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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> | <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> | <div class="legend"><b>Figure 1: </b>Structure of nerves<sup>[2]</sup></div> | ||
<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> | <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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<div class="block title"><h3 style="text-align: left;">1. Electric model of an axon</h3></div> | <div class="block title"><h3 style="text-align: left;">1. Electric model of an axon</h3></div> | ||
<div class="block full"><p>We used the model of Hodgkin-Huxley to describe the flow of electric current through the surface membrane of a giant nerve fiber. The electrical behaviour of the membrane may be represented by the network shown in Figure 2. Current can be carried through the membrane either by charging the membrane capacity or by movement of ions through the resistances in parallel with the capacity. The influence of membrane potential on permeability can be summarized by stating: first, that depolarization causes a transient increase in sodium conductance and a slower but maintained increase in potassium conductance; secondly, that these changes are graded and that they can be reversed by repolarizing the membrane. </p></div> | <div class="block full"><p>We used the model of Hodgkin-Huxley to describe the flow of electric current through the surface membrane of a giant nerve fiber. The electrical behaviour of the membrane may be represented by the network shown in Figure 2. Current can be carried through the membrane either by charging the membrane capacity or by movement of ions through the resistances in parallel with the capacity. The influence of membrane potential on permeability can be summarized by stating: first, that depolarization causes a transient increase in sodium conductance and a slower but maintained increase in potassium conductance; secondly, that these changes are graded and that they can be reversed by repolarizing the membrane. </p></div> | ||
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<div class="legend"><b>Figure 2: </b>Electrical circuit representing membrane. RNa = 1/g<sub>Na</sub>; R<sub>k</sub> = 1/g<sub>k</sub>; R<sub>l</sub> = 1/ḡ<sub>l</sub>. R<sub>Na</sub> and R<sub>k</sub> vary with time and membrane potential; the other components are constant.<sup>[3]</sup></div> | <div class="legend"><b>Figure 2: </b>Electrical circuit representing membrane. RNa = 1/g<sub>Na</sub>; R<sub>k</sub> = 1/g<sub>k</sub>; R<sub>l</sub> = 1/ḡ<sub>l</sub>. R<sub>Na</sub> and R<sub>k</sub> vary with time and membrane potential; the other components are constant.<sup>[3]</sup></div> | ||
<p>Thanks to this model, we are able to determine the evolution of the potential of membrane (V) and ionic current (J<sub>i</sub>) and capacitive (J<sub>CM</sub>) densities as a function of time (figure 3). </p> | <p>Thanks to this model, we are able to determine the evolution of the potential of membrane (V) and ionic current (J<sub>i</sub>) and capacitive (J<sub>CM</sub>) densities as a function of time (figure 3). </p> | ||
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− | <div class="legend"><b>Figure 3: Components of membrane current during propagated action potential. A, membrane potential. B, ionic current density (I<sub>i</sub>), capacity current density and total membrane current density I. C, ionic current density (I<sub>i</sub>), sodium current density (I<sub>Na</sub>) and potassium current density (I<sub>K</sub>).<sup>[3]</sup | + | <div class="legend"><b>Figure 3 </b>: Components of membrane current during propagated action potential. A, membrane potential. B, ionic current density (I<sub>i</sub>), capacity current density and total membrane current density I. C, ionic current density (I<sub>i</sub>), sodium current density (I<sub>Na</sub>) and potassium current density (I<sub>K</sub>).<sup>[3]</sup></div> |
<div class="block title"><h3 style="text-align: left;">2. Simulation of the transmembrane nodal currents</h3></div> | <div class="block title"><h3 style="text-align: left;">2. Simulation of the transmembrane nodal currents</h3></div> | ||
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<p>It is possible to implement this model in the NEURON software (http://www.neuron.yale.edu/neuron/) to be able to simulate the transmembrane nodal currents. This software calculates the densities of the currents presented previously.</p> | <p>It is possible to implement this model in the NEURON software (http://www.neuron.yale.edu/neuron/) to be able to simulate the transmembrane nodal currents. This software calculates the densities of the currents presented previously.</p> | ||
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<div class="legend"><b>Figure 4: </b>Result of the NEURON simulation of the electrical activity (evolution as a function of the time of the transmembrane current) of a Ranvier node of a typical axon of 8.7 <FONT face="Raleway">μ</FONT>m diameter.<sup>[4]</sup></div> | <div class="legend"><b>Figure 4: </b>Result of the NEURON simulation of the electrical activity (evolution as a function of the time of the transmembrane current) of a Ranvier node of a typical axon of 8.7 <FONT face="Raleway">μ</FONT>m diameter.<sup>[4]</sup></div> | ||
<p>The value of the electrical parameters of the model is based on the study of C. C. MCINTYRE and al.<sup>[5]</sup>. The simulations make it possible to generate a large database comprising, for each of the nodes of Ranvier of each of the fibers, the representation of the transmembrane current as a function of time. For the study of electrode selectivity, it is the distribution of these nodal currents in the space that must be defined. In other words, we need to know at a fixed time t0 the distribution of the currents generated by the nodes of Ranvier. In fact, Figure 5 represents the total activity of forty nodes of Ranvier. It can be noted that of these forty nodes of Ranvier, only a few are active simultaneously. The envelope of the generated currents is represented in dashed lines.</p> | <p>The value of the electrical parameters of the model is based on the study of C. C. MCINTYRE and al.<sup>[5]</sup>. The simulations make it possible to generate a large database comprising, for each of the nodes of Ranvier of each of the fibers, the representation of the transmembrane current as a function of time. For the study of electrode selectivity, it is the distribution of these nodal currents in the space that must be defined. In other words, we need to know at a fixed time t0 the distribution of the currents generated by the nodes of Ranvier. In fact, Figure 5 represents the total activity of forty nodes of Ranvier. It can be noted that of these forty nodes of Ranvier, only a few are active simultaneously. The envelope of the generated currents is represented in dashed lines.</p> | ||
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<div class="legend"><b>Figure 5: </b>Spatial distribution of the amplitudes of the nodal currents generated by several Ranvier nodes at a given instant.<sup>[4]</sup></div> | <div class="legend"><b>Figure 5: </b>Spatial distribution of the amplitudes of the nodal currents generated by several Ranvier nodes at a given instant.<sup>[4]</sup></div> | ||
<p>If we take into account realistic data of characteristic and propagation of an action potential (duration between 0.1 and 0.2ms and propagation speed between 50 and 100 ms<sup>-1</sup>) we find a wavelength of the order of 10mm. This wavelength value is characteristic of the propagation of action potential in a peripheral nerve, it is used to size the spacing between the poles of a conventional electroneurogram measuring electrode.</p> | <p>If we take into account realistic data of characteristic and propagation of an action potential (duration between 0.1 and 0.2ms and propagation speed between 50 and 100 ms<sup>-1</sup>) we find a wavelength of the order of 10mm. This wavelength value is characteristic of the propagation of action potential in a peripheral nerve, it is used to size the spacing between the poles of a conventional electroneurogram measuring electrode.</p> | ||
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<p>J. H. Meier and al. developed a nonhomogeneous and anisotropic model of a nerve [6]. We consider a cylindrical nerve of radius a and an axon at a distance r from the axis of the nerve (figure 6). The nodes of Ranvier are separated by a distance lmy. The point M, where the extracellular potential will be calculated, is located at a distance d = <FONT face="Raleway">ρ</FONT> - a from the surface of the nerve and at a distance <FONT face="Raleway">ρ</FONT>0 from the axon considered. The angle <FONT face="Raleway">θ</FONT> is the angle constructed from the radial position of the axon and the observation point M. The conductivity inside the nerve is different (<FONT face="Raleway">σ</FONT>z and <FONT face="Raleway">σ</FONT><FONT face="Raleway">ρ</FONT>) depending on the direction, longitudinal or radial. We consider two media other than the interior of the nerve: the perineural sheath that surrounds the nerve, of conductivity <FONT face="Raleway">σ</FONT>s, and the outside of the nerve of conductivity <FONT face="Raleway">σ</FONT>e.</p> | <p>J. H. Meier and al. developed a nonhomogeneous and anisotropic model of a nerve [6]. We consider a cylindrical nerve of radius a and an axon at a distance r from the axis of the nerve (figure 6). The nodes of Ranvier are separated by a distance lmy. The point M, where the extracellular potential will be calculated, is located at a distance d = <FONT face="Raleway">ρ</FONT> - a from the surface of the nerve and at a distance <FONT face="Raleway">ρ</FONT>0 from the axon considered. The angle <FONT face="Raleway">θ</FONT> is the angle constructed from the radial position of the axon and the observation point M. The conductivity inside the nerve is different (<FONT face="Raleway">σ</FONT>z and <FONT face="Raleway">σ</FONT><FONT face="Raleway">ρ</FONT>) depending on the direction, longitudinal or radial. We consider two media other than the interior of the nerve: the perineural sheath that surrounds the nerve, of conductivity <FONT face="Raleway">σ</FONT>s, and the outside of the nerve of conductivity <FONT face="Raleway">σ</FONT>e.</p> | ||
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<div class="legend"><b>Figure 6: </b>Model of the nerve fascicle, with perineural sheath labeled “s” and external surroundings labeled “e”.<sup>[6]</sup></div> | <div class="legend"><b>Figure 6: </b>Model of the nerve fascicle, with perineural sheath labeled “s” and external surroundings labeled “e”.<sup>[6]</sup></div> | ||
<p>The active fiber is simulated as an array of active point current sources along the fiber bundle. Each source represents a node of Ranvier. A nodal distance <FONT face="Raleway">λ</FONT> of 1000 <FONT face="Raleway">μ</FONT>m is chosen. Current values correspond to the nodal currents of an active fiber of 10 <FONT face="Raleway">μ</FONT>m diameter at one instant of time.</p> | <p>The active fiber is simulated as an array of active point current sources along the fiber bundle. Each source represents a node of Ranvier. A nodal distance <FONT face="Raleway">λ</FONT> of 1000 <FONT face="Raleway">μ</FONT>m is chosen. Current values correspond to the nodal currents of an active fiber of 10 <FONT face="Raleway">μ</FONT>m diameter at one instant of time.</p> | ||
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<div class="legend"><b>Figure 7: </b>The line represents the nodal current and the bars represent the currents of the active nodes. Each bar corresponds to one node.<sup>[6]</sup></div> | <div class="legend"><b>Figure 7: </b>The line represents the nodal current and the bars represent the currents of the active nodes. Each bar corresponds to one node.<sup>[6]</sup></div> | ||
<p>From this model, the calculation of the extracellular potential is based on the calculation of the electric field in a cylindrical medium. J. H. MEIER and al. established an analytic expression of the extracellular action potential using the medium transfer function in the spatio-frequency domain[6]. With the simplifications we have chosen, this transfer function is the sum of the series presented below by separating, for each term, a factor depending on the respective positions of the axon and the point of observation, and two factors dependent on the physical characteristics of the nerve considered.</p> | <p>From this model, the calculation of the extracellular potential is based on the calculation of the electric field in a cylindrical medium. J. H. MEIER and al. established an analytic expression of the extracellular action potential using the medium transfer function in the spatio-frequency domain[6]. With the simplifications we have chosen, this transfer function is the sum of the series presented below by separating, for each term, a factor depending on the respective positions of the axon and the point of observation, and two factors dependent on the physical characteristics of the nerve considered.</p> | ||
<p>From this expression, it is possible to determine the "spatial" transfer function h2(z). To do this, it suffices to calculate the inverse Fourier transform of H<FONT face="Raleway">ω</FONT>2, which is done numerically as it can not be practiced analytically[4]. The behavior of these two views of the transfer function is shown in Figure 8 for two reference distances <FONT face="Raleway">ρ</FONT>0 between the observation point M and the axon.</p> | <p>From this expression, it is possible to determine the "spatial" transfer function h2(z). To do this, it suffices to calculate the inverse Fourier transform of H<FONT face="Raleway">ω</FONT>2, which is done numerically as it can not be practiced analytically[4]. The behavior of these two views of the transfer function is shown in Figure 8 for two reference distances <FONT face="Raleway">ρ</FONT>0 between the observation point M and the axon.</p> | ||
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<div class="legend"><b>Figure 8: </b>Spatial transfer function h2(z) and space-frequency H2(k) of the nonhomogeneous and anisotropic medium for two distances <FONT face="Raleway">ρ</FONT>0 = 200 and 500 <FONT face="Raleway">μ</FONT>m between the observation point and the axon (depth of the axon in the nerve).<sup>6]</sup></div> | <div class="legend"><b>Figure 8: </b>Spatial transfer function h2(z) and space-frequency H2(k) of the nonhomogeneous and anisotropic medium for two distances <FONT face="Raleway">ρ</FONT>0 = 200 and 500 <FONT face="Raleway">μ</FONT>m between the observation point and the axon (depth of the axon in the nerve).<sup>6]</sup></div> | ||
<p>It is of course possible to go from the nonhomogeneous and anisotropic model to the homogeneous isotropic model by considering in the nonhomogeneous and anisotropic model that the conductivities of the media are equal to each other and that the perineurium is infinite. It is clear that the nonhomogeneous and anisotropic model is more realistic but the two models give relatively close medium transfer function variation trends.</p> | <p>It is of course possible to go from the nonhomogeneous and anisotropic model to the homogeneous isotropic model by considering in the nonhomogeneous and anisotropic model that the conductivities of the media are equal to each other and that the perineurium is infinite. It is clear that the nonhomogeneous and anisotropic model is more realistic but the two models give relatively close medium transfer function variation trends.</p> | ||
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<p>The objective of this part is to study the spatial and spatio-frequency properties of the extracellular potential created by such an axon.</p> | <p>The objective of this part is to study the spatial and spatio-frequency properties of the extracellular potential created by such an axon.</p> | ||
<p>Two reference distances (<FONT face="Raleway">ρ</FONT>'=200 <FONT face="Raleway">μ</FONT>m and <FONT face="Raleway">ρ</FONT>'=500 <FONT face="Raleway">μ</FONT>m) are considered between the observation point and the axon. The action potential at the surface and along the nerve is represented figure 9 for the two previous models. Whatever the model used, the low-frequency component - space current envelope (see Figure 9) - is found on the action potential generated. It can be concluded that the action potential has the same wavelength as the nodal currents at Ranvier's node. The nonhomogeneous and anisotropic model, closer to reality, gives a greater amplitude than the homogeneous isotropic model. This phenomenon - due partly to the insulating effect of the extraneural electrode - allows to expect a more easily measurable amplitude at the electrode. We also note that the extracellular action potential has a high frequency ripple, we will call this ripple: high spatial frequency component (HSF). This ripple is due to the spatial discretization of transmembrane current sources due to the nodes of Ranvier. The position of each peak is related to the location of a Ranvier node and the period of the ripple is directly related to the distance between two Ranvier nodes. Finally, it is interesting to note that the amplitude of these peaks is significant for the low <FONT face="Raleway">ρ</FONT>' distances, that is to say when the axon is close to the surface of the nerve, and this whatever the model used. </p> | <p>Two reference distances (<FONT face="Raleway">ρ</FONT>'=200 <FONT face="Raleway">μ</FONT>m and <FONT face="Raleway">ρ</FONT>'=500 <FONT face="Raleway">μ</FONT>m) are considered between the observation point and the axon. The action potential at the surface and along the nerve is represented figure 9 for the two previous models. Whatever the model used, the low-frequency component - space current envelope (see Figure 9) - is found on the action potential generated. It can be concluded that the action potential has the same wavelength as the nodal currents at Ranvier's node. The nonhomogeneous and anisotropic model, closer to reality, gives a greater amplitude than the homogeneous isotropic model. This phenomenon - due partly to the insulating effect of the extraneural electrode - allows to expect a more easily measurable amplitude at the electrode. We also note that the extracellular action potential has a high frequency ripple, we will call this ripple: high spatial frequency component (HSF). This ripple is due to the spatial discretization of transmembrane current sources due to the nodes of Ranvier. The position of each peak is related to the location of a Ranvier node and the period of the ripple is directly related to the distance between two Ranvier nodes. Finally, it is interesting to note that the amplitude of these peaks is significant for the low <FONT face="Raleway">ρ</FONT>' distances, that is to say when the axon is close to the surface of the nerve, and this whatever the model used. </p> | ||
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<div class="legend"><b>Figure 9: </b>Example of calculation of the extracellular action potential at the surface of the nerve for two reference distances (<FONT face="Raleway">ρ</FONT>0=200<FONT face="Raleway">μ</FONT>m and <FONT face="Raleway">ρ</FONT>0=500<FONT face="Raleway">μ</FONT>m) between the observation point and the axon. (1a and 1b) Distribution of nodal currents along the axon (identical). (2a and 2b) Transfer function of the medium according to models homogeneous isotropic model (2a) and nonhomogeneous anisotropic model (2b). (3a and 3b) Distribution of the extracellular action potential as a function of the position of the observation point along the nerve<sup>[4]</sup>. </div> | <div class="legend"><b>Figure 9: </b>Example of calculation of the extracellular action potential at the surface of the nerve for two reference distances (<FONT face="Raleway">ρ</FONT>0=200<FONT face="Raleway">μ</FONT>m and <FONT face="Raleway">ρ</FONT>0=500<FONT face="Raleway">μ</FONT>m) between the observation point and the axon. (1a and 1b) Distribution of nodal currents along the axon (identical). (2a and 2b) Transfer function of the medium according to models homogeneous isotropic model (2a) and nonhomogeneous anisotropic model (2b). (3a and 3b) Distribution of the extracellular action potential as a function of the position of the observation point along the nerve<sup>[4]</sup>. </div> | ||
<p>Whatever the model, the transfer functions behave like low-pass filters whose slope depends on the depth of the axon within the nerve. The high frequencies are all the more attenuated that the axon is far from the point of observation.</p> | <p>Whatever the model, the transfer functions behave like low-pass filters whose slope depends on the depth of the axon within the nerve. The high frequencies are all the more attenuated that the axon is far from the point of observation.</p> | ||
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<p>The purpose of this section is to ensure that axon studies can be generalized to fascicles. It is a question of understanding the evolution of the high frequency component of the signal during the superposition of action potentials. Finally, the extracellular action potential will be estimated using the homogeneous and isotropic model.</p> | <p>The purpose of this section is to ensure that axon studies can be generalized to fascicles. It is a question of understanding the evolution of the high frequency component of the signal during the superposition of action potentials. Finally, the extracellular action potential will be estimated using the homogeneous and isotropic model.</p> | ||
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<div class="block full"><b>Figure 10: </b>Histology of a nerve <sup>[8]</sup></div> | <div class="block full"><b>Figure 10: </b>Histology of a nerve <sup>[8]</sup></div> | ||
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<div class="legend"><b>Figure 11: </b>Random model of complete nerve with one elliptic fascicle<sup>[4]</sup>. </div> | <div class="legend"><b>Figure 11: </b>Random model of complete nerve with one elliptic fascicle<sup>[4]</sup>. </div> | ||
<p>It can be observed that the fascicles are roughly elliptical. Our nerve model is therefore based on an assembly of elliptical fascicles. The shape (round, rectangular, etc.), the dimensions of the nerve and the number of fascicles being determined, the forms and positions of the fascicles inside the nerve are fixed randomly. A fascicle is itself built by placing many fibers close to each other, positions and types of these fibers are also generated randomly (according to a uniform law).</p> | <p>It can be observed that the fascicles are roughly elliptical. Our nerve model is therefore based on an assembly of elliptical fascicles. The shape (round, rectangular, etc.), the dimensions of the nerve and the number of fascicles being determined, the forms and positions of the fascicles inside the nerve are fixed randomly. A fascicle is itself built by placing many fibers close to each other, positions and types of these fibers are also generated randomly (according to a uniform law).</p> | ||
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<div class="legend"><b>Figure 12: </b>Example of spatial extracellular action potential along a fascicle. For two distances: <FONT face="Raleway">ρ</FONT>0=200 <FONT face="Raleway">μ</FONT>m (solid line) and <FONT face="Raleway">ρ</FONT>0=500 <FONT face="Raleway">μ</FONT>m (dotted line)<sup>[9]</sup>.</div> | <div class="legend"><b>Figure 12: </b>Example of spatial extracellular action potential along a fascicle. For two distances: <FONT face="Raleway">ρ</FONT>0=200 <FONT face="Raleway">μ</FONT>m (solid line) and <FONT face="Raleway">ρ</FONT>0=500 <FONT face="Raleway">μ</FONT>m (dotted line)<sup>[9]</sup>.</div> | ||
<p>The extracellular action potentials for the configuration described in figure 11 are presented in figure 12 for observation sites (M1 and M2). One being close to the fascicle (<FONT face="Raleway">ρ</FONT>0=200 <FONT face="Raleway">μ</FONT>m), and the other being further away (<FONT face="Raleway">ρ</FONT>0=500 <FONT face="Raleway">μ</FONT>m).</p> | <p>The extracellular action potentials for the configuration described in figure 11 are presented in figure 12 for observation sites (M1 and M2). One being close to the fascicle (<FONT face="Raleway">ρ</FONT>0=200 <FONT face="Raleway">μ</FONT>m), and the other being further away (<FONT face="Raleway">ρ</FONT>0=500 <FONT face="Raleway">μ</FONT>m).</p> |
Revision as of 16:44, 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.
Membrane
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.