Publications by authors named "Blaise Yvert"

33 Publications

MRI-Compatible and Conformal Electrocorticography Grids for Translational Research.

Adv Sci (Weinh) 2021 05 8;8(9):2003761. Epub 2021 Mar 8.

Bertarelli Foundation Chair in Neuroprosthetic Technology Laboratory for Soft Bioelectronic Interfaces Institute of Microengineering Institute of Bioengineering Center for Neuroprosthetics Ecole Polytechnique Fédérale de Lausanne (EPFL) Geneva 1202 Switzerland.

Intraoperative electrocorticography (ECoG) captures neural information from the surface of the cerebral cortex during surgeries such as resections for intractable epilepsy and tumors. Current clinical ECoG grids come in evenly spaced, millimeter-sized electrodes embedded in silicone rubber. Their mechanical rigidity and fixed electrode spatial resolution are common shortcomings reported by the surgical teams. Here, advances in soft neurotechnology are leveraged to manufacture conformable subdural, thin-film ECoG grids, and evaluate their suitability for translational research. Soft grids with 0.2 to 10 mm electrode pitch and diameter are embedded in 150 µm silicone membranes. The soft grids are compatible with surgical handling and can be folded to safely interface hidden cerebral surface such as the Sylvian fold in human cadaveric models. It is found that the thin-film conductor grids do not generate diagnostic-impeding imaging artefacts (<1 mm) nor adverse local heating within a standard 3T clinical magnetic resonance imaging scanner. Next, the ability of the soft grids to record subdural neural activity in minipigs acutely and two weeks postimplantation is validated. Taken together, these results suggest a promising future alternative to current stiff electrodes and may enable the future adoption of soft ECoG grids in translational research and ultimately in clinical settings.
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http://dx.doi.org/10.1002/advs.202003761DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8097365PMC
May 2021

Digital Normativity: A Challenge for Human Subjectivation.

Front Artif Intell 2020 28;3:27. Epub 2020 Apr 28.

Inserm and Univ Grenoble Alpes, BrainTech Lab U1205, Gières, France.

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http://dx.doi.org/10.3389/frai.2020.00027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861289PMC
April 2020

Observation and assessment of acoustic contamination of electrophysiological brain signals during speech production and sound perception.

J Neural Eng 2020 10 15;17(5):056028. Epub 2020 Oct 15.

Inserm, BrainTech Lab, U1205, Grenoble, France. University Grenoble Alpes, BrainTech Lab, U1205, Grenoble, France.

Objective: A current challenge of neurotechnologies is to develop speech brain-computer interfaces aiming at restoring communication in people unable to speak. To achieve a proof of concept of such system, neural activity of patients implanted for clinical reasons can be recorded while they speak. Using such simultaneously recorded audio and neural data, decoders can be built to predict speech features using features extracted from brain signals. A typical neural feature is the spectral power of field potentials in the high-gamma frequency band, which happens to overlap the frequency range of speech acoustic signals, especially the fundamental frequency of the voice. Here, we analyzed human electrocorticographic and intracortical recordings during speech production and perception as well as a rat microelectrocorticographic recording during sound perception. We observed that several datasets, recorded with different recording setups, contained spectrotemporal features highly correlated with those of the sound produced by or delivered to the participants, especially within the high-gamma band and above, strongly suggesting a contamination of electrophysiological recordings by the sound signal. This study investigated the presence of acoustic contamination and its possible source.

Approach: We developed analysis methods and a statistical criterion to objectively assess the presence or absence of contamination-specific correlations, which we used to screen several datasets from five centers worldwide.

Main Results: Not all but several datasets, recorded in a variety of conditions, showed significant evidence of acoustic contamination. Three out of five centers were concerned by the phenomenon. In a recording showing high contamination, the use of high-gamma band features dramatically facilitated the performance of linear decoding of acoustic speech features, while such improvement was very limited for another recording showing no significant contamination. Further analysis and in vitro replication suggest that the contamination is caused by the mechanical action of the sound waves onto the cables and connectors along the recording chain, transforming sound vibrations into an undesired electrical noise affecting the biopotential measurements.

Significance: Although this study does not per se question the presence of speech-relevant physiological information in the high-gamma range and above (multiunit activity), it alerts on the fact that acoustic contamination of neural signals should be proofed and eliminated before investigating the cortical dynamics of these processes. To this end, we make available a toolbox implementing the proposed statistical approach to quickly assess the extent of contamination in an electrophysiological recording (https://doi.org/10.5281/zenodo.3929296).
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http://dx.doi.org/10.1088/1741-2552/abb25eDOI Listing
October 2020

Neuroprosthetic Speech: The Ethical Significance of Accuracy, Control and Pragmatics.

Camb Q Healthc Ethics 2019 10;28(4):657-670

Neuroprosthetic speech devices are an emerging technology that can offer the possibility of communication to those who are unable to speak. Patients with 'locked in syndrome,' aphasia, or other such pathologies can use covert speech-vividly imagining saying something without actual vocalization-to trigger neural controlled systems capable of synthesizing the speech they would have spoken, but for their impairment.We provide an analysis of the mechanisms and outputs involved in speech mediated by neuroprosthetic devices. This analysis provides a framework for accounting for the ethical significance of accuracy, control, and pragmatic dimensions of prosthesis-mediated speech. We first examine what it means for the output of the device to be accurate, drawing a distinction between technical accuracy on the one hand and semantic accuracy on the other. These are conceptual notions of accuracy.Both technical and semantic accuracy of the device will be necessary (but not yet sufficient) for the user to have sufficient control over the device. Sufficient control is an ethical consideration: we place high value on being able to express ourselves when we want and how we want. Sufficient control of a neural speech prosthesis requires that a speaker can reliably use their speech apparatus as they want to, and can expect their speech to authentically represent them. We draw a distinction between two relevant features which bear on the question of whether the user has sufficient control: voluntariness of the speech and the authenticity of the speech. These can come apart: the user might involuntarily produce an authentic output (perhaps revealing private thoughts) or might voluntarily produce an inauthentic output (e.g., when the output is not semantically accurate). Finally, we consider the role of the interlocutor in interpreting the content and purpose of the communication.These three ethical dimensions raise philosophical questions about the nature of speech, the level of control required for communicative accuracy, and the nature of 'accuracy' with respect to both natural and prosthesis-mediated speech.
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http://dx.doi.org/10.1017/S0963180119000604DOI Listing
October 2019

Volitional control of single-electrode high gamma local field potentials by people with paralysis.

J Neurophysiol 2019 04 20;121(4):1428-1450. Epub 2019 Feb 20.

Department of Neuroscience, Brown University , Providence, Rhode Island.

Intracortical brain-computer interfaces (BCIs) can enable individuals to control effectors, such as a computer cursor, by directly decoding the user's movement intentions from action potentials and local field potentials (LFPs) recorded within the motor cortex. However, the accuracy and complexity of effector control achieved with such "biomimetic" BCIs will depend on the degree to which the intended movements used to elicit control modulate the neural activity. In particular, channels that do not record distinguishable action potentials and only record LFP modulations may be of limited use for BCI control. In contrast, a biofeedback approach may surpass these limitations by letting the participants generate new control signals and learn strategies that improve the volitional control of signals used for effector control. Here, we show that, by using a biofeedback paradigm, three individuals with tetraplegia achieved volitional control of gamma LFPs (40-400 Hz) recorded by a single microelectrode implanted in the precentral gyrus. Control was improved over a pair of consecutive sessions up to 3 days apart. In all but one session, the channel used to achieve control lacked distinguishable action potentials. Our results indicate that biofeedback LFP-based BCIs may potentially contribute to the neural modulation necessary to obtain reliable and useful control of effectors. NEW & NOTEWORTHY Our study demonstrates that people with tetraplegia can volitionally control individual high-gamma local-field potential (LFP) channels recorded from the motor cortex, and that this control can be improved using biofeedback. Motor cortical LFP signals are thought to be both informative and stable intracortical signals and, thus, of importance for future brain-computer interfaces.
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http://dx.doi.org/10.1152/jn.00131.2018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485734PMC
April 2019

An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting.

Int J Neural Syst 2019 Oct 27;29(8):1850059. Epub 2018 Dec 27.

BrainTech Laboratory U1205, INSERM, 2280 Rue de la Piscine, 38400 Saint-Martin-d'Hères, France.

Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal in an online and unsupervised fashion. The signal stream is continuously fed to the network and processed through several layers to output spike trains matching the truth after a short learning period requiring only few data. The network features an attention mechanism to handle the scarcity of action potential occurrences in the signal, and a threshold adaptation mechanism to handle patterns with different sizes. This method outperforms two existing spike-sorting algorithms at low signal-to-noise ratio (SNR) and can be adapted to process several channels simultaneously in the case of tetrode recordings. Such attention-based STDP network applied to spike-sorting opens perspectives to embed neuromorphic processing of neural data in future brain implants.
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http://dx.doi.org/10.1142/S0129065718500594DOI Listing
October 2019

Editorial.

J Physiol Paris 2016 11;110(4 Pt A):315

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http://dx.doi.org/10.1016/j.jphysparis.2017.09.001DOI Listing
November 2016

Corrigendum: Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

Front Neurosci 2017 29;11:486. Epub 2017 Aug 29.

Laboratoire d'Électronique et de Technologie de l'Information (LETI), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA)Grenoble, France.

[This corrects the article on p. 474 in vol. 10, PMID: 27857680.].
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http://dx.doi.org/10.3389/fnins.2017.00486DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581870PMC
August 2017

Key considerations in designing a speech brain-computer interface.

J Physiol Paris 2016 11 7;110(4 Pt A):392-401. Epub 2017 Aug 7.

INSERM, BrainTech Laboratory U1205, F-38000 Grenoble, France; Univ. Grenoble Alpes, BrainTech Laboratory U1205, F-38000 Grenoble, France. Electronic address:

Restoring communication in case of aphasia is a key challenge for neurotechnologies. To this end, brain-computer strategies can be envisioned to allow artificial speech synthesis from the continuous decoding of neural signals underlying speech imagination. Such speech brain-computer interfaces do not exist yet and their design should consider three key choices that need to be made: the choice of appropriate brain regions to record neural activity from, the choice of an appropriate recording technique, and the choice of a neural decoding scheme in association with an appropriate speech synthesis method. These key considerations are discussed here in light of (1) the current understanding of the functional neuroanatomy of cortical areas underlying overt and covert speech production, (2) the available literature making use of a variety of brain recording techniques to better characterize and address the challenge of decoding cortical speech signals, and (3) the different speech synthesis approaches that can be considered depending on the level of speech representation (phonetic, acoustic or articulatory) envisioned to be decoded at the core of a speech BCI paradigm.
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http://dx.doi.org/10.1016/j.jphysparis.2017.07.002DOI Listing
November 2016

Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces.

PLoS Comput Biol 2016 Nov 23;12(11):e1005119. Epub 2016 Nov 23.

INSERM, BrainTech Laboratory U1205, Grenoble, France.

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.
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http://dx.doi.org/10.1371/journal.pcbi.1005119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5120792PMC
November 2016

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

Front Neurosci 2016 3;10:474. Epub 2016 Nov 3.

Laboratoire d'Électronique et de Technologie de l'Information (LETI), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA)Grenoble, France; Université Grenoble AlpesGrenoble, France.

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 (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.
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http://dx.doi.org/10.3389/fnins.2016.00474DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093145PMC
November 2016

Generation of Locomotor-Like Activity in the Isolated Rat Spinal Cord Using Intraspinal Electrical Microstimulation Driven by a Digital Neuromorphic CPG.

Front Neurosci 2016 7;10:67. Epub 2016 Mar 7.

Centre National de la Recherche Scientifique, Institute for Cognitive and Integrative Neuroscience (INCIA), UMR 5287Talence, France; Institute for Cognitive and Integrative Neuroscience (INCIA), UMR 5287, University of BordeauxTalence, France; Institut National de la Santé et de la Recherche Médicale, Clinatec-Lab, U1205Grenoble, France; Université Grenoble Alpes, Clinatec-Lab, U1205Grenoble, France.

Neural prostheses based on electrical microstimulation offer promising perspectives to restore functions following lesions of the central nervous system (CNS). They require the identification of appropriate stimulation sites and the coordination of their activation to achieve the restoration of functional activity. On the long term, a challenging perspective is to control microstimulation by artificial neural networks hybridized to the living tissue. Regarding the use of this strategy to restore locomotor activity in the spinal cord, to date, there has been no proof of principle of such hybrid approach driving intraspinal microstimulation (ISMS). Here, we address a first step toward this goal in the neonatal rat spinal cord isolated ex vivo, which can display locomotor-like activity while offering an easy access to intraspinal circuitry. Microelectrode arrays were inserted in the lumbar region to determine appropriate stimulation sites to elicit elementary bursting patterns on bilateral L2/L5 ventral roots. Two intraspinal sites were identified at L1 level, one on each side of the spinal cord laterally from the midline and approximately at a median position dorso-ventrally. An artificial CPG implemented on digital integrated circuit (FPGA) was built to generate alternating activity and was hybridized to the living spinal cord to drive electrical microstimulation on these two identified sites. Using this strategy, sustained left-right and flexor-extensor alternating activity on bilateral L2/L5 ventral roots could be generated in either whole or thoracically transected spinal cords. These results are a first step toward hybrid artificial/biological solutions based on electrical microstimulation for the restoration of lost function in the injured CNS.
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http://dx.doi.org/10.3389/fnins.2016.00067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4779903PMC
March 2016

TRPP2 modulates ryanodine- and inositol-1,4,5-trisphosphate receptors-dependent Ca2+ signals in opposite ways in cerebral arteries.

Cell Calcium 2015 Nov 30;58(5):467-75. Epub 2015 Jul 30.

Univ. Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, F-33000 Bordeaux, France; CNRS, Institut des Maladies Neurodégénératives, UMR 5293, F-33000 Bordeaux, France. Electronic address:

TRPP2 is a cationic channel expressed in plasma membrane and in sarcoplasmic reticulum. In several cell lines, TRPP2 is described as a reticulum Ca(2+) leak channel but it also interacts with ryanodine and inositol 1,4,5-trisphosphate (InsP3) receptors to inhibit and increase the release of Ca(2+) stores, respectively. TRPP2 is known to be expressed in vascular smooth muscle cells, however its function in Ca(2+) signals remains poorly described in native cells, principally because the pharmacology is not developed. TRPP2 was expressed in cerebral arteries. Triptolide evoked Ca(2+) responses in a Ca(2+)-free solution as well as permeabilized arteries. This Ca(2+) 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 Ca(2+) responses and in vivo contraction. It also decreased the InsP3-evoked Ca(2+) responses. Finally, aging affected the regulations in which TRPP2 is engaged, whereas the triptolide-evoked Ca(2+) response was not modified. Taken together, our results have shown that TRPP2 is implicated in triptolide-induced Ca(2+) release from intracellular Ca(2+) stores. TRPP2 functionally interacts with both ryanodine and InsP3 receptors. These interactions were not similar in adult and old mice.
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http://dx.doi.org/10.1016/j.ceca.2015.07.003DOI Listing
November 2015

3D-nanostructured boron-doped diamond for microelectrode array neural interfacing.

Biomaterials 2015 Jun 13;53:173-83. Epub 2015 Mar 13.

INSERM, UA01, Clinatec Laboratory, Biomedical Research Center Edmond J. Safra, F-38 000 Grenoble, France; Université Grenoble Alpes, UA01, Clinatec Laboratory, Biomedical Research Center Edmond J. Safra, F-38 000 Grenoble, France; CEA, LETI, Clinatec, F-38000 Grenoble, France. Electronic address:

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.
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http://dx.doi.org/10.1016/j.biomaterials.2015.02.021DOI Listing
June 2015

Current approaches to model extracellular electrical neural microstimulation.

Front Comput Neurosci 2014 19;8:13. Epub 2014 Feb 19.

Université de Bordeaux, Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, UMR5287 Bordeaux, France ; CNRS, Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, UMR5287 Bordeaux, France ; Inserm, Clinatec, U1167 Grenoble, France ; CEA, LETI, Clinatec Grenoble, France.

Nowadays, high-density microelectrode arrays provide unprecedented possibilities to precisely activate spatially well-controlled central nervous system (CNS) areas. However, this requires optimizing stimulating devices, which in turn requires a good understanding of the effects of microstimulation on cells and tissues. In this context, modeling approaches provide flexible ways to predict the outcome of electrical stimulation in terms of CNS activation. In this paper, we present state-of-the-art modeling methods with sufficient details to allow the reader to rapidly build numerical models of neuronal extracellular microstimulation. These include (1) the computation of the electrical potential field created by the stimulation in the tissue, and (2) the response of a target neuron to this field. Two main approaches are described: First we describe the classical hybrid approach that combines the finite element modeling of the potential field with the calculation of the neuron's response in a cable equation framework (compartmentalized neuron models). Then, we present a "whole finite element" approach allowing the simultaneous calculation of the extracellular and intracellular potentials, by representing the neuronal membrane with a thin-film approximation. This approach was previously introduced in the frame of neural recording, but has never been implemented to determine the effect of extracellular stimulation on the neural response at a sub-compartment level. Here, we show on an example that the latter modeling scheme can reveal important sub-compartment behavior of the neural membrane that cannot be resolved using the hybrid approach. The goal of this paper is also to describe in detail the practical implementation of these methods to allow the reader to easily build new models using standard software packages. These modeling paradigms, depending on the situation, should help build more efficient high-density neural prostheses for CNS rehabilitation.
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http://dx.doi.org/10.3389/fncom.2014.00013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3928616PMC
March 2014

Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments.

Front Neurosci 2013 21;7:215. Epub 2013 Nov 21.

Laboratoire IMS, UMR Centre National de la Recherche Scientifique, University of Bordeaux Talence, France.

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin-Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development.
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http://dx.doi.org/10.3389/fnins.2013.00215DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836270PMC
December 2013

Multichannel boron doped nanocrystalline diamond ultramicroelectrode arrays: design, fabrication and characterization.

Sensors (Basel) 2012 7;12(6):7669-81. Epub 2012 Jun 7.

CEA-LIST, Diamond Sensors Laboratory, Gif-sur-Yvette 91191, France.

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 (k(0)) 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)(6)(4-) ion at 100 mV·s(-1)) of 1.8 nA. The UMEAs are targeted for electrophysiological as well as analytical applications.
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http://dx.doi.org/10.3390/s120607669DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3435996PMC
January 2013

Extracellular neural microstimulation may activate much larger regions than expected by simulations: a combined experimental and modeling study.

PLoS One 2012 7;7(8):e41324. Epub 2012 Aug 7.

Université Bordeaux, Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, UMR5287, Bordeaux, Talence, France.

Electrical stimulation of the central nervous system has been widely used for decades for either fundamental research purposes or clinical treatment applications. Yet, very little is known regarding the spatial extent of an electrical stimulation. If pioneering experimental studies reported that activation threshold currents (TCs) increase with the square of the neuron-to-electrode distance over a few hundreds of microns, there is no evidence that this quadratic law remains valid for larger distances. Moreover, nowadays, numerical simulation approaches have supplanted experimental studies for estimating TCs. However, model predictions have not yet been validated directly with experiments within a common paradigm. Here, we present a direct comparison between experimental determination and modeling prediction of TCs up to distances of several millimeters. First, we combined patch-clamp recording and microelectrode array stimulation in whole embryonic mouse spinal cords to determine TCs. Experimental thresholds did not follow a quadratic law beyond 1 millimeter, but rather tended to remain constant for distances larger than 1 millimeter. We next built a combined finite element--compartment model of the same experimental paradigm to predict TCs. While theoretical TCs closely matched experimental TCs for distances <250 microns, they were highly overestimated for larger distances. This discrepancy remained even after modifications of the finite element model of the potential field, taking into account anisotropic, heterogeneous or dielectric properties of the tissue. In conclusion, these results show that quadratic evolution of TCs does not always hold for large distances between the electrode and the neuron and that classical models may underestimate volumes of tissue activated by electrical stimulation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041324PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3413686PMC
January 2013

Combined macro-/mesoporous microelectrode arrays for low-noise extracellular recording of neural networks.

J Neurophysiol 2012 Sep 27;108(6):1793-803. Epub 2012 Jun 27.

Université Bordeaux, ISM, UMR5255, Bordeaux, France.

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.
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http://dx.doi.org/10.1152/jn.00711.2011DOI Listing
September 2012

Focalizing electrical neural stimulation with penetrating microelectrode arrays: a modeling study.

J Neurosci Methods 2012 Jul 4;209(1):250-4. Epub 2012 Jun 4.

Univ. Bordeaux, Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, UMR 5287, Bordeaux, F-33405 Talence, France.

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.
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http://dx.doi.org/10.1016/j.jneumeth.2012.05.006DOI Listing
July 2012

Modeling extracellular electrical neural stimulation: from basic understanding to MEA-based applications.

J Physiol Paris 2012 May-Aug;106(3-4):146-58. Epub 2011 Oct 20.

CNRS, Institut des Neurosciences Cognitives et Intégratives d’Aquitaine, UMR 5287, Bordeaux F-33000, France.

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.
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http://dx.doi.org/10.1016/j.jphysparis.2011.10.003DOI Listing
November 2012

Nanostructuration strategies to enhance microelectrode array (MEA) performance for neuronal recording and stimulation.

J Physiol Paris 2012 May-Aug;106(3-4):137-45. Epub 2011 Oct 18.

CNRS, Institut des Sciences Moléculaires, UMR5255, Bordeaux F-33000, France.

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.
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http://dx.doi.org/10.1016/j.jphysparis.2011.10.001DOI Listing
November 2012

Artificial CSF motion ensures rhythmic activity in the developing CNS ex vivo: a mechanical source of rhythmogenesis?

J Neurosci 2011 Jun;31(24):8832-40

Institut des Neurosciences Cognitives et Intégratives d'Aquitaine, CNRS, Université de Bordeaux, UMR5287, Talence, F-33405 France.

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.
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http://dx.doi.org/10.1523/JNEUROSCI.1354-11.2011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6622937PMC
June 2011

NeuroMap: A Spline-Based Interactive Open-Source Software for Spatiotemporal Mapping of 2D and 3D MEA Data.

Front Neuroinform 2011 31;4:119. Epub 2011 Jan 31.

Centre National de la Recherche Scientifique, INCIA, UMR5287 Bordeaux, France.

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.
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http://dx.doi.org/10.3389/fninf.2010.00119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3034234PMC
July 2011

BioMEA: a versatile high-density 3D microelectrode array system using integrated electronics.

Biosens Bioelectron 2010 Apr 13;25(8):1889-96. Epub 2010 Jan 13.

CEA-LETI, Grenoble, France.

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.
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http://dx.doi.org/10.1016/j.bios.2010.01.001DOI Listing
April 2010

The "mirror" estimate: an intuitive predictor of membrane polarization during extracellular stimulation.

Biophys J 2009 May;96(9):3495-508

CNRS, Université de Bordeaux, UMR5228, Bordeaux, F-33000 France.

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.
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http://dx.doi.org/10.1016/j.bpj.2008.12.3961DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2711410PMC
May 2009

Improved focalization of electrical microstimulation using microelectrode arrays: a modeling study.

PLoS One 2009 12;4(3):e4828. Epub 2009 Mar 12.

Université de Bordeaux, CNRS, Centre de Neurosciences Intégratives et Cognitives, UMR Talence, France.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0004828PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2652101PMC
May 2009

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

IEEE Trans Neural Syst Rehabil Eng 2008 Apr;16(2):149-60

CEA/LETI, Minatec, Grenoble, France.

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.
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http://dx.doi.org/10.1109/TNSRE.2007.914467DOI Listing
April 2008

A new 3-D finite-element model based on thin-film approximation for microelectrode array recording of extracellular action potential.

IEEE Trans Biomed Eng 2008 Feb;55(2 Pt 1):683-92

LETI-CEA Recherche Technologique, Grenoble 38054, France.

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.
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http://dx.doi.org/10.1109/TBME.2007.903522DOI Listing
February 2008

Localization of human supratemporal auditory areas from intracerebral auditory evoked potentials using distributed source models.

Neuroimage 2005 Oct 21;28(1):140-53. Epub 2005 Jul 21.

Inserm Unité 280, Bron, France.

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.
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http://dx.doi.org/10.1016/j.neuroimage.2005.05.056DOI Listing
October 2005