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. 2018 Jun;26(6):1111-1120.
doi: 10.1109/TNSRE.2018.2832055.

Increasing Electrical Stimulation Efficacy in Degenerated Retina: Stimulus Waveform Design in a Multiscale Computational Model

Kyle Loizos, Robert Marc, Mark Humayun, James R Anderson, Bryan W Jones, Gianluca Lazzi

Increasing Electrical Stimulation Efficacy in Degenerated Retina: Stimulus Waveform Design in a Multiscale Computational Model

Kyle Loizos et al. IEEE Trans Neural Syst Rehabil Eng. 2018 Jun.

Abstract

A computational model of electrical stimulation of the retina is proposed for investigating current waveforms used in prosthetic devices for restoring partial vision lost to retinal degenerative diseases. The model framework combines a connectome-based neural network model characterized by accurate morphological and synaptic properties with an admittance method model of bulk tissue and prosthetic electronics. In this model, the retina was computationally "degenerated," considering cellular death and anatomical changes that occur early in disease, as well as altered neural behavior that develops throughout the neurodegeneration and is likely interfering with current attempts at restoring vision. A resulting analysis of stimulation range and threshold of ON ganglion cells within the retina that are either healthy or in beginning stages of degeneration is presented for currently used stimulation waveforms, and an asymmetric biphasic current stimulation for subduing spontaneous firing to allow increased control over ganglion cell firing patterns in degenerated retina is proposed. Results show that stimulation thresholds of retinal ganglion cells do not notably vary after beginning stages of retina degeneration. In addition, simulation of proposed asymmetric waveforms showed the ability to enhance the control of ganglion cell firing via electrical stimulation.

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Figures

Fig. 1
Fig. 1
Diagram of the timeline of retina degeneration.
Fig. 2
Fig. 2
Diagram of the multi-scale model of electrical stimulation of retinal tissue, including (Top) a discretized Admittance Method model consisting of a layered structure describing the retina and a 6x10 electrode array placed 0.05 mm away from the retina surface, and (bottom) a rendering of the NEURON model of an ON ganglion cell network, which was tiled to populate the entire ganglion cell, inner plexiform, and bipolar cell layers beneath the electrode array. This resulted in 888 cellular networks, each simulated independently.
Fig. 3
Fig. 3
Connectivity diagram of the neural network model, showing the ON ganglion cell considered in this study and every presynaptic cell (with the morphology depicted in Fig. 2), as extracted from the connectomics dataset. The node at the center is the ON ganglion cell and the nodes on the outside of the circular diagram are bipolar cells (blue) and amacrine cells (orange). The edges represent connections, including ribbon synapses (green), conventional synapses (red), and gap junctions (yellow).
Fig. 4
Fig. 4
Slice of the resulting voltage for a single electrode stimulation for a healthy retina (top) and degenerated retina (bottom).
Fig. 5
Fig. 5
Computed stimulation threshold for a given 1ms biphasic pulse for a single electrode, or simultaneous 60-electrode stimulation, considering direct and/or indirect stimulation for retina before and after early degeneration by considering modified retina layer thickness as given in Table I.
Fig. 6
Fig. 6
Computed stimulation range of ganglion cells, providing the number of ganglion cells stimulated for varying current magnitude and a 1ms wide biphasic pulse.
Fig. 7
Fig. 7
Induced spontaneous activity in the retina neural network model without electrical stimulation intervention, showing the membrane potential for (a) a single cone bipolar cell coupled to an AII cell (reproduction of results in [37] with gap junction conductance of 500 pS); (b) ganglion cell that is post-synaptic to the bipolar cell in (a), showing induced spontaneous firing; (c) the same ganglion cell as plotted in (b), but with all 47 AII amacrine cells integrated into the model, coupled with CBC’s appropriately via gap junctions, and all excitatory synapses included in the neural network model showing increased firing; and (d) the same as in (c) but with all excitatory and inhibitory synapses and gap junctions included in the neural network model, showing additional spontaneous activity.
Fig. 8
Fig. 8
Comparison of a symmetric and an asymmetric biphasic waveform. (a) Symmetric biphasic current stimulus applied in an Admittance Method simulation. (b) Asymmetric biphasic current stimulus applied in an Admittance Method simulation. (c) Simulated ganglion cell membrane voltage using the symmetric current stimulus shown in (a). (d) Simulated ganglion cell membrane voltage resulting from the asymmetric current stimulus shown in (b), illustrating ability to control spiking, eliminating spontaneous firing and limiting action potential to single firing at the time of cathodic pulse.
Fig. 9
Fig. 9
Images of human retina, both before degeneration (top) and after extensive degeneration due to retinitis pigmentosa (bottom).
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