Dekker, T

Farahbakhsh, M

Jones, PR

English

UCL Discovery

Development of Contrast Sensitivity in children using a novel child-friendly methodMahtab Farahbakhsh1, Pete R Jones1,2, Tessa M., Dekker1,31UCL Institute of Ophthalmology, London, UK3 UCL Division of Psychology and Language Sciences, London, UK2 NIHR Moorfields Biomedical Research Centre, London, UKBackground- There exists disagreements on the time course of the development ofcontrast sensitivity in children. This knowledge may influence decisionsabout when and if a treatment method is useful in children [1].- This disagreement is due to lack of a robust, child-friendly measure of theCSF.- Bayesian adaptive estimation (e.g., the “qCSF[2]”) provides faster and morerobust measures of CSF. However, this has not been tested in children yet.Aims- Evaluation of a novel implementation of Bayesian adaptive CSF estimation,using QUEST+: Assessment of the accuracy, speed, and test-retest reliabilityof this novel method in children.- Quantifying the development of CSF in children aged 4 – 14 years.MethodsParticipants: 59 children aged 4-14 years and 28 adults (see Fig 5 for agedistribution). Participants had normal or corrected-to-normal vision and had nohistory of non-refractive visual impairment.Stimuli: Horizontal Gabor patches: ranging from 2 to 30 cycles per degree(cpd) in Spatial Frequency, and from 0.0001 to 1 in Michelson Contrast. TheGabor patches were presented at 3.8eccentric.Procedure: Four alternative forced choice (4AFC) detection. Participantscompleted the same test using two different algorithms: a weighted staircase(the reference standard) and QUEST+ (the novel comparison).Reference Algorithm: 8 weighted “down-2 up-1” staircases performedindependently at 8 spatial frequencies (CSF fitted post-hoc).Test Algorithm: A 3-parameter CSF model (with a 2D stimulus space) fittedusing QUEST+[3] (see Fig 2 for details). The key advantages of the QUEST+algorithm are that it: 1. provides rapid and flexible estimation of the parametersof a psychophysical model, 2. using prior information, advises the user on themost appropriate stimuli to present and testing termination time, 3. determinesthe maximally informative stimulus on each trial, 4. fits arbitrarily complexmodels.Our (MATLAB) implementation of QUEST+ is freely available at:https://github.com/petejonze/QuestPlus.Fig 1. Experiment set-up. A) An eye-tracker (Tobii X120) was used to record the participant’s gazelocation. B) Each trial started with a fixation point and continued by presentation of the stimuli andpresentation of feedback (i.e., happy zebra (hit) or sad face (miss)).("#$%, '#$%))Fig 2. Our QUEST+ CSF estimation model (similar to the qCSF[2]). The model has 3 freeparameters: (1) peak contrast sensitivity, *+,-; (2) peak frequency, .+,-; (3) rate of fall-off, /. Themodel also contained 3 fixed parameters: lapse rate (0.1), psychometric slope (3), and guess rate(0.25). The stimulus (i.e., Gabor patch) has two parameters: frequency and contrast.ResultsBenefits of our novel test in comparison to conventional methodsThere were no significant differences in CSFs acquired using QUEST+ and theadaptive staircase after 50 trials (~2 minutes), providing substantive speedbenefits in comparison to the traditional staircase method (~12 minutes). Inaddition, Estimates were more reliable with the QUEST+ than with the traditionalstaircase method. This was true for all 3 parameters and both age groups,although children’s measures were consistently less reliable than adults.Test Duration and AccuracyFig 3. Test duration, accuracy and reliability. A) Mean  95%CI CSFs: the QUEST+ CSF (black) and theadaptive staircase method (blue). Each panel shows the average QUEST+ CSF estimate for all theparticipants obtained with different number of trials (adaptive staircase CSF estimate using all ~350 trials isshown in every panel, for reference). Shaded region: bootstrapped CI95%. B) Coefficient of repeatability foreach of the three CSF estimation parameters, broken down by method and age-group (orange: child,purple: adult). Larger values = less reliable.ChildAdultFig 4. Development of CSF with age. Mean CSF grouped by age for young children (<9yr; red), old children(>=9yr; blue), and adults (>=18yr; black). Each marker represents mean contrast sensitivity as a function ofspatial frequency: derived using the combined data from both adaptive methods (QUEST+ and Staircase).The shaded region is the bootstrapped CI95%.Development of contrast sensitivityBased on the CSFs acquired using our novel child-friendly method, there weresignificant difference in contrast sensitivity at low frequencies (i.e., 2 to 5 cpd) acrosschildren and adults.Fig 5. Development of CSF parameters across age. Each data point represents one participant. The blackline is the least mean squared regression slope. Participants who had very low VA (lower than 6/9 Snellenacuity), are plotted in red and were not included in the analyses. Left: Peak Contrast Sensitivity (Gmax)across Age, Middle: Peak Frequency (Fmax) Across age, Right: Rate of fall-off (/) across Age.CSF Parameters across ageParameters fmax and Beta were not observed to change with age. However, Gmax(peak contrast sensitivity) did exhibit a significant developmental effect (F112 = 23.2, p< .001, r2 = 0.17). Development of contrast sensitivity (7.44; mean difference betweenyoung children and adults) was larger than the individual variability between observers(4.60; median absolute deviation from the regression line shown in the left panel ofFig 5).DiscussionBayesian adaptive methods allow CSF estimates to be made in children. Specifically:- CSF estimates were more reliable than traditional psychophysical methods.- Robust estimates could be made in children aged 4 to 14 in only 50 trials (~2mins).Adults are better at detecting low contrast Gabor patches at low spatial frequencies(i.e., 2 to 5 cpd), even when compared to children older than 9 years old.Development of peak contrast sensitivity was larger than the amount of individualvariability within age groups. However, there were no significant differences betweenthe peak frequency and rate of CSF fall-off between children and adults.Future WorkWe are currently evaluating the feasibility of making CSF estimates in children withinherited retinal dystrophies (i.e., achromatopsia).If you are interested in using this test for your own projects then please get in touch(m.farahbakhsh.16@ucl.ac.uk).References[1] Leat, S. J., Yadav, N. K., & Irving, E. L. (2009). Development of visual acuity and contrast sensitivity in children. Journal of Optometry, 2(1), 19-26.[2 Lesmes, L. A., Lu, Z. L., Baek, J., & Albright, T. D. (2010). Bayesian adaptive estimation of the contrast sensitivity function: The quick CSF method. Journal of Vision, 10(3), 17-17. [3] Watson, A. B. (2017). QUEST+: A general multidimensional Bayesian adaptive psychometric method. Journal of Vision, 17(3), 10-10.This work was supported by Moorfields Eye Charity (R160035A), TheESRC (ES/N000838/1), NIHR Biomedical Research Centre (BRC) atMoorfields Eye Hospital and the UCL Institute of Ophthalmology,Ardalan Family Scholarship and the Persia Educational FoundationMaryam Mirzakhani Scholarship.For further information please contact: m.farahbakhsh.16@ucl.ac.uk

Development of Contrast Sensitivity in Children: Using a Novel Child-Friendly Method

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Development of Contrast Sensitivity in Children: Using a Novel Child-Friendly Method

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