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.

Dirty-data-based alarm prediction in self-optimizing large-scale optical networks.

Machine-learning-based solutions are showing promising results for several critical issues in large-scale optical networks. Alarm (caused by failure, disaster, etc.) prediction is an important use-case, where machine learning can assist in predicting events, ahead of time. Accurate prediction enables network administrators to undertake preventive measures. For such alarm prediction applications, high-quality data sets for training and testing are crucial. However, the collected performance and alarm data from large-scale optical networks are often dirty, i.e., these data are incomplete, inconsistent, and lack certain behaviors or trends. Such data are likely to contain several errors, when collected from old-fashioned optical equipment, in particular. Even after appropriate data preprocessing, feature distribution can be extremely unbalanced, limiting the performance of machine learning algorithms. This paper demonstrates a Dirty-data-based Alarm Prediction (DAP) method for Self-Optimizing Optical Networks (SOONs). Experimental results on a commercial large-scale field topology with 274 nodes and 487 links demonstrate that the proposed DAP method can achieve high accuracy for different types of alarms.

SOON: self-optimizing optical networks with machine learning.

As optical networks undergo rapid development, the trade-offs among higher network service capability, and increasing operating expense (OPEX) about operations, administration and maintenance (OAM) become telecom operators' key obstacles. Intelligent and automatic OAM is considered to effectively satisfy service requirements, while dampening OPEX growth. In particular, machine learning (ML) has been investigated as a possible method of replacing human image recognition, nature language processing, automatic drive, and so forth. This is because of its essential feature extraction ability. ML application in optical networks was studied in a preliminary way recently. In ML-enabled optical networks, huge data storage and powerful computing resources are required to handle computer-intensive tasks performed in order to analyze features from big data sets. Integration of these two key resources into existing optical network architectures, in order to improve network performance, is an emerging challenge for ML-enabled optical networks. This article proposes a novel optical network architecture, which is based on software-defined networking (SDN), which is also named self-optimizing optical networks (SOON). First, we comb through intelligence development of optical networks, and introduce SOON as an OAM-oriented optical network architecture. Second, we demonstrate four typical applications within SOON, including tidal traffic prediction, alarm prediction, anomaly action detection, and routing and wavelength assignment. Finally, we discuss some open issues.

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.

The emergence of abnormal hypersynchronization in the anatomical structural network of human brain.

Brain activity depends on transient interactions between segregated neuronal populations. While synchronization between distributed neuronal clusters reflects the dynamics of cooperative patterns, the emergence of abnormal cortical hypersynchronization is typically associated with spike-wave discharges, which are characterized by a sudden appearance of synchronous around 3Hz large amplitude spike-wave discharges of the electroencephalogram. While most existing studies focus on the cellular and synaptic mechanisms, the aim of this article is to study the role of structural connectivity in the origin of the large-scale synchronization of the brain. Simulating oscillatory dynamics on a human brain network, we find the space-time structure of the coupling defined by the anatomical connectivity and the time delays can be the primary component contributing to the emergence of global synchronization. Our results suggest that abnormal white fiber connections may facilitate the generation of spike-wave discharges. Furthermore, while neural populations can exhibit oscillations in a wide range of frequency bands, we show that large-scale synchronization of the brain only occurs at low frequencies. This may provide a potential explanation for the low characteristic frequencies of spike-wave discharges. Finally, we find the global synchronization has a clear anterior origin involving discrete areas of the frontal lobe. These observations are in agreement with existing brain recordings and in favor of the hypothesis that initiation of spike-wave discharges originates from specific brain areas. Further graph theory analysis indicates that the original areas are highly ranked across measures of centrality. These results underline the crucial role of structural connectivity in the generation of spike-wave discharges.

An integrative view of mechanisms underlying generalized spike-and-wave epileptic seizures and its implication on optimal therapeutic treatments.

Many types of epileptic seizures are characterized by generalized spike-and-wave discharges. In the past, notable effort has been devoted to understanding seizure dynamics and various hypotheses have been proposed to explain the underlying mechanisms. In this paper, by taking an integrative view of the underlying mechanisms, we demonstrate that epileptic seizures can be generated by many different combinations of synaptic strengths and intrinsic membrane properties. This integrative view has important medical implications: the specific state of a patient characterized by a set of biophysical characteristics ultimately determines the optimal therapeutic treatment. Through the same view, we further demonstrate the potentiation effect of rational polypharmacy in the treatment of epilepsy and provide a new angle to resolve the debate on polypharmacy. Our results underscore the need for personalized medicine and demonstrate that computer modeling and simulation may play an important role in assisting the clinicians in selecting the optimal treatment on an individual basis.

Reduced order modeling of passive and quasi-active dendrites for nervous system simulation.

Accurate neuron models at the level of the single cell are composed of dendrites described by a large number of compartments. The network-level simulation of complex nervous systems requires highly compact yet accurate single neuron models. We present a systematic, numerically efficient and stable model order reduction approach to reduce the complexity of large dendrites by orders of magnitude. The resulting reduced dendrite models match the impedances of the full model within the frequency range of biological signals and reproduce the original action potential output waveforms.