**Purpose**:
To examine whether a microperimetry testing strategy based on quantifying the spatial extent of functional abnormalities (termed “defect-mapping” strategy) could improve the detection of progressive changes in deep scotomas compared to the conventional thresholding strategy.

**Methods**:
A total of 30 healthy participants underwent two microperimetry examinations, each using the defect-mapping and thresholding strategies at the first visit to examine the test–retest variability of each method. Testing was performed using an isotropic stimulus pattern centered on the optic nerve head (ONH), which acted as a model of a deep scotoma. These tests were repeated at a second visit, except using a smaller stimulus pattern and thereby increasing the proportion of test locations falling within the ONH (to simulate the progressive enlargement of a deep scotoma). The extent of change detected between visits relative to measurement variability was compared between the two strategies.

**Results**:
Relative to their effective dynamic ranges, the test–retest variability of the defect-mapping strategy (1.8%) was significantly lower compared to the thresholding strategy (3.3%; *P* < 0.001). The defect-mapping strategy also captured a significantly greater extent of change between visits relative to variability (−4.70 t^{−1}) compared to the thresholding strategy (2.74 t^{−1}; *P* < 0.001).

**Conclusions**:
A defect-mapping microperimetry testing strategy shows promise for capturing the progressive enlargement of deep scotomas more effectively than the conventional thresholding strategy.

**Translational Relevance**:
Microperimetry testing with the defect-mapping strategy could provide a more accurate clinical trial outcome measure for capturing progressive changes in deep scotomas in eyes with atrophic retinal diseases, warranting further investigations.

^{1}), are urgently needed to improve the feasibility of evaluating new treatments for atrophic AMD and IRDs.

^{2–14}or are being evaluated in observational studies in preparation for clinical trials,

^{15–19}for atrophic AMD and IRDs.

^{20}) in a proof-of-principle study of this approach.

^{20,21}but briefly: Goldmann Size III stimuli (0.43° in diameter) were presented for 200 ms against a background of 1.27 cd/m

^{2}, with the maximum and minimum luminance of the stimuli that could be presented being 318 cd/m

^{2}and 1.35 cd/m

^{2}, respectively (thus, providing a 36 dB dynamic range of differential contrast). Stimulus presentations at precise retinal locations were enabled by using a line-scanning laser ophthalmoscope with an infrared illumination system to track the fundus at 25 frames per second. Only one eye was examined in this study, and the study eye was selected at random.

- Effective Dynamic Range: The effective dynamic range of the thresholding strategy was defined as the average threshold of the edge points (
*n*= 20) of the stimulus pattern from all tests of all participants at each visit, since these locations represent areas of normal function. Similarly, the effective dynamic range of the defect-mapping strategy was defined as the average PLS of the edge points (*n*= 48). The average sensitivity and PLS were determined using random-coefficients models, a form of linear mixed-effects model to account for within-eye correlations. - Test–Retest Variability: Based on probability theory, the standard deviation (SD) of test–retest differences that follow a normal distribution with a mean of zero can be calculated by multiplying the mean absolute difference by
Display Formula \(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\bf{\alpha}}\)\(\def\bupbeta{\bf{\beta}}\)\(\def\bupgamma{\bf{\gamma}}\)\(\def\bupdelta{\bf{\delta}}\)\(\def\bupvarepsilon{\bf{\varepsilon}}\)\(\def\bupzeta{\bf{\zeta}}\)\(\def\bupeta{\bf{\eta}}\)\(\def\buptheta{\bf{\theta}}\)\(\def\bupiota{\bf{\iota}}\)\(\def\bupkappa{\bf{\kappa}}\)\(\def\buplambda{\bf{\lambda}}\)\(\def\bupmu{\bf{\mu}}\)\(\def\bupnu{\bf{\nu}}\)\(\def\bupxi{\bf{\xi}}\)\(\def\bupomicron{\bf{\micron}}\)\(\def\buppi{\bf{\pi}}\)\(\def\buprho{\bf{\rho}}\)\(\def\bupsigma{\bf{\sigma}}\)\(\def\buptau{\bf{\tau}}\)\(\def\bupupsilon{\bf{\upsilon}}\)\(\def\bupphi{\bf{\phi}}\)\(\def\bupchi{\bf{\chi}}\)\(\def\buppsy{\bf{\psy}}\)\(\def\bupomega{\bf{\omega}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\(2/\sqrt \pi \). Thus, the SD was calculated using this method for each participant during each visit and then expressed as a percentage of the effective dynamic range (providing a normalized value of variability). - Comparison of Normalized Variability: The differences in the normalized SD between the two methods then were compared using another random-coefficients model.

^{22–24}that were calculated as follows:

- Variability of Each Method: This parameter was calculated by dividing the change in MS or PLS between visits by an estimate of its variability for each method. The estimates of variability were determined by first calculating best linear unbiased predictions for each eye using a linear mixed model (which are in essence predictions of the degree of change between the two visits, like a slope estimate). The SDs of the difference between the measured and predicted values then were calculated for each method to provide the estimate of variability.
- Normalization of Change: The changes in MS and PLS between the two visits then were divided by their respective variability estimates to provide normalized estimates of change (or longitudinal SNRs); note that longitudinal SNRs are expressed as a ratio (which is a dimensionless quantity) per unit of time (t
^{−1}). - Comparison of Normalized Estimates of Change: Another random-coefficients model was used to compare the normalized estimates of change (or longitudinal SNRs) between the two methods.

*P*< 0.001). Plots of the test–retest differences against the average of the two tests for the unadjusted values are shown in Figure 2 separately for each visit.

*P*< 0.001) and PLS (−5.4%, 95% CI = −6.3% to −4.4%;

*P*< 0.001) between the two visits (being the estimates of “signal”). The standard deviations of the residuals (being an estimate of “noise”) were 0.5 dB for MS and 1.1% for PLS. As such, the longitudinal SNR was significantly more negative (or a greater degree of change was detected relative to variability) for PLS (−4.70 t

^{−1}) compared to MS (−2.74 t

^{−1}; difference = −1.96 t

^{−1}, 95% CI = −2.93 to −1.00 t

^{−1};

*P*< 0.001). The best linear unbiased predictions of the normalized values of change are illustrated in Figure 4.

*P*< 0.001) for the microperimetry tests using the defect-mapping strategy (5.3 minutes) compared to the threshold strategy (5.8 minutes). Furthermore, a total of seven (5.8%) and three (2.5%) tests using the thresholding and defect-mapping strategy, respectively, had to be repeated due to poor reliability.

^{20}meaning that this approach may have a limited ability to accurately measure the extent of functional abnormalities in eyes with deep scotomas. Second, the simulated progressive enlargement of deep scotomas may not be captured with the thresholding strategy due to the larger spacing between stimuli with this approach compared to the defect-mapping strategy. This may perhaps account for the recent observation of a nonsignificant treatment effect on the change in microperimetric threshold sensitivities in a recent trial of eyes with macular telangiectasia type 2, despite a significant treatment effect on the change in area of photoreceptor loss and a significant correlation between those two outcome measures.

^{10}Finally, it is possible that the observer response characteristics are different during a detection task where visual stimuli spans a wide range of intensities (and often near the visual threshold), compared to a detection task involving only suprathreshold stimuli; future studies are required to examine this. Anecdotally, most participants reported that the defect-mapping strategy being easier to perform, and there were, indeed, more unreliable tests recorded when using the thresholding strategy.

^{25,26}indicating how visual sensitivity losses are often deep and localized within atrophic regions. A similar observation of a marked difference in visual sensitivity within areas of photoreceptor loss or retinal pigment epithelium degeneration and unaffected retinal regions also has been reported in eyes with IRDs.

^{15,27–29}However, it should be acknowledged that use of the ONH in young, healthy volunteers as a model of deep scotomas and simulation of its progressive enlargement through reducing the size of the stimulus pattern is only intended to provide a preliminary assessment of this approach. Future studies are needed to compare the effectiveness of the defect-mapping and thresholding strategies in actual eyes with atrophic AMD and IRDs, and also to understand which approach better reflects self-reported visual disability.

^{30–32}Improved testing efficiency could also be achieved by incorporating structural information to guide the customized placement of test locations.

^{26,33–35}

**Z. Wu**, None;

**R. Cimetta**, None;

**E. Caruso**, None;

**R.H. Guymer**, Bayer, Novartis, Roche Genentech, and Apellis (outside the submitted work; I), Bayer (outside the submitted work; F)

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