Difference between revisions of "Team:Pasteur Paris/Membrane"

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                 <h1 id="Introduction">MEMBRANE</h1>
 
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                 <p><i>When manipulating genetically engineered organisms, it is crucial to guarantee the confinement of these organisms. In our case, we want genetically modified bacteria to stay at the interface between the prosthesis and the external organic medium. At the same time, one of the main issues our project wants to tackle is the conduction of the neuron influx to the prosthesis. The answer to these questions came as a double solution: confinement of the bacteria by conductive nanoporous membranes. The membrane’s nanoporosity allows substances produced by our modified biofilm to pass through the membrane, but the bacteria remain confined. We tested the conductivity and biocompatibility of two types of membranes.</i></p></div>
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                 <p><i>When manipulating genetically engineered organisms, it is crucial to guarantee the confinement of these organisms. In our case, we want genetically modified bacteria to stay at the interface between the prosthesis and the external organic medium. At the same time, one of the main issues our project wants to tackle is the conduction of the neuron influx to the prosthesis. The answer to these questions came as a double solution: confinement of the bacteria by conductive nanoporous membranes. The membrane’s nanoporosity allows substances produced by our modified biofilm to pass through the membrane, but the bacteria remain confined. We tested the conductivity and biocompatibility of two types of membranes.</i></p>
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               <p>As seen in the other parts of this wiki, we chose to use a nanoporous membrane in our device. The first goal of the membrane was to confine our biofilm, so it does not escape the prosthesis. Moreover, we also used our membrane as a conductive electrode. This solution was interesting since we didn’t have enough time to develop an entire electrical device which collects and treat the signal of the nerves. However, we know we still need to improve our interface if we want the patient to fully control his prosthesis. That is why we decided to look at what is already made in this field. So, first, we detailed how it is possible to model the electrical characteristics of a nerve. Then, we searched for information on electrodes and signal treatment. </p>
 
               <p>As seen in the other parts of this wiki, we chose to use a nanoporous membrane in our device. The first goal of the membrane was to confine our biofilm, so it does not escape the prosthesis. Moreover, we also used our membrane as a conductive electrode. This solution was interesting since we didn’t have enough time to develop an entire electrical device which collects and treat the signal of the nerves. However, we know we still need to improve our interface if we want the patient to fully control his prosthesis. That is why we decided to look at what is already made in this field. So, first, we detailed how it is possible to model the electrical characteristics of a nerve. Then, we searched for information on electrodes and signal treatment. </p>
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              <p><i>This section is principaly based on the thesis of Olivier Rossel: Dispositifs de mesure et d’interprétation de l’activité d’un nerf. Electronique. Université Montpellier II - Sciences et Techniques du Languedoc, 2012. Français. </i></p>
 
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                    <h4>Nerve modelisation </h4>
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                  <div class="block title" style="margin-top: 35px;"><h3 style="text-align: left;" id="Nerve">Gold-coated membranes</h3></div>
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                    <p>One of the goals of NeuronArch is to use or even develop a neural signal collection solution that is both non-invasive for the nerve and highly selective.In this context, we seek to develop an innovative architecture to significantly improve the selectivity of extraneural electrodes. In order to be able to develop such a solution, we must be able to estimate the electrical potential created on the surface of the nerve by the propagation of transmembrane currents at the level of the axons.</p>
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                    <p>For this study, we are only interested in the myelinated axons present in the peripheral nervous system. There are models to represent the extracellular voltage produced by the passage of an action potential for this type of fiber. The evolution of the extracellular voltage in the space separating two nodes of Ranvier can be described by these models.</p>
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                    <p>First, we are going to detail the physiological characteristics of the human nervous system. Then, we are going to modelize the electrical currents of an axon. Finally, we will estimate the influence of such currents at the surface of a nerve and modelize an entire nerve. </p>
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                  <div class="block title"><h1>I. physiological characteristics of the human nervous system<sup>[1]</sup></h1></div>
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                    <p>The nervous system is divided in two different parts: the central nervous system (CNS) and the peripheral nervous system (PNS). We will be focused on the peripheral nervous system as it transports the informations between the organs and the nervous system. Moreover, it includes the somatic nervous system which consists of afferent nerves or sensory nerves, and efferent nerves or motor nerves. Afferent nerves are responsible for relaying sensation from the body to the central nervous system; efferent nerves are responsible for sending out commands from the CNS to the body, stimulating muscle contraction; they include all the non-sensory neurons connected with skeletal muscles and skin. Generally the fibers of the somatic nervous system have a insulating sheath called myelin sheath.</p>
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                    <div class="legend"><b>Figure 1: </b>Structure of nerves<sup>[2]</sup></div>
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                    <p>Nerve fibers, consisting of axons and associated Schwann cells are grouped together in fascicles, sheathed by the perineurium. This one is constituted of layers of perineural cells. About half of the fascicular surface is occupied by the fibers, the rest is composed of the endoneurium which partitions the inside of the fascicle into several groups of nerve fibers which will then form new fascicles.</p>
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                    <p>The fascicles are finally contained in an isolar connective tissue called epineurium containing fibroblasts, collagen and fat in different proportions. This envelope participates in the fixation of the nerve on the surrounding structures. It contains the lymphatic and vascular network which crosses the perineurium to communicate with the network of arterioles and venules of the endoneurium. The epineurium constitutes 30 to 70% of the total area section of a nerve.</p>
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                    <p>The fascicular architecture is ordered only distally, close to the emergence of a nerve trunk.Going up to the proximal part, the fascicles divide and some fibers change their fascicle, the size of the fascicles decreases and their number increases. An orderly organization relative to the target organ is found only in the final branches that innervate a muscle, a group of muscles or sensory receptors.</p>
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                  <div class="block title"><h1>II. Propagation of nerve impulses:</h1></div>
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                    <p>The nerve impulse is initiated by action potentials that are created by successive openings and closings of the ion channels. The membrane current due to ionic flux creates an electric field in the nerve that produces a potential difference outside the nerve called extracellular voltage. It is this extracellular voltage that a measuring electrode will perceive. For a myelinated axon these ionic currents appear only at the nodes of Ranvier.</p>
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                  <div class="block title"><h1>III. Modelisation of the currents of a axon’s membrane</h1></div>
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                    <p>Although our objective is the calculation of the extracellular action potential, it is necessary to know the currents produced at the level of an axon. </p>
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                    <p>From an electrical point of view, the myelin sheath of the axon acts as an insulator, preventing the appearance of transmembrane currents elsewhere than at Ranvier's nodes. In fact, seen from the outside of the axon, the action potential seems to jump from one node of Ranvier to the other. Let us now consider how to model this propagation, in order to extract the transmembrane currents at the nodes of Ranvier.</p>
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Revision as of 08:41, 16 October 2018

""

MEMBRANE

When manipulating genetically engineered organisms, it is crucial to guarantee the confinement of these organisms. In our case, we want genetically modified bacteria to stay at the interface between the prosthesis and the external organic medium. At the same time, one of the main issues our project wants to tackle is the conduction of the neuron influx to the prosthesis. The answer to these questions came as a double solution: confinement of the bacteria by conductive nanoporous membranes. The membrane’s nanoporosity allows substances produced by our modified biofilm to pass through the membrane, but the bacteria remain confined. We tested the conductivity and biocompatibility of two types of membranes.

Figure 1: Bacteria + Conductive Nanoporous Membrane = Confined Bacteria

Gold-coated membranes

Sterlitech Polycarbonate Gold-Coated Membrane Filters were the first membranes we tested. The pores have a diameter of 0.4 micrometer, which is small enough to confine Escherichia coli bacteria, which diameter and size are respectively about 1 micrometer and 2 micrometers. These membranes were relatively easy to manipulate with a forceps because of their high flexibility.

Figure 2: Gold-Coated Membrane
Figure 3: Gold-Coated Membrane (Electron Microscope)

Polymer-coated membranes

The other membranes were Sterlitech Alumina Oxide Membrane Filters with 0.2-micrometer pores. Their higher rigidity compared to the gold-coated membranes led to several membranes being broken while manipulating them with a forceps. We used these membranes as a support for different conductive and biocompatible polymers: PEDOT:PSS (poly(3,4-ethylenedioxythiophene) polystyrene sulfonate), PEDOT:Cl and PEDOT:Ts.

Figure 4: Alumnia Oxyde Membrane in grey
Figure 5: Alumnia Oxyde Membrane in grey (electron microscope)

For PEDOT:PSS, an aqueous solution of PEDOT:PSS was prepared ([1]) and alumina oxide membranes were dipped for 24 hours in this solution. Electron microscopy of the membranes before and after the experiment showed the deposit of a substance on their surface; however, its nature has not been tested.

Figure 6: PEDOT:PSS-coated membrane
Figure 7: PEDOT:PSS-coated membrane (electron microscope)

Vapor-phase polymerization of PEDOT:Cl and PEDOT:Ts ([2]) also induced a change in the surface of the membranes (its exact nature also has not been verified).

Figure 8: PEDOT:TS (left) / PEDOT:CL (right) - coated membranes
Figure 9: PEDOT:CL - coated membrane (electron microscope)

Biocompatibility

The first issue to tackle for such an interface is its biocompatibility, so its ability to coexist with a living organism. Experiments in self-made PDMS culture wells with E. coli showed a low biocompatibility for the gold coated membrane, but an enhanced biocompatibility for the polymer-coated membranes.

Conductivity

The second criterion for a fully functional interface is its ability to conduct a neuron’s influx. Thus, conductivity measurements were made for signals of different frequencies on the membranes. Results showed excellent conductive properties for the gold-coated membranes and very good conductive properties for the polymer-coated membranes.

CONCLUSION

Biocompatible polymers like PEDOT:PSS represent ideal materials for engineering biocompatible and conductive interfaces, that are also relatively easy to produce, thus making them our preferred choice in our project. However, it is worth mentioning that we are totally aware of the fact that we can’t just expect neuron axons to bind to our interface and produce an electric signal. The electric signal transmitted by a nerve is heavily limited to the interior of the nerve by myelin covering the axon, and the signal transmitted by the axon is purely chemical. So it requires special electrodes, like Fine or Cuff electrodes, to detect an electric signal. We might explore these solutions in the continuation of our project to enhance our interface’s ability to transmit neuron signals.

REFERENCES

  • Jikui Wang, Guofeng Cai, Xudong Zhu, Xiaping Zhou, Oxidative Chemical Polymerization of 3,4-Ethylenedioxythiophene and its Applications in Antistatic coatings, Journal of Applied Polymer Science, 2012, Vol. 124, 109-115 .

  • Alexis E. Abelow, Kristin M. Persson, Edwin W.H. Jager, Magnus Berggren, Ilya Zharov, Electroresponsive Nanoporous Membranes by Coating Anodized Alumina with Poly(3,4ethylenedioxythiophene) and Polypyrrole. 2014, 299, 190-197.

Nerve modelisation

As seen in the other parts of this wiki, we chose to use a nanoporous membrane in our device. The first goal of the membrane was to confine our biofilm, so it does not escape the prosthesis. Moreover, we also used our membrane as a conductive electrode. This solution was interesting since we didn’t have enough time to develop an entire electrical device which collects and treat the signal of the nerves. However, we know we still need to improve our interface if we want the patient to fully control his prosthesis. That is why we decided to look at what is already made in this field. So, first, we detailed how it is possible to model the electrical characteristics of a nerve. Then, we searched for information on electrodes and signal treatment.

This section is principaly based on the thesis of Olivier Rossel: Dispositifs de mesure et d’interprétation de l’activité d’un nerf. Electronique. Université Montpellier II - Sciences et Techniques du Languedoc, 2012. Français.

Gold-coated membranes

One of the goals of NeuronArch is to use or even develop a neural signal collection solution that is both non-invasive for the nerve and highly selective.In this context, we seek to develop an innovative architecture to significantly improve the selectivity of extraneural electrodes. In order to be able to develop such a solution, we must be able to estimate the electrical potential created on the surface of the nerve by the propagation of transmembrane currents at the level of the axons.

For this study, we are only interested in the myelinated axons present in the peripheral nervous system. There are models to represent the extracellular voltage produced by the passage of an action potential for this type of fiber. The evolution of the extracellular voltage in the space separating two nodes of Ranvier can be described by these models.

First, we are going to detail the physiological characteristics of the human nervous system. Then, we are going to modelize the electrical currents of an axon. Finally, we will estimate the influence of such currents at the surface of a nerve and modelize an entire nerve.

I. physiological characteristics of the human nervous system[1]

The nervous system is divided in two different parts: the central nervous system (CNS) and the peripheral nervous system (PNS). We will be focused on the peripheral nervous system as it transports the informations between the organs and the nervous system. Moreover, it includes the somatic nervous system which consists of afferent nerves or sensory nerves, and efferent nerves or motor nerves. Afferent nerves are responsible for relaying sensation from the body to the central nervous system; efferent nerves are responsible for sending out commands from the CNS to the body, stimulating muscle contraction; they include all the non-sensory neurons connected with skeletal muscles and skin. Generally the fibers of the somatic nervous system have a insulating sheath called myelin sheath.

Figure 1: Structure of nerves[2]

Nerve fibers, consisting of axons and associated Schwann cells are grouped together in fascicles, sheathed by the perineurium. This one is constituted of layers of perineural cells. About half of the fascicular surface is occupied by the fibers, the rest is composed of the endoneurium which partitions the inside of the fascicle into several groups of nerve fibers which will then form new fascicles.

The fascicles are finally contained in an isolar connective tissue called epineurium containing fibroblasts, collagen and fat in different proportions. This envelope participates in the fixation of the nerve on the surrounding structures. It contains the lymphatic and vascular network which crosses the perineurium to communicate with the network of arterioles and venules of the endoneurium. The epineurium constitutes 30 to 70% of the total area section of a nerve.

The fascicular architecture is ordered only distally, close to the emergence of a nerve trunk.Going up to the proximal part, the fascicles divide and some fibers change their fascicle, the size of the fascicles decreases and their number increases. An orderly organization relative to the target organ is found only in the final branches that innervate a muscle, a group of muscles or sensory receptors.

II. Propagation of nerve impulses:

The nerve impulse is initiated by action potentials that are created by successive openings and closings of the ion channels. The membrane current due to ionic flux creates an electric field in the nerve that produces a potential difference outside the nerve called extracellular voltage. It is this extracellular voltage that a measuring electrode will perceive. For a myelinated axon these ionic currents appear only at the nodes of Ranvier.

III. Modelisation of the currents of a axon’s membrane

Although our objective is the calculation of the extracellular action potential, it is necessary to know the currents produced at the level of an axon.

From an electrical point of view, the myelin sheath of the axon acts as an insulator, preventing the appearance of transmembrane currents elsewhere than at Ranvier's nodes. In fact, seen from the outside of the axon, the action potential seems to jump from one node of Ranvier to the other. Let us now consider how to model this propagation, in order to extract the transmembrane currents at the nodes of Ranvier.

The more selective an electrode is the simpler the extraction of information. Thus, the maximum selectivity, being reduced to the activity of a single fiber, is required for the measurement interfaces. Unfortunately, this search for selectivity will lead to a search for proximity between the electrode and the fibers, at the detriment of the nerve’s physical integrity. Indeed, the risk of infection or trauma to the body increases with the invasiveness of the electrodes. Electrodes can therefore be classified according to criteria such as selectivity and invasiveness. The ideal electrode is one that has the highest selectivity while remaining the least invasive possible. To make a choice, a compromise must be made between the selectivity and the degree of invasiveness of the electrode. The "secondary" criteria are stability and repeatability. We will present the neural electrodes by exposing their performances in terms of selectivity and level of invasiveness.

I. Extra-neural electrodes

1. Helicoidal electrode interface:

Helicoidal electrodes are placed surrounding the nerve and are made of flexible metal ribbon in a helical design. This design allows the electrode to conform to the size and shape of the nerve to minimize mechanical trauma. The structural design causes low selectivity. Helicoidal electrodes are currently used for functional electrical stimulation, to control intractable epilepsy, sleep apnea, and to treat depressive syndromes.

2. Cuff electrode:

Considered as extraneural electrodes, cuff electrodes are widely used to perform basic and applied electro-neurophysiology studies and are particularly interesting for their ability to achieve good nerve recruitment with low thresholds. The cuff-style electrode provides a cylindrical electrode contact with a nerve for each of an arbitrary number of contacts, is easy to place and remove in an acute nerve preparation, and is designed to fit on the nerve (Cf. Figure 1). For each electrode, the electrical contacts were cut from metal foil as an array so as to maintain their positions relative to each other within the cuff. Lead wires were soldered to each intended contact. The structure was then molded in silicone elastomer, and individual contacts were electrically isolated. The final electrode is curved into a cylindrical shape with an inner diameter corresponding to that of the intended target nerve. These electrodes have been successfully used for nerve stimulation, recording, and conduction block in a number of different acute animal experiments by several investigators.

Figure 1: Schematic of a nerve cuff electrode. Retrieved on Oct. 14th from MicroProbes for Life Science[1]

The activity recorded by the cuff electrode represents the simultaneous activity of a large number of active axons. The potential of action seen by the electrode is overlapped, allowing only a "global" image of the activity inside the nerve. As a result, the selectivity of the recording is limited by the number of axons undergoing simultaneous discharge and by the position and surface of the contact of the cuff electrode. This type of measurement, therefore, does not allow the identification of fiber activity alone.

Increasing the number of electrode poles allows increasing the selectivity of this type of electrode. A multi-pole cuff electrode is then called a cuff electrode having more than three contacts. These contacts can be rings or segments of rings.

3. FINE electrode:

The flat-interface nerve electrode (FINE) was designed for selective nerve recording by realigning the fascicles and reshaping the nerve into a more flattened cross section which increases the surface area of the exposed nerve and offers greater access to fascicles. This kind of electrode is particularly interesting as it was possible to achieve more than 90% selectivity (Cf. Figure 2)

Figure 2: Cross section and schematic of a FINE electrode[2]

II. Information extraction:

1. Extraction of the discharge frequency:

The most relevant information to extract is the discharge frequency of active fibers because it represents the means of coding information by the nervous system. Significantly, such processing must be applied to signals representing the activity of a limited number of fibers. In fact, the published examples relate exclusively to intra-neural collection: the only method, today, which allows to observe the activity of fibers alone. However, since we don’t want to use intra-neural electrode in our device we will not detail how to extract the discharge frequency.

2. Envelope extraction:

Rectification and Bin-Integration (RBI) of the nerve raw signal is widely used in rehabilitation application. This point of RBI ENG is found by calculating the average of the absolute value of ENG samples spread over a given period of time. This period is called “bin” and its value depends on the application. It ranges from 10 ms to 200 ms. The smoothed envelope-like signal created by RBI makes it easy to extract information about the innervated organ.

III. Improvement of the electroneurogram records selectivity

1. ENG-EMG selectivity:

The body is made so that a nerve is never very far from a muscle. However, the triggering and control of muscle contractions use a similar mechanism to the propagation of nerve impulses. Thus, the vicinity of a muscle is the seat of important extracellular currents because of the large number of muscle fibers excited simultaneously. The potential differences associated with these currents are called EMG, for electromyogram. Action potentials in muscle have mV amplitude, larger than a neural signal, and their spectra overlap. Minimizing these forms of interference is there for essential.

In order to attenuate the EMG signal, tripolar cuff electrode are used (Cf. Figure 3). For the nerve signal, the main point of the cuff is that it reduces the volume of tissue in which the action currents flow and, therefore, increases the potential differences between the electrodes. For the EMG interference, the fact that the cuff is a tube of uniform cross-sectional area means that the gradient inside, due to each external source, is approximately constant and, therefore, the potential differences between the pairs of electrodes are equal and cancel. How they are canceled depends on the amplifier configuration but the principle is that out-of-cuff signals are canceled while neural signals do not.

The variation of the ENG is not linear over the entire length of the electrode, it is at a maximum in the center of the cuff. Moreover, the average value of the EMG potential is zero or close.

Thus, the impact of EMG on the measurement is significantly attenuated, while the ENG is preserved

Figure 3: Comparison of the potential in the cuff due to EMG and ENG sources.

The electronic realization of this treatment is very simple, it can be done in two different ways using either one or three differential amplifiers, these structures are named respectively "quasi-tripole" and "true-tripole" (Cf. Figure 4)

Figure 4: (a) The QT amplifier configuration connected to a tripolar cuff. (b) The TT amplifier configuration.

2. Type of nerve fiber selectivity:

Nerves carry a lot of different neural signals, with both afferent and efferent traffic. However, by recording the signal we reduce it to only one artificial signal and we lose a lot of information. As the different types of signals are transmitted by fibers of different diameters, it should be interesting to select the fiber we record according to its diameter.

The method (Cf. Figure 5) uses a double differential array of amplifiers ('tripole amplifiers') and, for each selected velocity (of either sign), artificial time delays, as well as an adder and a narrow-band filter. An action potential transiting the nerve will be perceived in the same way by each tripole, but with delays inversely proportional to the speed of propagation of the action potential. If this time offset is compensated by the delay added by the measurement system, the action potentials appear simultaneously at the output of the delay stages. Thus, summing them to each other, the amplitude of the action potential is amplified. This system makes it possible to amplify the measurement for this particular action potential. Whereas, for another action potential having a different speed or direction of propagation, the amplification will not take place because the delay implemented in the system does not correspond to the delay due to the propagation of the action potential. This system is therefore selective for a given propagation speed.

Figure 5: Multi-electrode cuff (MEC), array of tripole amplifiers and signal processing unit for selecting one velocity.

3. Spatial selectivity:

Methods aim to increase the spatial selectivity of extra-neural electrodes to discriminate active fascicles, in order to determine the activity of each nerve branch.

One way to increase the spatial selectivity is to increase the number of measurement points. The issue is to separate the sources. In this context, in order to increase the spatial selectivity of the extra-neural electrodes, the multipolar cuffs or FINE electrode have been designed. These structures make it possible to increase the number of contacts, thus the number of measured signals.

Another way is to use algorithms. Blind source separation techniques are able to decompose fascicular signals from FINE electrodes. Several other methods have been described in the literature. They aim to localize or separate nerve trunk signals. For instance, Neurofuzzy algorithms use an artificial neural network.

We can also mention the method based on antenna array beamforming. This seems to be one of the most advanced methods to distinguish fascicular activity inside a nerve. It would be possible to distinguish up to five active fascicles at the same time.

IV. An example of the development of a multi-channel acquisition device

Olivier Rossel, in his thesis, chose to work on improving the selectivity of the cuff electrodes. He chose this type of electrode because they respect the integrity of the nerve and its fascicles membranes and that they make it possible to limit both the number of implants and the complexity surgical gesture. The electrode need reject the EMG signals and to measure local ENGs at multiple sites around the nerve.

In this part, all the results use the electrical models of the nerve developed in the previous part.

1. Tripolar electrode

As we saw previously, it is possible to reject the EMG signal by using a tripolar cuff electrode (Cf. Figure 6). A tripolar cuff and the adapted electrical treatment is used.

Figure : 6Schematic of a tripolar cuff electrode.

2. Tripolar treatment analysis

The tripolar cuff electrode is considered as a spatial filter with a 1/h periodic frequency response, where h is the distance between the poles. For the spatial frequencies inferior at 1/h, the filter is a bandpass filter with a gain of 2 and a bandwidth of -6 dB between 1/4h and 3/4h.

If we consider the larger “d” of the electrode poles (Cf. Figure 6), the impulse response associated with each of the poles of the electrode is then a gate function of width d and amplitude 1/d.

3. Electrode sizing

n order to increase neural information relative to the noise, it is vital to optimize the cuff dimensions. The literature suggests that the best compromise between cuff length and the available place is a cuff length close to the wavelength of the transmembrane action potential. This one is approximately linear with fiber diameter.

According to Struijk, the action potential propagation velocity can be approximated as 55.800 nodes/s and the duration, of the transmembrane action potential, is approximately 0,4 ms.

Thus, to have an optimal measurement, the cuff electrode must cover 22 nodes of Ranvier. The inter-pole distance must, therefore, be adjusted to h = 11 lmy (lmy is the length of myelin separating two nodes of Ranvier). So, for a typical fiber, the inter-electrode distance h should be about 1 cm, which is used in most ENG measuring electrodes.

Figure 7: Extra-neural potential of monopolar action according to the position of the measuring point. The diagram at the top left shows the simulated situation. At the top right, the simulation corresponding to this configuration is represented: calculation of twice five monopolar potentials, for a typical axon (diameter of 8.7 μm, and lmy= 1 mm). The distances from this axon to the measurement points are ρ1=100 μm for site A and ρ2=500 μm for site B. Below, the monopolar signals at points “a” to “e” are shown for each of the measurement sites.

Knowing the characteristics of the electrode we want, it is possible to evaluate the distance h between the poles. This distance is of the order of a hundred micrometers which is much lower than that of a classical tripole which is of the order of a centimeter. This is why we will call, in the rest of this work, the tripole proposed a "small tripole".

4. Local variations of the potential

The spatial low frequencies of the electric field generated by an active axon, has almost the same amplitude at each point of the nerve surface, regardless of the location of the axon inside the nerve. Conversely, the amplitudes of the high-frequency components of this electric field depending on the distance between the axon and the point of observation.

It was possible for several poles placed online, to determine the depth of the axon. Indeed, for axons close to the surface of the nerve, there is a difference in amplitude (as a function of the relative position of each pole relative to that of Ranvier's nodes), while for those who are far from the surface the measured amplitude is the same for each of the poles (Cf. Figure 7). Thus, it is necessary to suppress the common mode and amplify only the difference of the signals collected on several poles.

5. Sensibility of a small:

For the small tripole, we have a fast attenuation depending on the distance compared to big tripole (Cf. Figure 8). As figure 8 confirms it, the small tripole is much more selective than the big tripole. Moreover, figure 9 shows that despite the low power level of the targeted signals and the spatial filtering performed, the peak-to-peak amplitude of the output signals of the tripole can reach 6 μV for a single active fiber. Considering the superposition of signals - the simultaneous activity of several fibers - we can hope to reach larger amplitude. Even if it is the case, the output signals of a small tripole remain of very low amplitude and it will thus be necessary to be very attentive to the sources of noise to maintain an acceptable signal-to-noise ratio.

Figure 8: Peak-to-peak amplitudes of the output action potential of a small tripole and of a big one
Figure 9: Peak-to-peak amplitudes measure at the output of a big tripole (left) or a small tripole (right) in function of the position of the active axon (diameter 8, 7 µm, and lmy=1 mm) in a cylindrical nerve of 300 µm in diameter.

6. Selectivity study:

Olivier ROSSEL developed a new electrode architecture he compared to the FINE electrode. The FINE electrode used is the one developed by Paul YOO and Dominique DURAND (Cf. Figure 7).

Figure 10: FINE electrode, h = 0,5 mm.

Olivier ROSSEL tried to improve this electrode replacing each measure point by a small tripole and by deleting two external ring. He called this electrode the FORTE electrode for “FINE with Original Recording Tripolar Electrode” (Cf. Figure 11). The main difference between these two electrodes is the inter-poles distance in the longitudinal way.

Figure 11: FORTE electrode, h = 375 μm.
Figure 12: Two fascicles represented in the electrode. These disposition of the fascicles is the one used for the simulations made to obtain figure 13 and figure 14

The activity of two fascicles is simulated (Cf. Figure 12) and the peak-to-peak amplitudes of the output signals are compared (Cf. Figure 13). The first difference we see is the signal from the FORTE electrode is attenuated 20 dB compared to the FINE electrode. In figure 13, we see that when only one fascicle is active, the FORTE electrode makes it possible to locate the active fascicle much more easily than the FINE electrode.

Figure 13: Peak-to-peak tension received by the tripole n (from tripole 1 to tripole 13) for the red and black fascicles.

Moreover, in the general case of a simultaneous activity of different fascicles, the signals from the different active fascicles are summed at the level of each tripoles (Cf. Figure 14). We can see that for the FINE electrode the amplitude measured makes it impossible to differentiate the active fascicles. However, for the FORTE electrode, since the small tripole is locally sensitive, we can’t see the difference between the figure 13 and the figure 14. It is easy to differentiate the active fascicles.

Figure 14: Simulated ENG for FINE electrode (A) and for the FORTE electrode (B) for the two fascicles in the case of a simultaneous activity. The contribution of each fascicles is designed by the color avec the fascicles in the figure 13. Each fascicle contains around two hundred active axons.

Finally, we see the FORTE electrode can surpass in selectivity the FINE electrode. The FORTE electrode is a great example of an electrode we could use for our device.

Thus, thanks to this example, we understand that it is possible to develop our own type of electrode. We gathered a lot of different information. First, having a good electrical model of the nerve is crucial to understand what are the parameters we need to take into account to develop our electrode. Moreover, it is primordial in order to be able to simulate the performance of an electrode. We now know that different algorithms that improve the output signal of an electrode already exist. We would like to test and use such algorithms for our device. Finally, thanks to the example of the FORTE electrode, we have already thought about how it will be possible to incorporate such an electrode in our device.

References

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