue interface impedance. By comparing it with a point source approximated neuron, it is also shown that the neuron three-dimensional volume should be taken into account to simulate the extracellular action potential recording. Finally, the influence of the electrode size on the signal amplitude is evaluated. This parameter, together with the microelectrode noise, should be taken into account in order to optimize future microelectrode designs in terms of the signal-to-noise ratio.

A 64 channels CMOS chip dedicated to in-vitro simultaneous recording and stimulation of neurons using microelectrode arrays has been developed. It includes, for each channel, a low noise, variable gain (10, 75 or 750), 0.08 Hz-3 kHz bandwidth measurement path with unity-gain for lower frequencies to allow measurement of the electrochemical potential. A snapshot style SampleHold circuitry allows to have "images" of the 64 channels at a maximum sampling frequency of 50 kHz. Input-referred noise of the measurement path is 4.3 microV RMS integrated from 0.08 Hz to 3 kHz. To get rid of the random, slowly varying, DC offset potential that exists at the electrode-electrolyte interface, the ASIC can be supplied with floating VSS, VDD. Circuit size is 2.4 mm per 11.2 mm (0.35 microm CMOS process) and its power consumption is about 125 mW.

In order to understand the dynamics of large neural networks, where information is widely distributed over thousands of cells, one of today's challenges is to successfully record the simultaneous activities of as many neurons as possible. This is made possible by using microelectrodes arrays (MEAs) positioned in contact with the neural tissue. Thanks to microelectronics' microfabrication technologies, it now becomes possible to build high density MEAs containing several hundreds of microelectrodes. However, increasing the number of electrodes using conventional electronics is difficult to achieve. Moreover, high density devices addressing all channels independently for simultaneous recording and stimulation are not readily available. Here, we present a 256-channel in vitro MEA system with integrated electronics allowing simultaneous recording and stimulation of neural networks. Both actions are performed independently on all channels.

The first electrophysiological study of the human lateral geniculate nucleus (LGN), optic radiation, striate, and extrastriate visual areas is presented in the context of presurgical evaluation of three epileptic patients (Patients 1, 2, and 3). Visual-evoked potentials to pattern reversal and face presentation were recorded with depth intracranial electrodes implanted stereotactically. For Patient 1, electrode anatomical registration, structural magnetic resonance imaging, and electrophysiological responses confirmed the location of two contacts in the geniculate body and one in the optic radiation. The first responses peaked approximately 40 milliseconds in the LGN in Patient 1 and 60 milliseconds in the V1/V2 complex in Patients 2 and 3. Moreover, steady state visual-evoked potentials evoked by the unperceived but commonly experienced video-screen flicker were recorded in the LGN, optic radiation, and V1/V2 visual areas. This study provides topographic and temporal propagation characteristics of steady state visual-evoked potentials along human visual pathways. We discuss the possible relationship between the oscillating signal recorded in subcortical and cortical areas and the electroencephalogram abnormalities observed in patients suffering from photosensitive epilepsy, particularly video-game epilepsy. The consequences of high temporal frequency visual stimuli delivered by ubiquitous video screens on epilepsy, headaches, and eyestrain must be considered.

We describe documented simultaneous intracerebral auditory evoked potentials from the auditory cortex and medial geniculate body (MGB) of a human patient. The MGB response lasted300 ms, with an initial negativity at 13.5 ms (N13), two positive peaks P21 and P29, and two broader negativities N50 and N200. P21 and N50 amplitudes were strongest for lowest tone frequencies, suggesting possible MGB tonotopic organization. Thalamic peaks were strongly interlaced with cortical activities recorded in Heschl's gyri before 30 ms: N13 preceded the first cortical component by 3.5 ms, then P21 and P29 preceded and lagged, respectively, the following two cortical polarity reversals by 1.5-2 ms. This study provides new functional data on the human MGB, and supports a more complex than simply relay-like role of the thalamus in sound perception.

The supratemporal sources of the earliest auditory cortical responses (20–80 ms) were identified using simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) data. Both hemispheres of six subjects were recorded two or three times in different sessions in response to 8000 right-ear 1 kHz pure tones stimuli. Four components were identified: Pa (28 ms), Nb (40 ms), and two subcomponents of the Pb complex, termed Pb1 (52 ms) and Pb2 (74 ms). Based on MEG data, the corresponding sources were localized on the anatomy using individual realistic head models: Pa in the medial portion of Heschl’s gyri (H1/H2); Nb/Pb1 in the lateral aspect of the supratemporal gyrus (STG); and Pb2 in the antero-lateral portion of Heschl’s gyri. All sources were oriented antero-superiorly. This pattern was clearest in the contralateral hemisphere, where these three activities could be statistically dissociated. Results agree with previous invasive human intracerebral recordings, with animal studies reporting secondary areas involved in the generation of middle latency auditory-evoked components, and with positron emission tomography and functional magnetic resonance imaging studies often reporting these three active areas although without temporal information. The early STG activity may be attributed to parallel thalamo-cortical connections, or to cortico-cortical connections between the primary auditory cortex and the STG, as recently described in humans.

An increasing number of studies on realistic head modelling for solving the inverse EEG and MEG problems have been emerging since the late 1980s. Recent evaluations of realistically meshed models showed that it was possible to correctly compute forward problems for any dipole depth and orientation, by using locally refined meshes [1, 2]. This being achieved, it is now possible to evaluate what error is made using various spherical models. A few previous works already adressed this question either using EEG [13] or MEG [4] data. These studies tested a small number of dipole locations, namely of the order of 10 and found localization errors of about 1cm for MEG and l–2cm for EEG, assuming negligible errors on the forward Solution. However, the numerical precision turns out to significantly depend upon the dipole orientation and depth within the brain.

This paper presents a study of the intrinsic localization error bias due to the use of a spherical geometry model on EEG simulated data obtained from realistically shaped models. About 2000 dipoles were randomly chosen on the segmented cortex surface of a particular subject. Forward calculations were performed using a uniformly meshed model for each dipole located at a depth greater than 20 mm below the brain surface, and locally refined models were used for shallower dipoles. Inverse calculations were performed using four different spherical models and another uniformly meshed model. It was found that the best spherical model lead to localization errors of 5-6 mm in the upper part of the head, and of 15-25 mm in the lower part. The influence of the number of electrodes upon this intrinsic bias was also studied. It was found that using 32 electrodes instead of 19 improves the localization by 2.7 mm on average, while using 63 instead of 32 electrodes lead to improvements of less than 1 mm. Finally, simulations involving two simultaneously active dipoles (one in the vicinity of each auditory cortex) show localization errors increasing by about 2-3 mm.

not Available.