Age-related macular degeneration (AMD) is the primary cause of irreversible vision loss in the developed world.1 It is a degenerative disease of the central retina that typically presents after 50 years of age and affects the photoreceptors, retinal pigment epithelium (RPE), Bruch's membrane, and choriocapillaris. The early stage of the disease, before the development of noticeable vision loss, is known as age-related maculopathy (ARM).2
Current treatment strategies, such as photodynamic therapy3 and anti-vascular endothelial growth factor (anti-VEGF) therapy,4,5 target only the exudative form of the disease. There is as yet no effective therapy for the treatment of atrophic AMD. However, the development of tests sensitive to early macular changes and capable of monitoring subtle changes in visual function would help to expedite the development of new interventions and to monitor outcomes.
Color vision,6 flicker sensitivity,7 temporal thresholds,8–10 and dark adaptation7,11–14 abnormalities have all been reported to precede the loss of visual acuity and the appearance of retinal abnormalities in people developing AMD. In the few studies that have measured more than one of these parameters in people with AMD,6,7,14 dark adaptation abnormalities appear to be the single most sensitive markers for the condition. For example, an evaluation of the data presented in Fig. 2 of Eisner et al.6 shows that while both color vision and dark adaptation parameters provided 100% specificity in AMD, the sensitivity of photopic dark adaptation (65%) was superior to that of the color matching (48%). Similarly, Owsley et al.14 and Phipps et al.7 found dark adaptation kinetics to be more sensitive to AMD than steady state measures of visual function.
However, although many studies have reported dark adaptation deficits in people with AMD, the literature is not entirely consistent. For example, although there is a clear consensus that dark adaptation is delayed in rods,14–18 evidence for abnormal cone dark adaptation is equivocal.17,18 More specifically, while Dimitrov et al.18 reported significant delays in cone-mediated dark adaptation in people with AMD, Owsley et al.17 found no such difference in their study of a similar patient group.
One explanation for the contradictory evidence regarding cone dark adaptation in ARM is that the cone dark adaptation deficit in ARM is dependent on retinal location. The observation that delays in cone adaptation have been reported for stimuli centered on the fovea7,18,19 but not for small spot stimuli presented at more eccentric retinal locations17 leads to the hypothesis that cone dark adaptation deficits in ARM are greatest for centrally presented stimuli.
A number of studies have examined the effect of retinal location on rod and cone thresholds in ARM.16,20–23 These studies tend to show a modest reduction in threshold at peripheral locations and a larger deficit in the macular region. However, only one study has systematically described the topographical variation in dark adaptation kinetics in the disease.20 In this study, Brown et al.20 monitored cone dark adaptation at four locations in the central 40° of visual field, in six patients with geographic atrophy and six healthy controls. However, while cone threshold was elevated at all retinal locations in the subjects with geographic atrophy, there were no significant differences in the rate of cone dark adaptation between groups at any of the locations studied.
Clearly, there is ambiguity surrounding the extent to which cone photoreceptors are affected by ARM and the effect, if any, of the retinal location at which dark adaptation is measured. The current study aims to quantify any topographical differences in cone dark adaptation between people with ARM and healthy controls. More specifically, this study determines the diagnostic potential of cone dark adaptation as a function of retinal eccentricity and compares this with the diagnostic potential of the time to rod-cone-break (RCB), the aspect of rod recovery that can be obtained most rapidly in the clinic.
Twenty subjects (10 people with ARM and 10 healthy controls) were recruited. All participants were aged at least 55 years, had a corrected visual acuity of 6/9 or better in the test eye and none had a history of systemic disease or medication known to affect visual function, or significant media opacity.
Participants with ARM were recruited from the Eye Unit at the University Hospital of Wales, Cardiff, and the Eye Clinic at Cardiff University and had a diagnosis of ARM according to the Age-Related Eye Disease Study severity scale,2 in the absence of any co-existing ocular or fundus abnormality. The assessment of fundus status was based on 37° fundus photographs (Canon CR-DGi Camera) obtained at the baseline examination. Ten age-matched control subjects were recruited from the Eye Clinic at Cardiff University. All had a normal retinal appearance in both eyes.
All subjects provided informed written consent before participation. The study was approved by the South East Wales Research Ethics Committee and all procedures adhered to the tenets of the Declaration of Helsinki.
Apparatus and Psychophysical Methods
All stimuli were presented on a calibrated, high-resolution CRT monitor (Iiyama LS 902UT) driven by an 8-bit (nVIDIA Geforce 9) graphics board under software control (Matlab). The luminance output of the monitor was γ-corrected24,25 and modified by neutral density filters mounted on the screen to expose the full range of recovery. The background luminance of the CRT was −0.85 log cd/m2, which was further attenuated by a 1.2 ND filter that was in place throughout all recordings. The computer emitted a sound to signal that the lower end of the luminance range was approaching, at which point an additional 2.1 ND filter was added to further attenuate the luminance. Although the γ-corrected monitor generated a linear output over a 3 log unit range, only 2.1 log units of this range were used during testing.
Thresholds were recorded in response to a foveal spot (radius 0.5°) and three achromatic annuli (2, 7, and 12° in radius), all 0.5° wide, centered on the fovea (Fig. 1). Subjects were instructed to fixate the center of the screen, marked by a fixation cross, and to indicate perception of the stimulus using the computer keyboard.
Dark adaptation was monitored using a psychophysical method, which was previously implemented by Jackson et al.26 using a modified Humphrey perimeter. The stimulus was presented for 200 ms, followed by a 600 ms response window and then a randomly determined interstimulus delay of 0.9 to 2.4 s. This procedure generated a stimulus, on average, every 2.25 s. If the subject responded to the stimulus within 600 ms, the luminance was reduced by 0.3 log units for the next presentation. Conversely, if the subject took longer than 600 ms to respond to the stimulus, or failed to respond at all the intensity was increased by 0.1 log units on each of the following presentations. Threshold was recorded when the stimulus first became visible on an ascending staircase. Using this method, recovery was sampled approximately every 7.2 s on average.
Participants attended the laboratory on 2 days. Baseline examinations, including patient history, logMAR visual acuity (Early Treatment Diabetic Retinopathy Study), central visual field screening (C-40, Humphrey Field Analyzer), stereoscopic fundus examination, fundus photography (Canon CR-DGi Camera), and media opacity grading27 were completed at the start of the first visit.
Before dark adaptation, subjects' eyes were dilated with one drop of 1.0% Tropicamide in each eye. The eye selected for testing was the eye with ARM, or the eye with the better visual acuity in bilateral ARM or control subjects. The contralateral eye was occluded and refractive correction was worn if required.
Each subject was instructed how to use the dark adaptation program, before undergoing a 5-min practice recording session. This was repeated until subjects produced consistent thresholds and the investigator considered the subject to be competent with the procedure.
A Maxwellian view optical system was used to deliver an 80% bleach (5.1 log phot. Td for 120 s) of cone photopigment28 to the central 43.6° of the test eye. On cessation of the bleach, subjects placed their chin on the rest in front of the computer screen and the computer program commenced immediately. Dark adaptation was monitored in response to one of the four stimuli, selected at random, until the RCB occurred or for 25 min. The investigator deemed the RCB to have occurred when threshold fell by at least 1 log unit below the cone plateau. For the 0.5° stimulus, recovery was only monitored for 10 min, as no RCB was expected for a small stimulus, presented to the rod-free fovea. This procedure was repeated for each of the remaining stimuli. Generally, two stimulus sizes were completed at each session, with a washout period of an hour between bleaches. In addition, as the study used a long duration adapting light, sufficient to produce an equilibrium level of bleach,28 all individuals should have reached the same level of photopigment bleach regardless of any small differences in prebleach adaptational status.
The dynamics of cone recovery and the time to RCB were determined by fitting an exponential model of dark adaptation to the cone threshold recovery data and a linear model to any rod threshold recovery data, after McGwin et al.29 (Eq. 1), on a least squares basis, using Microsoft Excel. Threshold was recorded when the stimulus first became visible on an ascending staircase. An alternative rate-based model of cone recovery18,30 was also evaluated, but was rejected in favor of the exponential model29 because although the rate-based model is theoretically superior, it did not fit the data and the exponential one.
where T is the threshold (log cd/m2) at time t after cessation of the bleach, a is the final cone threshold, b is the change in cone threshold from t = 0, τ is the time constant of cone recovery, c is the slope of the second component of rod recovery, max is a logic statement, and rcb denotes the time from bleach offset to the RCB. Although the RCB was the only aspect of rod recovery assessed during the analysis, the second component of rod recovery was modeled to identify the time to RCB.
The parameters of interest were cone τ, the final cone threshold, and the time to RCB. The mean (±SD) was calculated for each parameter, before independent sample t tests were used to make comparisons between ARM and control groups. Receiver operating characteristic curves were constructed using statistical software (SPSS, version 16.0) to assess the diagnostic potential of the parameters that showed a statistically significant difference between groups.
The clinical characteristics of the ARM group are shown in Table 1. There were no significant differences in age between ARM (mean age = 68.3 ± 7.3 SD years) and control (mean age = 70.0 ± 4.7 SD years) groups (p = 0.54). Fifty percent of ARM subjects had ARM in their fellow eye and the remaining 50% had exudative changes. There were no significant differences in logMAR acuity between the test eyes of ARM and control groups (mean acuity = 0.09 ± 0.11 SD logMAR for ARM subjects and −0.002 ± 0.10 SD logMAR for control subjects; p = 0.11).
Fig. 2A shows the time course of dark adaptation for a typical control subject, in response to the four stimuli. An example of the dark adaptation curves for an ARM subject is shown in Fig. 2B. This ARM subject had prolonged cone adaptation and only displayed a RCB within 25 min for the 12° stimulus.
The mean dark adaptation parameters for each group are summarized in Table 2. Where there was no RCB within 25 min, the RCB was given as 25 min. This means a conservative estimate of the delay in rod adaptation was included in all statistics. There were no significant differences in final cone threshold between control and ARM groups for any of the locations studied. In contrast, there were significant differences in the cone time constant of recovery (τ) between groups for stimuli located 2, 7, and 12° from fixation (all p < 0.05). In addition, there was a significant difference in the time to RCB at 12° (p < 0.001) between groups. The mean τ, final cone threshold, and time to RCB for control and ARM groups at each retinal location are summarized in Fig. 3. Although it can be seen that the greatest absolute difference in recovery rate (Fig. 3A) between those with ARM and healthy controls was observed for the central stimulus, this difference failed to reach significance because of the variability in the data obtained at this location. The most significant difference between groups was observed for the stimulus located at 12° where the variability in the data set was minimal.
Equation 1 fitted the data well as exemplified by the RMS error, which was 0.140, 0.146, 0.146, and 0.178, for stimuli located at 0.5, 2, 7, and 12° for the ARM group and 0.152, 0.157, 0.180, and 0.216, for those without ARM at the same locations.
Fig. 4 shows the receiver operating characteristic curves for all the parameters that differed significantly between groups on univariate analysis. The diagnostic capacity of each parameter is expressed as the area under the curve (AUC). The 12° annulus was the best stimulus for discriminating subjects with ARM from healthy controls, yielding an AUC of 0.99 ± 0.02 for cone τ and 0.96 ± 0.04 for time to RCB, respectively. This equates to 100% sensitivity and 90% specificity for a cone τ of 1.04 min and 90% sensitivity and 90% specificity for a RCB of 11.98 min.
Table 3 shows that the sensitivity and specificity of the parameters differed significantly on univariate analysis according to the normal reference range (defined as two standard deviations around the mean of the control group). This further supports the high diagnostic ability of the 12° stimulus, which showed 90% sensitivity and 90% specificity for cone τ.
A separate analysis was undertaken to check for test order effects (Table 4). There were no significant differences in dark adaptation parameters recorded for the first and second bleaches within a single session. This analysis indicates that there is no learning, fatigue, or bleach carry-over effects within the dataset.
Contrary to expectations, these results show that cone τ and time to the RCB are highly diagnostic for ARM for stimuli located 12° from the fovea, with cone τ discriminating participants with ARM from healthy controls with 100% sensitivity and 90% specificity. To a lesser extent, cone τ was also diagnostic for ARM for stimuli located at 2 and 7°. Interestingly, although the greatest absolute difference in mean cone τ between the groups was observed at the fovea, a finding consistent with previous reports,7,18,19 this difference failed to reach significance because of the variability of the data at this retinal location (see size of 95% CI in Fig. 3A). Consequently, in terms of diagnostic potential, the functional deficit at 12° from fixation provides the best separation between groups.
Although a previous study reported that dark adaptation was impaired at 12° in ARM, the impairment was thought to affect rods only.17 In contrast, this study has shown that cone dark adaptation is also highly abnormal at this location. This has significant clinical implications because in the clinic, “time is of the essence” and cone dark adaptation may generally be assessed in less time than rod adaptation.
What then explains the discrepancies between the current study and the results of previous investigations?
The following paragraphs provide three related explanations based on the well-established rate-limiting step in dark adaptation, that is, the local availability of 11-cis retinal.31
The first explanation relates to the bleaching method used (photoflash vs. steady state bleach) and its effect on the rate of cone dark adaptation in people with AMD. Both rods and cones need 11-cis retinal to regenerate visual pigment but while rods can only obtain that supply from the RPE, there is good evidence to suggest that cones have access to a secondary supply, derived from Müller cells.32–34 Rods and cones compete for RPE-derived 11-cis retinal but the additional Müller cell pathway provides cones with an exclusive secondary source that helps them regenerate photopigment much more rapidly than rods. Unlike photoflashes, steady state bleaches involve both sustained phototransduction and activation of the visual cycle. If this sustained metabolic activity adversely affected the Müller cell retinoid recycling pathway, cone photopigment regeneration following a long duration bleach would be relatively slow and more dependent on RPE derived 11-cis-retinal, that is, the part of the retina that is abnormal in ARM. Any impairment to cone-mediated dark adaptation in ARM would therefore be more likely to manifest under the conditions used here than following the 11 ms bleach used previously.17
The second explanation provides a physiologically plausible rationale for the cone dark adaptation deficit at 12°. As discussed, the long duration adapting light makes both rods and cones reliant on the RPE for regeneration of 11-cis retinal, that is, any deficit in RPE function or in the local supply of retinoid is likely to affect both rod and cone adaptation. Rod photoreceptor density reaches a peak of about 150,000 cells/mm2 about 12° from the fovea,35 and it has previously been shown that rod adaptation is significantly impaired at this eccentricity.14,17 Direct competition between the rods and cones for the same limited supply of retinoid will be greatest at this location and therefore, providing the cones' intraretinal source of 11-cis-retinal is deficient following a long duration bleach, cone recovery at this site would also be expected to be highly abnormal. That is, the important topographical parameter in ARM is not photoreceptor type but the local availability of 11-cis-retinal on which both rods and cones are dependent.
The third explanation relates to the size of the stimuli used to track dark adaptation and its effect on variability and the results we obtained at the fovea. Cone density peaks at the fovea (200,000 cells/mm235); hence, competition for 11-cis-retinal will be relatively high and, as we observed, recovery was particularly slow at this retinal location for those with ARM. However, small spot stimuli sample the retina at a precise location. If the disease being investigated is characterized by localized abnormalities (i.e., it is heterogeneous), then the results obtained with small spots will be influenced by chance, that is, the chance that the stimulus is located on a healthy or unhealthy part of the retina. Conversely, thresholds obtained in response to large annuli will be determined by the part of the retina that is most functional/healthy because small areas of abnormality will not contribute to the threshold measured. Hence, we might expect large annuli to produce relatively consistent results based on the functional ability of the retina at a given eccentricity and spots to produce more variable data based on their “hit or miss” sampling of heterogeneous retina. This is exemplified in Table 2, where the standard deviations reported for the ARM group are relatively small for our largest annuli, for example, the standard deviation was on average only 0.57 times the size of the mean recovery time. This contrasts sharply with the data reported by Owsley et al.17 who sampled the retina at the same distance from the fovea (12° in the inferior visual field) but who used a spot stimulus (1.7° diameter). They reported standard deviations that were more than twice the value of the mean cone τ for their “intermediate” ARM group. This hypothesis appears to provide an explanation for the relatively increased variability observed for the 0.5° data reported here and may have also contributed to the variability observed in other studies that used relatively small spot stimuli.14,17
The heterogeneity argument proposed above may also explain why Dimitrov et al.18 found cone dark adaptation to be highly diagnostic for ARM (AUC = 0.98). They also used a large (area = 12.6 deg2) stimulus that would have been relatively unaffected by focal abnormalities and reported relatively little variability in their dataset (the standard deviation of their recovery rate was only 0.35 of the mean value).18 Taken together with the current findings, this suggests that the size of the stimulus may be just as important as location because larger stimuli are associated with reduced variability and hence better diagnostic power.
Previously, Brown et al.20 reported no differences in cone dark adaptation dynamics within the central 40° of visual field between control subjects and those with geographic atrophy. However, that patient group had end-stage AMD and was very different from those studied here.
A group of eight individuals with ARM failed to reach a RCB within the 25 min recording period for one or more of the experimental stimuli. This group included all five of the participants diagnosed with exudative disease in their fellow eye. It is known that there is a higher incidence of choroidal neovascularization in the fellow eyes of patients with unilateral exudative AMD.36–38 Therefore, the results support evidence to suggest that dark adaptation is more severely impaired in eyes with an increased risk of exudative changes.6
We collected data over a maximum recording period of 25 min. This time constraint was implemented to evaluate the diagnostic potential of cone dark adaptation and the time to RCB within a clinically viable time frame. Although rod dark adaptation can be assessed in as little as 20 min using an alternative protocol,39 the results presented here are particularly significant because cone τ is highly diagnostic for ARM and may be quantified in as little as 10 min.
A limitation of our rod parameter, time to RCB, is that it is dependent not only on the rate of rod adaptation but also on changes in cone final threshold. Therefore, it cannot be considered to be a pure measure of rod adaptation. However, this study showed no evidence of elevation in the cone plateau of participants with early ARM, therefore the finding of a significantly delayed RCB is likely to be attributable to delayed rod adaptation in these individuals.
Although our sample size (n = 20) is modest, our primary interest was in distinguishing those with early ARM from healthy controls, that is, detecting clinically significant differences, rather than identifying small differences in mean values. Even with a relatively modest sample size, there was a marked separation of participants with ARM and controls in the cone recovery and RCB data.
In conclusion, this study has demonstrated the diagnostic potential of cone dark adaptation in the detection of ARM and the effect of the retinal location at which dark adaptation is measured. Our results provide compelling evidence supporting the use of cone dark adaptation and the use of large, annular stimuli at 12° in the diagnosis of ARM.
This study was funded by a research grant from the College of Optometrists, UK.
Tom H. Margrain
School of Optometry and Vision Sciences
Cathays, Cardiff, CF24 4LU, United Kingdom
1. Resnikoff S, Pascolini D, Etya'ale D, Kocur I, Pararajasegaram R, Pokharel GP, Mariotti SP. Global data on visual impairment in the year 2002. Bull World Health Organ 2004;82:844–51.
2. Davis MD, Gangnon RE, Lee LY, Hubbard LD, Klein BE, Klein R, Ferris FL, III, Bressler SB, Milton RC. The Age-Related Eye Disease Study severity scale for age-related macular degeneration: AREDS Report No. 17. Arch Ophthalmol 2005;123:1484–98.
3. Bressler NM, Bressler SB. Photodynamic therapy with verteporfin (Visudyne): impact on ophthalmology and visual sciences. Invest Ophthalmol Vis Sci 2000;41:624–8.
4. Brown DM, Michels M, Kaiser PK, Heier JS, Sy JP, Ianchulev T. Ranibizumab versus verteporfin photodynamic therapy for neovascular age-related macular degeneration: two-year results of the ANCHOR study. Ophthalmology 2009;116:57–65.
5. Mitchell P, Korobelnik JF, Lanzetta P, Holz FG, Prunte C, Schmidt-Erfurth U, Tano Y, Wolf S. Ranibizumab (Lucentis) in neovascular age-related macular degeneration: evidence from clinical trials. Br J Ophthalmol 2010;94:2–13.
6. Eisner A, Stoumbos VD, Klein ML, Fleming SA. Relations between fundus appearance and function. Eyes whose fellow eye has exudative age-related macular degeneration. Invest Ophthalmol Vis Sci 1991;32:8–20.
7. Phipps JA, Guymer RH, Vingrys AJ. Loss of cone function in age-related maculopathy. Invest Ophthalmol Vis Sci 2003;44:2277–83.
8. Mayer MJ, Spiegler SJ, Ward B, Glucs A, Kim CB. Mid-frequency loss of foveal flicker sensitivity in early stages of age-related maculopathy. Invest Ophthalmol Vis Sci 1992;33:3136–42.
9. Mayer MJ, Spiegler SJ, Ward B, Glucs A, Kim CB. Foveal flicker sensitivity discriminates ARM-risk from healthy eyes. Invest Ophthalmol Vis Sci 1992;33:3143–9.
10. Mayer MJ, Spiegler SJ, Ward B, Glucs A, Kim CB. Preliminary evaluation of flicker sensitivity as a predictive test for exudative age-related maculopathy. Invest Ophthalmol Vis Sci 1992;33:3150–5.
11. Collins M, Brown B. Glare recovery and age-related maculopathy. Clin Vis Sci 1989;4:145–53.
12. Sandberg MA, Gaudio AR. Slow photostress recovery and disease severity in age-related macular degeneration. Retina 1995;15:407–12.
13. Midena E, Degli Angeli C, Blarzino MC, Valenti M, Segato T. Macular function impairment in eyes with early age-related macular degeneration. Invest Ophthalmol Vis Sci 1997;38:469–77.
14. Owsley C, Jackson GR, White M, Feist R, Edwards D. Delays in rod-mediated dark adaptation in early age-related maculopathy. Ophthalmology 2001;108:1196–202.
15. Brown B, Kitchin JL. Dark adaptation and the acuity/luminance response in senile macular degeneration (SMD). Am J Optom Physiol Opt 1983;60:645–50.
16. Brown B, Adams AJ, Coletta NJ, Haegerstrom-Portnoy G. Dark adaptation in age-related maculopathy. Ophthal Physiol Opt 1986;6:81–4.
17. Owsley C, McGwin G, Jr., Jackson GR, Kallies K, Clark M. Cone- and rod-mediated dark adaptation impairment in age-related maculopathy. Ophthalmology 2007;114:1728–35.
18. Dimitrov PN, Guymer RH, Zele AJ, Anderson AJ, Vingrys AJ. Measuring rod and cone dynamics in age-related maculopathy. Invest Ophthalmol Vis Sci 2008;49:55–65.
19. Binns AM, Margrain TH. Evaluating retinal function in age-related maculopathy with the ERG photostress test. Invest Ophthalmol Vis Sci 2007;48:2806–13.
20. Brown B, Tobin C, Roche N, Wolanowski A. Cone adaptation in age-related maculopathy. Am J Optom Physiol Opt 1986;63:450–4.
21. Steinmetz RL, Haimovici R, Jubb C, Fitzke FW, Bird AC. Symptomatic abnormalities of dark adaptation in patients with age-related Bruch's membrane change. Br J Ophthalmol 1993;77:549–54.
22. Owsley C, Jackson GR, Cideciyan AV, Huang Y, Fine SL, Ho AC, Maguire MG, Lolley V, Jacobson SG. Psychophysical evidence for rod vulnerability in age-related macular degeneration. Invest Ophthalmol Vis Sci 2000;41:267–73.
23. Zele AJ, Dang TM, O'Loughlin RK, Guymer RH, Harper A, Vingrys AJ. Adaptation mechanisms, eccentricity profiles, and clinical implementation of red-on-white perimetry. Optom Vis Sci 2008;85:309–17.
24. Metha AB, Vingrys AJ, Badcock DR. Calibration of a color monitor for visual psychophysics. Behav Res Method Instrum Comput 1993;25:371–83.
25. Brainard DH, Pelli DG, Robson T Display characterization. In: Hornak J, ed. The Encyclopaedia of Imaging Science and Technology, vol 18. Hoboken, NJ: Wiley; 2001:172–88.
26. Jackson GR, Owsley C, McGwin G. Aging and dark adaptation. Vision Res 1999;39:3975–82.
27. Chylack LT, Jr., Wolfe JK, Singer DM, Leske MC, Bullimore MA, Bailey IL, Friend J, McCarthy D, Wu SY. The Lens Opacities Classification System III. The Longitudinal Study of Cataract Study Group. Arch Ophthalmol 1993;111:831–6.
28. Hollins M, Alpern M. Dark adaptation and visual pigment regeneration in human cones. J Gen Physiol 1973;62:430–47.
29. McGwin G, Jr., Jackson GR, Owsley C. Using nonlinear regression to estimate parameters of dark adaptation. Behav Res Methods Instrum Comput 1999;31:712–7.
30. Pianta MJ, Kalloniatis M. Characterisation of dark adaptation in human cone pathways: an application of the equivalent background hypothesis. J Physiol 2000;528:591–608.
31. Lamb TD, Pugh EN, Jr. Dark adaptation and the retinoid cycle of vision. Prog Retin Eye Res 2004;23:307–80.
32. Mata NL, Radu RA, Clemmons RC, Travis GH. Isomerization and oxidation of vitamin a in cone-dominant retinas: a novel pathway for visual-pigment regeneration in daylight. Neuron 2002;36:69–80.
33. Wang JS, Kefalov VJ. An alternative pathway mediates the mouse and human cone visual cycle. Curr Biol 2009;19:1665–9.
34. Wang JS, Kefalov VJ. The cone-specific visual cycle. Prog Retin Eye Res 2011;30:115–28.
35. Curcio CA, Sloan KR, Kalina RE, Hendrickson AE. Human photoreceptor topography. J Comp Neurol 1990;292:497–523.
36. Klaver CC, Assink JJ, van Leeuwen R, Wolfs RC, Vingerling JR, Stijnen T, Hofman A, de Jong PT. Incidence and progression rates of age-related maculopathy: the Rotterdam Study. Invest Ophthalmol Vis Sci 2001;42:2237–41.
37. Mitchell P, Wang JJ, Foran S, Smith W. Five-year incidence of age-related maculopathy lesions: the Blue Mountains Eye Study. Ophthalmology 2002;109:1092–7.
38. Klein R, Klein BE, Knudtson MD, Meuer SM, Swift M, Gangnon RE. Fifteen-year cumulative incidence of age-related macular degeneration: the Beaver Dam Eye Study. Ophthalmology 2007;114:253–62.
39. Jackson GR, Edwards JG. A short-duration dark adaptation protocol for assessment of age-related maculopathy. J Ocul Biol Dis Infor 2008;1:7–11.
Keywords:© 2011 American Academy of Optometry
age-related maculopathy; dark adaptation; retinal eccentricity; diagnostic potential; psychophysics