Yes / PURPOSE. To compare microperimetric sensitivity around the monocular preferred retinal locus (mPRL) in age-related macular degeneration (AMD) to normative data, and to describe the characteristics of visual field defects around the mPRL in AMD. METHODS. Participants with AMD (total n ¼ 185) were either prospectively recruited (n ¼ 135) or retrospectively reviewed from an existing database (n ¼ 50). Participants underwent microperimetry using a test pattern (37 point, 58 radius) centered on their mPRL. Sensitivities were compared to normative data by spatial interpolation, and conventional perimetric indices were calculated. The location of the mPRL relative to the fovea and to visual field defects was also investigated. RESULTS. Location of mPRL varied approximately 158 horizontally and vertically. Visual field loss within 58 of the mPRL was considerable in the majority of participants (median mean deviation 14.7 dB, interquartile range [IQR] 19.6 to 9.6 dB, median pattern standard deviation 7.1 dB [IQR 4.8–9.0 dB]). Over 95% of participants had mean total deviation worse than 2 dB across all tested locations and similarly within 18 of their mPRL. A common pattern of placing the mPRL just foveal to a region of normal pattern deviation was found in 78% of participants. Total deviation was outside normal limits in this region in 68%. CONCLUSIONS. Despite altering fixation to improve vision, people with AMD exhibit considerable visual field loss at and around their mPRL. The location of the mPRL was typically just foveal to, but not within, a region of relatively normal sensitivity for the individual, suggesting that a combination of factors drives mPRL selection. / This report presents independent research funded by the NIHR

Yes / Microperimetry, also referred to as fundus perimetry or fundus-driven perimetry, enables simultaneous acquisition of visual sensitivity and eye movement data. We present sensitivity data collected from 60 participants with normal vision using gaze-contingent perimetry. A custom designed spatially dense test grid was used to collect data across the visual field within 13° of fixation. These data are supplemental to a study in which we demonstrated a spatial interpolation method that facilitates comparison of acquired data from any set of spatial locations to normative data and thus screening of individuals with both normal and non-foveal fixation (Denniss and Astle, 2016)[1].

京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第12917号 / 論医博第2092号 / 新制||医||1009(附属図書館) / 32127 / 京都大学大学院医学研究科医学専攻 / (主査)教授 河野 憲二, 教授 大森 治紀, 教授 渡邉 大 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

Yes / Purpose: To demonstrate methods that enable visual field sensitivities to be compared with normative data without restriction to a fixed test pattern. Methods: Healthy participants (n = 60, age 19–50) undertook microperimetry (MAIA-2) using 237 spatially dense locations up to 13° eccentricity. Surfaces were fit to the mean, variance, and 5th percentile sensitivities. Goodness-of-fit was assessed by refitting the surfaces 1000 times to the dataset and comparing estimated and measured sensitivities at 50 randomly excluded locations. A leave-one-out method was used to compare individual data with the 5th percentile surface. We also considered cases with unknown fovea location by adding error sampled from the distribution of relative fovea–optic disc positions to the test locations and comparing shifted data to the fixed surface. Results: Root mean square (RMS) difference between estimated and measured sensitivities were less than 0.5 dB and less than 1.0 dB for the mean and 5th percentile surfaces, respectively. Root mean square differences were greater for the variance surface, median 1.4 dB, range 0.8 to 2.7 dB. Across all participants 3.9% (interquartile range, 1.8–8.9%) of sensitivities fell beneath the 5th percentile surface, close to the expected 5%. Positional error added to the test grid altered the number of locations falling beneath the 5th percentile surface by less than 1.3% in 95% of participants. Conclusions: Spatial interpolation of normative data enables comparison of sensitivity measurements from varied visual field locations. Conventional indices and probability maps familiar from standard automated perimetry can be produced. These methods may enhance the clinical use of microperimetry, especially in cases of nonfoveal fixation.

Yes / Purpose Perimetry is increasingly being used to measure sensitivity at central visual field locations. For many tasks, the central (0°, 0°) location is functionally the most important, however threshold estimates at this location may be affected by masking by the nearby spatial structure of the fixation target. We investigated this effect. Methods First we retrospectively analysed microperimetry (MAIA-2; CenterVue, Padova, Italy) data from 60 healthy subjects, tested on a custom grid with 1° central spacing. We compared sensitivity at (0°, 0°) to the mean sensitivity at the eight adjacent locations. We then prospectively tested 15 further healthy subjects on the same instrument using a cross-shaped test pattern with 1° spacing. Testing was carried out with and without the central fixation target, and sensitivity estimates at (0°, 0°) were compared. We also compared sensitivity at (0°, 0°) to the mean of the adjacent four locations in each condition. Three subjects undertook 10 repeated tests with the fixation target in place to assess within-subject variability of the effect. Results In the retrospective analysis, central sensitivity was median 2.8 dB lower (95% range 0.1–8.8 dB lower, p < 0.0001) than the mean of the adjacent locations. In the prospective study, central sensitivity was median 2.0 dB lower with the fixation target vs without (95% range 0.4–4.7 dB lower, p = 0.0011). With the fixation target in place central sensitivity was median 2.5 dB lower than mean sensitivity of adjacent locations (95% range 0.8–4.2 dB lower, p = 0.0007), whilst without the fixation target there was no difference (mean 0.4 dB lower, S.D. 0.9 dB, p = 0.15). These differences could not be explained by reduced fixation stability. Mean within subject standard deviation in the difference between central and adjacent locations' sensitivity was 1.84 dB for the repeated tests. Conclusions Perimetric sensitivity estimates from the central (0°, 0°) location are, on-average, reduced by 2 to 3 dB, corresponding to a 60–100% increase in stimulus luminance at threshold. This effect can be explained by masking by the nearby fixation target. The considerable within- and between-subject variability in magnitude, and the unknown effects of disease may hamper attempts to compensate threshold estimates for this effect. Clinicians should interpret central perimetric sensitivity estimates with caution, especially in patients with reduced sensitivity due to disease.

Yes / Purpose: To investigate how well visual field sensitivity predicts visual acuity at the same locations in macular disease, and to assess whether such predictions may be useful for selecting an optimum area for fixation training. Methods: Visual field sensitivity and acuity were measured at nine locations in the central 10° in 20 people with AMD and stable foveal fixation. A linear mixed model was constructed to predict acuity from sensitivity, taking into account within-subject effects and eccentricity. Cross validation was used to test the ability to predict acuity from sensitivity in a new patient. Simulations tested whether sensitivity can predict nonfoveal regions with greatest acuity in individual patients. Results: Visual field sensitivity (P < 0.0001), eccentricity (P = 0.007), and random effects of subject on eccentricity (P = 0.043) improved the model. For known subjects, 95% of acuity prediction errors (predicted − measured acuity) fell within −0.21 logMAR to +0.18 logMAR (median +0.00 logMAR). For unknown subjects, cross validation gave 95% of acuity prediction errors within −0.35 logMAR to +0.31 logMAR (median −0.01 logMAR). In simulations, the nonfoveal location with greatest predicted acuity had greatest “true” acuity on median 26% of occasions, and median difference in acuity between the location with greatest predicted acuity and the best possible location was +0.14 logMAR (range +0.04 to +0.17). Conclusions: The relationship between sensitivity and acuity in macular disease is not strongly predictive. The location with greatest sensitivity on microperimetry is unlikely to represent the location with the best visual acuity, even if eccentricity is taken into account. / College of Optometrists Postdoctoral Research Award (JD and ATA; London, UK) and National Institute for Health Research (NIHR) Postdoctoral Fellowship (ATA; London, UK). Presents independent research funded by the NIHR. / Research Development Fund Publication Prize Award winner, August 2018.

Yes / Fundus-controlled perimetry (FCP, also called 'microperimetry') allows for spatially-resolved mapping of visual sensitivity and measurement of fixation stability, both in clinical practice as well as research. The accurate spatial characterization of visual function enabled by FCP can provide insightful information about disease severity and progression not reflected by best-corrected visual acuity in a large range of disorders. This is especially important for monitoring of retinal diseases that initially spare the central retina in earlier disease stages. Improved intra- and inter-session retest-variability through fundus-tracking and precise point-wise follow-up examinations even in patients with unstable fixation represent key advantages of these technique. The design of disease-specific test patterns and protocols reduces the burden of extensive and time-consuming FCP testing, permitting a more meaningful and focused application. Recent developments also allow for photoreceptor-specific testing through implementation of dark-adapted chromatic and photopic testing. A detailed understanding of the variety of available devices and test settings is a key prerequisite for the design and optimization of FCP protocols in future natural history studies and clinical trials. Accordingly, this review describes the theoretical and technical background of FCP, its prior application in clinical and research settings, data that qualify the application of FCP as an outcome measure in clinical trials as well as ongoing and future developments.