Blaise Yvert

Improved Focalization of Electrical Microstimulation Using Microelectrode Arrays: A Modeling Study

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.

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

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.

BioMEA™: A versatile high-density 3D microelectrode array system using integrated electronics

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.

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

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.

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

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.

The “Mirror” Estimate: An Intuitive Predictor of Membrane Polarization during Extracellular Stimulation

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.

A 64-Channel ASIC for In-Vitro Simultaneous Recording and Stimulation of Neurons using Microelectrode Arrays

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 Sample & Hold 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 muV 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 mum CMOS process) and its power consumption is about 125 mW.

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

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 50u2009μm) 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.

Extracellular Neural Microstimulation May Activate Much Larger Regions than Expected by Simulations: A Combined Experimental and Modeling Study

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.

Microfabrication of new microelectrode arrays equipped with a ground surface configuration for focal neural microstimulation

Extracellular electrical stimulation of the central nervous system has been used empirically for decades, with both fundamental and clinical goals. Currently, microelectrode arrays (MEAs) offer new possibilities for CNS microstimulation, allowing in principle to activate only neurons located in the vicinity of the stimulation sites. To overcome the lack of focality of monopolar stimulations, multipolar approaches are commonly used, multiplying therefore the number of electrodes of the arrays and the complexity of the connection system behind. To overcome these limitations, we developed a ground surface configuration consisting in surrounding all the electrodes with a conductive surface laying over the MEA substrate, and using it for the stimulation current return. We first report the microfabrication of a prototype of MEA equipped with this configuration. We also perform experimental recordings of the potential field induced by microstimulations and confirm the expected increased focality with the ground surface configuration. This will open the way to focal pixel-like microstimulation of neural networks using MEAs.