An Engineering Platform for Clinical Application of Optogenetic Therapy in Retinal Degenerative Diseases.

Optogenetics is a new approach for controlling neural circuits with numerous applications in both basic and clinical science. In retinal degenerative diseases, the photoreceptors die, but inner retinal cells remain largely intact. By expressing light sensitive proteins in the remaining cells, optogenetics has the potential to offer a novel approach to restoring vision. In the past several years, optogenetics has advanced into an early clinical stage, and promising results have been reported. At the current stage, there is an urgent need to develop hardware and software for clinical training, testing, and rehabilitation in optogenetic therapy, which is beyond the capability of existing ophthalmic equipment. In this paper, we present an engineering platform consisting of hardware and software utilities, which allow clinicians to interactively work with patients to explore and assess their vision in optogenetic treatment, providing the basis for prosthetic design, customization, and prescription. This approach is also applicable to other therapies that utilize light activation of neurons, such as photoswitches.Clinical and Translational Impact Statement-The engineering platform allows clinicians to conduct training, testing, and rehabilitation in optogenetic gene therapy for retinal degenerative diseases, providing the basis for prosthetic design, customization, and prescription.

A clinically viable approach to restoring visual function using optogenetic gene therapy.

Optogenetic gene therapies offer a promising strategy for restoring vision to patients with retinal degenerative diseases, such as retinitis pigmentosa (RP). Several clinical trials have begun in this area using different vectors and optogenetic proteins (Clinical Identifiers: NCT02556736, NCT03326336, NCT04945772, and NCT04278131). Here we present preclinical efficacy and safety data for the NCT04278131 trial, which uses an AAV2 vector and Chronos as the optogenetic protein. Efficacy was assessed in mice in a dose-dependent manner using electroretinograms (ERGs). Safety was assessed in rats, nonhuman primates, and mice, using several tests, including immunohistochemical analyses and cell counts (rats), electroretinograms (nonhuman primates), and ocular toxicology assays (mice). The results showed that Chronos-expressing vectors were efficacious over a broad range of vector doses and stimulating light intensities, and were well tolerated: no test article-related findings were observed in the anatomical and electrophysiological assays performed.

An Embedded Real-Time Processing Platform for Optogenetic Neuroprosthetic Applications.

Optogenetics offers a powerful new approach for controlling neural circuits. It has numerous applications in both basic and clinical science. These applications require stimulating devices with small processors that can perform real-time neural signal processing, deliver high-intensity light with high spatial and temporal resolution, and do not consume a lot of power. In this paper, we demonstrate the implementation of neuronal models in a platform consisting of an embedded system module and a portable digital light processing projector. As a replacement for damaged neural circuitry, the embedded module processes neural signals and then directs the projector to optogenetically activate a downstream neural pathway. We present a design in the context of stimulating circuits in the visual system, but the approach is feasible for a broad range of biomedical applications.

Developing an Outcome Measure With High Luminance for Optogenetics Treatment of Severe Retinal Degenerations and for Gene Therapy of Cone Diseases.

PURPOSE: To present stimuli with varied sizes, colors, and patterns over a large range of luminance. METHODS: The filter bar used in scotopic MP1 was replaced with a custom slide-in tray that introduces light from an external projector driven by an additional computer. MP1 software was modified to provide retinal tracking information to the computer driving the projector. Retinal tracking performance was evaluated by imaging the system input and the output simultaneously with a high-speed video system. Spatial resolution was measured with achromatic and chromatic grating/background combinations over scotopic and photopic ranges. RESULTS: The range of retinal illuminance achievable by the modification was up to 6.8 log photopic Trolands (phot-Td); however, in the current work, only a lower range over -4 to +3 log phot-Td was tested in human subjects. Optical magnification was optimized for low-vision testing with gratings from 4.5 to 0.2 cyc/deg. In normal subjects, spatial resolution driven by rods, short wavelength-sensitive (S-) cones, and long/middle wavelength-sensitive (L/M-) cones was obtained by the choice of adapting conditions and wavelengths of grating and background. Data from a patient with blue cone monochromacy was used to confirm mediation. CONCLUSIONS: The modified MP1 can be developed into an outcome measure for treatments in patients with severe retinal degeneration, very low vision, and abnormal eye movements such as those for whom treatment with optogenetics is planned, as well as for patients with cone disorders such as blue cone monochromacy for whom treatment with gene therapy is planned to improve L/M-cone function above a normal complement of rod and S-cone function.

Maintaining ocular safety with light exposure, focusing on devices for optogenetic stimulation.

Optogenetics methods are rapidly being developed as therapeutic tools for treating neurological diseases, in particular, retinal degenerative diseases. A critical component of the development is testing the safety of the light stimulation used to activate the optogenetic proteins. While the stimulation needs to be sufficient to produce neural responses in the targeted retinal cell class, it also needs to be below photochemical and photothermal limits known to cause ocular damage. The maximal permissible exposure is determined by a variety of factors, including wavelength, exposure duration, visual angle, pupil size, pulse width, pulse pattern, and repetition frequency. In this paper, we develop utilities to systematically and efficiently assess the contributions of these parameters in relation to the limits, following directly from the 2014 American National Standards Institute (ANSI). We also provide an array of stimulus protocols that fall within the bounds of both safety and effectiveness. Additional verification of safety is provided with a case study in rats using one of these protocols.

Interacting linear and nonlinear characteristics produce population coding asymmetries between ON and OFF cells in the retina.

The early visual system is a model for understanding the roles of cell populations in parallel processing. Cells in this system can be classified according to their responsiveness to different stimuli; a prominent example is the division between cells that respond to stimuli of opposite contrasts (ON vs OFF cells). These two cell classes display many asymmetries in their physiological characteristics (including temporal characteristics, spatial characteristics, and nonlinear characteristics) that, individually, are known to have important roles in population coding. Here we describe a novel distinction between the information that ON and OFF ganglion cell populations carry in mouse--that OFF cells are able to signal motion information about both light and dark objects, while ON cells have a selective deficit at signaling the motion of dark objects. We found that none of the previously reported asymmetries in physiological characteristics could account for this distinction. We therefore analyzed its basis via a recently developed linear-nonlinear-Poisson model that faithfully captures input/output relationships for a broad range of stimuli (Bomash et al., 2013). While the coding differences between ON and OFF cell populations could not be ascribed to the linear or nonlinear components of the model individually, they had a simple explanation in the way that these components interact. Sensory transformations in other systems can likewise be described by these models, and thus our findings suggest that similar interactions between component properties may help account for the roles of cell classes in population coding more generally.

A virtual retina for studying population coding.

At every level of the visual system - from retina to cortex - information is encoded in the activity of large populations of cells. The populations are not uniform, but contain many different types of cells, each with its own sensitivities to visual stimuli. Understanding the roles of the cell types and how they work together to form collective representations has been a long-standing goal. This goal, though, has been difficult to advance, and, to a large extent, the reason is data limitation. Large numbers of stimulus/response relationships need to be explored, and obtaining enough data to examine even a fraction of them requires a great deal of experiments and animals. Here we describe a tool for addressing this, specifically, at the level of the retina. The tool is a data-driven model of retinal input/output relationships that is effective on a broad range of stimuli - essentially, a virtual retina. The results show that it is highly reliable: (1) the model cells carry the same amount of information as their real cell counterparts, (2) the quality of the information is the same - that is, the posterior stimulus distributions produced by the model cells closely match those of their real cell counterparts, and (3) the model cells are able to make very reliable predictions about the functions of the different retinal output cell types, as measured using Bayesian decoding (electrophysiology) and optomotor performance (behavior). In sum, we present a new tool for studying population coding and test it experimentally. It provides a way to rapidly probe the actions of different cell classes and develop testable predictions. The overall aim is to build constrained theories about population coding and keep the number of experiments and animals to a minimum.

A system for optically controlling neural circuits with very high spatial and temporal resolution.

Optogenetics offers a powerful new approach for controlling neural circuits. It has a vast array of applications in both basic and clinical science. For basic science, it opens the door to unraveling circuit operations, since one can perturb specific circuit components with high spatial (single cell) and high temporal (millisecond) resolution. For clinical applications, it allows new kinds of selective treatments, because it provides a method to inactivate or activate specific components in a malfunctioning circuit and bring it back into a normal operating range [1-3]. To harness the power of optogenetics, though, one needs stimulating tools that work with the same high spatial and temporal resolution as the molecules themselves, the channelrhodopsins. To date, most stimulating tools require a tradeoff between spatial and temporal precision and are prohibitively expensive to integrate into a stimulating/recording setup in a laboratory or a device in a clinical setting [4, 5]. Here we describe a Digital Light Processing (DLP)-based system capable of extremely high temporal resolution (sub-millisecond), without sacrificing spatial resolution. Furthermore, it is constructed using off-the-shelf components, making it feasible for a broad range of biology and bioengineering labs. Using transgenic mice that express channelrhodopsin-2 (ChR2), we demonstrate the system's capability for stimulating channelrhodopsin-expressing neurons in tissue with single cell and sub-millisecond precision.

Determining the role of correlated firing in large populations of neurons using white noise and natural scene stimuli.

The role of correlated firing in representing information has been a subject of much discussion. Several studies in retina, visual cortex, somatosensory cortex, and motor cortex, have suggested that it plays only a minor role, carrying <10% of the total information carried by the neurons (Gawne & Richmond, 1993; Nirenberg et al., 2001; Oram et al., 2001; Petersen, Panzeri, & Diamond, 2001; Rolls et al., 2003). A limiting factor of these studies, however, is that they were carried out using pairs of neurons; how the results extend to large populations was not clear. Recently, new methods for modeling network firing patterns have been developed (Nirenberg & Pandarinath, 2012; Pillow et al., 2008), opening the door to answering this question for more complete populations. One study, Pillow et al. (2008), showed that including correlations increased information by a modest amount, ~20%; however, this work used only a single retina (primate) and a white noise stimulus. Here we performed the analysis using several retinas (mouse) and both white noise and natural scene stimuli. The results showed that correlations added little information when white noise stimuli were used (~13%), similar to Pillow et al.'s findings, and essentially no information when natural scene stimuli were used. Further, the results showed that ignoring correlations did not change the quality of the information carried by the population (as measured by comparing the full pattern of decoding errors). These results suggest generalization: the pairwise analysis in several species show that correlations account for very little of the total information. Now, the analysis with large populations in two species show a similar result, that correlations still account for only a small fraction of the total information, and, most significantly, the amount is not statistically significant when natural stimuli are used, making rapid advances in the study of population coding possible.

Retinal prosthetic strategy with the capacity to restore normal vision.

Retinal prosthetics offer hope for patients with retinal degenerative diseases. There are 20-25 million people worldwide who are blind or facing blindness due to these diseases, and they have few treatment options. Drug therapies are able to help a small fraction of the population, but for the vast majority, their best hope is through prosthetic devices [reviewed in Chader et al. (2009) Prog Brain Res 175:317-332]. Current prosthetics, however, are still very limited in the vision that they provide: for example, they allow for perception of spots of light and high-contrast edges, but not natural images. Efforts to improve prosthetic capabilities have focused largely on increasing the resolution of the device's stimulators (either electrodes or optogenetic transducers). Here, we show that a second factor is also critical: driving the stimulators with the retina's neural code. Using the mouse as a model system, we generated a prosthetic system that incorporates the code. This dramatically increased the system's capabilities--well beyond what can be achieved just by increasing resolution. Furthermore, the results show, using 9,800 optogenetically stimulated ganglion cell responses, that the combined effect of using the code and high-resolution stimulation is able to bring prosthetic capabilities into the realm of normal image representation.

Symmetry breakdown in the ON and OFF pathways of the retina at night: functional implications.

Several recent studies have shown that the ON and OFF channels of the visual system are not simple mirror images of each other, that their response characteristics are asymmetric (Chichilnisky and Kalmar, 2002; Sagdullaev and McCall, 2005). How the asymmetries bear on visual processing is not well understood. Here, we show that ON and OFF ganglion cells show a strong asymmetry in their temporal adaptation to photopic (day) and scotopic (night) conditions and that the asymmetry confers a functional advantage. Under photopic conditions, the ON and OFF ganglion cells show similar temporal characteristics. Under scotopic conditions, the two cell classes diverge-ON cells shift their tuning to low temporal frequencies, whereas OFF cells continue to respond to high. This difference in processing corresponds to an asymmetry in the natural world, one produced by the Poisson nature of photon capture and persists over a broad range of light levels. This work characterizes a previously unknown divergence in the ON and OFF pathways and its utility to visual processing. Furthermore, the results have implications for downstream circuitry and thus offer new constraints for models of downstream processing, since ganglion cells serve as building blocks for circuits in higher brain areas. For example, if simple cells in visual cortex rely on complementary interactions between the two pathways, such as push-pull interactions (Alonso et al., 2001; Hirsch, 2003), their receptive fields may be radically different under scotopic conditions, when the ON and OFF pathways are out of sync.

A novel mechanism for switching a neural system from one state to another.

An animal's ability to rapidly adjust to new conditions is essential to its survival. The nervous system, then, must be built with the flexibility to adjust, or shift, its processing capabilities on the fly. To understand how this flexibility comes about, we tracked a well-known behavioral shift, a visual integration shift, down to its underlying circuitry, and found that it is produced by a novel mechanism - a change in gap junction coupling that can turn a cell class on and off. The results showed that the turning on and off of a cell class shifted the circuit's behavior from one state to another, and, likewise, the animal's behavior. The widespread presence of similar gap junction-coupled networks in the brain suggests that this mechanism may underlie other behavioral shifts as well.

Heterogeneous response dynamics in retinal ganglion cells: the interplay of predictive coding and adaptation.

To make efficient use of their limited signaling capacity, sensory systems often use predictive coding. Predictive coding works by exploiting the statistical regularities of the environment--specifically, by filtering the sensory input to remove its predictable elements, thus enabling the neural signal to focus on what cannot be guessed. To do this, the neural filters must remove the environmental correlations. If predictive coding is to work well in multiple environments, sensory systems must adapt their filtering properties to fit each environment's statistics. Using the visual system as a model, we determine whether this happens. We compare retinal ganglion cell dynamics in two very different environments: white noise and natural. Because natural environments have more power than that of white noise at low temporal frequencies, predictive coding is expected to produce a suppression of low frequencies and an enhancement of high frequencies, compared with the behavior in a white-noise environment. We find that this holds, but only in part. First, predictive coding behavior is not uniform: most on cells manifest it, whereas off cells, on average, do not. Overlaid on this nonuniformity between cell classes is further nonuniformity within both cell classes. These findings indicate that functional considerations beyond predictive coding play an important role in shaping the dynamics of sensory adaptation. Moreover, the differences in behavior between on and off cell classes add to the growing evidence that these classes are not merely homogeneous mirror images of each other and suggest that their roles in visual processing are more complex than expected from the classic view.

Pairwise maximum entropy models for studying large biological systems: when they can work and when they can't.

One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here, we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, then the results may have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of large biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point.

Ruling out and ruling in neural codes.

The subject of neural coding has generated much debate. A key issue is whether the nervous system uses coarse or fine coding. Each has different strengths and weaknesses and, therefore, different implications for how the brain computes. For example, the strength of coarse coding is that it is robust to fluctuations in spike arrival times; downstream neurons do not have to keep track of the details of the spike train. The weakness, though, is that individual cells cannot carry much information, so downstream neurons have to pool signals across cells and/or time to obtain enough information to represent the sensory world and guide behavior. In contrast, with fine coding, individual cells can carry much more information, but downstream neurons have to resolve spike train structure to obtain it. Here, we set up a strategy to determine which codes are viable, and we apply it to the retina as a model system. We recorded from all the retinal output cells an animal uses to solve a task, evaluated the cells' spike trains for as long as the animal evaluates them, and used optimal, i.e., Bayesian, decoding. This approach makes it possible to obtain an upper bound on the performance of codes and thus eliminate those that are insufficient, that is, those that cannot account for behavioral performance. Our results show that standard coarse coding (spike count coding) is insufficient; finer, more information-rich codes are necessary.

Indices for testing neural codes.

One of the most critical challenges in systems neuroscience is determining the neural code. A principled framework for addressing this can be found in information theory. With this approach, one can determine whether a proposed code can account for the stimulus-response relationship. Specifically, one can compare the transmitted information between the stimulus and the hypothesized neural code with the transmitted information between the stimulus and the behavioral response. If the former is smaller than the latter (i.e., if the code cannot account for the behavior), the code can be ruled out. The information-theoretic index most widely used in this context is Shannon's mutual information. The Shannon test, however, is not ideal for this purpose: while the codes it will rule out are truly nonviable, there will be some nonviable codes that it will fail to rule out. Here we describe a wide range of alternative indices that can be used for ruling codes out. The range includes a continuum from Shannon information to measures of the performance of a Bayesian decoder. We analyze the relationship of these indices to each other and their complementary strengths and weaknesses for addressing this problem.

Ganglion cell adaptability: does the coupling of horizontal cells play a role?

BACKGROUND: The visual system can adjust itself to different visual environments. One of the most well known examples of this is the shift in spatial tuning that occurs in retinal ganglion cells with the change from night to day vision. This shift is thought to be produced by a change in the ganglion cell receptive field surround, mediated by a decrease in the coupling of horizontal cells. METHODOLOGY/PRINCIPAL FINDINGS: To test this hypothesis, we used a transgenic mouse line, a connexin57-deficient line, in which horizontal cell coupling was abolished. Measurements, both at the ganglion cell level and the level of behavioral performance, showed no differences between wild-type retinas and retinas with decoupled horizontal cells from connexin57-deficient mice. CONCLUSION/SIGNIFICANCE: This analysis showed that the coupling and uncoupling of horizontal cells does not play a dominant role in spatial tuning and its adjustability to night and day light conditions. Instead, our data suggest that another mechanism, likely arising in the inner retina, must be responsible.

Analyzing the activity of large populations of neurons: how tractable is the problem?

Understanding how the brain performs computations requires understanding neuronal firing patterns at successive levels of processing-a daunting and seemingly intractable task. Two recent studies have made dramatic progress on this problem by showing how its dimensionality can be reduced. Using the retina as a model system, they demonstrated that multineuronal firing patterns can be predicted by pairwise interactions.

Synergy, redundancy, and independence in population codes, revisited.

Decoding the activity of a population of neurons is a fundamental problem in neuroscience. A key aspect of this problem is determining whether correlations in the activity, i.e., noise correlations, are important. If they are important, then the decoding problem is high dimensional: decoding algorithms must take the correlational structure in the activity into account. If they are not important, or if they play a minor role, then the decoding problem can be reduced to lower dimension and thus made more tractable. The issue of whether correlations are important has been a subject of heated debate. The debate centers around the validity of the measures used to address it. Here, we evaluate three of the most commonly used ones: synergy, DeltaI(shuffled), and DeltaI. We show that synergy and DeltaI(shuffled) are confounded measures: they can be zero when correlations are clearly important for decoding and positive when they are not. In contrast, DeltaI is not confounded. It is zero only when correlations are not important for decoding and positive only when they are; that is, it is zero only when one can decode exactly as well using a decoder that ignores correlations as one can using a decoder that does not, and it is positive only when one cannot decode as well. Finally, we show that DeltaI has an information theoretic interpretation; it is an upper bound on the information lost when correlations are ignored.

Computing and stability in cortical networks.

Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the cortex is remarkably stable: normal brains do not exhibit the kind of runaway excitation one might expect of such a system. How does the cortex maintain stability in the face of this massive excitatory feedback? More importantly, how does it do so during computations, which necessarily involve elevated firing rates? Here we address these questions in the context of attractor networks-networks that exhibit multiple stable states, or memories. We find that such networks can be stabilized at the relatively low firing rates observed in vivo if two conditions are met: (1) the background state, where all neurons are firing at low rates, is inhibition dominated, and (2) the fraction of neurons involved in a memory is above some threshold, so that there is sufficient coupling between the memory neurons and the background. This allows "dynamical stabilization" of the attractors, meaning feedback from the pool of background neurons stabilizes what would otherwise be an unstable state. We suggest that dynamical stabilization may be a strategy used for a broad range of computations, not just those involving attractors.