A prosthetic eye to treat blindness - Sheila Nirenberg

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I study how the brain processes information; that is, how it takes information in from the outside world and converts it into patterns of electrical activity. Then, how it uses those patterns to allow you to do things—to see, to hear, to reach for an object. So I'm really a basic scientist, not a clinician. But in the last year and a half, I've started to switch over to use what we've been learning about these patterns of activity to develop prosthetic devices. What I wanted to do today is show you an example of this. It's really our first foray into this; it's the development of a prosthetic device for treating blindness.

Okay, so let me start in on the problem. There are 10 million people in the US and many more worldwide who are blind or facing blindness due to diseases of the retina, diseases like macular degeneration. There's little that can be done for them. There are some drug treatments, but they're only effective on a small fraction of the population. So for the vast majority of patients, their best hope for regaining sight is through prosthetic devices. The problem is that current prosthetics don’t work very well; they’re still very limited in the vision that they can provide. For example, with these devices, patients can see simple things like bright lights and high contrast edges—not very much more. Nothing close to normal vision has been possible.

So what I'm going to tell you about today is a device that we've been working on that I think has the potential to make a difference—to be much more effective. And what I wanted to do is show you how it works.

Okay, so let me back up a little bit and show you how a normal retina works first so you can see the problem that we were trying to solve. Here, you have a retina. You have an image, a retina, and a brain. When you look at something like this image of this baby's face, it goes into your eye and lands on your retina, on the front end cells here, the photoreceptors. Then what happens is the retinal circuitry, the middle part, goes to work on it. What it does is it performs operations on it, it extracts information from it, and it converts that information into a code.

The code is in the form of these patterns of electrical pulses that get sent up to the brain. So the key thing is that the image ultimately gets converted into a code. And when I say code, I do literally mean code. Like this pattern of pulses here actually means baby's face. When the brain gets this pattern of pulses, it knows that what was out there was a baby's face. If it got a different pattern, it would know that what was out there was, say, a dog or another pattern would be a house. Anyway, you get the idea.

In real life, it's all dynamic, meaning that it's changing all the time. The patterns of pulses are changing all the time because the world you're looking at is changing all the time, too. So, you know, it's sort of a complicated thing—you have these patterns of pulses coming out of your eye every millisecond telling your brain what it is that you're seeing.

Okay, so what happens when a person gets a retinal degenerative disease like macular degeneration? What happens is that the front end cells die. The photoreceptors die, and over time, all the cells and the circuits that are connected to them die too, until the only things that you have left are these cells here, the output cells, the ones that send the signals to the brain. But because of all that degeneration, they aren't sending any signals anymore—they aren't getting any input. So the person's brain no longer gets any visual information—that is, he or she is blind.

So a solution to the problem then would be to build a device that could mimic the actions of that front end circuitry and send signals to the retina's output cells. Then they can go back to doing their normal job of sending signals to the brain. So this is what we've been working on. This is what our prosthetic does.

It consists of two parts: what we call an encoder and a transducer. The encoder does just what I was saying; it mimics the actions of the front end circuitry. It takes images in and converts them into the retina's code. Then the transducer makes the output cells send the code on up to the brain. The result is a retinal prosthetic that can produce normal retinal output. So a completely blind retina, even one with no front end circuitry at all, no photoreceptors, can now send out normal signals—signals that the brain can understand. So no other device has been able to do this.

Okay, so I just want to take a sentence or two to say something about the encoder and what it's doing because it's really the key part, and it's sort of interesting—and kind of cool. Not sure "cool" is really the right word, but you know what I mean. So what it's doing is it's replacing the retinal circuitry—really the guts of the retinal circuitry—with a set of equations. A set of equations that we can implement on a chip. So it's just math. In other words, we're not literally replacing the components of the retina. It's not like we're making a little mini-device for each of the different cell types. We’ve just abstracted what the retina is doing with a set of equations.

In a way, the equations are serving as sort of a code book. An image comes in, goes through the set of equations, and out come streams of electrical pulses just like a normal retina would produce.

Okay, so now let me put my money where my mouth is and show you that we can actually produce normal output and what the implications of this are.

Okay, so here are three sets of firing patterns. The top one's from a normal animal, the middle one's from a blind animal that's been treated with this encoder-transducer device, and the bottom one's from a blind animal treated with a standard prosthetic. The bottom one is the state-of-the-art device that's out there right now, which is basically made up of light detectors but no encoder.

So what we did was we presented movies of, you know, everyday things—people, babies, park benches—you know, regular things happening. We recorded the responses from the retinas of these three groups of animals. Just to orient you, each box is showing the firing patterns of several cells, and just as in the previous slides, each row is a different cell. I just made the pulses a little bit smaller and thinner so I could show you like a long stretch of data.

Okay, so as you can see, the firing patterns from the blind animal treated with the encoder-transducer really do very closely match the normal firing patterns. It’s not perfect, but it’s pretty good. The blind animal treated with the standard prosthetic, the responses really don’t match. So with a standard method, the cells do fire; they just don't fire in the normal firing patterns because they don’t have the right code.

How important is this? Like, what’s the potential impact on a patient's ability to see? So I'm just going to show you one bottom line experiment that answers this. Of course, I've got a lot of other data, so if you're interested, I'm happy to show more.

Okay, so the experiment is called a reconstruction experiment. So what we did is we took a moment in time from these recordings and asked, what was the retina seeing at that moment? Can we reconstruct what the retina was seeing from the responses from the firing patterns? So when we did this for responses from the standard method and from our encoder and transducer, let me show you. I'm going to start with the standard method first.

Okay, so you can see that it's pretty limited. Because the firing patterns aren't in the right code, they're very limited in what they can tell you about what's out there. So you can see that there's something there, but it's not so clear what that something is. This just circles back to what I was saying in the beginning—that with a standard method, patients can see high contrast edges, they can see light, but it doesn't easily go further than that.

Okay, so what was the image? It was a baby's face.

Okay, so what about with our approach, adding the code? You can see that it's much better. Not only can you tell that it's a baby's face, but you can tell that it's this baby's face, which is a really challenging task.

Okay, so on the left is the encoder alone, and on the right is from an actual blind retina—the encoder and the transducer. But the key one really is the encoder alone, because we can team up the encoder with the different transducer. This was just actually the first one that we tried.

I want to say something about the standard method. When this first came out, it was just a really exciting thing—the idea that you could even make a blind retina respond at all. But there was this limiting factor—the issue of the code and how to make the cells respond, produce normal responses. This was our contribution.

Okay, so now I just want to wrap up. As I was mentioning earlier, of course, I have a lot of other data if you're interested. But I just wanted to give this sort of basic idea—that this idea of being able to communicate with the brain in its language and the potential power of being able to do that.

It's different from the motor prosthetics where you're communicating from the brain to a device. Here, we have to communicate from the outside world into the brain and be understood—be understood by the brain.

Okay, and then the last thing I wanted to emphasize is that the idea generalizes. The same strategy that we use to find the code for the retina, we can also use to find the code for other areas—for example, the auditory system and the motor system. So for treating deafness and for motor disorders.

Just the same way that we were able to jump over the damaged circuitry in the retina to get to the retina's output cells, we can jump over the damaged circuitry in the auditory nerve or jump over damaged areas in the cortex in the motor cortex to bridge the gap produced by a stroke.

Okay, so I just want to end with a simple message: that understanding the code is really, really important. If we can understand the code—the language of the brain—things become possible that didn't seem obviously possible before.

Thank you.

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