The effect of morphology upon electrophysiological responses of retinal ganglion cells: simulation results

Targeted Stimulation of Retinal Ganglion Cells in Epiretinal Prostheses: A Multiscale Computational Study

Retinal prostheses aim at restoring partial sight to patients that are blind due to retinal degenerative diseases by electrically stimulating the surviving healthy retinal neurons. Ideally, the electrical stimulation of the retina is intended to induce localized, focused, percepts only; however, some epiretinal implant subjects have reported seeing elongated phosphenes in a single electrode stimulation due to the axonal activation of retinal ganglion cells (RGCs). This issue can be addressed by properly devising stimulation waveforms so that the possibility of inducing axonal activation of RGCs is minimized. While strategies to devise electrical stimulation waveforms to achieve a focal RGCs response have been reported in literature, the underlying mechanisms are not well understood. This article intends to address this gap; we developed morphologically and biophysically realistic computational models of two classified RGCs: D1-bistratified and A2-monostratified. Computational results suggest that the sodium channel band (SOCB) is less sensitive to modulations in stimulation parameters than the distal axon (DA), and DA stimulus threshold is less sensitive to physiological differences among RGCs. Therefore, over a range of RGCs distal axon diameters, short-pulse symmetric biphasic waveforms can enhance the stimulation threshold difference between the SOCB and the DA. Appropriately designed waveforms can avoid axonal activation of RGCs, implying a consequential reduction of undesired strikes in the visual field.

Type-specific dendritic integration in mouse retinal ganglion cells

Neural computation relies on the integration of synaptic inputs across a neuron’s dendritic arbour. However, it is far from understood how different cell types tune this process to establish cell-type specific computations. Here, using two-photon imaging of dendritic Ca²⁺ signals, electrical recordings of somatic voltage and biophysical modelling, we demonstrate that four morphologically distinct types of mouse retinal ganglion cells with overlapping excitatory synaptic input (transient Off alpha, transient Off mini, sustained Off, and F-mini Off) exhibit type-specific dendritic integration profiles: in contrast to the other types, dendrites of transient Off alpha cells were spatially independent, with little receptive field overlap. The temporal correlation of dendritic signals varied also extensively, with the highest and lowest correlation in transient Off mini and transient Off alpha cells, respectively. We show that differences between cell types can likely be explained by differences in backpropagation efficiency, arising from the specific combinations of dendritic morphology and ion channel densities.

Neurons compute by integrating synaptic inputs across their dendritic arbor. Here, the authors show that distinct cell-types of mouse retinal ganglion cells that receive similar excitatory inputs have different biophysical mechanisms of input integration to generate their unique response tuning.

Automated 3D Soma Segmentation with Morphological Surface Evolution for Neuron Reconstruction

The automatic neuron reconstruction is important since it accelerates the collection of 3D neuron models for the neuronal morphological studies. The majority of the previous neuron reconstruction methods only focused on tracing neuron fibres without considering the somatic surface. Thus, topological errors often present around the soma area in the results obtained by these tracing methods. Segmentation of the soma structures can be embedded in the existing neuron tracing methods to reduce such topological errors. In this paper, we present a novel method to segment the soma structures with complex geometry. It can be applied along with the existing methods in a fully automated pipeline. An approximate bounding block is firstly estimated based on a geodesic distance transform. Then the soma segmentation is obtained by evolving the surface with a set of morphological operators inside the initial bounding region. By evaluating the methods against the challenging images released by the BigNeuron project, we showed that the proposed method can outperform the existing soma segmentation methods regarding the accuracy. We also showed that the soma segmentation can be used for enhancing the results of existing neuron tracing methods.

Simulation of retinal ganglion cell response using fast independent component analysis

Advances in neurobiology suggest that neuronal response of the primary visual cortex to natural stimuli may be attributed to sparse approximation of images, encoding stimuli to activate specific neurons although the underlying mechanisms are still unclear. The responses of retinal ganglion cells (RGCs) to natural and random checkerboard stimuli were simulated using fast independent component analysis. The neuronal response to stimuli was measured using kurtosis and Treves-Rolls sparseness, and the kurtosis, lifetime and population sparseness were analyzed. RGCs exhibited significant lifetime sparseness in response to natural stimuli and random checkerboard stimuli. About 65 and 72% of RGCs do not fire all the time in response to natural and random checkerboard stimuli, respectively. Both kurtosis of single neurons and lifetime response of single neurons values were larger in the case of natural than in random checkerboard stimuli. The population of RGCs fire much less in response to random checkerboard stimuli than natural stimuli. However, kurtosis of population sparseness and population response of the entire neurons were larger with natural than random checkerboard stimuli. RGCs fire more sparsely in response to natural stimuli. Individual neurons fire at a low rate, while the occasional "burst" of neuronal population transmits information efficiently.

Minimizing activation of overlying axons with epiretinal stimulation: The role of fiber orientation and electrode configuration

Currently, a challenge in electrical stimulation of the retina with a visual prosthesis (bionic eye) is to excite only the cells lying directly under the electrode in the ganglion cell layer, while avoiding excitation of axon bundles that pass over the surface of the retina in the nerve fiber layer. Stimulation of overlying axons results in irregular visual percepts, limiting perceptual efficacy. This research explores how differences in fiber orientation between the nerve fiber layer and ganglion cell layer leads to differences in the electrical activation of the axon initial segment and axons of passage. Approach. Axons of passage of retinal ganglion cells in the nerve fiber layer are characterized by a narrow distribution of fiber orientations, causing highly anisotropic spread of applied current. In contrast, proximal axons in the ganglion cell layer have a wider distribution of orientations. A four-layer computational model of epiretinal extracellular stimulation that captures the effect of neurite orientation in anisotropic tissue has been developed using a volume conductor model known as the cellular composite model. Simulations are conducted to investigate the interaction of neural tissue orientation, stimulating electrode configuration, and stimulation pulse duration and amplitude. Main results. Our model shows that simultaneous stimulation with multiple electrodes aligned with the nerve fiber layer can be used to achieve selective activation of axon initial segments rather than passing fibers. This result can be achieved while reducing required stimulus charge density and with only modest increases in the spread of activation in the ganglion cell layer, and is shown to extend to the general case of arbitrary electrode array positioning and arbitrary target volume. Significance. These results elucidate a strategy for more targeted stimulation of retinal ganglion cells with experimentally-relevant multi-electrode geometries and achievable stimulation requirements.

Modeling electrical stimulation of retinal ganglion cell with optimizing additive noises for reducing threshold and energy consumption

BACKGROUND

Epiretinal prosthesis is one device for the treatment of blindness, which target retinal ganglion cells (RGCs) by electrodes on retinal surface. The stimulating current of epiretinal prosthesis is an important factor that influences the safety threshold and visual perception. Stochastic resonance (SR) can be used to enhance the detection and transmission of subthreshold stimuli in neurons. Here, it was assumed that SR was a potential way to improve the performance of epiretinal prosthesis. The effect of noises on the response of RGCs to electrical stimulation and the energy of stimulating current was studied based on a RGC model.

METHODS

The RGC was modeled as a multi-compartment model consisting of dendrites and its branches, soma and axon. To evoke SR, a subthreshold signal, a series of bipolar rectangular pulse sequences, plus stochastic biphasic pulse sequences as noises, were used as a stimulus to the model. The SR-type behavior in the model was characterized by a "power norm" measure. To decrease energy consumption of the stimulation waveform, the stochastic biphasic pulse sequences were only added to the cathode and anode phase of the subthreshold pulse and the noise parameters were optimized by using a genetic algorithm (GA).

RESULTS

When certain intensity of noise is added to the subthreshold signal, RGC model can fire. With the noise's RMS amplitudes increased, more spikes were elicited and the curve of power norm presents the inverted U-like graph. The larger pulse width of stochastic biphasic pulse sequences resulted in higher power norm. The energy consumption and charges of the single bipolar rectangular pulse without noise in threshold level are 468.18 pJ, 15.30 nC, and after adding optimized parameters's noise to the subthreshold signal, they became 314.8174 pJ, 11.9281 nC and were reduced by 32.8 and 22.0%, respectively.

CONCLUSIONS

The SR exists in the RGC model and can enhance the representation of RGC model to the subthreshold signal. Adding the stochastic biphasic pulse sequences to the cathode and anode phase of the subthreshold signal helps to reduce stimulation threshold, energy consumption and charge of RGC stimulation. These may be helpful for improving the performance of epiretinal prosthesis.

Optimal voltage stimulation parameters for network-mediated responses in wild type and rd10 mouse retinal ganglion cells

To further improve the quality of visual percepts elicited by microelectronic retinal prosthetics, substantial efforts have been made to understand how retinal neurons respond to electrical stimulation. It is generally assumed that a sufficiently strong stimulus will recruit most retinal neurons. However, recent evidence has shown that the responses of some retinal neurons decrease with excessively strong stimuli (a non-monotonic response function). Therefore, it is necessary to identify stimuli that can be used to activate the majority of retinal neurons even when such non-monotonic cells are part of the neuronal population. Taking these non-monotonic responses into consideration, we establish the optimal voltage stimulation parameters (amplitude, duration, and polarity) for epiretinal stimulation of network-mediated (indirect) ganglion cell responses. We recorded responses from 3958 mouse retinal ganglion cells (RGCs) in both healthy (wild type, WT) and a degenerating (rd10) mouse model of retinitis pigmentosa-using flat-mounted retina on a microelectrode array. Rectangular monophasic voltage-controlled pulses were presented with varying voltage, duration, and polarity. We found that in 4-5 weeks old rd10 mice the RGC thresholds were comparable to those of WT. There was a marked response variability among mouse RGCs. To account for this variability, we interpolated the percentage of RGCs activated at each point in the voltage-polarity-duration stimulus space, thus identifying the optimal voltage-controlled pulse (-2.4 V, 0.88 ms). The identified optimal voltage pulse can activate at least 65% of potentially responsive RGCs in both mouse strains. Furthermore, this pulse is well within the range of stimuli demonstrated to be safe and effective for retinal implant patients. Such optimized stimuli and the underlying method used to identify them support a high yield of responsive RGCs and will serve as an effective guideline for future in vitro investigations of retinal electrostimulation by establishing standard stimuli for each unique experimental condition.

Single-compartment models of retinal ganglion cells with different electrophysiologies

There are more than 15 different types of retinal ganglion cells (RGCs) in the mammalian retina. To model responses of RGCs to electrical stimulation, we use single-compartment Hodgkin-Huxley-type models and run simulations in the Neuron environment. We use our recently published in vitro data of different morphological cell types to constrain the model, and study the effects of electrophysiology on the cell responses separately from the effects of morphology. We find simple models that can match the spike patterns of different types of RGCs. These models, with different input-output properties, may be used in a network to study retinal network dynamics and interactions.

Electrical activity of ON and OFF retinal ganglion cells: a modelling study

OBJECTIVE

Retinal ganglion cells (RGCs) demonstrate a large range of variation in their ionic channel properties and morphologies. Cell-specific properties are responsible for the unique way RGCs process synaptic inputs, as well as artificial electrical signals such as that from a visual prosthesis. A cell-specific computational modelling approach allows us to examine the functional significance of regional membrane channel expression and cell morphology.

APPROACH

In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs.

MAIN RESULTS

With well-defined cell-specific model parameters and the incorporation of detailed cell morphologies, these models were able to closely reconstruct and predict ON and OFF RGC response properties recorded experimentally.

SIGNIFICANCE

The resulting models were used to study the contribution of different ion channel properties and spatial structure of neurons to RGC activation. The techniques of this study are generally applicable to other excitable cell models, increasing the utility of theoretical models in accurately predicting the response of real biological neurons.

Ionic channel changes in glaucomatous retinal ganglion cells: multicompartment modeling

This research takes a step towards discovering underlying ionic channel changes in the glaucomatous ganglion cells. Glaucoma is characterized by a gradual death of retinal ganglion cells. In this paper, we propose a hypothesis that the ionic channel concentrations change during the progression of glaucoma. We use computer simulation of a multi-compartment morphologically correct model of a mouse retinal ganglion cell to verify our hypothesis. Using published experimental data, we alter the morphology of healthy ganglion cells to replicate glaucomatous cells. Our results suggest that in glaucomatous cell, the sodium channel concentration decreases in the soma by 30% and by 60% in the dendrites, calcium channel concentration decreases by 10% in all compartments, and leak channel concentration increases by 40% in the soma and by 100% in the dendrites.

Multicompartment retinal ganglion cells response to high frequency bi-phasic pulse train stimulation: Simulation results

Retinal ganglion cells (RGCs) are the sole output neurons of the retina that carry information about a visual scene to the brain. By stimulating RGCs with electrical stimulation, it is possible to elicit a sensation of light for people with macular degeneration or retinitis pigmentosa. To investigate the responses of RGCs to high frequency bi-phasic pulse train stimulation, we use previously constrained models of multi-compartment OFF RGCs. The morphologies of mouse RGCs are taken from the Chalupa set of the NeuroMorpho database. The cell models are divided into compartments representing the dendrites, soma and axon that vary between the cells. A total of 132 cells are simulated in the NEURON environment. Results show that the cell morphology plays an important role in the response characteristics of the cell to high frequency bi-phasic pulse train stimulation.