Electrical Field Shaping Techniques in a Feline Model of Retinal Degeneration

Development of a rabbit model of Adenosine triphosphate-induced monocular retinal degeneration for optimization of retinal prostheses

Optimization of retinal prostheses requires preclinical animal models that mimic features of human retinal disease, have appropriate eye sizes to accommodate implantable arrays, and provide options for unilateral degeneration so as to enable a contralateral, within-animal control eye. In absence of a suitable non-human primate model and shortcomings of our previous feline model generated through intravitreal injections of Adenosine Triphosphate (ATP), we aimed in the present study to develop an ATP induced degeneration model in the rabbit. Six normally sighted Dutch rabbits were monocularly blinded with this technique. Subsequent retinal degeneration was assessed with optical coherence tomography, electroretinography, and histological assays. Overall, there was a 42% and 26% reduction in a-wave and oscillatory potential amplitudes in the electroretinograms respectively, along with a global decrease in retinal thickness, with increased variability. Qualitative inspection also revealed that there were variable levels of retinal degeneration and remodeling both within and between treated eyes, mimicking the disease heterogeneity observed in retinitis pigmentosa. These findings confirm that ATP can be utilized to unilaterally induce blinding in rabbits and, potentially present an ideal model for future cortical recording experiments aimed at optimizing vision restoration strategies.Clinical Relevance- A rapid, unilaterally induced model of retinal degeneration in an animal with low binocular overlap and large eyes will allow for clinically valid recordings of downstream cortical activity following retinal stimulation. Such a model would be highly beneficial for the optimization of clinically appropriate vision restoration approaches.

Classifying Retinal Degeneration in Histological Sections Using Deep Learning

Purpose

Artificial intelligence (AI) techniques are increasingly being used to classify retinal diseases. In this study we investigated the ability of a convolutional neural network (CNN) in categorizing histological images into different classes of retinal degeneration.

Methods

Images were obtained from a chemically induced feline model of monocular retinal dystrophy and split into training and testing sets. The training set was graded for the level of retinal degeneration and used to train various CNN architectures. The testing set was evaluated through the best architecture and graded by six observers. Comparisons between model and observer classifications, and interobserver variability were measured. Finally, the effects of using less training images or images containing half the presentable context were investigated.

Results

The best model gave weighted-F1 scores in the range 85% to 90%. Cohen kappa scores reached up to 0.86, indicating high agreement between the model and observers. Interobserver variability was consistent with the model-observer variability in the model's ability to match predictions with the observers. Image context restriction resulted in model performance reduction by up to 6% and at least one training set size resulted in a model performance reduction of 10% compared to the original size.

Conclusions

Detecting the presence and severity of up to three classes of retinal degeneration in histological data can be reliably achieved with a deep learning classifier.

Translational Relevance

This work lays the foundations for future AI models which could aid in the evaluation of more intricate changes occurring in retinal degeneration, particularly in other types of clinically derived image data.

Neural activity shaping utilizing a partitioned target pattern

Electrical stimulation of neural tissue is used in both clinical and experimental devices to evoke a desired spatiotemporal pattern of neural activity. These devices induce a local field that drives neural activation, referred to as an activating function or generator signal. In visual prostheses, the spread of generator signal from each electrode within the neural tissue results in a spread of visual perception, referred to as a phosphene. In cases where neighboring phosphenes overlap, it is desirable to use current steering or neural activity shaping strategies to manipulate the generator signal between the electrodes to provide greater control over the total pattern of neural activity. Applying opposite generator signal polarities in neighboring regions of the retina forces the generator signal to pass through zero at an intermediate point, thus inducing low neural activity that may be perceived as a high-contrast line. This approach provides a form of high contrast visual perception, but it requires partitioning of the target pattern into those regions that use positive or negative generator signals. This discrete optimization is an NP-hard problem that is subject to being trapped in detrimental local minima. This investigation proposes a new partitioning method using image segmentation to determine the most beneficial positive and negative generator signal regions. Utilizing a database of 1000 natural images, the method is compared to alternative approaches based upon the mean squared error of the outcome. Under nominal conditions and with a set computation limit, partitioning provided improvement for 32% of these images. This percentage increased to 89% when utilizing image pre-processing to emphasize perceptual features of the images. The percentage of images that were dealt with most effectively with image segmentation increased as lower computation limits were imposed on the algorithms.

Computational challenges and opportunities for a bi-directional artificial retina

A future artificial retina that can restore high acuity vision in blind people will rely on the capability to both read (observe) and write (control) the spiking activity of neurons using an adaptive, bi-directional and high-resolution device. Although current research is focused on overcoming the technical challenges of building and implanting such a device, exploiting its capabilities to achieve more acute visual perception will also require substantial computational advances. Using high-density large-scale recording and stimulation in the primate retina with an ex vivo multi-electrode array lab prototype, we frame several of the major computational problems, and describe current progress and future opportunities in solving them. First, we identify cell types and locations from spontaneous activity in the blind retina, and then efficiently estimate their visual response properties by using a low-dimensional manifold of inter-retina variability learned from a large experimental dataset. Second, we estimate retinal responses to a large collection of relevant electrical stimuli by passing current patterns through an electrode array, spike sorting the resulting recordings and using the results to develop a model of evoked responses. Third, we reproduce the desired responses for a given visual target by temporally dithering a diverse collection of electrical stimuli within the integration time of the visual system. Together, these novel approaches may substantially enhance artificial vision in a next-generation device.

Retinal Prosthetic Approaches to Enhance Visual Perception for Blind Patients

Retinal prostheses are implantable devices that aim to restore the vision of blind patients suffering from retinal degeneration, mainly by artificially stimulating the remaining retinal neurons. Some retinal prostheses have successfully reached the stage of clinical trials; however, these devices can only restore vision partially and remain insufficient to enable patients to conduct everyday life independently. The visual acuity of the artificial vision is limited by various factors from both engineering and physiological perspectives. To overcome those issues and further enhance the visual resolution of retinal prostheses, a variety of retinal prosthetic approaches have been proposed, based on optimization of the geometries of electrode arrays and stimulation pulse parameters. Other retinal stimulation modalities such as optics, ultrasound, and magnetics have also been utilized to address the limitations in conventional electrical stimulation. Although none of these approaches have been clinically proven to fully restore the function of a degenerated retina, the extensive efforts made in this field have demonstrated a series of encouraging findings for the next generation of retinal prostheses, and these could potentially enhance the visual acuity of retinal prostheses. In this article, a comprehensive and up-to-date overview of retinal prosthetic strategies is provided, with a specific focus on a quantitative assessment of visual acuity results from various retinal stimulation technologies. The aim is to highlight future directions toward high-resolution retinal prostheses.