Evidence map›Paper›PMID 32692825›Full record

ArticleJournal of vision2020

Combining optical and neural components in physiological visual image quality metrics as functions of luminance and age.

Gareth D Hastings, Jason D Marsack, Larry N Thibos, Raymond A Applegate

Open access · goldAbstract readComparative Study
In one paragraph

Article in Journal of vision, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Article
  3. Structure-Function Relationship in Keratoconus: Spatial and Depth Vision.Translational vision science & technology · 2023
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Clinical applications of personalising the neural components of visual image quality metrics for individual eyes.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2022
    Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 4 institutions in 2 countries.

Gareth D Hastings,.
Jason D Marsack,.
Larry N Thibos,.
Raymond A Applegate,.
Grieg Seafood (Norway) · NOIndiana University Bloomington · USJDSU (United States) · USUniversity of Houston · US

Funding

Research Programming ModuleP30EY007551 · NEI · UNIVERSITY OF HOUSTON · PI VALLABH E DAS · 1988 to 2026
$16.4M
Optical aberration and visual performance in highly aberrated eyesR01EY019105 · NEI · UNIVERSITY OF HOUSTON · PI APPLEGATE, RAYMOND A, MARSACK, JASON D · 2009 to 2018
$2.9M
ABERRATIONS OF EYES IN NORMAL CLINICAL POPULATIONR01EY008520 · NEI · UNIVERSITY OF TEXAS HLTH SCI CTR SAN ANT · PI APPLEGATE, RAYMOND A · 1991 to 2010
$2.8M
NEI NIH HHS P30 EY007551NEI NIH HHS R01 EY008520NEI NIH HHS R01 EY019105
6 · The paper itself

Abstract

Visual image quality metrics combine comprehensive descriptions of ocular optics (from wavefront error) with a measure of the neural processing of the visual system (neural contrast sensitivity). To improve the ability of these metrics to track real-world changes in visual performance and to investigate the roles and interactions of those optical and neural components in foveal visual image quality as functions of age and target luminance, models of neural contrast sensitivity were constructed from the literature as functions of (1) retinal illuminance (Trolands, td), and (2) retinal illuminance and age. These models were then incorporated into calculation of the visual Strehl ratio (VSX). Best-corrected VSX values were determined at physiological pupil sizes over target luminances of 104 to 10-3 cd/m2 for 146 eyes spanning six decades of age. Optical and neural components of the metrics interact and contribute to visual image quality in three ways. At target luminances resulting in >900 td at physiological pupil size, neural processing is constant, and only aberrations (that change as pupil size changes with luminance) affect the metric. At low mesopic luminances below where pupil size asymptotes to maximum, optics are constant (maximum pupil), and only the neural component changes with luminance. Between these two levels, both optical and neural components of the metrics are affected by changes in target luminance. The model that accounted for both retinal illuminance and age allowed VSX, termed VSX(td,a), to best track visual acuity trends (measured at 160 and 200 cd/m2) as a function of age (20s through 70s) from the literature. Best-corrected VSX(td,a) decreased by 2.24 log units between maximum and minimum target luminances in the youngest eyes and by 2.58 log units in the oldest. The decrease due to age was more gradual at high target luminances (0.70 log units) and more pronounced as target luminance decreased (1.04 log units).

Indexed as

LightAdultAgedAged, 80 and overAgingContrast SensitivityFemaleHumansMaleMiddle AgedPupilRetinaVision, OcularVisual AcuityVisual PerceptionYoung Adult

Identifiers

PMID32692825
PMCPMC7424108
OpenAlexW3044846656

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.