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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.

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., Competing Interests: B.Y., W.W.H., and S.N. have financial interests in Bionic Sight, Inc. B.Y. is a consultant for Bionic Sight, Inc. S.N. is the principal and founder of Bionic Sight, Inc. W.W.H. is a cofounder of Bionic Sight, Inc., (© 2023 The Author(s).)

Background: Patients with heart failure (HF) are at high risk for adverse outcomes when they have COVID-19. Reports of COVID-19 vaccine-related cardiac complications may contribute to vaccine hesitancy in patients with HF., Methods: To analyze the impact of COVID-19 vaccine status on clinical outcomes in patients with HF, we conducted a retrospective cohort study of the association of COVID-19 vaccination status with hospitalizations, intensive care unit admission and mortality after adjustment for covariates. Inverse probability treatment-weighted models were used to adjust for potential confounding., Results: Of 7094 patients with HF, 645 (9.1%) were partially vaccinated, 2200 (31.0%) were fully vaccinated, 1053 were vaccine-boosted (14.8%), and 3196 remained unvaccinated (45.1%) by January 2022. The mean age was 73.3 ± 14.5 years, and 48% were female. Lower mortality rates were observed in patients who were vaccine-boosted, followed by those who were fully vaccinated; they experienced lower mortality rates (HR 0.33; CI 0.23, 0.48) and 0.36 (CI 0.30, 0.43), respectively, compared to unvaccinated individuals (P< 0.001) over the mean follow-up time of 276.5 ± 104.9 days, whereas no difference was observed between those who were unvaccinated or only partially vaccinated., Conclusion: COVID-19 vaccination was associated with significant reduction in all-cause hospitalization rates and mortality rates, lending further evidence to support the importance of vaccination implementation in the high-risk population of patients living with HF., Competing Interests: Declaration of Competing Interest Dr. Lala has received personal fees from Zoll, outside the submitted work. All other authors report no relationships relevant to the contents of this article., (Copyright © 2022. Published by Elsevier Inc.)

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Background: Disparate COVID-19 outcomes have been observed between Hispanic, non-Hispanic Black, and White patients. The underlying causes for these disparities are not fully understood., Methods: This was a retrospective study utilizing electronic medical record data from five hospitals within a single academic health system based in New York City. Multivariable logistic regression models were used to identify demographic, clinical, and lab values associated with in-hospital mortality., Results: A total of 3,086 adult patients with self-reported race/ethnicity information presenting to the emergency department and hospitalized with COVID-19 up to April 13, 2020, were included in this study. While older age (multivariable odds ratio (OR) 1.06, 95% CI 1.05-1.07) and baseline hypoxia (multivariable OR 2.71, 95% CI 2.17-3.36) were associated with increased mortality overall and across all races/ethnicities, non-Hispanic Black (median age 67, interquartile range (IQR) 58-76) and Hispanic (median age 63, IQR 50-74) patients were younger and had different comorbidity profiles as compared to non-Hispanic White patients (median age 73, IQR 62-84; p < 0.05 for both comparisons). Among inflammatory markers associated with COVID-19 mortality, there was a significant interaction between the non-Hispanic Black population and interleukin-1-beta (interaction p-value 0.04)., Conclusions: This analysis of a multiethnic cohort highlights the need for inclusion and consideration of diverse populations in ongoing COVID-19 trials targeting inflammatory cytokines., Competing Interests: EW has consulting agreements with and/or is on the scientific advisory board for Merck, Elstar, Janssen, Jounce, Related Sciences, Synthekine, and Surface Oncology. EW is a founder of Surface Oncology and Arsenal Biosciences. EW has a patent licensing agreement on the PD-1 pathway with Roche/Genentech. TJ is employed by and owns stock in Sema4. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Jun, Mathew, Sharma, Nirenberg, Huang, Kovatch, Wherry and Huang.)

Introduction: The shift from in-person visits to telehealth visits during the COVID-19 pandemic presented unique challenges for patients with pain. Disparities in health care access already existed, and the impact of telehealth on these inequities has not been studied., Objectives: To identify sociodemographic characteristics of patients with pain obtaining care through video, telephone, and in-person visits as social distancing restrictions evolved during the COVID-19 pandemic., Methods: Using our institutional clinical data warehouse, we identified 3314 patients with pain receiving care at a large academic institution in New York City during a baseline period (September 23, 2019-March 22, 2020) and counted telephone, video, and in-person visits during the following conditions: a shutdown period (March 23, 2020-May 23, 2020), when nonessential in-person visits were strictly limited, and a reopening period (May 23, 2020-September 23, 2020), when restrictions were relaxed and in-person visits were available. Patients were categorized into 4 groups based on the technology used to complete a visit: (1) video, (2) telephone, (3) in-person, and (4) no visit., Results: Patients who were older, publicly insured, and identified as Black or Hispanic were overrepresented in the telephone visit group during shutdown and the in-person group during reopening. A video visit during shutdown increased the likelihood of continued video visit use during reopening despite the return of in-person visits., Conclusions: Results show differences in how patients with pain accessed clinical care in a socially distanced world and that flexibility in method of health care delivery may reduce barriers to access. Future research will identify factors (eg, Internet access, digital literacy, provider-patient relationships) driving heterogeneity in telehealth use in patients with pain., Competing Interests: The authors have no conflict of interest to declare. This work was submitted as a poster and presented orally at the HEAL annual meeting from May 17, 2021, to May 19, 2021.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The International Association for the Study of Pain.)

COVID-19 affects multiple organs. Clinical data from the Mount Sinai Health System show that substantial numbers of COVID-19 patients without prior heart disease develop cardiac dysfunction. How COVID-19 patients develop cardiac disease is not known. We integrated cell biological and physiological analyses of human cardiomyocytes differentiated from human induced pluripotent stem cells (hiPSCs) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the presence of interleukins (ILs) with clinical findings related to laboratory values in COVID-19 patients to identify plausible mechanisms of cardiac disease in COVID-19 patients. We infected hiPSC-derived cardiomyocytes from healthy human subjects with SARS-CoV-2 in the absence and presence of IL-6 and IL-1β. Infection resulted in increased numbers of multinucleated cells. Interleukin treatment and infection resulted in disorganization of myofibrils, extracellular release of troponin I, and reduced and erratic beating. Infection resulted in decreased expression of mRNA encoding key proteins of the cardiomyocyte contractile apparatus. Although interleukins did not increase the extent of infection, they increased the contractile dysfunction associated with viral infection of cardiomyocytes, resulting in cessation of beating. Clinical data from hospitalized patients from the Mount Sinai Health System show that a significant portion of COVID-19 patients without history of heart disease have elevated troponin and interleukin levels. A substantial subset of these patients showed reduced left ventricular function by echocardiography. Our laboratory observations, combined with the clinical data, indicate that direct effects on cardiomyocytes by interleukins and SARS-CoV-2 infection might underlie heart disease in COVID-19 patients. IMPORTANCE SARS-CoV-2 infects multiple organs, including the heart. Analyses of hospitalized patients show that a substantial number without prior indication of heart disease or comorbidities show significant injury to heart tissue, assessed by increased levels of troponin in blood. We studied the cell biological and physiological effects of virus infection of healthy human iPSC-derived cardiomyocytes in culture. Virus infection with interleukins disorganizes myofibrils, increases cell size and the numbers of multinucleated cells, and suppresses the expression of proteins of the contractile apparatus. Viral infection of cardiomyocytes in culture triggers release of troponin similar to elevation in levels of COVID-19 patients with heart disease. Viral infection in the presence of interleukins slows down and desynchronizes the beating of cardiomyocytes in culture. The cell-level physiological changes are similar to decreases in left ventricular ejection seen in imaging of patients' hearts. These observations suggest that direct injury to heart tissue by virus can be one underlying cause of heart disease in COVID-19.

Importance: Up to two-thirds of African American individuals carry the benign rs2814778-CC genotype that lowers total white blood cell (WBC) count., Objective: To examine whether the rs2814778-CC genotype is associated with an increased likelihood of receiving a bone marrow biopsy (BMB) for an isolated low WBC count., Design, Setting, and Participants: This retrospective genetic association study assessed African American patients younger than 90 years who underwent a BMB at Vanderbilt University Medical Center, Mount Sinai Health System, or Children's Hospital of Philadelphia from January 1, 1998, to December 31, 2020., Exposure: The rs2814778-CC genotype., Main Outcomes and Measures: The proportion of individuals with the CC genotype who underwent BMB for an isolated low WBC count and had a normal biopsy result compared with the proportion of individuals with the CC genotype who underwent BMB for other indications and had a normal biopsy result., Results: Among 399 individuals who underwent a BMB (mean [SD] age, 41.8 [22.5] years, 234 [59%] female), 277 (69%) had the CC genotype. A total of 35 patients (9%) had clinical histories of isolated low WBC counts, and 364 (91%) had other histories. Of those with a clinical history of isolated low WBC count, 34 of 35 (97%) had the CC genotype vs 243 of 364 (67%) of those without a low WBC count history. Among those with the CC genotype, 33 of 34 (97%) had normal results for biopsies performed for isolated low WBC counts compared with 134 of 243 individuals (55%) with biopsies performed for other histories (P < .001)., Conclusions and Relevance: In this genetic association study, among patients of African American race who had a BMB with a clinical history of isolated low WBC counts, the rs2814778-CC genotype was highly prevalent, and 97% of these BMBs identified no hematologic abnormality. Accounting for the rs2814778-CC genotype in clinical decision-making could avoid unnecessary BMB procedures.

The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.

Background: Sex has consistently been shown to affect COVID-19 mortality, but it remains unclear how each sex's clinical outcome may be distinctively shaped by risk factors., Methods: We studied a primary cohort of 4930 patients hospitalized with COVID-19 in a single healthcare system in New York City from the start of the pandemic till August 5, 2020, and a validation cohort of 1645 patients hospitalized with COVID-19 in the same healthcare system from August 5, 2020, to January 13, 2021., Results: Here we show that male sex was independently associated with in-hospital mortality, intubation, and ICU care after adjusting for demographics and comorbidities. Using interaction analysis and sex-stratified models, we found that hypoxia interacted with sex to preferentially increase women's mortality risk while obesity interacted with sex to preferentially increase women's risk of intubation and intensive care in our primary cohort. In the validation cohort, we observed that male sex remained an independent risk factor for mortality, but sex-specific interactions were not replicated., Conclusions: We conducted a comprehensive sex-stratified analysis of a large cohort of hospitalized COVID-19 patients, highlighting clinical factors that may contribute to sex differences in the outcome of COVID-19., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2021.)

Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66-1.92]), male sex (OR, 1.57 [95% CI 1.30-1.90]), higher BMI (OR, 1.03 [95% CI 1.102-1.05]), higher heart rate (OR, 1.01 [95% CI 1.00-1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03-1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93-0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20-1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.

Objective: To assess association of clinical features on COVID-19 patient outcomes., Design: Retrospective observational study using electronic medical record data., Setting: Five member hospitals from the Mount Sinai Health System in New York City (NYC)., Participants: 28 336 patients tested for SARS-CoV-2 from 24 February 2020 to 15 April 2020, including 6158 laboratory-confirmed COVID-19 cases., Main Outcomes and Measures: Positive test rates and in-hospital mortality were assessed for different racial groups. Among positive cases admitted to the hospital (N=3273), we estimated HR for both discharge and death across various explanatory variables, including patient demographics, hospital site and unit, smoking status, vital signs, lab results and comorbidities., Results: Hispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to their representation in the overall NYC population (p<0.05); however, no differences in mortality rates were observed in hospitalised patients based on race. Outcomes differed significantly between hospitals (Gray's T=248.9; p<0.05), reflecting differences in average baseline age and underlying comorbidities. Significant risk factors for mortality included age (HR 1.05, 95% CI 1.04 to 1.06; p=1.15e-32), oxygen saturation (HR 0.985, 95% CI 0.982 to 0.988; p=1.57e-17), care in intensive care unit areas (HR 1.58, 95% CI 1.29 to 1.92; p=7.81e-6) and elevated creatinine (HR 1.75, 95% CI 1.47 to 2.10; p=7.48e-10), white cell count (HR 1.02, 95% CI 1.01 to 1.04; p=8.4e-3) and body mass index (BMI) (HR 1.02, 95% CI 1.00 to 1.03; p=1.09e-2). Deceased patients were more likely to have elevated markers of inflammation., Conclusions: While race was associated with higher risk of infection, we did not find racial disparities in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. In addition, we identified key clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk of a severe infection response and predict survival., Competing Interests: Competing interests: WKO is a paid consultant to Astellas, Astra Zeneca, Bayer, Janssen, Sanofi, Sema4, and TeneoBio., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Several studies have revealed that the hyper-inflammatory response induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major cause of disease severity and death. However, predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α and IL-1β in hospitalized patients with coronavirus disease 2019 (COVID-19) upon admission to the Mount Sinai Health System in New York. Patients (n = 1,484) were followed up to 41 d after admission (median, 8 d), and clinical information, laboratory test results and patient outcomes were collected. We found that high serum IL-6, IL-8 and TNF-α levels at the time of hospitalization were strong and independent predictors of patient survival (P < 0.0001, P = 0.0205 and P = 0.0140, respectively). Notably, when adjusting for disease severity, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF-α serum levels remained independent and significant predictors of disease severity and death. These findings were validated in a second cohort of patients (n = 231). We propose that serum IL-6 and TNF-α levels should be considered in the management and treatment of patients with COVID-19 to stratify prospective clinical trials, guide resource allocation and inform therapeutic options.

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.

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.

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., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

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.

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.

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., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

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.

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.

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.

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.

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.

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.

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.

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.

We adopt a system theoretic approach and explore the model of retinal ganglion cells as linear filters followed by a maximum-likelihood Bayesian predictor. We evaluate the model by using cross-validation, i.e., first the model parameters are estimated using a training set, and then the prediction error is computed (by comparing the stochastic rate predicted by the model with the rate code of the response) for a test set. As in system identification theory, we present spatially uniform stimuli to the retina, whose temporal intensity is drawn independently from a Gaussian distribution, and we simultaneously record the spike trains from multiple neurons. The optimal linear filter for each cell is obtained by maximizing the mutual information between the filtered stimulus values and the output of the cell (as measured in terms of a stochastic rate code). Our results show that the model presented in this paper performs well on the test set, and it outperforms the identity Bayesian model and the traditional linear model. Moreover, in order to reduce the number of optimal filters needed for prediction, we cluster the cells based on the filters' shapes, and use the cluster consensus filters to predict the firing rates of all neurons in the same class. We obtain almost the same performance with these cluster filters. These results provide hope that filter-based retinal prosthetics might be an effective and feasible idea.

There is considerable recent interest in both (i) modelling the retinal ganglion cells, so that the models can generate output that approximates the actual response of the retina (such models will help design retinal prosthetics); and (ii) understanding how relevant information is encoded in the spike patterns generated by the ganglion cells (these neuronal codes will help understand how the brain analyzes visual scenes). Since the signals (as captured by ISI) are fundamentally stochastic, any modelling or analysis tool will have to track, and make assumptions about, the fluctuations or noise inherently present in these signals. Even though there have been recent work claiming that the fluctuations are fractal in nature, showing long-range dependencies, almost all modelling and analysis work continue to assume Poisson fluctuations. The widespread use of the Poisson model is partly for the sake of convenience, and partly due to the fact that those claiming on fractal nature of ISI are contradictory: In [1] a long-range dependency (i.e., Hurst parameter [2], H > 0.5) is claimed in cat's retina, and in [3] an H < 0.5 and a long-range anti-correlation are claimed for paddlefish electroreceptors. We resolve this issue by studying the ISI of more than 50 ganglion cells recorded from two different mouse retinas, and (i) Conclusively show that the Hurst parameter is less than 0.5; we also show why the results presented in [1] are erroneous: methods that do not detrend the data were used.

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.

Several recent studies have suggested that the spatial tuning of retinal ganglion cells may be a more complex process than previously thought. The working hypothesis for many years was that the tuning was shaped by operations performed in the first synaptic layer of the retina, but recent work shows that operations in the second synaptic layer, involving amacrine cells, also play a significant role (Cook and McReynolds, 1998; Taylor, 1999; Flores-Herr et al., 2001). Although it is clear that amacrine cells are involved, the precise roles of the different amacrine subtypes in the many aspects of spatial tuning have not yet been established. Here we used a cell class ablation method to remove one subtype, the neuropeptide Y-expressing cells (NPY cells), and tapped into a part of the circuitry that tunes ganglion cells toward large spatial patterns (low spatial frequencies). When the subtype was ablated, ganglion cells tuned toward low spatial frequencies, both ON- and OFF-type cells, lost this preferential tuning. The effect was specific because ablation of another amacrine subtype did not produce it. Further analysis showed that the change in tuning was attributable to a decrease in the receptive field surround size of the ganglion cell. Other parameters, such as the size, strength, and asymmetry of the center and the strength of the surround, were not statistically significantly affected. These results thus show a mechanism for tuning cells to low spatial frequencies; an operation in the second synaptic layer, mediated by NPY cells, extends the surround of the ganglion cell.

Numerous studies have shown that retinal ganglion cells exhibit an array of responses to visual stimuli. This has led to the idea that these cells can be sorted into distinct physiological classes, such as linear versus nonlinear or on versus off. Although many classification schemes are widely accepted, few studies have provided statistical support to favor one scheme over another. Here we test whether some of the most widely used classification schemes can be statistically verified, using the mouse retina as the model system. We used a cluster analysis approach and focused on 4 standard response parameters: 1) response latency, 2) response duration, 3) relative amplitude of the on and off responses, and 4) degree of nonlinearity in the stimulus-to-response transformation. For each parameter, we plotted its distribution and tested quantitatively, using a bootstrap method, whether it divided into distinct clusters. Our analysis showed that mouse ganglion cells clustered into several groups based on response latency, duration, and relative amplitude of the on and off responses, but did not cluster into more than one group based on degree of nonlinearity-the latter formed a single, large, continuous group. Thus while some well-known schemes for classifying ganglion cells could be statistically verified, others could not. Knowledge of which schemes can be confirmed is important for building models of how retinal output is processed and how retinal circuits are built. Finally, this cluster analysis approach is general and can be used to test other classification proposals as well, both physiological and anatomical.