A novel Bayesian adaptive method for mapping the visual field

TORONTO: A trial-oriented multidimensional psychometric testing algorithm

Bayesian adaptive methods for sensory threshold determination were conceived originally to track a single threshold. When applied to the testing of vision, they do not exploit the spatial patterns that underlie thresholds at different locations in the visual field. Exploiting these patterns has been recognized as key to further improving visual field test efficiency. We present a new approach (TORONTO) that outperforms other existing methods in terms of speed and accuracy. TORONTO generalizes the QUEST/ZEST algorithm to estimate simultaneously multiple thresholds. After each trial, without waiting for a fully determined threshold, the trial-oriented approach updates not only the location currently tested but also all other locations based on patterns in a reference data set. Since the availability of reference data can be limited, techniques are developed to overcome this limitation. TORONTO was evaluated using computer-simulated visual field tests: In the reliable condition (false positive [FP] = false negative [FN] = 3%), the median termination and root mean square error (RMSE) of TORONTO was 153 trials and 2.0 dB, twice as fast with equal accuracy as ZEST. In the FP = FN = 15% condition, TORONTO terminated in 151 trials and was 2.2 times faster than ZEST with better RMSE (2.6 vs. 3.7 dB). In the FP = FN = 30% condition, TORONTO achieved 4.2 dB RMSE in 148 trials, while all other techniques had > 6.5 dB RMSE and terminated much slower. In conclusion, TORONTO is a fast and accurate algorithm for determining multiple thresholds under a wide range of reliability and subject conditions.

Visual Function and Driving Performance Under Different Lighting Conditions in Older Drivers: Preliminary Results From an Observational Study

BACKGROUND

Age-related vision changes significantly contribute to fatal crashes at night among older drivers. However, the effects of lighting conditions on age-related vision changes and associated driving performance remain unclear.

OBJECTIVE

This pilot study examined the associations between visual function and driving performance assessed by a high-fidelity driving simulator among drivers 60 and older across 3 lighting conditions: daytime (photopic), nighttime (mesopic), and nighttime with glare.

METHODS

Active drivers aged 60 years or older participated in visual function assessments and simulated driving on a high-fidelity driving simulator. Visual acuity (VA), contrast sensitivity function (CSF), and visual field map (VFM) were measured using quantitative VA, quantitative CSF, and quantitative VFM procedures under photopic and mesopic conditions. VA and CSF were also obtained in the presence of glare in the mesopic condition. Two summary metrics, the area under the log CSF (AULCSF) and volume under the surface of VFM (VUSVFM), quantified CSF and VFM. Driving performance measures (average speed, SD of speed [SDspeed], SD of lane position (SDLP), and reaction time) were assessed under daytime, nighttime, and nighttime with glare conditions. Pearson correlations determined the associations between visual function and driving performance across the 3 lighting conditions.

RESULTS

Of the 20 drivers included, the average age was 70.3 years; 55% were male. Poor photopic VA was significantly correlated with greater SDspeed (r=0.26; P<.001) and greater SDLP (r=0.31; P<.001). Poor photopic AULCSF was correlated with greater SDLP (r=-0.22; P=.01). Poor mesopic VUSFVM was significantly correlated with slower average speed (r=-0.24; P=.007), larger SDspeed (r=-0.19; P=.04), greater SDLP (r=-0.22; P=.007), and longer reaction times (r=-0.22; P=.04) while driving at night. For functional vision in the mesopic condition with glare, poor VA was significantly correlated with longer reaction times (r=0.21; P=.046) while driving at night with glare; poor AULCSF was significantly correlated with slower speed (r=-0.32; P<.001), greater SDLP (r=-0.26; P=.001) and longer reaction times (r=-0.2; P=.04) while driving at night with glare. No other significant correlations were observed between visual function and driving performance under the same lighting conditions.

CONCLUSIONS

Visual functions differentially affect driving performance in different lighting conditions among older drivers, with more substantial impacts on driving during nighttime, especially in glare. Additional research with larger sample sizes is needed to confirm these results.

The Random Step Method for Measuring the Point of Subjective Equality

Points of Subjective Equality (PSE) are commonly measured using staircase or constant stimuli methods. However, the staircase method is highly dependent on the step size, and the constant stimuli method is time-consuming. Thus, we wanted to develop an efficient and quick method to estimate both the PSE and the slope of the psychometric function. We developed a random-step algorithm in which a one-up-one-down rule is followed but with a random step size in a pre-defined range of test levels. Each stimulus would be chosen depending on the previous response of the subject. If the subject responded "up", any random level in the lower range would be picked for the next trial. And if the subject responded "down", any random level in the upper range would be picked for the next trial. This procedure would result in a bell-shaped distribution of the test levels around the estimated PSE, while a substantial amount of trials would still be dispersed at both bounds of the range. We then compared this method with traditional constant stimuli procedure on a task based on the Pulfrich phenomenon while the PSEs of participants could be varied using different neutral density filters. Our random-step method provided robust estimates of both the PSE and the slope under various noise levels with small trial counts, and we observed a significant correlation between the PSEs obtained with the two methods. The random-step method is an efficient way to measure the full psychometric function when testing time is critical, such as in clinical settings.

Adaptive Design Optimization as a Promising Tool for Reliable and Efficient Computational Fingerprinting

A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multidimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction, especially in clinical populations. Computational modeling may address this challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-the-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent work suggests that Bayesian adaptive design optimization (ADO) is a promising way to address these challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual's characteristics. In this review, we first describe the ADO methodology and then summarize recent work demonstrating that ADO increases the reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and proposing development of ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.

Consistency of preferred retinal locus across tasks and participants trained with a simulated scotoma

After loss of central vision following retinal pathologies such as macular degeneration (MD), patients often adopt compensatory strategies including developing a "preferred retinal locus" (PRL) to replace the fovea in tasks involving fixation. A key question is whether patients develop multi-purpose PRLs or whether their oculomotor strategies adapt to the demands of the task. While most MD patients develop a PRL, clinical evidence suggests that patients may develop multiple PRLs and switch between them according to the task at hand. To understand this, we examined a model of central vision loss in normally seeing individuals and tested whether they used the same or different PRLs across tasks after training. Nineteen participants trained for 10 sessions on contrast detection while in conditions of gaze-contingent, simulated central vision loss. Before and after training, peripheral looking strategies were evaluated during tasks measuring visual acuity, reading abilities and visual search. To quantify strategies in these disparate, naturalistic tasks, we measured and compared the amount of task-relevant information at each of 8 equally spaced, peripheral locations, while participants performed the tasks. Results showed that some participants used consistent viewing strategies across tasks whereas other participants' strategies differed depending on task. This novel method allows quantification of peripheral vision use even in relatively ecological tasks. These results represent one of the first examinations of peripheral viewing strategies across tasks in simulated vision loss. Results suggest that individual differences in peripheral looking strategies following simulated central vision loss may model those developed in pathological vision loss.

Assessing the contrast sensitivity function in myopic parafovea: A quick contrast sensitivity functions study

Purpose

Compare peripheral contrast sensitivity functions (CSF) between myopes and emmetropes to reveal potential myogenic risks during emmetropization.

Materials and methods

This observational, cross-sectional, non-consecutive case study included data from 19 myopes (23.42 ± 4.03 years old) and 12 emmetropes (22.93 ± 2.91 years old) who underwent central and peripheral quick CSF (qCSF) measurements. Summary CSF metrics including the cut-off spatial frequency (cut-off SF), area under log CSF (AULCSF), low-, intermediate-, and high-spatial-frequency AULCSFs (l-, i-, and h-SF AULCSFs), and log CS at 19 SFs in the fovea and 15 peripheral locations (superior, inferior, temporal, and nasal quadrants at 6, 12, 18, and 24° eccentricities, excluding the physiological scotoma at 18°) were analyzed with 3-way and 4-way between-subjects analysis of variance (ANOVA) (α = 0.05).

Results

Three-way ANOVA showed that myopes had significantly increased AULCSF at 6° (mean difference, 0.08; 95% CI, 0.02–0.13; P = 0.007) and 12° (mean difference, 0.09; 95% CI, 0.03–0.14; P = 0.003). Log CS at all 19 SFs were higher in the myopia group compared to the normal group (mean differencesuperior, 0.02; 95% CI, 0.01–0.20; P = 0.02 and mean differenceinferior, 0.11; 95% CI, 0.02–0.21; P = 0.01) at 12°. The h-SF AULCSF at 6° (mean differenceinferior, 1.27; 95% CI, 0.32–2.22; P = 0.009) and i-SF AULCSF at 12° (mean differencesuperior, 5.31; 95% CI, 4.35–6.27; P < 0.001; mean differenceinferior, 1.14; 95% CI, 0.19–2.10; P = 0.02) were higher in myopia vs. normal group.

Conclusion

We found myopia increased contrast sensitivity in superior and inferior visual field locations at 6° parafoveal and 12° perifoveal regions of the retina. The observation of increased contrast sensitivities within the macula visual field in myopia might provide important insights for myopia control during emmetropization.

Evaluating the Performance of qVFM in Mapping the Visual Field of Simulated Observers With Eye Diseases

PURPOSE

Recently, we developed a novel active learning framework, qVFM, to map visual functions in the visual field. The method has been implemented and validated in measuring light sensitivity and contrast sensitivity visual field maps (VFMs) of normal observers. In this study, we evaluated the performance of the qVFM method in mapping the light sensitivity VFM of simulated patients with peripheral scotoma, glaucoma, age-related macular degeneration (AMD), and cataract.

METHODS

For each simulated patient, we sampled 100 locations (60 × 60 degrees) of the visual field and compared the performance of the qVFM method with a procedure that tests each location independently (the qYN method) in a cued Yes/No task. Two different switch modules, the distribution sampling method (DSM) and parameter delivering method (PDM), were implemented in the qVFM method. Simulated runs of 1,200 trials were used to compare the accuracy and precision of the qVFM-DSM, qVFM-PDM and qYN methods.

RESULTS

The qVFM method with both switch modules can provide accurate, precise, and efficient assessments of the light sensitivity VFM for the simulated patients, with the qVFM-PDM method better at detecting VFM deficits in the simulated glaucoma.

CONCLUSIONS

The qVFM method can be used to characterize residual vision of simulated ophthalmic patients. The study sets the stage for further investigation with real patients and potential translation of the method into clinical practice.

Do Impairments in Visual Functions Affect Skiing Performance?

Nordic and alpine skiing-related visual tasks such as identifying hill contours, slope characteristics, and snow conditions increase demands on contrast processing and other visual functions. Prospective observational studies were conducted to assess the relationships between skiing performance and a broad range of visual functions in nordic and alpine skiers with vision impairments. The study hypothesized that contrast sensitivity (CS), visual acuity (VA), and visual field (VF) would be predictive of skiing performance. Binocular static VA, CS, light sensitivity, glare sensitivity, glare recovery, dynamic VA, translational and radial motion perception, and VF were assessed in elite Para nordic (n = 26) and Para alpine (n = 15) skiers. Skiing performance was assessed based on skiers’ raw race times. Performance on the visual function tests was compared with skiing performances using Kendall’s correlations (with and without Bonferroni–Holm corrections) and linear multivariable regressions (p < 0.05 considered significant). None of the vision variables were significantly correlated with performance in Para nordic or Para alpine skiing after Bonferroni–Holm corrections were applied. Before applying the corrections, VF extent (ρ = -0.37, p = 0.011), and static VA (ρ = 0.26, p = 0.066) demonstrated the strongest correlations with Para nordic skiing performance; in Para alpine skiing, static VA and CS demonstrated the strongest correlations with downhill (static VA: ρ = 0.54, p = 0.046, CS: ρ = -0.50, p = 0.06), super G (static VA: ρ = 0.50, p = 0.007, CS: ρ = -0.51, p = 0.017), and giant slalom (static VA: ρ = 0.57, p = 0.01, CS: ρ = -0.46, p = 0.017) performance. Dynamic VA and VF were significantly associated with downhill (ρ = 0.593, p = 0.04) and slalom (ρ = -0.49, p = 0.013) performances, respectively. Static VA was a significant predictor of giant slalom [(F(3,11) = 24.71, p < 0.001), and R of 0.87], super G [(F(3,9) = 17.34, p = 0.002), and R of 0.85], and slalom [(F(3,11) = 11.8, p = 0.002), and R of 0.80] performance, but CS and VF were not. Interestingly, static VA and CS were highly correlated in both Para nordic (ρ = -0.60, p < 0.001) and Para alpine (ρ = -0.80, p < 0.001) skiers. Of the vision variables, only static VA and VF were associated with skiing performance and should be included as the in Para nordic and Para alpine classifications. The strong correlations between static VA and CS in these skiers with vision impairment may have masked relationships between CS and skiing performance.

Psychophysical Validation of a Novel Active Learning Approach for Measuring the Visual Acuity Behavioral Function

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.

A Strategy for Seeding Point Error Assessment for Retesting (SPEAR) in Perimetry Applied to Normal Subjects, Glaucoma Suspects, and Patients With Glaucoma

PURPOSE

We sought to determine the impact of seeding point errors (SPEs) as a source of low test reliability in perimetry and to develop a strategy to mitigate this error early in the test.

DESIGN

Cross-sectional study.

METHODS

Visual field test results from 1 eye of 364 patients (77 normal eyes, 178 glaucoma suspect eyes, and 109 glaucoma eyes) were used to develop models for identifying SPE. Two test cohorts (326 undertaking Swedish interactive thresholding algorithm [SITA]-Faster and 327 glaucoma eyes undertaking SITA-Standard) were used to prospectively evaluate the models for identifying SPEs. Global visual field metrics were compared among reliable and unreliable results. Regression models were used to identify factors distinguishing SPEs from non-SPEs. Models were evaluated using receiver operating characteristic (ROC) curves.

RESULTS

In the test cohorts, SITA-Faster produced a higher rate of unreliable visual field results (30%-49.7%) compared with SITA-Standard (10.8%-16.6%). SPEs contributed to most of the unreliable results in SITA-Faster (57.5%-64.9%) compared with gaze tracker deviations accounting for most of the unreliable results in SITA-Standard (40%-77.8%). In SITA-Faster, results with SPEs had worse global indices and more clusters of sensitivity reduction than reliable results. Our best model (using 9 test locations) can identify SPEs with an area under the ROC curve of 0.89.

CONCLUSION

SPEs contribute to a large proportion of unreliable visual field test results, particularly when using SITA-Faster. We propose a useful model for identifying SPEs early in the test that can then guide retesting using both SITA algorithms. We provide a simplified framework for the perimetrist to improve the overall fidelity of the test result.

Mapping the Contrast Sensitivity of the Visual Field With Bayesian Adaptive qVFM

Current clinical evaluation, which focuses on central vision, could be improved through characterization of residual vision with peripheral testing of visual acuity, contrast sensitivity, color vision, crowding, and reading speed. Assessing visual functions in addition to light sensitivity, a comprehensive visual field map (VFM) would be valuable for detecting and managing eye diseases. In a previous study, we developed a Bayesian adaptive qVFM method that combines a global module for preliminary assessment of the VFM's shape and a local module for assessment at individual retinal locations. The method was validated in measuring the light sensitivity VFM. In this study, we extended the qVFM method to measure contrast sensitivity across the visual field. In both simulations and psychophysics, we sampled 64 visual field locations (48 x 48 deg) and compared the qVFM method with a procedure that tested each retinal location independently (qFC; Lesmes et al., 2015). In each trial, subjects were required to identify a single optotype (size: 2.5 x 2.5 deg), one of 10 filtered Sloan letters. To compare the accuracy and precision of the two methods, three simulated eyes were tested in 1,280 trials with each method. In addition, data were collected from 10 eyes (5 OS, 5 OD) of five normal observers. For simulations, the average RMSE of the estimated contrast sensitivity with the qVFM and qFC methods were 0.057 and 0.100 after 320 trials, and 0.037 and 0.041 after 1,280 trials [all in log10 units, represent as log(sensitivity)], respectively. The average SD of the qVFM and qFC estimates were 0.054 and 0.096 after 320 trials, and 0.032 and 0.041 after 1,280 trials, respectively. The within-run variability (68.2% HWCIs) were comparable to the cross-run variability (SD). In the psychophysics experiment, the average HWCI of the estimated contrast sensitivity from the qVFM and qFC methods across the visual field decreased from 0.33 on the first trial to 0.072 and 0.16 after 160, and to 0.060 and 0.10 after 320 trials. The RMSE between the qVFM and qFC estimates started at 0.26, decreased to 0.12 after 160 and to 0.11 after 320 qVFM trials. The qVFM provides an accurate, precise, and efficient mapping of contrast sensitivity across the entire visual field. The method might find potential clinical applications in monitoring vision loss, evaluating therapeutic interventions, and developing effective rehabilitation for visual diseases.