Ординатура / Офтальмология / Английские материалы / Artificial Sight Basic Research, Biomedical Engineering, and Clinical Advances_Humayun, Weiland, Chader_2007

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Figure 19.1. Single spikes are elicited by the leading edge of electrical pulses. (a) Voltage clamp response to a 3 ms 50 A cathodic pulse (solid trace). Large current transients are seen (horizontal arrows) at pulse onset and offset (timing given by square wave above. The response was sensitive to TTX (dotted trace) but only in the time period immediately following the pulse onset (gray box). (b) Subtracting the TTX record from control extracts the spike which is similar to light-elicited spikes (dotted trace). (c) Voltage clamp response to a 200 sec 50 A electrical pulse (solid line). Large transient currents are again seen at pulse onset and offset (horizontal arrows). The response here was also TTX sensitive (dotted trace). (d) Subtracting the TTX record from control extracts the spike.

pulse under control conditions and in TTX (Figure 19.1c, solid and dotted lines respectively) were different, again suggesting the presence of neuronal activity. Subtraction again revealed the presence of a spike (Figure 19.1d). The response to a short pulse in control conditions was always triphasic, consisting of an upward, downward and then second upward deflection. In TTX, the response was always biphasic (no second upward deflection). In later experiments with

required for the generation of these spikes, and suggests that long-duration pulses activate presynaptic neurons that release excitatory neurotransmitter. The early phase spike was not eliminated in the presence of these blockers (Figure 19.2b, inset), indicating that it arises from direct activation of the ganglion cell.

Reliable Generation of One Spike per Pulse

At higher stimulation frequencies, short duration pulses continued to elicit one spike per pulse (Figure 19.3a). The responses to 200 individual pulses, applied at 200 Hz (Figure 19.3a, solid lines), are all tri-phasic, indicating the presence of a neuronal spike in each. Comparison with the TTX response (dotted line) confirms that every stimulus pulse elicited a spike.

A single spike per pulse could be reliably elicited over a wide range of pulse amplitudes as shown in Figure 19.3b. For this cell, pulse amplitudes above 120 A consistently elicited a single spike. Above 360 A, late phase spikes were elicited, likely due to activation of presynaptic neurons.

Discussion

We have developed a stimulus paradigm that elicits one neuronal spike for each stimulus pulse and is effective over a wide range of stimulation frequencies and amplitudes. This is important because it will allow electrical prosthetic devices to generate prescribed patterns of spiking in different ganglion cell types, including those patterns that are normally generated by the retina in response to light stimulation.

Short pulses are likely to simplify the generation of spatially complex patterns of activity because they activate only ganglion cells. Longer pulses activate bipolar cells in addition to ganglion cells and are also likely to activate amacrine cells, either directly or indirectly from activated bipolar cell input. Nearly all activated amacrine cells will generate feedforward inhibitory input to ganglion cells and/or feedback inhibition to bipolar cells [16]. This suggests that the net effect from a single long pulse will be to raise the threshold for activation when a subsequent pulse (via direct stimulation) is delivered. In response to light, amacrine cells generate inhibitory signals with time constants on the order of 100 ms which further suggests that prosthetic stimulation which activates amacrine cells may have a limited temporal response. The use of short pulses avoids the long latency and broad spatial activity.

Short pulses elicit spiking in ganglion cells using less total charge than long pulses. Currently, charge density provides the limiting factor in reducing the size of stimulating electrodes [17]. If we can elicit ganglion cell responses with less total charge, we can reduce the size of the stimulating electrode and possibly generate a more focal response, a strong advantage when designing high-density microelectrode arrays for prosthetic stimulation.

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Acknowledgments. We wish to thank Matt McMahon and David Zhou from Second Sight, LLC for technical advice, Tommy Chong for help with data processing. This work was supported by grants from DARPA, NIH, and funding from Second Sight, LLC.

References

1.Friedman, D. S., B. J O’Colmain et al.(2004) The prevalence of age-related macular degeneration in the United States. “Arch Ophthal” 122; 564–572.

2.Foundation Fighting Blindness, Pamphlet on Retinitis Pigmentosa, 2004. http://www.blindness.org/RetinitisPigmentosa/

3.Bunker, C. H., E. L. Berson et al. (1984). “Prevalence of retinitis pigmentosa in Maine.” Am J Ophthalmol 97(3): 357–365.

4.Curcio, C. A., N. E. Medeiros et al. (1996). “Photoreceptor loss in age-related macular degeneration.” Invest Ophthalmol Vis Sci 37(7): 1236–1249.

5.Stone, J. L., W. E. Barlow et al. (1992). “Morphometric analysis of macular photoreceptors and ganglion cells in retinas with retinitis pigmentosa.” Arch Ophthalmol 110(11): 1634–1639.

6.Humayun, M. S., M. Prince et al. (1999). “Morphometric analysis of the extramacular retina from postmortem eyes with retinitis pigmentosa.” Invest Ophthalmol Vis Sci 40(1): 143–148.

7.Santos, A., M. S. Humayun et al. (1997). “Preservation of the inner retina in retinitis pigmentosa. A morphometric analysis.” Arch Ophthalmol 115(4): 511–515.

8.Humayun, M., R. Propst et al. (1994). “Bipolar surface electrical stimulation of the vertebrate retina.” Arch Ophthalmol 112(1): 110–116.

9.Rizzo, J. F., 3rd, J. Wyatt et al. (2003). “Perceptual efficacy of electrical stimulation of human retina with a microelectrode array during short-term surgical trials.” Invest Ophthalmol Vis Sci 44(12): 5362–5369.

10.Chow, A. Y., V. Y. Chow et al. (2004). “The artificial silicon retina microchip for the treatment of vision loss from retinitis pigmentosa.” Arch Ophthalmol 122(4):460–469

11.Zrenner, E., F. Gekeler et al. (2001). “[Subretinal microphotodiode array as replacement for degenerated photoreceptors?].” Ophthalmologe 98(4): 357–363.

12.Roska, B. and F. Werblin (2001). “Vertical interactions across ten parallel, stacked representations in the mammalian retina.” Nature 410(6828): 583–587.

13.O’Brien, B. J., T. Isayama et al. (2002). “Intrinsic physiological properties of cat retinal ganglion cells.” J Physiol 538(Pt 3): 787–802.

14.Berry, M. J., D. K. Warland et al. (1997). “The structure and precision of retinal spike trains.” Proc Natl Acad Sci USA 94(10): 5411–5416.

15.Berry, M. J., 2nd and M. Meister (1998). “Refractoriness and neural precision.” J Neurosci 18(6): 2200–2211.

16.Wassle, H. (2004). “Parallel processing in the mammalian retina.” Nat Rev Neurosci 5(10): 747–757.

17.Margalit, E., M. Maia et al. (2002). “Retinal prosthesis for the blind.” Surv Ophthalmol 47(4): 335–356.

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A

? B

? C

Figure 20.1. Knowledge of the input–output relationship of the diseased retina will aid prosthesis design, resulting in a higher-quality percept. On the left are two distinct visual scenes, on the right are the visual percepts under four different conditions (a–d, conceptual images only, not based on actual patient reports). (a) A healthy retina creates a high-quality percept. (b) A dysfunctional retina receives input from an electronic prosthesis. Initial clinical results with prototypes are encouraging, reporting percepts that are robust, but unpredictable and variable in size, shape, brightness, color, threshold, and retinotopic origin. (c) By using patient feedback to optimize the image coding strategy of the prosthesis, with larger numbers of electrodes, and through patient behavioral adaptation to the prosthesis (i.e. scanning the visual scene), it is anticipated that a percept containing a useful amount of information will be achieved. Note that a “useful” percept will be defined by individual patients, and may be very fundamental compared to normal vision. (d) With knowledge of the input– output relationship of the diseased retina (absent in (b) and (c)), it may be possible to further improve the quality of the percept by optimizing electrode design and stimulus parameters.

conceptually, the general development of retinal prostheses from a functional point of view. Two primary challenges are to understand the relationship between neural activity in the diseased retina and the formation of images in the mind’s eye, and the relationship between microelectrode-associated currents and neural activity in the diseased retina. We know almost nothing about the former, and very little about the latter.

The objectives of this chapter are to summarize techniques that have been used extensively during the past several decades to learn about the response of the retina to natural, photic stimuli, and to then describe how these techniques can be modified to understand the response of the retina to electrical stimulation. Results from two of these modified techniques, applied to understand the response of the retina to subretinal stimulating electrodes, are presented. The scope of this chapter is limited to in vivo electrophysiological techniques, or those methods which can be used to measure the responses of the retina when it is in as natural a state as possible, in the intact subject (animal or human). Psychophysical tests, in which a patient reports his or her perception of a stimulus, are also excluded due to the complexity of applying these approaches in animal studies (in which case the perception must be inferred from a behavioral response).

With knowledge of how the retina responds to artificial electrical stimulation gained from animal studies, and knowledge of the perceptions of humans for given electrical stimuli, we may be able to infer the activity in the human retina, understand the relationship between activity in the diseased retina and perceptions, and then make effective decisions to improve the design of the prosthesis.

Electrophysiology of Natural Vision

The light response of the retina has been measured by a number of electrophysiological methods, each involving one or more recording electrodes which transduce the ion-based potential differences within the body to electron-based potential differences in metal conductors. The measured potentials are either membrane potentials or field potentials. Membrane potentials are measured by placing one electrode inside a neuron, and measuring the potential difference relative to a second electrode outside the neuron. Due to the high mechanical precision and stability required to place the intracellular electrode without damaging the target neuron, these approaches are rarely used in vivo, and will not be covered here. It should be noted that membrane potential can also be measured using a membrane-bound dye that changes its fluorescent emission properties in a manner proportional to the potential across the membrane to which it is bound; for a number of reasons, these potentiometric probes, or voltage-sensitive dyes, are best suited to in vitro applications when single-cell resolution is the goal.

Local field potentials (LFPs) result from the combined contribution of all nearby neurons, each of which is associated with an electric field due to its

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membrane potential and shape. LFPs are recorded only from nearby neurons for two reasons: First, the magnitude of an electric field falls off with the square of the distance from the source. This is described by Poisson’s equation, and for the very weak electric fields generated by single neurons, means that the field is too weak to be detected above noise levels with typical electrophysiological recording technology. Second, the dipole moment, indicating the magnitude and direction of the electric field generated by an individual neuron, tends to be randomly oriented from neuron to neuron. Therefore, the contributions of a number of neurons sum to zero over any moderate distance from the dipole sources. LFP recordings tend to be low-frequency signals, where the recorded potentials reflect the summed electric fields generated by many action potentials traveling along nearby neurons.

Somewhere between intracellular recording (one neuron per electrode) and what is typically referred to as local field potential recording (roughly tens to hundreds of neurons per electrode) is the technique of extracellular recording. In this approach, a very small electrode (on the order of a neuron soma diameter,10 m) is placed in close proximity to one or more neurons, essentially in direct physical contact, and records the changes in membrane potential that accurately reflect the dynamics of single action potentials. Due to the small distance between recording electrode and target neuron, these signals are much larger in amplitude than the contributions of more distant neurons. Central nervous system neurons are properly referred to as units, and extracellular recordings are categorized as single-unit or multi-unit recordings. While single-unit recordings are generally preferred, the activity of individual neurons in a multi-unit recording can be separated using any number of available spike-sorting techniques (for reviews of available techniques, see Refs. [1, 2]). A straightforward approach would be a simple threshold, where the spikes attributed to two different neurons were distinguished by peak amplitude. Amplitude of the signal recorded from each neuron is strongly influenced by distance to the electrode and orientation of the effective dipole generated by each neuron’s activity. The potential recorded by an extracellular electrode does not directly reflect the membrane potential, Vm, but generally resembles a first or second time derivative of the membrane potential dVm/dt or d2Vm/dt2 , due to the capacitive nature of the coupling between the cell membrane and the metal conductor.

If large populations of neurons are aligned in space, and demonstrate activity coordinated in time, as in the highly organized and stratified retina, the dipole sources and associated field potentials of many millions of neurons sum and can be recorded some distance from the source. Measuring these field potential recordings is perhaps the simplest electrophysiological technique, and is the most common type of clinical measurement, because it can be performed noninvasively. For example, the electroencephalogram (EEG) and electrocardiogram (ECG or EKG) use electrodes applied to the surface of the skin. However, the simplicity of this approach comes at a cost of spatial resolution. That is, it is difficult to know the location of the dipole source within the body that is associated with a potential difference recorded at the skin. Further, recall that

the magnitude of an electric field falls of with the square of the distance from the source; this means that the contribution of an individual cortical neuron to the potential recorded at the scalp (a few centimeters away) is extremely small.

Field potential recordings made from the body surface are often referred to as surface potential recordings. These signals are potential differences measured between two points on the body surface. If you picture the dipole source of the potential difference within the conductive body, the associated electric field can be visualized as a series of increasing shells surrounding the source (like layers of an onion), where each shell represents all of the locations in space that are at the same potential at a given snapshot in time. These isopotential surfaces eventually intersect the boundary of the body, where the electrodes are located. The potential difference recorded on the body surface results from the two electrode locations intercepting two different isopotential surfaces. Attempts to mathematically infer the location of the dipole source from knowledge of the surface potentials is solving the inverse problem (as opposed to solving for the field potential a known distance from the dipole, the forward problem) and is called functional mapping; this is a highly developed area of biomedical modeling. Much of the challenge in functional mapping arises from the complex shapes of the isopotential surfaces due to anatomy and differences in conductivity of the various tissues between the dipole source and the electrodes.

Recent trends in electrophysiology are driven by attempts to overcome the limitations of each of the methods just described. The relative attributes of each method are summarized in Table 20.1.

New technologies and analysis strategies are aimed at the ultimate goal of recording from many individual neurons without sacrificing the ability to resolve individual action potentials. The development of multielectrode arrays attempts to scale up the extracellular recording approach, but is ultimately limited by the displacement of tissue required to introduce the many electrodes into the target structure. The spatial resolution of functional mapping from surface

Table 20.1. Summary of four basic strategies for recording neural activity. Existing methods are a compromise between resolution (temporal and spatial) and the number of individual units that can be resolved in the recorded signal.