Fang Hou

qCSF in Clinical Application: Efficient Characterization and Classification of Contrast Sensitivity Functions in Amblyopia

PURPOSE The qCSF method is a novel procedure for rapid measurement of spatial contrast sensitivity functions (CSFs). It combines Bayesian adaptive inference with a trial-to-trial information gain strategy, to directly estimate four parameters defining the observers CSF. In the present study, the suitability of the qCSF method for clinical application was examined. METHODS The qCSF method was applied to rapidly assess spatial CSFs in 10 normal and 8 amblyopic participants. The qCSF was evaluated for accuracy, precision, test-retest reliability, suitability of CSF model assumptions, and accuracy of amblyopia screening. RESULTS qCSF estimates obtained with as few as 50 trials matched those obtained with 300 Ψ trials. The precision of qCSF estimates obtained with 120 and 130 trials, in normal subjects and amblyopes, matched the precision of 300 Ψ trials. For both groups and both methods, test-retest sensitivity estimates were well matched (all R > 0.94). The qCSF model assumptions were valid for 8 of 10 normal participants and all amblyopic participants. Measures of the area under log CSF (AULCSF) and the cutoff spatial frequency (cutSF) were lower in the amblyopia group; these differences were captured within 50 qCSF trials. Amblyopia was detected at an approximately 80% correct rate in 50 trials, when a logistic regression model was used with AULCSF and cutSF as predictors. CONCLUSIONS The qCSF method is sufficiently rapid, accurate, and precise in measuring CSFs in normal and amblyopic persons. It has great potential for clinical practice.

Training in contrast detection improves motion perception of sinewave gratings in amblyopia.

PURPOSE. One critical concern about using perceptual learning to treat amblyopia is whether training with one particular stimulus and task generalizes to other stimuli and tasks. In the spatial domain, it has been found that the bandwidth of contrast sensitivity improvement is much broader in amblyopes than in normals. Because previous studies suggested the local motion deficits in amblyopia are explained by the spatial vision deficits, the hypothesis for this study was that training in the spatial domain could benefit motion perception of sinewave gratings. METHODS. Nine adult amblyopes (mean age, 22.1 ± 5.6 years) were trained in a contrast detection task in the amblyopic eye for 10 days. Visual acuity, spatial contrast sensitivity functions, and temporal modulation transfer functions (MTF) for sinewave motion detection and discrimination were measured for each eye before and after training. Eight adult amblyopes (mean age, 22.6 ± 6.7 years) served as control subjects. RESULTS. In the amblyopic eye, training improved (1) contrast sensitivity by 6.6 dB (or 113.8%) across spatial frequencies, with a bandwidth of 4.4 octaves; (2) sensitivity of motion detection and discrimination by 3.2 dB (or 44.5%) and 3.7 dB (or 53.1%) across temporal frequencies, with bandwidths of 3.9 and 3.1 octaves, respectively; (3) visual acuity by 3.2 dB (or 44.5%). The fellow eye also showed a small amount of improvement in contrast sensitivities and no significant change in motion perception. Control subjects who received no training demonstrated no obvious improvement in any measure. CONCLUSIONS. The results demonstrate substantial plasticity in the amblyopic visual system, and provide additional empirical support for perceptual learning as a potential treatment for amblyopia.

Contrast Gain Control in Stereo Depth and Cyclopean Contrast Perception

Although human observers can perceive depth from stereograms with considerable contrast difference between the images presented to the two eyes (Legge & Gu, 1989), how contrast gain control functions in stereo depth perception has not been systematically investigated. Recently, we developed a multipathway contrast gain-control model (MCM) for binocular phase and contrast perception (Huang, Zhou, Lu, & Zhou, 2011; Huang, Zhou, Zhou, & Lu, 2010) based on a contrast gain-control model of binocular phase combination (Ding & Sperling, 2006). To extend the MCM to simultaneously account for stereo depth and cyclopean contrast perception, we manipulated the contrasts (ranging from 0.08 to 0.4) of the dynamic random dot stereograms (RDS) presented to the left and right eyes independently and measured both disparity thresholds for depth perception and perceived contrasts of the cyclopean images. We found that both disparity threshold and perceived contrast depended strongly on the signal contrasts in the two eyes, exhibiting characteristic binocular contrast gain-control properties. The results were well accounted for by an extended MCM model, in which each eye exerts gain control on the other eyes signal in proportion to its own signal contrast energy and also gain control over the other eyes gain control; stereo strength is proportional to the product of the signal strengths in the two eyes after contrast gain control, and perceived contrast is computed by combining contrast energy from the two eyes. The new model provided an excellent account of our data (r(2) = 0.945), as well as some challenging results in the literature.

Using 10AFC to further improve the efficiency of the quick CSF method.

The contrast sensitivity function (CSF) provides a fundamental characterization of spatial vision, important for basic and clinical applications, but its long testing times have prevented easy, widespread assessment. The original quick CSF method was developed using a two-alternative forced choice (2AFC) grating orientation identification task (Lesmes, Lu, Baek, & Albright, 2010), and obtained precise CSF assessments while reducing the testing burden to only 50 trials. In this study, we attempt to further improve the efficiency of the quick CSF method by exploiting the properties of psychometric functions in multiple-alternative forced choice (m-AFC) tasks. A simulation study evaluated the effect of the number of alternatives m on the efficiency of the sensitivity measurement by the quick CSF method, and a psychophysical study validated the quick CS method in a 10AFC task. We found that increasing the number of alternatives of the forced-choice task greatly improved the efficiency of CSF assessment in both simulation and psychophysical studies. The quick CSF method based on a 10-letter identification task can assess the CSF with an averaged standard deviation of 0.10 decimal log unit in less than 2 minutes.

Noise Provides New Insights on Contrast Sensitivity Function

Sensitivity to luminance difference, or contrast sensitivity, is critical for animals to survive in and interact with the external world. The contrast sensitivity function (CSF), which measures visual sensitivity to spatial patterns over a wide range of spatial frequencies, provides a comprehensive characterization of the visual system. Despite its popularity and significance in both basic research and clinical practice, it hasn’t been clear what determines the CSF and how the factors underlying the CSF change in different conditions. In the current study, we applied the external noise method and perceptual template model to a wide range of external noise and spatial frequency (SF) conditions, and evaluated how the various sources of observer inefficiency changed with SF and determined the limiting factors underlying the CSF. We found that only internal additive noise and template gain changed significantly with SF, while the transducer non-linearity and coefficient for multiplicative noise were constant. The 12-parameter model provided a very good account of all the data in the 200 tested conditions (86.5%, 86.2%, 89.5%, and 96.4% for the four subjects, respectively). Our results suggest a re-consideration of the popular spatial vision model that employs the CSF as the front-end filter and constant internal additive noise across spatial frequencies. The study will also be of interest to scientists and clinicians engaged in characterizing spatial vision deficits and/or developing rehabilitation methods to restore spatial vision in clinical populations.

The external noise normalized gain profile of spatial vision

The contrast sensitivity function (CSF), a measure of visual sensitivity to a wide range of spatial frequencies, has been widely used as the gain profile of the front-end filter of the visual system to predict how we perceive spatial patterns. However, the CSF itself is determined by the gain profile and other processing inefficiencies of the visual system; it may be problematic to use the CSF as the gain profile in observer models. Here, we applied the external noise paradigm and the perceptual template model (PTM) to characterize several major properties of the visual system. With the external noise normalized gain profile, nonlinearity, and internal additive and multiplicative noises, the PTM accounted for 92.8% of the variance in the experiment data measured in a wide range of conditions and revealed the major processing components that determine the CSF. Unlike the CSF, the external noise normalized gain profile of the visual system is relatively flat across a wide range of spatial frequencies. The results may have major implications for understanding normal and abnormal spatial vision.

Evaluating the performance of the quick CSF method in detecting contrast sensitivity function changes

The contrast sensitivity function (CSF) has shown promise as a functional vision endpoint for monitoring the changes in functional vision that accompany eye disease or its treatment. However, detecting CSF changes with precision and efficiency at both the individual and group levels is very challenging. By exploiting the Bayesian foundation of the quick CSF method (Lesmes, Lu, Baek, & Albright, 2010), we developed and evaluated metrics for detecting CSF changes at both the individual and group levels. A 10-letter identification task was used to assess the systematic changes in the CSF measured in three luminance conditions in 112 naïve normal observers. The data from the large sample allowed us to estimate the test–retest reliability of the quick CSF procedure and evaluate its performance in detecting CSF changes at both the individual and group levels. The test–retest reliability reached 0.974 with 50 trials. In 50 trials, the quick CSF method can detect a medium 0.30 log unit area under log CSF change with 94.0% accuracy at the individual observer level. At the group level, a power analysis based on the empirical distribution of CSF changes from the large sample showed that a very small area under log CSF change (0.025 log unit) could be detected by the quick CSF method with 112 observers and 50 trials. These results make it plausible to apply the method to monitor the progression of visual diseases or treatment effects on individual patients and greatly reduce the time, sample size, and costs in clinical trials at the group level.

A hierarchical Bayesian approach to adaptive vision testing: A case study with the contrast sensitivity function.

Measurement efficiency is of concern when a large number of observations are required to obtain reliable estimates for parametric models of vision. The standard entropy-based Bayesian adaptive testing procedures addressed the issue by selecting the most informative stimulus in sequential experimental trials. Noninformative, diffuse priors were commonly used in those tests. Hierarchical adaptive design optimization (HADO; Kim, Pitt, Lu, Steyvers, & Myung, 2014) further improves the efficiency of the standard Bayesian adaptive testing procedures by constructing an informative prior using data from observers who have already participated in the experiment. The present study represents an empirical validation of HADO in estimating the human contrast sensitivity function. The results show that HADO significantly improves the accuracy and precision of parameter estimates, and therefore requires many fewer observations to obtain reliable inference about contrast sensitivity, compared to the method of quick contrast sensitivity function (Lesmes, Lu, Baek, & Albright, 2010), which uses the standard Bayesian procedure. The improvement with HADO was maintained even when the prior was constructed from heterogeneous populations or a relatively small number of observers. These results of this case study support the conclusion that HADO can be used in Bayesian adaptive testing by replacing noninformative, diffuse priors with statistically justified informative priors without introducing unwanted bias.