Purpose:To explore the relationship between visual performance and retinal morphology as assessed by optical coherence tomography (OCT), and the ability of OCT to reflect visual impairment in people with multiple sclerosis (PwMS) compared with healthy controls (HC). Methods:We gathered data from two neurology referral centers on PwMS and HC. Neurological and ophthalmological assessments, including OCT, high-contrast visual acuity (HCVA) and low-contrast visual acuity (LCVA), area under the log contrast sensitivity function (AULCSF), and vision-related quality of life (National Eye Institute Visual Function Questionnaire), were conducted between 2018 and 2020, with follow-up at 1 year. Results:A total of 137 PwMS (271 eyes) and 118 HC (236 eyes) were available for analysis. The peripapillary retinal nerve fiber layer (pRNFL) and the macular ganglion cell layer and inner plexiform layer volume (mGCIPL) volume were both reduced in PwMS (92 µm in PwMS vs 98 µm in HC [P < 0.001], 0.55 mm3 vs 0.62 mm3 [P < 0.001], respectively). A cutoff effect for visual impairment was observed in PwMS when pRNFL fell below 68.8 µm (HCVA), 71.4 µm (LCVA), and 72.6 µm (AULCSF). Using mixed effects models, the mGCIPL volume emerged as the variable most strongly associated with the AULCSF (P < 0.001). The AULCSF showed the strongest correlation with both pRNFL and mGCIPL (P < 0.001), with optic neuritis being a significant contributing factor (P < 0.001). Conclusions:AULCSF outperformed standard HCVA and LCVA, closely reflecting retinal atrophy. mGCIPL loss showed stronger associations with vision tests and detected neurodegeneration without the cutoff effect seen in pRNFL, making it the best marker for neuronal atrophy.

We developed and validated digital twins (DTs) for contrast sensitivity function (CSF) across 12 prediction tasks using a data-driven, generative model approach based on a hierarchical Bayesian model (HBM). For each prediction task, we utilized the HBM to compute the joint distribution of CSF hyperparameters and parameters at the population, subject, and test levels. This computation was based on a combination of historical data (N = 56), any new data from additional subjects (N = 56), and “missing data” from unmeasured conditions. The posterior distributions of the parameters in the unmeasured conditions were used as input for the CSF generative model to generate predicted CSFs. In addition to their accuracy and precision, these predictions were evaluated for their potential as informative priors that enable generation of synthetic quantitative contrast sensitivity function (qCSF) data or rescore existing qCSF data. The DTs demonstrated high accuracy in group level predictions across all tasks and maintained accuracy at the individual subject level when new data were available, with accuracy comparable to and precision lower than the observed data. DT predictions could reduce the data collection burden by more than 50% in qCSF testing when using 25 trials. Although further research is necessary, this study demonstrates the potential of DTs in vision assessment. Predictions from DTs could improve the accuracy, precision, and efficiency of vision assessment and enable personalized medicine, offering more efficient and effective patient care solutions.

Purpose: The purpose of this study is to introduce a nonparametric hierarchical Bayesian model (HBM) that enables advanced statistical inference on contrast sensitivity (CS) both at individual spatial frequencies (SFs) and across multiple SFs in clinical trials, where CS measurements are crucial for assessing safety and efficacy. Methods: The HBM computes the joint posterior distribution of CS at six Food and Drug Administration-designated SFs across the population, individual, and test levels. It incorporates covariances at both population and individual levels to capture the relationship between CSs across SFs. A Bayesian inference procedure (BIP) is also used to estimate the posterior distribution of CS at each SF independently. Both methods are applied to a quantitative CSF (qCSF) dataset of 112 subjects and compared in terms of precision, test-retest reliability of CS estimates, sensitivity, accuracy, and statistical power in detecting CS changes. Results: The HBM reveals correlations between CSs in pairs of SFs and provides significantly more precise estimates and higher test-retest reliability compared to the BIP. Additionally, it improves the average sensitivity and accuracy in detecting CS changes for individual subjects, as well as statistical power for detecting group-level CS changes at individual and combinations of multiple SFs between luminance conditions. Conclusions: The HBM establishes a comprehensive framework to enhance sensitivity, accuracy, and statistical power for detecting CS changes in hierarchical experimental designs. Translational Relevance: The HBM presents a valuable tool for advancing CS assessments in the clinic and clinical trials, potentially improving the evaluation of treatment efficacy and patient outcomes.

Purpose:Studies have reported that individuals with certain ocular disorders may have significant decreases in contrast sensitivity function (CSF) despite having normal or near normal visual acuity (VA). This study seeks to elucidate this phenomenon by investigating the relationship between VA and CSF. Methods:We analyzed data from 14 eyes tested with Electronic Early Treatment Diabetic Retinopathy and quantitative CSF under four Bangerter foil conditions (n = 56). From the CSF data, we estimated peak gain, peak frequency, and contrast sensitivity acuity (CSA). We explored the correlations between VA and various CSF parameters and evaluated five predictive models of VA using CSA alone and in combination with additional CSF parameters through ridge regression. Results:We found that similar VA scores can correspond with markedly different CSFs and observed significant correlations among all CSF parameters and between VA and each CSF parameter (all P < 0.001). The most effective predictive model, incorporating CSA and peak gain, explained 90.97% of the variance with a root mean squared error of 0.0676 logMAR, which is comparable with the average standard deviation of the VA scores (0.0627 logMAR) and accounted for 38.6% of the residual variance not explained by the CSA-alone model. Conclusions:This study offers the first empirical inference of the quantitative relationship between VA and CSF, suggesting that various CSF parameter combinations can yield identical VA. This might help to explain why some clinical populations with normal or near-normal VA exhibit significant CSF deficits and calls for further research in different clinical settings.

The qCSF has been used to assess spatial vision in normal and clinical populations. Because it uses the log parabola CSF model, the estimated contrast sensitivities (CSs) across different spatial frequencies (SFs) are not independent. Most studies have used the area under Log CSF (AULCSF) to simplify statistical inference. Here, we developed a hierarchical Bayesian model (HBM) with population, individual and test levels to compute the joint posterior CS distribution across all levels and SFs to enable statistical inference on CS at individual SFs, using covariance hyperparameters at the population and individual levels to capture the relationship between CSs across SFs. We applied the HBM to a dataset of 112 subjects tested with qCSF trials in each of three (L, M & H) luminance conditions (Hou et al., 2016). The HBM recovered correlations between CSs in pairs of 19 SFs (0.739 to 0.948, -0.065 to 0.451, and -0.044 to 0.340 at the population, individual and test levels, respectively), and precise CS estimates at individual SFs for each subject (average 68.2% HWCI=0.097 log10 units). CS difference distributions between luminance conditions were constructed from the joint posterior distribution. At the group level, luminance had highly significant effects on CS both across 19 SFs jointly and at each SF (all p<0.001). 112, 111, and 71 subjects exhibited significant luminance effects on CS across 19 SFs jointly in L-H, L-M, and H-M, respectively (p< 0.05), whereas 112, 112, and 101 subjects exhibited significant luminance effects in at least two SFs (p<0.05). With covariances, the HBM generated precise CS estimates at individual SFs for each subject based on only 150 optotypes in each condition. The joint posterior distribution from the HBM enabled statistical inference on CS at individual SFs and across SFs at both group and subject levels.

Clinical trials typically analyze multiple endpoints for signals of efficacy. To improve signal detection for treatment effects using the high-dimensional data collected in trials, we developed a hierarchical Bayesian joint model (HBJM) to compute a five-dimensional collective endpoint (CE5D) of contrast sensitivity function (CSF) and visual acuity (VA). The HBJM analyzes row-by-row CSF and VA data across multiple conditions, and describes visual functions across a hierarchy of population, individuals, and tests. It generates joint posterior distributions of CE5D that combines CSF (peak gain, peak frequency, and bandwidth) and VA (threshold and range) parameters. The HBJM was applied to an existing dataset of 14 eyes, each tested with the quantitative VA and quantitative CSF procedures in four Bangerter foil conditions. The HBJM recovered strong correlations among CE5D components at all levels. With 15 qVA and 25 qCSF rows, it reduced the variance of the estimated components by 72% on average. Combining signals from VA and CSF and reducing noises, CE5D exhibited significantly higher sensitivity and accuracy in discriminating performance differences between foil conditions at both the group and test levels than the original tests. The HBJM extracts valuable information about covariance of CSF and VA parameters, improves precision of the estimated parameters, and increases the statistical power in detecting vision changes. By combining signals and reducing noise from multiple tests for detecting vision changes, the HBJM framework exhibits potential to increase statistical power for combining multi-modality data in ophthalmic trials.

BackgroundNeurodegeneration in multiple sclerosis (MS) affects the visual system but dynamics and pathomechanisms over several years especially in primary progressive MS (PPMS) are not fully understood.MethodsWe assessed longitudinal changes in visual function, retinal neurodegeneration using optical coherence tomography, MRI and serum NfL (sNfL) levels in a prospective PPMS cohort and matched healthy controls. We investigated the changes over time, correlations between outcomes and with loss of visual function.ResultsWe followed 81 patients with PPMS (mean disease duration 5.9 years) over 2.7 years on average. Retinal nerve fibre layer thickness (RNFL) was reduced in comparison with controls (90.1 vs 97.8 µm; p<0.001). Visual function quantified by the area under the log contrast sensitivity function (AULCSF) remained stable over a continuous loss of RNFL (0.46 µm/year, 95% CI 0.10 to 0.82; p=0.015) up until a mean turning point of 91 µm from which the AULCSF deteriorated. Intereye RNFL asymmetry above 6 µm, suggestive of subclinical optic neuritis, occurred in 15 patients and was related to lower AULCSF but occurred also in 5 out of 44 controls. Patients with an AULCSF progression had a faster increase in Expanded Disability Status Scale (beta=0.17/year, p=0.043). sNfL levels were elevated in patients (12.2 pg/mL vs 8.0 pg/mL, p<0.001), but remained stable during follow-up (beta=–0.14 pg/mL/year, p=0.291) and were not associated with other outcomes.ConclusionWhereas neurodegeneration in the anterior visual system is already present at onset, visual function is not impaired until a certain turning point. sNfL is not correlated with structural or functional impairment in the visual system.

We make use of expected information gain to quantify the amount of knowledge obtained from measurements in a population. In the first application, we compared the expected information gain in the Snellen, ETDRS, and qVA visual acuity (VA) tests, as well as in the Pelli-Robson, CSV-1000, and qCSF contrast sensitivity (CS) tests. For the VA tests, ETDRS generated more expected information gain than Snellen. Additionally, the qVA test with 15 rows (or 45 optotypes) generated more expected information gain than ETDRS, whether scored with VA threshold alone or with both VA threshold and VA range. Regarding the CS tests, CSV-1000 generated more expected information gain than Pelli-Robson, and the qCSF test with 25 trials generated more expected information gain than CSV-1000, whether scored with AULCSF or with CSF at six spatial frequencies. The active learning-based qVA and qCSF tests have the potential to generate more expected information gain than traditional paper chart tests. Although we have specifically applied it to compare VA and CS tests, expected information gain is a general concept that can be used to compare measurements in any domain.

BackgroundImpairment of visual function is one of the major symptoms of people with multiple sclerosis (pwMS). A multitude of disease effects including inflammation and neurodegeneration lead to structural impairment in the visual system. However, the gold standard of disability quantification, the expanded disability status scale (EDSS), relies on visual assessment charts. A more comprehensive assessment of visual function is the full contrast sensitivity function (CSF), but most tools are time consuming and not feasible in clinical routine. The quantitative CSF (qCSF) test is a computerized test to assess the full CSF. We have already shown a better correlation with visual quality of life (QoL) than for classical high and low contrast charts in multiple sclerosis (MS).ObjectiveTo study the precision, test duration, and repeatability of the qCSF in pwMS. In order to evaluate the discrimination ability, we compared the data of pwMS to healthy controls.MethodsWe recruited two independent cohorts of MS patients. Within the precision cohort (n = 54), we analyzed the benefit of running 50 instead of 25 qCSF trials. The repeatability cohort (n = 44) was assessed by high contrast vision charts and qCSF assessments twice and we computed repeatability metrics. For the discrimination ability we used the data from all pwMS without any previous optic neuritis and compared the area under the log CSF (AULCSF) to an age-matched healthy control data set.ResultsWe identified 25 trials of the qCSF algorithm as a sufficient amount for a precise estimate of the CSF. The median test duration for one eye was 185 s (range 129–373 s). The AULCSF had better test–retest repeatability (Mean Average Precision, MAP) than visual acuity measured by standard high contrast visual acuity charts or CSF acuity measured with the qCSF (0.18 vs. 0.11 and 0.17, respectively). Even better repeatability (MAP = 0.19) was demonstrated by a CSF-derived feature that was inspired by low-contrast acuity charts, i.e., the highest spatial frequency at 25% contrast. When compared to healthy controls, the MS patients showed reduced CSF (average AULCSF 1.21 vs. 1.42, p < 0.01).ConclusionHigh precision, usability, repeatability, and discrimination support the qCSF as a tool to assess contrast vision in pwMS.

Purpose To evaluate the performance of the quantitative visual acuity (qVA) method in measuring the visual acuity (VA) behavioral function. Methods We evaluated qVA performance in terms of the accuracy, precision, and efficiency of the estimated VA threshold and range in Monte Carlo simulations and a psychophysical experiment. We also compared the estimated VA threshold from the qVA method with that from the Electronic Early Treatment Diabetic Retinopathy Study (E-ETDRS) and Freiburg Visual Acuity Text (FrACT) methods. Four repeated measures with all three methods were conducted in four Bangerter foil conditions in 14 eyes. Results In both simulations and psychophysical experiment, the qVA method quantified the full acuity behavioral function with two psychometric parameters (VA threshold and VA range) with virtually no bias and with high precision and efficiency. There was a significant correlation between qVA estimates of VA threshold and range in the psychophysical experiment. In addition, qVA threshold estimates were highly correlated with those from the E-ETDRS and FrACT methods. Conclusions The qVA method can provide an accurate, precise, and efficient assessment of the full acuity behavioral function with both VA threshold and range. Translational Relevance The qVA method can accurately, precisely, and efficiently assess the full VA behavioral function. Further research will evaluate the potential value of these rich measures for both clinical research and patient care.

In this short article we present our manual annotation of the eye movement events in a subset of the large-scale eye tracking data set Hollywood2. Our labels include fixations, saccades, and smooth pursuits, as well as a noise event type (the latter representing either blinks, loss of tracking, or physically implausible signals). In order to achieve more consistent annotations, the gaze samples were labelled by a novice rater based on rudimentary algorithmic suggestions, and subsequently corrected by an expert rater. Overall, we annotated eye movement events in the recordings corresponding to 50 randomly selected test set clips and 6 training set clips from Hollywood2, which were viewed by 16 observers and amount to a total of approximately 130 minutes of gaze data. In these labels, 62.4% of the samples were attributed to fixations, 9.1% – to saccades, and, notably, 24.2% – to pursuit (the remainder marked as noise). After evaluation of 15 published eye movement classification algorithms on our newly collected annotated data set, we found that the most recent algorithms perform very well on average, and even reach human-level labelling quality for fixations and saccades, but all have a much larger room for improvement when it comes to smooth pursuit classification. The data set is made available at https://gin.g- node.org/ioannis.agtzidis/hollywood2_em.