July 2008
Volume 49, Issue 7
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Glaucoma  |   July 2008
Mapping Standard Automated Perimetry to the Peripapillary Retinal Nerve Fiber Layer in Glaucoma
Author Affiliations
  • Antonio Ferreras
    From the Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain;
  • Luís E. Pablo
    From the Department of Ophthalmology, Miguel Servet University Hospital, Zaragoza, Spain;
  • David F. Garway-Heath
    NIHR (National Institute of Health Research) Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS (National Health Service) Foundation Trust and UCL (University College London) Institute of Ophthalmology, London, United Kingdom;
    G. B. Bietti Foundation-IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico), Rome, Italy; and the
  • Paolo Fogagnolo
    G. B. Bietti Foundation-IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico), Rome, Italy; and the
  • Julián García-Feijoo
    Department of Ophthalmology, San Carlos University Hospital, Madrid, Spain.
Investigative Ophthalmology & Visual Science July 2008, Vol.49, 3018-3025. doi:10.1167/iovs.08-1775
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      Antonio Ferreras, Luís E. Pablo, David F. Garway-Heath, Paolo Fogagnolo, Julián García-Feijoo; Mapping Standard Automated Perimetry to the Peripapillary Retinal Nerve Fiber Layer in Glaucoma. Invest. Ophthalmol. Vis. Sci. 2008;49(7):3018-3025. doi: 10.1167/iovs.08-1775.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

purpose. To establish a map relating visual field (VF) test points to corresponding areas of the retinal nerve fiber layer (RNFL) measured with optical coherence tomography (OCT) in patients with glaucomatous optic neuropathy.

methods. One hundred four consecutive subjects with open-angle glaucoma were prospectively selected. All subjects underwent standard automated perimetry (SAP) and imaging with OCT. Factor analyses of the mean thresholds for the SAP test points were performed, independently for each hemifield, to define regions of related points. Pearson correlations were then calculated between the VF regions and peripapillary RNFL thickness measured with OCT at each of the 12 clock-hour positions. A map relating the VF regions to the OCT sectors was plotted based on the strongest correlations between both techniques.

results. Factor analysis distributed the VF points into five VF regions for each hemifield. A slightly asymmetric distribution of VF regions was obtained for the upper and lower points, with respect to the horizontal meridian. Mild to moderate correlations were observed between the VF regions and RNFL thickness. The superior VF regions and RNFL segments correlated most strongly at the 6- and 7-o’clock positions (r = 0.4–0.5).

conclusions. There was a moderate association between the VF regions and the RNFL thickness in patients with glaucomatous optic neuropathy, as measured by OCT. Within sectors of the RNFL, there was some overlap in the representation of the VF regions. The map obtained validates previously reported clinical findings and contributes to a better understanding of the relationship between structure and function in patients with glaucoma.

Glaucoma is a progressive multifactorial optic neuropathy characterized by an acquired atrophy of the optic nerve due to the loss of retinal ganglion cells and their axons in the retina. 1 2 Damage to the retinal nerve fiber layer (RNFL) is usually associated with corresponding visual field (VF) defects. 
There is currently a lack of agreement as to whether functional or structural tests are the most sensitive for detecting early glaucomatous damage. Recent randomized clinical trials indicate that the first detectable glaucomatous change at early stages of the disease can be either functional or structural. 3 4 5 Thus, some have suggested that a combination of functional and structural tests will increase diagnostic sensitivity. 6 7  
Standard automated perimetry (SAP), particularly the 24-2 Swedish interactive threshold algorithm (SITA) standard strategy, 8 9 has become the clinical standard for diagnoses and monitoring of patients with glaucoma. Objective structural imaging instruments have been standardized for the diagnosis and follow-up of patients with, or at risk of, glaucoma. 8 One of these tools is optical coherence tomography (OCT), which provides quantitative and reproducible measurements of the RNFL. 10 11 12 13 14 15 16 The ability of OCT to detect RNFL changes in patients with glaucoma with VF loss has been widely validated. 17 18 19 20 21 22 23 It is not known, however, how well the RNFL changes correspond with functional deficits. 
In previous studies, investigators have attempted to produce anatomic maps of the correspondence between peripapillary RNFL thickness distribution and equivalent areas measured in SAP. 24 25 26 27 28 29 The most complete data set is based on RNFL monochromatic photographs, but because of the nonquantitative nature of the assessment of RNFL photographs, the method was based on transposing the RNFL defect distribution onto the VF; hence, no quantitative structure–function associations were measured. Therefore, the anatomic map must be validated in structure–function studies. 
SAP and OCT provide quantitative measurements, and therefore an objective relationship can be established between the two techniques. The 24-2 SITA program of the Humphrey visual field perimeter (Humphrey Field Analyzer; Carl Zeiss Meditec, Dublin, CA) displays 26 threshold point values for each hemifield, and the optical coherence tomographer (Stratus OCT 3000; Carl Zeiss Meditec) assesses RNFL thickness as the distance between the vitreoretinal interface and the RNFL posterior boundary 30 31 32 at the 12 clock-hour positions. This difference in the number of comparative variables and the results of previous studies 24 25 26 27 28 29 suggests that clusters of points, or VF regions of related points, can be compared with the anatomic-equivalent RNFL thickness distribution, meaning that single VF points can be merged into regions comprising related points. 
Factor analysis is a statistical data-reduction technique that is used to analyze interrelationships among a large number of variables and to explain these variables in terms of their common underlying dimensions or fewer unobserved variables called factors. 33 It is a useful statistical approach for condensing the information contained in several original variables into a smaller set of factors with a minimum loss of information. These groups of interrelated variables can then be correlated with other variables to determine how they are interrelated. 
To our knowledge, this is the first study conducted to assess the topographic correspondence between VF regions and RNFL thickness in humans, based completely on objective and quantitative statistical analysis procedures. Determining this topographic correspondence may clarify the relationship between structural and functional changes in open-angle glaucoma. 
Materials and Methods
Subjects and Measurement Protocol
The prospective study protocol was approved by the ethics committee of Miguel Servet University Hospital, and informed written consent was obtained from all participants. The design of the study adhered to the tenets of the Declaration of Helsinki for biomedical research. 
From April 2007 to September 2007, a sample of 110 consecutive glaucoma patients was prospectively preselected from the Department of Ophthalmology of Miguel Servet University Hospital, Zaragoza, Spain. Eligible subjects had to have glaucomatous optic nerve head (ONH) morphology (for a definition, see below), regardless of intraocular pressure and SAP results. All participants had to meet the following inclusion criteria: best corrected visual acuity (BCVA) of 20/40 or better, refractive error within ±5.00 D equivalent sphere and ±2.00 D astigmatism, transparent ocular media (nuclear color/opalescence, cortical or posterior subcapsular lens opacity <1) according to the Lens Opacities Classification System III system, 34 open anterior chamber angle, and glaucomatous optic disc appearance. Subjects who had undergone intraocular surgery, had diabetes or other systemic diseases, or had a history of ocular or neurologic disease or current use of a medication that could affect visual field sensitivity were excluded. 
Two of the preselected subjects did not provide informed consent, and four subjects did not complete all the required tests; all six of these subjects were excluded from further analysis. One hundred four eyes of Caucasian origin were included in the study. One eye of each subject was randomly chosen, unless only one eye met the inclusion criteria. 
Participants underwent a full ophthalmic examination: clinical history, visual acuity, slit lamp biomicroscopy of the anterior segment, gonioscopy, Goldmann applanation tonometry, central corneal ultrasonic pachymetry (model DGH 500; DGH Technology, Exton, PA), and ophthalmoscopy of the posterior segment. Simultaneous stereophotographs of the optic disc were taken after mydriasis (0.5% tropicamide; Alcon Laboratories Inc., Fort Worth, TX), with a fundus camera (model CF-60UV; Canon Inc., Tokyo, Japan). Glaucomatous damage was defined as focal or diffuse neuroretinal rim narrowing with concentric enlargement of the optic cup, localized notching, or both. 35 The photographs were evaluated by two independent glaucoma specialists who were masked to the patients’ identities and clinical histories. Any disagreement was resolved by consensus. 
At least two reliable SAP tests were performed (model 750i; Humphrey Field analyzer; Carl Zeiss Meditec, Inc.) with the SITA Standard 24-2 program. Near addition was added to the subject’s refractive correction. If fixation losses and false-positive or -negative rates were greater than 20%, the test was repeated. The last reliable perimetry test obtained was used in this study to minimize the learning effect. 36 37 Abnormal SAP results were defined as a pattern SD significantly elevated beyond the 5% level and/or a Glaucoma Hemifield Test outside normal limits. The subjects completed the perimetry tests before undergoing any clinical examination or structural test. Each SAP test was performed on different days to avoid a fatigue effect. 
The optical coherence tomograph (Stratus OCT 3000, software version 4.0.7; Carl Zeiss Meditec) was used to measure peripapillary RNFL thickness after pharmacologic mydriasis (0.5% tropicamide). OCT images were acquired by using the RNFL thickness 3.46-mm scanning protocol and were analyzed by using the RNFL thickness average (in both eyes) analysis protocol. Good-quality scans had to have focused images from the ocular fundus and a centered circular ring around the optic disc. Examinations with a signal-to-noise ratio ≤33 dB or <95% accepted A-scans were retaken (five cases, 5.2%). 31 The OCT variables included in this study were the RNFL thickness at each of the 12 clock-hour positions (with the 3-o’clock position as nasal, 6-o’clock as inferior, 9-o’clock as temporal, and 12-o’clock as superior, regardless of the side of the eye). To simplify the statistical analysis, the OCT measures were aligned according to the orientation of the right eye. Hence, in the left eyes, the 11-o’clock position was transformed to the 1-o’clock position, the 10-o’clock position to the 2-o’clock position, and so forth. Thus, the clock-hour 9 scan represented the temporal side in both eyes. All the ophthalmic examinations, perimetry tests, and the topographic analyses were performed within 1 month after the subject’s date of enrollment in the study. 
Statistical Analysis
All statistical analyses were calculated with commercial software (SPSS, ver. 15.0; SPSS Inc., Chicago, IL). We assumed that the upper and lower hemifields are anatomically separate, and therefore the statistical analyses were calculated for each hemifield separately. First, the Kolmogorov-Smirnov test was applied to check that the data were normally distributed. The second statistical analysis performed in the sample was a factor analysis that was intended to obtain groups of threshold-related points within the VF. Factor analysis is often used in data reduction to classify a small number of factors that explain most of the variance observed in a much larger number of manifest variables. It can be used to select a subset of variables from a larger set, based on which original variables have the highest correlations with the principal component factors. The variables should be quantitative, data should have a bivariate normal distribution for each pair of variables, and observations should be independent. Moreover, data for which Pearson correlation coefficients can be sensibly calculated are suitable for factor analysis. 38 39  
Factor analysis was calculated in an attempt to identify underlying variables or factors that explain the pattern of correlations within a set of observed variables. The factor analysis method used in this study was principal components analysis (the total variance in the data is considered), which searches for a linear combination of variables so that the maximum variance is extracted from the variables. It then removes this variance and seeks a second linear combination that explains the maximum proportion of the remaining variance, and so on. The sampling adequacy for factor analysis was checked by using the Kaiser-Meyer-Olkin (KMO) statistic, which predicts whether the data are likely to factor well. 38 A KMO > 0.60 is recommended for effective factor analysis. 
The next step is a rotation of factors, which is a transformation of the principal factors or components to approximate a simple structure. The varimax rotation is the most common rotation option, and it was used in our statistical analysis. 38 39 Varimax rotation is an orthogonal rotation of the factor axes to maximize the variance of the squared loadings of a factor on all the variables in the factor matrix. Each factor tends to have either large or small loadings of any particular variable. The rotated component matrix allows for each variable to be assigned to each factor. 
Each of the mean threshold values at each point of the VF was numbered (Fig. 1)and considered a variable for the factor analysis. We performed two independent-factor analyses. The first one included the points of the superior hemifield (points 1–26) and the second one included the points of the inferior hemifield (points 27–52). We fixed the maximum number of factors at six and the minimum total variance at 85%. The factor analyses determined five factors or VF regions for each hemifield, and assigned each of the 52 threshold points to the corresponding VF region. 
Raw sensitivities measured at each test point are indicated in decibels, which are tenths of a log unit. The white stimulus presented by the perimeter varied in intensity over a range of 5.1 log units (51 dB) between 0.08 and 10,000 apostilbs (asb). A 0 dB value corresponds to the maximum brightness than the perimeter can produce (stimulus intensity of 10,000 asb), and 51 dB to the minimum stimulus intensity (0.08 asb). 40 For example, a point with 31-dB sensitivity means that the maximum 10,000-asb white stimulus had to be attenuated 31 dB or 3.1 log units (or a factor of 1258), to reach the threshold value of detection. As VF test points are on a logarithmic scale, the decibel levels in each location of the raw numeric plot were converted to a linear scale before averaging the data within each VF region (i.e., the arithmetic mean within a region was calculated). Pearson correlation coefficients were calculated between the mean RNFL thickness at each clock-hour position measured with the OCT and the average sensitivity (linear scale) within each VF region. The highest correlations between corresponding RNFL thickness and VF regions were used to plot a map to determine the structure–function relationship for both tests. 
Results
The study sample included 104 patients with glaucoma (mean age: 64.6 ± 9.3 years; Table 1 ): 81 with primary open-angle glaucoma, 20 with pseudoexfoliative glaucoma, and 3 with pigmentary glaucoma; 97 of them had an abnormal SAP result, and 7 did not meet the abnormality criteria defined in the Methods section. Therefore, these criteria yielded a 93.2% sensitivity of SAP to detect glaucomatous optic discs. According to the Hodapp-Parrish-Anderson score, 41 the study included mainly patients with mild to moderate glaucoma (average mean deviation: −6.4 ± 6.0 dB; pattern SD: 5.0 ± 3.6 dB). 
The Kolmogorov-Smirnov test confirmed that all variables analyzed in this study had a normal distribution. The KMO statistic was 0.927 for the factor analysis of the upper hemifield points and 0.919 for the factor analysis of the lower hemifield points. The cumulative total variance explained with the five factors was 86.7% and 86.4% for the upper and lower hemifield analyses, respectively. 
For the superior hemifield, factor analysis revealed a rotated component matrix with five VF regions (Table 2) . Factor 1 comprised the following points: 1, 2, 5, 6, 7, 11, 12, 13, 14, 19, 20, and 21 (Fig. 2) ; factor 2 merged points 3, 4, 8, 9, and 10; factor 3 included points 15, 16, 17, 24, and 25; factor 4 was formed by points 18 and 26; and factor 5 comprised points 22 and 23. 
The rotated component matrix for the inferior hemifield also had five VF regions (Table 3) . Factor 1 comprised points 27, 28, 29, 30, 35, 36, 37, 38, 39, 40, 43, 44, 45, and 50; factor 2 included points 34, 41, 42, 48, 49, and 52; factor 3 comprised points 46, 47, and 51; factor 4 comprised points 32 and 33; and factor 5 included only point 31. 
According to the anatomic RNFL distribution of the bundles in the retina, 42 43 44 45 46 the mean thresholds of the points included in each factor or VF region were correlated with the mean RNFL thickness at the 12 clock-hour positions (Table 4) . Therefore, we correlated the lower OCT-measured RNFL thicknesses (3, 4, 5, 6, 7, 8, and 9 clock-hour segments) with the superior VF regions, and the upper OCT-measured RNFL thicknesses (9, 10, 11, 12, 1, 2, and 3 clock-hour segments) with the inferior VF regions. The 6- and 7-o’clock segment thicknesses correlated the most (r = 0.5) with the 1 and 3 VF regions of the superior hemifield. The inferior VF regions had weaker correlations with the upper RNFL thicknesses than those of the superior VF regions with the lower RNFL thicknesses. The weakest correlations were for the VF regions corresponding to the RNFL thickness at the 3 and 9 clock-hour positions (r = 0.1–0.2). 
Maps containing the related OCT sectors and VF regions are detailed in Figures 2 and 3 . Each RNFL sector measured with OCT had mild to moderate correlations with more than one VF region; however, the highest correlation with each RNFL sector was obtained with only one associated VF region. 
Discussion
Several studies have been conducted in an attempt to establish maps relating VF regions to sectors of the optic disc. 24 25 26 27 28 29 The most complete map was reported by Garway-Heath et al. 28 They established the anatomic relationship between VF test points in the Humphrey SAP test grid and regions of the ONH relating to RNFL defects evaluated with monochromatic photographs. Although their study offers a sound approach, the nonquantitative method of RNFL photograph evaluation (and absence of related functional data) makes it necessary to validate the results with studies that actually measure visual function and relate visual function measurements to the RNFL thickness determined quantitatively. 
Recently, other investigators 5 47 48 have used objective and highly reproducible techniques for RNFL measurement, such as OCT, 10 11 12 13 14 15 16 to study the structure–function relationship in glaucoma. Harwerth et al. 47 investigated this association in rhesus monkeys and found that SAP sensitivity and RNFL thickness measured with the OCT are correlated measures of the underlying populations of retinal ganglion cells. Hood et al. 5 48 developed a linear model to relate the loss in RNFL thickness measured with OCT to the loss in SAP sensitivity; they based their analysis on the Garway-Heath map, 28 and whereas there was a good fit between structure and function in glaucomatous VFs, in their model there was a very weak correlation between RNFL thickness and SAP sensitivity in healthy individuals. Although they concluded that SAP sensitivity in control subjects does not depend on RNFL thickness, the weak correlation may simply reflect the small range and imprecision of measurements among normal subjects. Several other studies 21 49 50 51 52 have demonstrated good correlations between OCT-measured RNFL thickness and VF sensitivity in glaucoma, even in the early stages of the disease. Strouthidis et al. 53 have also demonstrated a high level of association between the strength of correlation between the sensitivity of pairs of VF points and their relative location in the peripheral retina and the relative proximity of their respective RNFL bundle locations at the ONH. Our study validates the results of these previous studies and also introduces a new map obtained completely from objective analyses. This map can be used as a reference for developing future studies of the structure–function relationship in glaucoma. 
After the current definitions that glaucoma is an acquired optic neuropathy, 1 2 we took optic disc morphology into account to identify glaucomatous eyes. Intraocular pressure is considered a risk factor, and therefore eyes were selected for the study regardless of intraocular pressure. Also, because we were evaluating the relationship between SAP and OCT, we did not include abnormal SAP or OCT as a criterion for a diagnosis of glaucoma. Consequently, as we chose ONH appearance as the reference standard used to distinguish patients with the target condition (glaucoma) from those without it, the maps obtained from this sample allow us to assess the structure–function relationship for glaucomatous optic neuropathy without introducing bias from preconceived structure–function relationships. 54  
Based on the anatomic distribution of the RNFL bundles in the retina, 42 43 44 45 46 the superior hemifield areas are represented in the inferior RNFL bundles, and inferior hemifield areas are represented in the superior RNFL bundles; thus, the upper regions of the VF correlated with lower peripapillary RNFL thickness, and vice versa. We assumed that the degree of reduced visual sensitivity in a region of the VF was proportional to the amount of loss of ganglion cells in the corresponding area of the retina, 55 56 57 58 and therefore to the RNFL thickness in that region. 47 Our results are consistent with those in previous studies, 24 25 26 27 28 29 in which the distribution of the VF regions for the superior and inferior hemifields was reported to be asymmetric. Our findings were also in agreement regarding the poor representation of the temporal RNFL area (9 o’clock position) in the 24-2 Humphrey test. This area represents the papillomacular bundle in the neuroretinal rim at the ONH, which may be less sensitive for detecting early glaucomatous changes. 59 60 61 The superior and inferior poles of the ONH may be more commonly affected at early stages of glaucoma. 17 18 51 59 60 61 62 Thus, classically, the vertical cup-to-disc ratio is one of the best clinical parameters for glaucoma diagnosis. 35 63 64 Neither the 4- nor the 9-o’clock positions correlated with any VF region. Identification of change in OCT RNFL measurements in the horizontal meridian is more difficult because changes are numerically smaller, the lower the normal RNFL thicknesses. 
The strongest correlations between superior VF regions and RNFL segments were observed for the 6- and 7-o’clock segment thicknesses, which, according to the normal distribution of the RNFL bundles (ISNT rule), 17 18 51 62 are the thicker segments. These clock-hour positions correlated better with the upper VF region number 1, where a cluster of depressed points is more likely to be found. The same is true of the inferior hemifield. Nevertheless, the VF sampling may introduce some bias. These segments, corresponding to the poles of the ONH, match with the best subserved VF areas. The VF regions corresponding to the vertical meridian of the ONH are more densely tested by the perimeter, and consequently the most easily recognized VF loss is more likely to relate to those RNFL bundles. There is also unequal sampling of the VF with respect to the peripapillary sectors. The arcuate regions (e.g., VF region 1) contained many more test points than other VF regions. The consequence is that the VF regions containing the higher number of tested points had greater signal averaging, and therefore, a greater signal-to-noise ratio. Hence, when these VF regions were correlated to the RNFL sectors, it resulted in stronger correlations. 
The map derived from this study shows good agreement with other maps. 24 25 28 The differences between them arise from the differences in methods, samples, and statistical procedures used in the studies. With respect to the Garway-Heath map, 28 factor analysis generated 1 VF region more for each hemifield. As RNFL thickness measured with the OCT is divided into 12 clock-hour positions, we initially selected six possible factors to correlate with the 6 clock-hour positions of each hemifield. Statistical analysis determined that five factors was the optimal number for each hemifield. 
There was not a one-to-one correspondence between RNFL sector and VF region. Within sectors of the RNFL, there was an overlap in the representation of VF regions. In general, most VF regions correlated well with various OCT segments, although every VF region had a segment in the peripapillary bundles, which had the best correlation. For the upper hemifield, the strongest correlations were observed between 6-o’clock segment thickness and VF region 1, 5-o’clock segment thickness and VF regions 2 and 4, and 7-o’clock segment thickness and VF regions 3 and 5. In the lower VF regions, the strongest correlations were found between the 11-o’clock segment thickness and VF regions 1, 3, 4 and 5 and the 2-o’clock segment thickness and VF region 2. However, the implication of the strongest correlation requires consideration. The RNFL sectors are not completely independent, so that if one sector is damaged, the likelihood of an adjacent sector’s being damaged is greater than that of a sector farther away. Moreover, some of the RNFL sectors may have been more likely to be damaged, and/or have a greater range of measurements, than others. Therefore, the strongest correlations may be expected in RNFL sectors that are thickest in undamaged eyes and are thin in the glaucomatous eye. This fact may explain why the 11-o’clock sector maps to most of the VF regions in the lower hemifield. 
Factor analysis has limitations. Some disadvantages are that factor analysis is only as good as the data allow and cannot identify causality. 65 66 Factor analysis can classify factors with distant points within each hemifield. In our study, most VF regions obtained by factor analysis comprised contiguous points, although this statistical test does not necessarily provide that result, as occurred in region 2 of the inferior hemifield. 
The high variability of normal human ONH morphology and the intertest variability of SAP limit the generalization of a structure–function map. Other limitations of the study are that RNFL thickness measured with the OCT mainly contained the axons of the retinal ganglion cells, glial cells, and blood vessels. Particularly, the distribution of blood vessels in the peripapillary RNFL may have influenced interindividual measurement variation and the range of measurements. Other sources of variability in structure–function correspondence may be the position and tilt of the ONH in relation to the fovea, 28 and must be taken into account in clinical practice. 
The mild to moderate correlations observed between the VF regions and the RNFL thickness measurements obtained with the OCT in glaucomatous optic discs indicate a reasonable level of agreement in measuring different aspects of the same disease. In our analysis, we were forced to use the data as they were provided by the currently available techniques. On the one hand, Humphrey perimetry tests visual field points on a grid that is not arranged according to the anatomy of the nerve fiber bundle paths. On the other hand, peripapillary RNFL thickness measured with OCT is divided into 12 equal sectors independent of the anatomic distribution of the RNFL bundles around the optic disc. This fact and the anatomic differences between individuals contribute to the difficulties in developing structure–function relationship studies with the present technology. The suggested map shows the expected association between ONH locations and portions of the retina represented in the typical person, provides evidence of previously proposed hypothetical maps, and may help to elucidate the concordance between the structural and functional findings in patients with glaucoma. 
 
Figure 1.
 
Each of the test points of the 24-2 SITA standard strategy was numbered for the factor analysis.
Figure 1.
 
Each of the test points of the 24-2 SITA standard strategy was numbered for the factor analysis.
Table 1.
 
Clinical Characteristics of Both Populations Included in the Study
Table 1.
 
Clinical Characteristics of Both Populations Included in the Study
Minimum Maximum Mean SD
Age (y) 39 75 64.6 9.3
BCVA (Snellen) 0.7 1 0.83 0.12
IOP (mm Hg) 22 40 24.7 3.5
Cup-disc ratio 0.5 0.9 0.71 0.19
Pachymetry (μm) 487 624 541.6 35.4
MD SAP (dB) −18.70 −0.02 −6.41 5.98
PSD SAP (dB) 0.94 12.61 5.02 3.57
Table 2.
 
Rotated Component Matrix for the Superior Visual Field Points
Table 2.
 
Rotated Component Matrix for the Superior Visual Field Points
Point Component
1 2 3 4 5 6
1 0.836 0.274 0.079 0.223 0.126 0.180
2 0.763 0.164 0.124 0.345 0.055 0.317
3 0.513 0.552 0.180 0.344 0.301 0.092
4 0.367 0.637 0.035 0.529 0.099 −0.050
5 0.824 0.211 0.221 0.193 0.289 0.111
6 0.755 0.184 0.272 0.292 0.236 0.256
7 0.571 0.450 0.201 0.211 0.389 0.351
8 0.417 0.643 0.342 0.292 0.261 0.236
9 0.364 0.726 0.232 0.382 0.127 0.082
10 0.238 0.642 0.324 0.492 0.145 −0.038
11 0.847 0.264 0.235 0.183 0.134 −0.092
12 0.744 0.363 0.329 0.109 0.309 0.143
13 0.654 0.203 0.367 0.066 0.334 0.429
14 0.682 0.386 0.296 0.051 0.422 0.237
15 0.418 0.459 0.543 0.200 0.367 0.175
16 0.325 0.502 0.595 0.148 0.229 0.343
17 0.385 0.407 0.560 0.438 −0.022 0.273
18 0.200 0.160 0.114 0.888 0.129 0.032
19 0.800 0.348 0.351 0.052 −0.016 −0.164
20 0.843 0.218 0.317 0.131 0.244 −0.073
21 0.770 0.181 0.356 0.194 0.369 0.029
22 0.552 0.248 0.322 0.073 0.598 0.165
23 0.444 0.192 0.386 0.142 0.694 −0.016
24 0.336 0.048 0.832 0.117 0.287 0.004
25 0.246 0.250 0.830 0.239 0.135 0.024
26 0.100 0.351 0.231 0.794 0.009 0.072
Figure 2.
 
The relationship between the sectors, into which test points of the visual field were divided by the factor analysis, and the RNFL thickness, measured with OCT. The stronger the correlation between structure and function, the sharper the color in the 12 clock-hour maps.
Figure 2.
 
The relationship between the sectors, into which test points of the visual field were divided by the factor analysis, and the RNFL thickness, measured with OCT. The stronger the correlation between structure and function, the sharper the color in the 12 clock-hour maps.
Table 3.
 
Rotated Component Matrix for the Inferior Visual Field Points
Table 3.
 
Rotated Component Matrix for the Inferior Visual Field Points
Point Component
1 2 3 4 5 6
27 0.732 0.232 0.130 0.158 0.111 0.530
28 0.811 0.215 0.141 0.212 −0.004 0.357
29 0.915 0.194 0.178 0.157 0.091 0.090
30 0.828 0.299 0.207 0.304 −0.042 −0.105
31 0.490 0.265 0.240 0.209 0.645 0.117
32 0.570 0.271 0.188 0.650 −0.130 −0.055
33 0.264 0.335 0.200 0.804 0.178 0.133
34 0.092 0.689 0.162 0.347 0.484 −0.049
35 0.892 0.156 0.216 0.028 0.121 0.094
36 0.892 0.149 0.236 0.161 0.085 0.041
37 0.907 0.159 0.247 0.183 −0.005 −0.006
38 0.850 0.312 0.138 0.234 −0.078 −0.019
39 0.663 0.549 0.047 0.297 0.014 −0.282
40 0.663 0.288 0.416 0.347 −0.040 0.022
41 0.263 0.654 0.452 0.259 −0.219 0.140
42 0.115 0.922 0.119 0.079 −0.008 0.019
43 0.911 0.134 0.176 0.057 0.069 0.053
44 0.844 0.147 0.345 0.038 0.216 0.130
45 0.721 0.229 0.382 0.204 0.109 0.185
46 0.349 0.273 0.824 0.139 0.126 −0.054
47 0.280 0.522 0.674 0.264 −0.123 0.047
48 0.265 0.815 0.275 0.177 0.188 0.012
49 0.473 0.548 0.282 0.007 0.483 0.218
50 0.554 0.318 0.547 0.112 0.286 0.291
51 0.412 0.552 0.588 0.079 0.132 0.173
52 0.349 0.666 0.352 0.258 −0.103 0.298
Table 4.
 
Pearson Correlations between the Mean Threshold for Each Division of the Visual Field According to the Results of This Study and the RNFL Thickness at Each of the 12 Clock-Hour Positions Measured with OCT
Table 4.
 
Pearson Correlations between the Mean Threshold for Each Division of the Visual Field According to the Results of This Study and the RNFL Thickness at Each of the 12 Clock-Hour Positions Measured with OCT
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12
Sector 1 superior
 Pearson correlation 0.140 0.150 0.449 0.531 0.510 0.442 0.095
P 0.271 0.238 <0.001 <0.001 <0.001 <0.001 0.457
Sector 2 superior
 Pearson correlation 0.114 0.092 0.365 0.340 0.289 0.305 0.090
P 0.372 0.471 0.003 0.006 0.021 0.014 0.478
Sector 3 superior
 Pearson correlation 0.126 0.183 0.362 0.415 0.504 0.490 0.186
P 0.321 0.148 0.003 0.001 <0.001 <0.001 0.142
Sector 4 superior
 Pearson correlation 0.094 0.106 0.265 0.214 0.202 0.198 0.119
P 0.458 0.404 0.034 0.090 0.109 0.117 0.351
Sector 5 superior
 Pearson correlation 0.040 0.003 0.305 0.394 0.456 0.335 0.054
P 0.752 0.981 0.014 0.001 <0.001 0.007 0.673
Sector 1 inferior
 Pearson correlation 0.276 0.282 0.216 0.091 0.296 0.504 0.307
P 0.027 0.024 0.087 0.476 0.017 <0.001 0.014
Sector 2 inferior
 Pearson correlation 0.203 0.342 0.307 0.039 0.145 0.231 0.116
P 0.108 0.006 0.014 0.760 0.255 0.067 0.362
Sector 3 inferior
 Pearson correlation 0.242 0.189 0.171 0.008 0.166 0.274 0.214
P 0.054 0.135 0.178 0.947 0.190 0.029 0.089
Sector 4 inferior
 Pearson correlation 0.234 0.249 0.188 0.146 0.295 0.466 0.242
P 0.063 0.053 0.138 0.249 0.018 <0.001 0.054
Sector 5 inferior
 Pearson correlation 0.281 0.251 0.102 0.002 0.186 0.341 0.260
P 0.024 0.045 0.421 0.989 0.140 0.006 0.038
Figure 3.
 
Global structure–function relationship determined in the study. The different sectors in which the visual field was divided and the corresponding areas in the RNFL overlapped for each hemifield. The 4-and 9-o’clock positions had no association with any visual field region (white sectors). The width of the colored segments is proportional to the strength and number of the RNFL segments that correlated with each visual field region.
Figure 3.
 
Global structure–function relationship determined in the study. The different sectors in which the visual field was divided and the corresponding areas in the RNFL overlapped for each hemifield. The 4-and 9-o’clock positions had no association with any visual field region (white sectors). The width of the colored segments is proportional to the strength and number of the RNFL segments that correlated with each visual field region.
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Figure 1.
 
Each of the test points of the 24-2 SITA standard strategy was numbered for the factor analysis.
Figure 1.
 
Each of the test points of the 24-2 SITA standard strategy was numbered for the factor analysis.
Figure 2.
 
The relationship between the sectors, into which test points of the visual field were divided by the factor analysis, and the RNFL thickness, measured with OCT. The stronger the correlation between structure and function, the sharper the color in the 12 clock-hour maps.
Figure 2.
 
The relationship between the sectors, into which test points of the visual field were divided by the factor analysis, and the RNFL thickness, measured with OCT. The stronger the correlation between structure and function, the sharper the color in the 12 clock-hour maps.
Figure 3.
 
Global structure–function relationship determined in the study. The different sectors in which the visual field was divided and the corresponding areas in the RNFL overlapped for each hemifield. The 4-and 9-o’clock positions had no association with any visual field region (white sectors). The width of the colored segments is proportional to the strength and number of the RNFL segments that correlated with each visual field region.
Figure 3.
 
Global structure–function relationship determined in the study. The different sectors in which the visual field was divided and the corresponding areas in the RNFL overlapped for each hemifield. The 4-and 9-o’clock positions had no association with any visual field region (white sectors). The width of the colored segments is proportional to the strength and number of the RNFL segments that correlated with each visual field region.
Table 1.
 
Clinical Characteristics of Both Populations Included in the Study
Table 1.
 
Clinical Characteristics of Both Populations Included in the Study
Minimum Maximum Mean SD
Age (y) 39 75 64.6 9.3
BCVA (Snellen) 0.7 1 0.83 0.12
IOP (mm Hg) 22 40 24.7 3.5
Cup-disc ratio 0.5 0.9 0.71 0.19
Pachymetry (μm) 487 624 541.6 35.4
MD SAP (dB) −18.70 −0.02 −6.41 5.98
PSD SAP (dB) 0.94 12.61 5.02 3.57
Table 2.
 
Rotated Component Matrix for the Superior Visual Field Points
Table 2.
 
Rotated Component Matrix for the Superior Visual Field Points
Point Component
1 2 3 4 5 6
1 0.836 0.274 0.079 0.223 0.126 0.180
2 0.763 0.164 0.124 0.345 0.055 0.317
3 0.513 0.552 0.180 0.344 0.301 0.092
4 0.367 0.637 0.035 0.529 0.099 −0.050
5 0.824 0.211 0.221 0.193 0.289 0.111
6 0.755 0.184 0.272 0.292 0.236 0.256
7 0.571 0.450 0.201 0.211 0.389 0.351
8 0.417 0.643 0.342 0.292 0.261 0.236
9 0.364 0.726 0.232 0.382 0.127 0.082
10 0.238 0.642 0.324 0.492 0.145 −0.038
11 0.847 0.264 0.235 0.183 0.134 −0.092
12 0.744 0.363 0.329 0.109 0.309 0.143
13 0.654 0.203 0.367 0.066 0.334 0.429
14 0.682 0.386 0.296 0.051 0.422 0.237
15 0.418 0.459 0.543 0.200 0.367 0.175
16 0.325 0.502 0.595 0.148 0.229 0.343
17 0.385 0.407 0.560 0.438 −0.022 0.273
18 0.200 0.160 0.114 0.888 0.129 0.032
19 0.800 0.348 0.351 0.052 −0.016 −0.164
20 0.843 0.218 0.317 0.131 0.244 −0.073
21 0.770 0.181 0.356 0.194 0.369 0.029
22 0.552 0.248 0.322 0.073 0.598 0.165
23 0.444 0.192 0.386 0.142 0.694 −0.016
24 0.336 0.048 0.832 0.117 0.287 0.004
25 0.246 0.250 0.830 0.239 0.135 0.024
26 0.100 0.351 0.231 0.794 0.009 0.072
Table 3.
 
Rotated Component Matrix for the Inferior Visual Field Points
Table 3.
 
Rotated Component Matrix for the Inferior Visual Field Points
Point Component
1 2 3 4 5 6
27 0.732 0.232 0.130 0.158 0.111 0.530
28 0.811 0.215 0.141 0.212 −0.004 0.357
29 0.915 0.194 0.178 0.157 0.091 0.090
30 0.828 0.299 0.207 0.304 −0.042 −0.105
31 0.490 0.265 0.240 0.209 0.645 0.117
32 0.570 0.271 0.188 0.650 −0.130 −0.055
33 0.264 0.335 0.200 0.804 0.178 0.133
34 0.092 0.689 0.162 0.347 0.484 −0.049
35 0.892 0.156 0.216 0.028 0.121 0.094
36 0.892 0.149 0.236 0.161 0.085 0.041
37 0.907 0.159 0.247 0.183 −0.005 −0.006
38 0.850 0.312 0.138 0.234 −0.078 −0.019
39 0.663 0.549 0.047 0.297 0.014 −0.282
40 0.663 0.288 0.416 0.347 −0.040 0.022
41 0.263 0.654 0.452 0.259 −0.219 0.140
42 0.115 0.922 0.119 0.079 −0.008 0.019
43 0.911 0.134 0.176 0.057 0.069 0.053
44 0.844 0.147 0.345 0.038 0.216 0.130
45 0.721 0.229 0.382 0.204 0.109 0.185
46 0.349 0.273 0.824 0.139 0.126 −0.054
47 0.280 0.522 0.674 0.264 −0.123 0.047
48 0.265 0.815 0.275 0.177 0.188 0.012
49 0.473 0.548 0.282 0.007 0.483 0.218
50 0.554 0.318 0.547 0.112 0.286 0.291
51 0.412 0.552 0.588 0.079 0.132 0.173
52 0.349 0.666 0.352 0.258 −0.103 0.298
Table 4.
 
Pearson Correlations between the Mean Threshold for Each Division of the Visual Field According to the Results of This Study and the RNFL Thickness at Each of the 12 Clock-Hour Positions Measured with OCT
Table 4.
 
Pearson Correlations between the Mean Threshold for Each Division of the Visual Field According to the Results of This Study and the RNFL Thickness at Each of the 12 Clock-Hour Positions Measured with OCT
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12
Sector 1 superior
 Pearson correlation 0.140 0.150 0.449 0.531 0.510 0.442 0.095
P 0.271 0.238 <0.001 <0.001 <0.001 <0.001 0.457
Sector 2 superior
 Pearson correlation 0.114 0.092 0.365 0.340 0.289 0.305 0.090
P 0.372 0.471 0.003 0.006 0.021 0.014 0.478
Sector 3 superior
 Pearson correlation 0.126 0.183 0.362 0.415 0.504 0.490 0.186
P 0.321 0.148 0.003 0.001 <0.001 <0.001 0.142
Sector 4 superior
 Pearson correlation 0.094 0.106 0.265 0.214 0.202 0.198 0.119
P 0.458 0.404 0.034 0.090 0.109 0.117 0.351
Sector 5 superior
 Pearson correlation 0.040 0.003 0.305 0.394 0.456 0.335 0.054
P 0.752 0.981 0.014 0.001 <0.001 0.007 0.673
Sector 1 inferior
 Pearson correlation 0.276 0.282 0.216 0.091 0.296 0.504 0.307
P 0.027 0.024 0.087 0.476 0.017 <0.001 0.014
Sector 2 inferior
 Pearson correlation 0.203 0.342 0.307 0.039 0.145 0.231 0.116
P 0.108 0.006 0.014 0.760 0.255 0.067 0.362
Sector 3 inferior
 Pearson correlation 0.242 0.189 0.171 0.008 0.166 0.274 0.214
P 0.054 0.135 0.178 0.947 0.190 0.029 0.089
Sector 4 inferior
 Pearson correlation 0.234 0.249 0.188 0.146 0.295 0.466 0.242
P 0.063 0.053 0.138 0.249 0.018 <0.001 0.054
Sector 5 inferior
 Pearson correlation 0.281 0.251 0.102 0.002 0.186 0.341 0.260
P 0.024 0.045 0.421 0.989 0.140 0.006 0.038
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