Name: computational modeling of retinal neurons for visual prosthesis research - fundamental approaches
Uploaded: 2022-06-21T13:00:00
Description: Watch this Scientific Journal Video about Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches at JoVE.com

This research is funded by The National Health and Medical Research Council Project Grant (Grant Number 1109056).

1. Setting up the finite element model for electric potential calculations
<ol>
	<li>Determine the simulation steps and the complexity of the model 
	NOTE: The aim of the first step is to clarify the purpose of the modeling, which will determine the necessary elements of the model and the simulation procedure. An important point to consider is the behavior of neural cells that needs to be shown by the model, and what testing protocol would be needed to demonstrate that behavior. This study shows a...

<ol>
	<li>Greenberg, R. J., Velte, T. J., Humayun, M. S., Scarlatis, G. N., de Juan, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+computational+model+of+electrical+stimulation+of+the+retinal+ganglion+cell.">A computational model of electrical stimulation of the retinal ganglion cell.</a> IEEE Transactions on Bio-medical Engineering. 46 (5), 505-514 (1999).</li><li>Guo, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mediating+retinal+ganglion+cell+spike+rates+using+high-frequency+electrical+stimulation.">Mediating retinal ganglion cell spike rates using high-frequency electrical stimulation.</a> Frontiers in Neuroscience. 13, 413 (2019).</li><li>Loizos, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increasing+electrical+stimulation+efficacy+in+degenerated+retina:+Stimulus+waveform+design+in+a+multiscale+computational+model.">Increasing electrical stimulation efficacy in degenerated retina: Stimulus waveform design in a multiscale computational model.</a> IEEE Transactions on Neural Systems and Rehabilitation Engineering. 26 (6), 1111-1120 (2018).</li><li>Cao, X., Sui, X., Lyu, Q., Li, L., Chai, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+different+three-dimensional+electrodes+on+epiretinal+electrical+stimulation+by+modeling+analysis.">Effects of different three-dimensional electrodes on epiretinal electrical stimulation by modeling analysis.</a> Journal of Neuroengineering and Rehabilitation. 12 (1), 73 (2015).</li><li>Wilke, R. G. H., Moghadam, G. K., Lovell, N. H., Suaning, G. J., Dokos, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electric+crosstalk+impairs+spatial+resolution+of+multi-electrode+arrays+in+retinal+implants.">Electric crosstalk impairs spatial resolution of multi-electrode arrays in retinal implants.</a> Journal of Neural Engineering. 8 (4), 046016 (2011).</li><li>AC/DC module user's guide. COMSOL AB Available from: <a target="_blank" href="https://doc.comsol.com/5.4/doc/com.comsol.help.acdc/ACDCModuleUsersGuide.pdf">https://doc.comsol.com/5.4/doc/com.comsol.help.acdc/ACDCModuleUsersGuide.pdf</a> (2018)</li><li>Malmivuo, P., Malmivuo, J., Plonsey, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. , (1995).</li><li>Rall, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrophysiology+of+a+dendritic+neuron+model.">Electrophysiology of a dendritic neuron model.</a> Biophysical Journal. 2, 145-167 (1962).</li><li>Carnevale, N. T., Hines, M. 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F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+description+of+membrane+current+and+its+application+to+conduction+and+excitation+in+nerve.">A quantitative description of membrane current and its application to conduction and excitation in nerve.</a> The Journal of Physiology. 117 (4), 500-544 (1952).</li><li>Liang, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Threshold+suprachoroidal-transretinal+stimulation+current+required+by+different-size+electrodes+in+rabbit+eyes.">Threshold suprachoroidal-transretinal stimulation current required by different-size electrodes in rabbit eyes.</a> Ophthalmic Research. 45 (3), 113-121 (2011).</li><li>Jensen, R. J., Rizzo, J. F., Ziv, O. R., Grumet, A., Wyatt, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thresholds+for+activation+of+rabbit+retinal+ganglion+cells+with+an+ultrafine,+extracellular+microelectrode.">Thresholds for activation of rabbit retinal ganglion cells with an ultrafine, extracellular microelectrode.</a> Investigative Ophthalmology and Visual Science. 44 (8), 3533-3543 (2003).</li><li>Kim, W., Choi, M., Kim, S. -. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+normative+retinal+and+choroidal+thicknesses+of+the+rabbit+as+revealed+by+spectral+domain+optical+coherence+tomography.">The normative retinal and choroidal thicknesses of the rabbit as revealed by spectral domain optical coherence tomography.</a> Journal of the Korean Ophthalmological Society. 62 (3), 354-361 (2021).</li><li>Guo, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+cell+morphology+in+a+computational+model+of+ON+and+OFF+retinal+ganglion+cells.">Influence of cell morphology in a computational model of ON and OFF retinal ganglion cells.</a> 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2013, 4553-4556 (2013).</li><li>Haberbosch, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Safety+aspects,+tolerability+and+modeling+of+retinofugal+alternating+current+stimulation.">Safety aspects, tolerability and modeling of retinofugal alternating current stimulation.</a> Frontiers in Neuroscience. 13, 783 (2019).</li><li>Sheasby, B. W., Fohlmeister, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impulse+encoding+across+the+dendritic+morphologies+of+retinal+ganglion+cells.">Impulse encoding across the dendritic morphologies of retinal ganglion cells.</a> Journal of Neurophysiology. 81 (4), 1685-1698 (1999).</li><li>Rockhill, R. L., Daly, F. J., MacNeil, M. A., Brown, S. P., Masland, R. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+diversity+of+ganglion+cells+in+a+mammalian+retina.">The diversity of ganglion cells in a mammalian retina.</a> Journal of Neuroscience. 22 (9), 3831-3843 (2002).</li><li>Lukasiewicz, P., Werblin, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+slowly+inactivating+potassium+current+truncates+spike+activity+in+ganglion+cells+of+the+tiger+salamander+retina.">A slowly inactivating potassium current truncates spike activity in ganglion cells of the tiger salamander retina.</a> The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 8 (12), 4470-4481 (1988).</li><li>Van Rossum, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Python Reference Manual. , (1995).</li><li>. Welcome to Spyder's Documentation - Spyder 5 documentation Available from: <a target="_blank" href="https://docs.spyder-idle.org/current/index.html">https://docs.spyder-idle.org/current/index.html</a> (2022)</li><li>Rattay, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ways+to+approximate+current-distance+relations+for+electrically+stimulated+fibers.">Ways to approximate current-distance relations for electrically stimulated fibers.</a> Journal of Theoretical Biology. 125 (3), 339-349 (1987).</li><li>Tsai, D., Morley, J. W., Suaning, G. J., Lovell, N. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+activation+and+temporal+response+properties+of+rabbit+retinal+ganglion+cells+following+subretinal+stimulation.">Direct activation and temporal response properties of rabbit retinal ganglion cells following subretinal stimulation.</a> Journal of Neurophysiology. 102 (5), 2982-2993 (2009).</li><li>Tsai, D., Morley, J. W., Suaning, G. J., Lovell, N. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frequency-dependent+reduction+of+voltage-gated+sodium+current+modulates+retinal+ganglion+cell+response+rate+to+electrical+stimulation.">Frequency-dependent reduction of voltage-gated sodium current modulates retinal ganglion cell response rate to electrical stimulation.</a> Journal of Neural Engineering. 8 (6), 066007 (2011).</li><li>Joucla, S., Gli&#232;re, A., Yvert, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+approaches+to+model+extracellular+electrical+neural+microstimulation.">Current approaches to model extracellular electrical neural microstimulation.</a> Frontiers in Computational Neuroscience. 8, 13 (2014).</li><li>. OpenFOAM Available from: <a target="_blank" href="https://www.openfoam.com/">https://www.openfoam.com/</a> (2022)</li><li>Barba, L., Forsyth, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CFD+Python:+The+12+steps+to+Navier-Stokes+equations.">CFD Python: The 12 steps to Navier-Stokes equations.</a> Journal of Open Source Education. 1 (9), 21 (2018).</li></ol>

In this paper, we have demonstrated a modeling workflow that combined finite element and biophysical neuron modeling. The model is highly flexible, as it can be modified in its complexity to fit different purposes, and it provides a way to validate the results against empirical findings. We also demonstrated how we parameterized the model to enable automation.
The two-step modeling method combines the advantages of using FEM and neuron computational suite to solve the neuron's cable equation i...

Visual neuroprostheses are a group of devices that deliver stimulations (electrical, light, etc.) to the neural cells in the visual pathway to create phosphenes or sensation of seeing the light. It is a treatment strategy that has been in clinical use for almost a decade for people with permanent blindness caused by degenerative retinal diseases. Typically, a complete system would include an external camera that captures the visual information around the user, a power supply and computing unit to process and translate the image to a series of electrical pulses, and an implanted electrode array that interfaces the neural tissue and deliver the electrical pulses to the neural cells. The working principle allows a visual neuroprosthesis to be placed in different sites along the visual pathway from the retina to the visual cortex, as long as it is downstream from the damaged tissue. A majority of current research in visual neuroprostheses focuses on increasing the efficacy of the stimulation and improving the spatial acuity to provide a more natural vision.
In the efforts to improve the efficacy of the stimulation, computational modeling has been a cost- and time-effective method to validate a prosthesis design and simulate its visual outcome. Computational modeling in this field gained popularity since 1999 as Greenberg1 modeled the response of a retinal ganglion cell to extracellular electrical stimuli. Since then, computational modeling has been used to optimize the parameters of the electrical pulse2,3 or the geometrical design of the electrode4,5. Despite the variation in complexity and research questions, these models work by determining the electrical voltage distribution in the medium (e.g., neural tissue) and estimating the electrical response that the neurons in the vicinity will produce due to the electrical voltage.
The electrical voltage distribution in a conductor can be found by solving the Poisson equations6 at all locations:
<img alt="Equation 1" src="/files/ftp_upload/63792/63792eq01.jpg" style="margin: auto;" />
<img alt="Equation 2" src="/files/ftp_upload/63792/63792eq02.jpg" style="margin: auto;" />
where E is the electric field, V the electric potential, J the current density, and&#160;&#963; is the electrical conductivity. The <img alt="Equation 12" src="/files/ftp_upload/63792/63792eq12.jpg" style="margin: auto;" /> in the equation indicates a gradient operator. In the case of stationary current, the following boundary conditions are imposed on the model:
<img alt="Equation 3" src="/files/ftp_upload/63792/63792eq3.jpg" style="margin: auto;" />
<img alt="Equation 4" src="/files/ftp_upload/63792/63792eq04.jpg" style="margin: auto;" />
where n is the normal to the surface, &#937; represents the boundary, and I0 represents the specific current. Together, they create electrical insulation at the external boundaries and create a current source for a selected boundary. If we assume a monopolar point source in a homogeneous medium with an isotropic conductivity, the extracellular electric potential at an arbitrary location can be calculated by7:
<img alt="Equation 5" src="/files/ftp_upload/63792/63792eq05.jpg" style="margin: auto;" />
where Ie&#160;is the current and is the distance between the electrode and the point of measurement. When the medium is inhomogeneous or anisotropic, or the electrode array has multiple electrodes, a computational suite to numerically solve the equations can be convenient. A finite-element modeling software6 breaks up the volume conductor into small sections known as &#39;elements&#39;. The elements are interconnected with each other such that the effects of change in one element influence change in others, and it solves the physical equations that serve to describe these elements. With the increasing computational speed of modern computers, this process can be completed within seconds. Once the electric potential is calculated, one can then estimate the electrical response of the neuron.
A neuron sends and receives information in the form of electrical signals. Such signals come in two forms - graded potentials and action potentials. Graded potentials are temporary changes to the membrane potential wherein the voltage across the membrane becomes more positive (depolarization) or negative (hyperpolarization). Graded potentials typically have localized effects. In cells that produce them, action potentials are all-or-nothing responses that can travel long distances along the length of an axon. Both graded and action potentials are sensitive to the electrical as well as the chemical environment. An action potential spike can be produced by various neuronal cell types, including the retinal ganglion cells, when a threshold transmembrane potential is crossed. The action potential spiking and propagation then trigger synaptic transmission of signals to downstream neurons. A neuron can be modeled as a cable that is divided into cylindrical segments, where each segment has capacitance and resistance due to the lipid bilayer membrane8. A neuron computational programme9 can estimate the electrical activity of an electrically-excitable cell by discretizing the cell into multiple compartments and solving the mathematical model10:
<img alt="Equation 6" src="/files/ftp_upload/63792/63792eq06.jpg" style="margin: auto;" />
In this equation, Cm is the membrane capacitance, Ve,n is the extracellular potential at node n, Vi,n the intracellular potential at node n, Rn the intracellular (longitudinal) resistance at node n, and Iion is the ionic current going through the ion channels at node n. The values of V from the FEM model are implemented as Ve,n for all nodes in the neuron when the stimulation is active.
The transmembrane currents from ion channels can be modeled by using Hodgkin-Huxley formulations11:
<img alt="Equation 7" src="/files/ftp_upload/63792/63792eq07.jpg" style="margin: auto;" />
where gi is the specific conductance of the channel, Vm the transmembrane potential (Vi,n - Ve,n) and Eion the reversal potential of the ion channel. For voltage-gated channels, such as Na channel, dimensionless parameters, m, and h, that describe the probability of opening or closing of the channels are introduced:
<img alt="Equation 8" src="/files/ftp_upload/63792/63792eq08.jpg" style="margin: auto;" />
where <img alt="Equation 9" src="/files/ftp_upload/63792/63792eq09.jpg" style="margin: auto;" /> is the maximum membrane conductance for the particular ion channel, and the values of parameters m and h are defined by differential equations:
<img alt="Equation 10" src="/files/ftp_upload/63792/63792eq10.jpg" style="margin: auto;" />
where &#945;x and&#160;&#946;x are voltage-dependent functions that define the rate constants of the ion channel. They generally take the form:
<img alt="Equation 11" src="/files/ftp_upload/63792/63792eq11v2.jpg" style="margin: auto;" />
The values of the parameters in these equations, including maximal conductance, as well as the constants A, B, C, and D, were typically found from empirical measurements.
With these building blocks, models of different complexities can be built by following the steps described. An FEM software is useful when the Poisson equation cannot be solved analytically, such as in the case of inhomogeneous or anisotropic conductance in the volume conductor or when the geometry of the electrode array is complex. After the extracellular potential values have been solved, the neuron cable model can then be numerically solved in the neuron computational software. Combining the two software enables the computation of a complex neuron cell or network to a non-uniform electric field.
A simple two-step model of a retinal ganglion cell under a suprachoroidal stimulation will be built using the aforementioned programs. In this study, the retinal ganglion cell will be subjected to a range of magnitudes of electrical current pulses. The location of the cell relative to the stimulus is also varied to show the distance-threshold relationship. Moreover, the study includes a validation of the computational result against an in vivo study of the cortical activation threshold using different sizes of stimulation electrode12, as well as an in vitro study showing the relationship between the electrode-neuron distance and the activation threshold13.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer workstation</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Windows 64-bit operating system, at least 4GB of RAM, at least 3 GB of disk space</td>
		</tr>
		<tr>
			<td>Anaconda Python</td>
			<td>Anaconda Inc.</td>
			<td>Version 3.9</td>
			<td>The open source Individual Edition containing Python 3.9 and preinstalled packages to perform data manipulation, as well as Spyder Integrated Development Environment. It could be used to control the simulation, as well as to display and analyse the simulation data.</td>
		</tr>
		<tr>
			<td>COMSOL Multiphysics</td>
			<td>COMSOL</td>
			<td>Version 5.6</td>
			<td>The simulation suite to perform finite element modelling. The licence for the AC/DC module should be purchased. The Application Builder capability should be included in the licence to follow the automation tutorial.</td>
		</tr>
		<tr>
			<td>NEURON</td>
			<td>NEURON</td>
			<td>Version 8.0</td>
			<td>A freely-distributed software to perform the computation of neuronal cells and/or neural networks.</td>
		</tr>
	</tbody>
</table>

computational modeling of retinal neurons for visual prosthesis research - fundamental approaches

Computational modeling has become an increasingly important method in neural engineering due to its capacity to predict behaviors of in vivo and in vitro systems. This has the key advantage of minimizing the number of animals required in a given study by providing an often very precise prediction of physiological outcomes. In the field of visual prosthesis, computational modeling has an array of practical applications, including informing the design of an implantable electrode array and prediction of visual percepts that may be elicited through the delivery of electrical impulses from the said array. Some models described in the literature combine a three-dimensional (3D) morphology to compute the electric field and a cable model of the neuron or neural network of interest. To increase the accessibility of this two-step method to researchers who may have limited prior experience in computational modeling, we provide a video of the fundamental approaches to be taken in order to construct a computational model and utilize it in predicting the physiological and psychophysical outcomes of stimulation protocols deployed via a visual prosthesis. The guide comprises the steps to build a 3D model in a finite element modeling (FEM) software, the construction of a retinal ganglion cell model in a multi-compartmental neuron computational software, followed by the amalgamation of the two. A finite element modeling software to numerically solve physical equations would be used to solve electric field distribution in the electrical stimulations of tissue. Then, specialized software to simulate the electrical activities of a neural cell or network was used. To follow this tutorial, familiarity with the working principle of a neuroprosthesis, as well as neurophysiological concepts (e.g., action potential mechanism and an understanding of the Hodgkin-Huxley model), would be required.

We conducted two simulation protocols to demonstrate the use of the model. The first protocol involved varying the electrode size while keeping the location of the neuron and the electrical pulse parameters the same. The second protocol involved shifting the neuron in the x-direction in 100 &#181;m steps, while the size of the electrode remained constant. For both protocols, the pulse used was a single cathodic-first biphasic pulse of 0.25 ms width with a 0.05 ms interphase gap. For the first protocol, the radius of the ...

Watch this Scientific Journal Video about Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches at JoVE.com

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

We summarize a workflow to computationally model a retinal neuron's behaviors in response to electrical stimulation. The computational model is versatile and includes automation steps that are useful in simulating a range of physiological scenarios and anticipating the outcomes of future in vivo/in vitro studies.

Computational modeling has become an increasingly important method in neural engineering due to its capacity to predict behaviors of in vivo and in vitro ...

This protocol helps predict the response of neural cells to different stimuli, allowing us to explore novel ideas in neural stimulation without needing physical prototype or life tissue. It can be used as a preliminary study to test concepts that are challenging to emulate physically, and it is cost and time-efficient, enabling testing of large numbers of parameters. This method can predict neural response in other neural stimulation systems besides the retina.It is also not limited to electrical stimulations, but can be used for light stimuli as well. To begin, run the FEM software and click on model wizard, then 3D. In the select physics list box, expand AC/DC and select electric fields and current.Click on study and add a stationary study under the general studies option, and then click on done. In the geometry settings, change the unit of length from meter to micrometer. Right-click on geometry one, then click block to create a block domain.Repeat this step two more times to create three blocks in total. For all blocks, set both the depth and width to 5, 000 micrometers and assign the height value for the block. Change the base option to center and assign Z-values for each block.To create a work plane for adding an electrode to the model, right-click on the geometry one in the model tree and choose work plane. Click on work plane one and change the plane type to face parallel. Click on the activate selection button below the plane type and choose the bottom surface of block one.Click on parameters one and define the value for the radius of the electrode. To draw a disk electrode on the work plane, click on plane geometry under work plane one and click on sketch in the main toolbar. Select circle, click anywhere inside the rectangle in the graphics tab, and drag to create a disk electrode.Change the radius to a predefined value in micrometer, xw and yw to zero micrometer, and then click on build all. In the model tree, right-click on material, then click blank material, and then click on material one and change the selection to manual. Click on the domains in the graphics window so that only domain one is chosen.Choose material properties, basic properties, then click electrical conductivity and click on the add to material button. Change the electrical conductivity value to 0.043 siemens per meter. Repeat the steps for domains two and three with the electrical conductivity values of 0.7 and 1.55 siemens per meter, respectively.To mesh a 3D model, go to the model tree and right-click on mesh one, then click free tetrahedral. Click on free tetrahedral one and choose build all. To apply physics to FEM, expand electric currents one in model tree and check whether current conservation one, electric insulation one, and initial values one are listed.Then right-click on the electric currents. Then click ground and apply this to the surface furthest from the electrode. Next, right-click on the electric currents.Then click floating potential assigned to the disk electrode and change the I0 value to one microampere to apply a unitary current. To run the simulation with a parametric sweep, in the model tree, right-click on study one, then click parametric sweep. Click on parametric sweep, and in the study setting table, click on add, and then choose elec_rad for the parameter name.Type 50, 150, 350, 500 for the parameter value list and micrometer for the parameter unit. Then click on compute to run the study. To import the morphology using the CellBuilder feature, run nrngui from the NEURON Computational Suite's installation folder.Next, click on tools, then click miscellaneous, import 3D, and then tick the choose a file box. Locate the downloaded SWC file and click on read. Once the geometry has been imported, click on export, then CellBuilder.To create a HOC file of the imported cell morphology, go to the subsets tab and observe the subsets that have been predefined in the model. Tick the continuous create box, go to management, then click export and export the morphology as rgc.hoc. To view the morphology of the cell, click on tool, model view, one real cells.Then click root soma zero on the toolbar. Right-click on the appearing window and click on access type and view access. By visual inspection, this model's dendritic field diameter should be around 250 micrometers.Close the NEURON windows for now. Open the FEM software. Go to the Application Builder.Right-click on methods in the Application Builder tree. Choose new method and click on okay. Go to file, then click preferences and methods.Tick the view all codes box and click on okay. Write the HOC file that loads the coordinates of the neurons'segments to a text file. Use the FEM method script to shift the values to match the desired location and save a text file containing the coordinate values for the new location of the cell.Open the COMSOL method and save the shifted coordinate and voltage values. To run the automated steps in the FEM software, switch to the model builder, developer, run method, and click method one. This will produce DAT files with the appropriate voltage values.Loop the simulations in a general-purpose programming language by opening the chosen IDE and click on new file to make a new script, as determined in the text manuscript. Finally, click on run or press F5 to run the script, which will also open the NEURON Computational Suite's GUI. Graph the response of the NEURON model to the extracellular stimulation in the NEURON Computational Suite's GUI.To do this, run stimulation. hoc, click on graph, then click voltage access from the toolbar, and on the graph window, right-click anywhere and choose, plot what? Type in axon.v1 in the variable to graph field, which means that it will plot the transmembrane potential of the last segment of the axon per time step. The model described here confirmed that increasing the suprachoroidal electrode size at a 0.25 millisecond pulse width increased the activation threshold of the model neuron. The action potential characteristics were observed to validate the model.The latency or the time between the stimulus onset and the peak of the action potential spike ranged from 1 to 2.2 milliseconds. This corresponded to the short latency spiking due to non-network-mediated retinal activation. The spike width of this model was one millisecond and this is in the same range as the spike widths of rabbit RGCs measured in vitro.The model showed the lowest threshold when the narrow segment of the axon was immediately above the disk electrode and it increased as the X distance became larger. Moving the electrode further toward the distal axon produced a lower threshold as compared to moving the electrode toward the dendrites due to the presence of the axon initial segment and the narrow segment where the sodium channels are more prevalent. The described method is easy to apply and it accelerates researchers in producing proofs of concept for novel stimulation methods or neuro-electro designs.

computational-modeling-retinal-neurons-for-visual-prosthesis-research

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Neuroscience

Cut-loading: A Useful Tool for Examining the Extent of Gap Junction Tracer Coupling Between Retinal Neurons

In addition to chemical synaptic transmission, neurons that are connected by gap junctions can also communicate rapidly via electrical synaptic transmission. Increasing evidence indicates that gap junctions not only permit electrical current flow and synchronous activity between interconnected or coupled cells, but that the strength or effectiveness of electrical communication between coupled cells can be modulated to a great extent1,2. In addition, the large internal diameter (~1.2 nm) of many gap junction channels permits not only electric current flow, but also the diffusion of intracellular signaling molecules and small metabolites between interconnected cells, so that gap junctions may also mediate metabolic and chemical communication. The strength of gap junctional communication between neurons and its modulation by neurotransmitters and other factors can be studied by simultaneously electrically recording from coupled cells and by determining the extent of diffusion of tracer molecules, which are gap junction permeable, but not membrane permeable, following iontophoretic injection into single cells. However, these procedures can be extremely difficult to perform on neurons with small somata in intact neural tissue.
Numerous studies on electrical synapses and the modulation of electrical communication have been conducted in the vertebrate retina, since each of the five retinal neuron types is electrically connected by gap junctions3,4. Increasing evidence has shown that the circadian (24-hour) clock in the retina and changes in light stimulation regulate gap junction coupling3-8. For example, recent work has demonstrated that the retinal circadian clock decreases gap junction coupling between rod and cone photoreceptor cells during the day by increasing dopamine D2 receptor activation, and dramatically increases rod-cone coupling at night by reducing D2 receptor activation7,8. However, not only are these studies extremely difficult to perform on neurons with small somata in intact neural retinal tissue, but it can be difficult to adequately control the illumination conditions during the electrophysiological study of single retinal neurons to avoid light-induced changes in gap junction conductance.
Here, we present a straightforward method of determining the extent of gap junction tracer coupling between retinal neurons under different illumination conditions and at different times of the day and night. This cut-loading technique is a modification of scrape loading9-12, which is based on dye loading and diffusion through open gap junction channels. Scrape loading works well in cultured cells, but not in thick slices such as intact retinas. The cut-loading technique has been used to study photoreceptor coupling in intact fish and mammalian retinas7, 8,13, and can be used to study coupling between other retinal neurons, as described here.

An easy and convenient method to determine the extent of gap junction tracer coupling between retinal neurons is described. This technique enables one to investigate the function of the electrical synapses between neurons in the intact retina under different illumination conditions and at different times of the day and night.

In addition to chemical synaptic transmission, neurons that are connected by gap junctions can also communicate rapidly via electrical synaptic ...

Single-cell Profiling of Developing and Mature Retinal Neurons

Highly specialized, but exceedingly small populations of cells play important roles in many tissues. The identification of cell-type specific markers and gene expression programs for extremely rare cell subsets has been a challenge using standard whole-tissue approaches. Gene expression profiling of individual cells allows for unprecedented access to cell types that comprise only a small percentage of the total tissue1-7. In addition, this technique can be used to examine the gene expression programs that are transiently expressed in small numbers of cells during dynamic developmental transitions8.
This issue of cellular diversity arises repeatedly in the central nervous system (CNS) where neuronal connections can occur between quite diverse cells9. The exact number of distinct cell types is not precisely known, but it has been estimated that there may be as many as 1000 different types in the cortex itself10. The function(s) of complex neural circuits may rely on some of the rare neuronal types and the genes they express. By identifying new markers and helping to molecularly classify different neurons, the single-cell approach is particularly useful in the analysis of cell types in the nervous system. It may also help to elucidate mechanisms of neural development by identifying differentially expressed genes and gene pathways during early stages of neuronal progenitor development.
As a simple, easily accessed tissue with considerable neuronal diversity, the vertebrate retina is an excellent model system for studying the processes of cellular development, neuronal differentiation and neuronal diversification. However, as in other parts of the CNS, this cellular diversity can present a problem for determining the genetic pathways that drive retinal progenitors to adopt a specific cell fate, especially given that rod photoreceptors make up the majority of the total retinal cell population11. Here we report a method for the identification of the transcripts expressed in single retinal cells (Figure 1). The single-cell profiling technique allows for the assessment of the amount of heterogeneity present within different cellular populations of the retina2,4,5,12. In addition, this method has revealed a host of new candidate genes that may play role(s) in the cell fate decision-making processes that occur in subsets of retinal progenitor cells8. With some simple adjustments to the protocol, this technique can be utilized for many different tissues and cell types.

A method for the isolation of single retinal cells and subsequent amplification of their cDNAs is described. Single-cell transcriptomics reveals the degree of cellular heterogeneity present in a tissue and uncovers new marker genes for rare cell populations. The accompanying protocol can be adjusted to suit many different cell types.

Highly specialized, but exceedingly small populations of cells play important roles in many tissues. The identification of cell-type specific markers and ...

Simultaneous Whole-cell Recordings from Photoreceptors and Second-order Neurons in an Amphibian Retinal Slice Preparation

One of the central tasks in retinal neuroscience is to understand the circuitry of retinal neurons and how those connections are responsible for shaping the signals transmitted to the brain. Photons are detected in the retina by rod and cone photoreceptors, which convert that energy into an electrical signal, transmitting it to other retinal neurons, where it is processed and communicated to central targets in the brain via the optic nerve. Important early insights into retinal circuitry and visual processing came from the histological studies of Cajal1,2 and, later, from electrophysiological recordings of the spiking activity of retinal ganglion cells - the output cells of the retina3,4.
A detailed understanding of visual processing in the retina requires an understanding of the signaling at each step in the pathway from photoreceptor to retinal ganglion cell. However, many retinal cell types are buried deep in the tissue and therefore relatively inaccessible for electrophysiological recording. This limitation can be overcome by working with vertical slices, in which cells residing within each of the retinal layers are clearly visible and accessible for electrophysiological recording.
Here, we describe a method for making vertical sections of retinas from larval tiger salamanders (Ambystoma tigrinum). While this preparation was originally developed for recordings with sharp microelectrodes5,6, we describe a method for dual whole-cell voltage clamp recordings from photoreceptors and second-order horizontal and bipolar cells in which we manipulate the photoreceptor's membrane potential while simultaneously recording post-synaptic responses in horizontal or bipolar cells. The photoreceptors of the tiger salamander are considerably larger than those of mammalian species, making this an ideal preparation in which to undertake this technically challenging experimental approach. These experiments are described with an eye toward probing the signaling properties of the synaptic ribbon - a specialized synaptic structure found in a only a handful of neurons, including rod and cone photoreceptors, that is well suited for maintaining a high rate of tonic neurotransmitter release7,8 - and how it contributes to the unique signaling properties of this first retinal synapse.

We describe the preparation of thin retinal slices from aquatic tiger salamanders (Ambystoma tigrinum) and explain how we use these slices to study synaptic processing in the retina by obtaining dual whole-cell voltage clamp recordings from photoreceptors and second-order horizontal and bipolar cells.

One of the central tasks in retinal neuroscience  is to understand the circuitry of retinal neurons and how those connections are  responsible for shaping ...

Modeling Neuronal Death and Degeneration in Mouse Primary Cerebellar Granule Neurons

Cerebellar granule neurons (CGNs) are a commonly used neuronal model, forming an abundant homogeneous population in the cerebellum. In light of their post-natal development, abundance, and accessibility, CGNs are an ideal model to study neuronal processes, including neuronal development, neuronal migration, and physiological neuronal activity stimulation. In addition, CGN cultures provide an excellent model for studying different modes of cell death including excitotoxicity and apoptosis. Within a week in culture, CGNs express N-methyl-D-aspartate (NMDA) receptors, a specific ionotropic glutamate receptor with many critical functions in neuronal health and disease. The addition of low concentrations of NMDA in conjunction with membrane depolarization to rodent primary CGN cultures has been used to model physiological neuronal activity stimulation while the addition of high concentrations of NMDA can be employed to model excitotoxic neuronal injury. Here, a method of isolation and culturing of CGNs from 6 day old pups as well as genetic manipulation of CGNs by adenoviruses and lentiviruses are described. We also present optimized protocols on how to stimulate NMDA-induced excitotoxicity, low-potassium-induced apoptosis, oxidative stress and DNA damage following transduction of these neurons.

This protocol describes a simple method for isolating and culturing primary mouse cerebral granule neurons (CGNs) from 6-7 day old pups, efficient transduction of CGNs for loss and gain of function studies, and modelling NMDA-induced neuronal excitotoxicity, low-potassium-induced cell death, DNA-damage, and oxidative stress using the same culture model.

Cerebellar granule neurons (CGNs) are a commonly used neuronal model, forming an abundant homogeneous population in the cerebellum. In light of their ...

Generation of Human Neurons and Oligodendrocytes from Pluripotent Stem Cells for Modeling Neuron-Oligodendrocyte Interactions

In Alzheimer&#8217;s disease (AD) and other neurodegenerative disorders, oligodendroglial failure is a common early pathological feature, but how it contributes to disease development and progression, particularly in the gray matter of the brain, remains largely unknown. The dysfunction of oligodendrocyte lineage cells is hallmarked by deficiencies in myelination and impaired self-renewal of oligodendrocyte precursor cells (OPCs). These two defects are caused at least in part by the disruption of interactions between neuron and oligodendrocytes along the buildup of pathology. OPCs give rise to myelinating oligodendrocytes during CNS development. In the mature brain cortex, OPCs are the major proliferative cells (comprising ~5% of total brain cells) and control new myelin formation in a neural activity-dependent manner. Such neuron-to-oligodendrocyte communications are significantly understudied, especially in the context of neurodegenerative conditions such as AD, due to the lack of appropriate tools. In recent years, our group and others have made significant progress to improve currently available protocols to generate functional neurons and oligodendrocytes individually from human pluripotent stem cells. In this manuscript, we describe our optimized procedures, including the establishment of a co-culture system to model the neuron-oligodendrocyte connections. Our illustrative results suggest an unexpected contribution from OPCs/oligodendrocytes to the brain amyloidosis and synapse integrity and highlight the utility of this methodology for AD research. This reductionist approach is a powerful tool to dissect the specific hetero-cellular interactions out of the inherent complexity inside the brain. The protocols we describe here are expected to facilitate future studies on oligodendroglial defects in the pathogenesis of neurodegeneration.

The neuron-glial interactions in neurodegeneration are not well understood due to inadequate tools and methods. Here, we describe optimized protocols to obtain induced neurons, oligodendrocyte precursor cells, and oligodendrocytes from human pluripotent stem cells and provide examples of the values of these methods in understanding cell-type-specific contributions in Alzheimer&#8217;s disease.

In Alzheimer&#8217;s disease (AD) and other neurodegenerative disorders, oligodendroglial failure is a common early pathological feature, but how it ...

Mouse Retina OCT Fibergram Alignment with Confocal Images

Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina

In recent years, in vivo retinal imaging, which provides non-invasive, real-time, and longitudinal information about biological systems and processes, has been increasingly applied to obtain an objective assessment of neural damage in eye diseases. Ex vivo confocal imaging of the same retina is often necessary to validate the in vivo findings especially in animal research. In this study, we demonstrated a method for aligning an ex vivo confocal image of the mouse retina with its in vivo images. A new clinical-ready imaging technology called visible light optical coherence tomography fibergraphy (vis-OCTF) was applied to acquire in vivo images of the mouse retina. We then performed the confocal imaging of the same retina as the &#34;gold standard&#34; to validate the in vivo vis-OCTF images. This study not only enables further investigation of the molecular and cellular mechanisms but also establishes a foundation for a sensitive and objective evaluation of neural damage in vivo.

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment 

The present protocol outlines the steps for aligning in vivo visible-light optical coherence tomography fibergraphy (vis-OCTF) images with ex vivo confocal images of the same mouse retina for the purpose of verifying the observed retinal ganglion cell axon bundle morphology in the in vivo images.

In recent years, in vivo retinal imaging, which provides non-invasive, real-time, and longitudinal information about biological systems and processes, has ...

Advancing Visual Cortex Mapping for Enhanced Evaluation of Vision Disorders

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

High-resolution retinotopic blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) with a wide-view presentation can be used to functionally map the peripheral and central visual cortex. This method for measuring functional changes of the visual brain allows for functional mapping of the occipital lobe, stimulating &gt;100&#176; (&plusmn;50&#176;) or more of the visual field, compared to standard fMRI visual presentation setups which usually cover &lt;30&#176; of the visual field. A simple wide-view stimulation system for BOLD fMRI can be set up using common MR-compatible projectors by placing a large mirror or screen close to the subject's face and using only the posterior half of a standard head coil to provide a wide-viewing angle without obstructing their vision. The wide-view retinotopic fMRI map can then be imaged using various retinotopic stimulation paradigms, and the data can be analyzed to determine the functional activity of visual cortical regions corresponding to central and peripheral vision. This method provides a practical, easy-to-implement visual presentation system that can be used to evaluate changes in the peripheral and central visual cortex due to eye diseases such as glaucoma and the vision loss that may accompany them.

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping 

We have developed techniques for mapping the visual cortex function utilizing more of the visual field than is commonly used. This approach has the potential to enhance the evaluation of vision disorders and eye diseases.

High-resolution retinotopic blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) with a wide-view presentation can be ...

Visualizing Retinal Synaptic Structures in 3D with Volume Electron Microscopy

Preparation of Retinal Samples for Volume Electron Microscopy

Volume electron microscopy (Volume EM) has emerged as a powerful tool for visualizing the 3D structure of cells and tissues with nanometer-level precision. Within the retina, various types of neurons establish synaptic connections in the inner and outer plexiform layers. While conventional EM techniques have yielded valuable insights into retinal subcellular organelles, their limitation lies in providing 2D image data, which can hinder accurate measurements. For instance, quantifying the size of three distinct synaptic vesicle pools, crucial for synaptic transmission, is challenging in 2D. Volume EM offers a solution by providing large-scale, high-resolution 3D data. It is worth noting that sample preparation is a critical step in Volume EM, significantly impacting image clarity and contrast. In this context, we outline a sample preparation protocol for the 3D reconstruction of photoreceptor axon terminals in the retina. This protocol includes three key steps: retina dissection and fixation, sample embedding processes, and selection of the area of interest.

Author Spotlight: Retinal Neuroscience Studies with Volume Electron Microscopy

This protocol describes the detailed steps for preparing retinal samples for volume electron microscopy, focusing on the structural features of retinal photoreceptor terminals.

Volume electron microscopy (Volume EM) has emerged as a powerful tool for visualizing the 3D structure of cells and tissues with nanometer-level ...

Optimized Protocol for Retinal Organoid Sample Preparation for TEM

Preparing Retinal Organoid Samples for Transmission Electron Microscopy

Retinal organoids (ROs) are a three-dimensional culture system mimicking human retinal features that have differentiated from induced pluripotent stem cells (iPSCs) under specific conditions. Synapse development and maturation in ROs have been studied immunocytochemically and functionally. However, the direct evidence of the synaptic contact ultrastructure is limited, containing both special ribbon synapses and conventional chemical synapses. Transmission electron microscopy (TEM) is characterized by high resolution and a respectable history elucidating retinal development and synapse maturation in humans and various species. It is a powerful tool to explore synaptic structure in ROs and is widely used in the research field of ROs. Therefore, to better explore the structure of RO synaptic contacts at the nanoscale and obtain high-quality microscopic evidence, we developed a simple and repeatable method of RO TEM sample preparation. This paper describes the protocol, reagents used, and detailed steps, including RO fixation preparation, post fixation, embedding, and visualization.

Author Spotlight: Analyzing the Synaptic Ultrastructure in Mature Retinal Organoids Using TEM

This protocol provides an optimized and elaborate preparation procedure for retinal organoid samples for transmission electron microscopy. It is suitable for applications that involve the analysis of synapses in mature retinal organoids.

Retinal organoids (ROs) are a three-dimensional culture system mimicking human retinal features that have differentiated from induced pluripotent stem ...

Synaptic Circuits Analysis and Protein Localization in the Mouse Retina

Investigating Retinal Circuits and Molecular Localization by Pre&#45;Embedding Immunoelectron Microscopy

The retina comprises numerous cells forming diverse neuronal circuits, which constitute the first stage of the visual pathway. Each circuit is characterized by unique features and distinct neurotransmitters, determining its role and functional significance. Given the intricate cell types within its structure, the complexity of neuronal circuits in the retina poses challenges for exploration. To better investigate retinal circuits and cross-talk, such as the link between cone and rod pathways, and precise molecular localization (neurotransmitters or neuropeptides), such as the presence of substance P-like immunoreactivity in the mouse retina, we employed a pre-embedding immunoelectron microscopy (immuno-EM) method to explore synaptic connections and organization. This approach enables us to pinpoint specific intercellular synaptic connections and precise molecular localization and could play a guiding role in exploring its function. This article describes the protocol, reagents used, and detailed steps, including (1) retina fixation preparation, (2) pre-embedding immunostaining, and (3) post-fixation and embedding.

Author Spotlight: Understanding the Ultrastructural Basis of Retinal Synaptic Connectivity and Neurotransmitter Localization in Mice 

This protocol outlines the detailed steps of pre-embedding immunoelectron microscopy, with a focus on exploring synaptic circuits and protein localization in the retina.