October 2008
Volume 49, Issue 10
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Glaucoma  |   October 2008
Age-Related Losses of Retinal Ganglion Cells and Axons
Author Affiliations
  • Ronald S. Harwerth
    From the College of Optometry, University of Houston, Houston, Texas.
  • Joe L. Wheat
    From the College of Optometry, University of Houston, Houston, Texas.
  • Nalini V. Rangaswamy
    From the College of Optometry, University of Houston, Houston, Texas.
Investigative Ophthalmology & Visual Science October 2008, Vol.49, 4437-4443. doi:10.1167/iovs.08-1753
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      Ronald S. Harwerth, Joe L. Wheat, Nalini V. Rangaswamy; Age-Related Losses of Retinal Ganglion Cells and Axons. Invest. Ophthalmol. Vis. Sci. 2008;49(10):4437-4443. doi: 10.1167/iovs.08-1753.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

purpose. Age-related losses in retinal nerve fiber layer (RNFL) thickness have been assumed to be the result of an age-dependent reduction of retinal ganglion cells (RGCs), but the published rates differ: age-related losses of RGCs of approximately 0.6%/year compared to 0.2%/year for thinning of the RNFL. An analysis of normative data for standard automated perimetry (SAP) sensitivities and optical coherence tomography (OCT) measures of RNFL thickness showed that the apparent disagreement in age-dependent losses of RGCs and axons in the RNFL can be reconciled by an age-dependent decrease in the proportion of the RNFL thickness that is composed of axons. The purpose of the present study was to determine whether the mechanisms of age-related losses that were suggested by the normative data can be confirmed with data from healthy, normal eyes.

methods. Data were obtained from visual fields (normal results in a Glaucoma Hemifield Test [GHT] on standard automated perimetry [SAP] 24-2 fields) and RNFL thickness measurements (standard OCT scan) of 55 patients (age range, 18–80 years; mean, 44.5 ± 17.3). The SAP measures of visual sensitivity and OCT measures of RNFL thickness for one eye of each patient were used to estimate neuron counts by each procedure.

results. The age-related thinning of RNFL was 0.27%/year when a constant axon density was used to derive axon counts from RNFL thickness, compared with 0.50%/year for the age-related loss of RGCs from SAP. In agreement with the model developed with normative clinical data, concordance between losses of axons and soma was achieved by an age-dependent reduction of 0.46%/year in the density of axons in the RNFL.

conclusions. The results suggest that the proportion of RNFL that is composed of RGC axons is not constant with age; rather, the proportion of the total thickness from non-neuronal tissue increases with age. If a similar compensation occurs in the RNFL thickness with axon loss from glaucoma, then a stage-dependent correction to translate OCT measurements to neuronal components is needed, in addition to the age-dependent correction.

Many visual abilities decline with age as a result of the normal, nonpathologic losses of neurons in the peripheral and central visual pathways. 1 2 A methodical loss of retinal ganglion cells (RGCs) is part of the normal aging process, which causes reduced visual sensitivity across the visual field in standard automated perimetry (SAP) 3 4 5 6 7 8 9 and a thinning of the retinal nerve fiber layer (RNFL) in optical coherence tomography (OCT). 10 11 12 13 14 15 16 17 In clinical practice, it is important to differentiate between the functional and structural changes that arise from normal nonpathologic losses versus pathologic losses, and clinical instruments for SAP 4 8 and RNFL 16 18 measurements therefore incorporate age-dependent normative data for the statistical evaluation of a patient’s data. However, the comparison to normative data does not allow direct interinstrument comparisons of measurements, nor does it provide information about the amount of a patient’s neuronal loss from either aging or glaucoma. 
The general phenomenon of age-related losses of neurons in the inner retina is well established. The effects of aging have been investigated by both histologic analysis of the RGC somas 19 20 21 or their axons in the optic nerve 21 22 23 24 25 and by optical measurements of the RNFL. 10 11 12 13 14 15 16 17 The investigations of the number of axons in the optic nerve have shown systematic age-related losses at rates that range from 0.3% to 0.6%/year, whereas the age-related thinning of RNFL occurs at somewhat lower rates of ∼0.2%/year. 16 The aging effects found by histology have been supported by statistical analysis of the slopes of the function for neuronal loss versus age, 19 20 21 22 23 24 25 but the rather low rates of RNFL thinning are problematic for the statistical analysis of an age-dependent effect, because shallow slopes are hard to define with confidence. For example, one study was unable to define a statistically significant effect of age on any of the OCT parameters for RNFL thickness, 14 whereas each of the others have demonstrated significant declines in the mean thickness, 10 11 12 13 15 16 but not always in thinning, within a quadrant, especially the inferior and nasal quadrants. 17 The source of the apparent discrepancies in age-dependent effects across experimental and measurement methods or within a restricted range of RNFL measurement is a likely consequence of the optical measurement of the total RNFL thickness. Total RNFL thickness does not separate the proportion of thickness that is composed of RGC axons from non-neuronal support tissue 26 27 28 and consequently the amount of thinning that was caused by the age-related loss of RGCs is not straightforward. 29 30  
Potentially, neuronal losses in the retina may be quantifiable from clinical measurements via an application of methods that were developed for experimental glaucoma in macaque monkeys. 29 31 32 If these methods are also appropriate for patients, then the evaluation of clinical data can be achieved by translation of SAP and OCT measurements into a common parameter related to RGCs. An initial study to determine whether the procedures could be extended to clinical patients was based on normative age-dependent data for SAP and OCT. 30 The principal result of the analysis was that, although the general methods seem appropriate, the quantification of neuronal populations from OCT measurements required an additional variable to reflect an age-dependent decrease in the RGC axon density in the RNFL. The additional factor was needed to account for the discrepancy in the rates of age-related thinning of the RNFL and the age-related losses of RGCs. To reconcile the effects of normal aging, opposing age-dependent alterations in the composition of the RNFL were incorporated into the model of normal aging that is illustrated in Figure 1 . By this model, the normal age-related thinning of the RNFL of approximately 0.21 μm/year (solid line) represents the sum of a neuronal component, the well-established age-related loss of RGC neurons that could cause a decline in RNFL thickness of approximately 0.55 μm/year (dashed line), and a non-neuronal component that increases at a rate of approximately 0.32 μm/year (dot-dash line). The smaller increases in non-neuronal, compared with the decreases in neuronal tissue, result in a net loss of RNFL thickness and an age-dependent reduction of axon density of 0.007 axons/μm2per year. Therefore, to achieve concordance between the subjective and objective methods of estimating neuronal populations, the model proposes that the thickness of the RNFL does not represent a constant proportion of RGC axons, but rather, that there are age-dependent decreases in the density of axons that are partially compensated for by increases in non-neuronal components. 
The normative databases are linear estimates of age-related changes for specific instrumentation and measurements, but the analytical form of the neuronal loss–age function cannot be addressed by these techniques, because the manufacturer’s methods of incorporating the data are unknown, and the accuracy of extracting the data from the instruments cannot be verified. In addition, modeling the average values for a population does not provide information about the variability and limits of agreement for the population. Therefore, although the analysis of normative data presents a strong suggestion for differences in the effects of aging on SAP and OCT measurements, the results must be confirmed by empiric data that were analyzed by the same model that was developed from the normative data. 30 Consequently, the purpose of the present study was to test the model of age-related losses of visual sensitivity from SAP and the thinning of the RNFL with OCT data from normal eyes of clinical patients. Some of the results of these studies have been presented in abstract form (Harwerth RS, et al. IOVS 2007;48:ARVO E-Abstract 492). 
Materials and Methods
Subjects
The results from data of 55 subjects are reported. The group data and general characteristics of the population are presented in Table 1 . The subjects were students, faculty, or staff of the college, or their relatives. They were initially identified on the basis of their self-reported normal vision and were subsequently enrolled in the study if their SAP results were identified as within normal limits by the Glaucoma Hemifield Test (GHT). The refractive errors in all the subjects were between 6 D of hyperopia and 6 D of myopia. The subjects ranged in age from 18.0 to 80.3 years (mean age, 44.5 ± 17.3) with at least seven subjects for each decade from the third through the seventh. The group included an approximately equal number of men and women (27/28). One eye of each subject was entered into the study (25 right and 30 left eyes), with alteration of the eye selected in successive subjects. The research adhered to the tenets of the Declaration of Helsinki and the experimental protocol was reviewed and approved by the University of Houston’s Committee for the Protection of Human Subjects. Informed consent was obtained from each of the subjects, and they were remunerated for their participation. 
SAP and OCT Data
The data for visual fields were obtained with SAP SITA standard 24-2 measurements (Humphrey Analyzer II; model 750; Carl Zeiss Meditec, Inc., Dublin, CA), taken twice for each eye during a single experimental session. Testing was conducted by one of the authors (JW or NR), who used standard clinical protocols for correction of refractive errors and with undilated pupils. Initial practice data were collected for both of the subject’s eyes, and the patient was given a short rest break. Subsequently, the visual field measurements were repeated in reverse order (i.e., left eye– right eye), and the data from the second measurement run for the eye selected for the study were used in the investigation. The point-by-point thresholds were entered into data files and, for the analysis of RGC densities, each threshold was decreased by 1 dB to compensate for the difference in data collected by SITA thresholding versus the full-threshold strategy. 33 34  
For the RNFL thickness measurements, the subject’s pupils were dilated, and the RNFL images were acquired (StratusOCT, running ver. 4.0.4 software; Carl Zeiss Meditec, Inc.). One of the authors (JW or NR) obtained RNFL scans using the standard RNFL thickness protocol of 512 measures in a nominal 10.87-mm scan length (circumference) of a circle of 3.4-mm diameter that was centered on the ONH. Only scans that were well-centered, with a signal strength of 7 or higher were saved for analysis. Three standard scans for each eye were subsequently exported to a computer to derive the mean thickness at each sample (pixel) of the scan for an analysis of axon densities across the RNFL. All the patients’ ametropias were 6 D, or less, and neither axial length nor refractive error was considered in the analysis of the RNFL thickness data. 
Translation of SAP and OCT Data to the Number of Neurons
The methods for quantification of the RGC populations underlying measurements of visual field sensitivities and RNFL thicknesses have been described in detail and will be presented only in brief. 31 32 The relationships between the number of ganglion cells and the perimetry measurement results, which were initially derived from data obtained by behavioral perimetry and retinal histology, are linear functions when both variables are expressed in logarithmic (decibel) units. However, the parameters of the linear functions vary with eccentricity and, thus, the RGC density, in decibel units, for a given location in the visual field was a product of the perimetric sensitivity and retinal eccentricity. The retinal eccentricities for the perimetry data were modified for application to data from human subjects because of the longer axial length of the human eye. The nodal point-to-retina distance in the human eye is approximately 16.7 mm, compared with 12.5 mm in a monkey’s eye, and therefore, the retinal eccentricities (in arc degrees) for point-wise estimations of ganglion cell densities from SAP measurements were increased by the ratio of these lengths. The number of RGCs in linear units at a given test field location was determined by multiplying the antilog of the cell density by the retinal area that corresponds to the 6 × 6-arc deg area of the visual field (i.e., an area set by the sampling density of SAP). 
The number of RGC axons in a section of the RNFL were estimated from the area defined by the RNFL thickness profile and the density of axons in the RNFL. The first factor, RNFL area, was determined by multiplying the OCT scan height at each pixel by a pixel length of 21.2 μm. The pixel areas were summed across a specific scan distance to obtain the RNFL area of a given region or over the entire RNFL scan around the optic nerve head (ONH) to obtain the total number of axons. The second value, the axon density, which translates an RNFL area to the number of axons, was either a fixed value of 1.23 axons/μm2 or an age-dependent variable, as is illustrated by the model in Figure 1 . The variable axon density in the present study was taken from the model of normative data, with a reduction of axon density at a rate of 0.007 axons/μm2 per year of age, starting from an initial value of 1.4 axons/μm2
The topographic mapping of the visual field onto the ONH was accomplished by an empiric strategy of dividing the RNFL scan and visual field into 10 corresponding sectors. 29 32 The RNFL data were divided into equal sectors of 51 pixels of the OCT scan and the SAP visual field locations, with the RGC axons entering each sector, were assigned to that sector. The number of test field locations assigned to a given sector varied between one SAP location near fixation corresponding to the temporal portion of the RNFL scan to 13 SAP locations for the arcuate locations of the visual field, with axons entering the superior and inferior poles of the ONH. 
Results
The mean thickness of the RNFL as a function of age is presented in Figure 2A . The data are well described by linear regression (r 2 = 0.3, P < 0.0001), with an age-dependent loss of RNFL thickness of −0.27%/year, which is in close agreement with the published results of several studies 13 14 16 17 and with the function derived from the normative OCT data (Fig. 1) . Thus, the data for the normal age-dependent thinning of the RNFL are consistent with both sets of published data, and the normative databases and should provide an adequate set of data for an evaluation of effectiveness of the model for estimating the number of RGC axons from RNFL thickness measures. 
The results of a linear transformation of RNFL thickness to axon numbers, using a constant value for the axon density across all ages, are presented in Figure 2B . The axon density for these conversions (1.23 axons/μm2) was the value that provided a close agreement between RGCs and their axons for the normative data of 25-year-old subjects. It is obvious that the simple rescaling of the RNFL data does not change the slope of the function and, as a result, it does not accurately correlate with the rate of age-related reduction in the number of RGCs that were estimated from the SAP data for these subjects (Fig. 2C) , nor for the published data for age-related losses of RGC somas or axons. 19 20 21 22 23 24 25 26 In contrast, an application of the age-dependent reduction of axon density in the RNFL produced an agreement between the two estimates of age-related losses of retinal neurons (Fig. 2D) . Therefore, the concordant results of the estimates of RGCs and their axons from the diverse measurements of visual sensitivities and RNFL thickness provides strong support for the concept of an age-related decrease in the proportion of the RNFL thickness that is composed of neuronal tissue and for the model of aging (Fig. 1)that has been proposed to reconcile SAP and OCT measurements. 
Examples of the concurrence between SAP and OCT measurements for individual subjects are illustrated in Figure 3 . These two subjects were in the youngest (Fig. 3A)and oldest (Fig. 3B)decades of life of the subjects in the study. The global indices (MD and PSD) of the SAP data of both subjects indicate that the subjects have normal age-adjusted visual fields, but on an absolute basis, the mean SAP sensitivity (shown at the upper-right of the SAP data) for the younger subject is approximately 4 dB higher than for the older subject. As well, the OCT measurements for both subjects are within their respective age-adjusted normal ranges, but in this case, the older subject’s mean RNFL thickness is almost 15 μm greater than the younger subject’s. These facial differences in retinal neurology were eliminated when the age-dependent density of axons was incorporated into the translation of RNFL thickness to axon number. The data in the bottom graph for each subject demonstrate that the SAP/OCT estimates of RGCs and their axons are, in fact, very similar in relation to the total sums of RGCs and axons. The two estimations of neuronal populations, approximately 1,000,000 for the younger subject and over 700,000 for the older subject, are within 12% of agreement for each subject, and reflect an age-appropriate reduction in neurons for the older subject compared with the younger subject. 
An assessment of the degree of intrasubject agreement between SAP and OCT estimates of RGCs and axons for all the subjects is presented in Figure 4 . The data represent the relationship between estimates of neurons, in decibel units, from OCT and SAP measurements for each of 8 of the 10 OCT sectors (2 nasal sectors were excluded 32 ) and the corresponding SAP test locations (Fig. 4A)or summed across the 8 sectors of the RNFL scan and related visual field locations (Fig. 4B) . By inspection, the data seem to represent a linear relationship that is accurately modeled by the 1:1 function that is illustrated by a solid line superimposed on each plot. The goodness-of-fit of the model of a unity relationship between the estimates and the empiric data was tested by two simple statistics (i.e., the mean absolute deviation [MAD] and the coefficient of determination for the 1:1 function [r 2]). The results of the analysis are shown in the insets of the graphs and illustrate that the average deviations from the 1:1 relationship are small (1 dB, or less) and the unity relationship is significant (P < 0.0001) for both the sector-by-sector analysis and for the whole field analysis. The sum of the analyses clearly supports the concurrence of estimates of RGCs and axons by clinical measures of function and structure when procedures for translating each measure to its neuronal substrate have been implemented. 
The analyses of subregions of the RNFL scan and SAP test locations, presented in Figure 5 , indicate that the effects of aging are similar for the mean RNFL data (Fig. 4)and the thicker RNFL regions at the superior and inferior ONH, but aging effects are less well correlated in the thinner temporal region. The areas analyzed for regional effects were based on the RNFL sectors developed for the correlation of OCT and RNFL data, but were close to the standard clock-hour sectors. Using the OCT scan circle as a reference, we analyzed three sectors: (1) the temporal ONH data (Fig. 5A)representing the papillomacular RNFL bundles in a 72-arc deg region of the ONH (324–36 arc deg or ∼8–10 o’clock), (2) the superior ONH data (Fig. 5B)including the arcuate bundles of the superior retina over a 108-arc deg sector of the ONH (36–144 arc deg, or ∼10–2 o’clock) and, (3) the inferior ONH data (Fig. 5C)representing the arcuate bundles from the inferior retina in a 108-arc deg sector of the ONH (from 216–324 arc deg, or ∼4–8 o’clock). The correlations between axons and cell bodies for each sector were analyzed by the same statistics as the composite data in Figure 4 . For the superior and inferior arcuate bundles, the MAD values (<0.7 dB in both regions) are similar to the data for the full visual field and, although the r 2 values for the regional analyses are lower than those for the full field, the 1:1 relationships are statistically significant (P < 0.004) in both cases. In contrast, the data for the papillomacular bundles are more scattered (MAD ∼ 1.0 dB) and the unity relationship is not significant (P < 0.12). However, in all three cases, the data clustered about the 1:1 line and the lower correlations are probably a result of the smaller range of data to define the relationship with confidence, and thus they were considered to be generally supportive of the structure–function models proposed for the study. 
Discussion
The present investigation was undertaken to determine how the age-dependent decrease in visual sensitivity for SAP measurements and thickness of the RNFL by OCT measurements are related to a person’s age-related losses in RGCs. Specifically, the present study was a test of a proposed model of RNFL thickness that incorporates a remodeling of the nerve fiber layer by an increase in non-neuronal tissue, which appears to be initiated by the axonal loss from normal aging. 26 The model was tested by an application of procedures to derive RGC density from SAP sensitivity and RGC axons from RNFL thickness, by using the data from normal eyes of subjects between approximately 18 and 80 years of age. These empiric results provided strong support for the model developed with normative SAP and OCT data, 30 and, thus, the present study is additional evidence that visual sensitivity measurements by SAP are an accurate reflection of RGC density, without an age-dependent variable, whereas the translation of RNFL thickness to RGC axons includes an age-dependent variable for axon density to produce an accurate estimate of the neuronal population. 
Compensations for the effects of normal aging have been incorporated in clinical measurements by SAP and OCT through an evaluation of the significance of an individual’s data with respect to the mean and range of values for an age-equivalent normal population. 4 8 18 This method of deriving statistical probabilities is an effective diagnostic procedure, but it does not provide information for comparison across diagnostic procedures or for an assessment of whether pathologic alterations of the structural and functional components of vision are in agreement. The present study suggests that the comparison of the results of objective and subjective testing for a given patient can be achieved by the translation of structural and functional measurements to a common parameter that is related to the population of RGCs, which in turn requires an age-dependent variable for RNFL thickness. The application of these procedures produced consistent estimates of RGC populations from SAP and OCT measurements for normal eyes of patients across five decades of life, with equal accuracy and precision for each decade (Fig. 4) . Thus, the normal age-dependent variation in neuronal populations sets a baseline for the evaluation of the pathologic alterations caused by glaucoma. 
The function for normal age-related thinning of the total RNFL thickness is shallow, with a decrease of the mean thickness of 3 μm per decade (Fig. 2A)and standard deviations of measurements for each decade of 5 to 8 μm (Table 1) . These properties of the aging function are similar to published data, 10 11 12 13 14 15 16 which have called into question the clinical significance of an aging effect and the age-adjustment for normative data. 16 However, the true neuronal aging effect is not reflected in the OCT measurement of RNFL total thickness because of the non-neuronal component of the measurement and, in fact, a 3-μm reduction in RNFL thickness represents a loss of approximately 60,000 RGCs (Fig. 2C) . In addition, the variability of the OCT data may not be an indication of measurement variability, but rather may represent intersubject variability in retinal neurology. With incorporation of a correction for the non-neuronal component of RNFL thickness, the age-related loss of neurons has a steeper slope (Fig. 2D)and a high degree of intrasubject correspondence between SAP and OCT measurements. For example, the data for the youngest and oldest subjects (Fig. 3)illustrate the concordance of neuronal estimates, which is confirmed by the group data (Fig. 4)showing that the average difference between procedures is less than 1.0 dB. 
The correlation between RNFL thickness and perimetric sensitivity for normal, healthy eyes that was found in the present study is stronger than the relationship reported recently by Hood and Kardon. 35 The contrasting results are probably a consequence of substantive methodological differences in (1) the translation of the visual sensitivity to a neural substrate, (2) the portion of the scan used in the analysis (cf., Figs. 4 5 ), and (3) the consideration of age as an independent variable. Therefore, because the methods of analyzing OCT and SAP data were substantially different, the results of the two studies cannot be compared directly. Nevertheless, although the methodological issues must be resolved, it seems reasonable that the function that relates OCT and SAP measurements to RGC losses from glaucoma should also relate these clinical measurements to RGC losses from aging. 
The methods used for deriving axon counts from RNFL thickness measures were developed to achieve correspondence between axon counts from OCT scans and the number of RGCs estimated by SAP data 29 30 32 and therefore are dependent on the veridical translation of visual sensitivity measures to RGC densities. The methods used for these translations were based on the general psychophysiological finding that visual thresholds are nonlinearly correlated to their neural substrate, 36 37 38 39 which is supported by the relationship between visual sensitivity and ganglion cell density across the retinal area tested by SAP. 31 The RGC densities for retinal areas that correspond to the central test locations are 10 dB (10 times) greater than for the more peripheral test locations (50,000 cells/mm2 at a 4-arc deg eccentricity vs. 5000 cells/mm2 at a 21-arc deg eccentricity), whereas the visual sensitivities vary by 5 dB, or threefold (35 dB vs. 30 dB) across this region of the visual field. More important, the psychophysiological link was strongly supported by empiric studies of point-wise SAP measurements and corresponding histologic counts of RGC densities in monkeys with experimental glaucoma 40 41 and in humans with clinical glaucoma. 42 Thus, although neither alternative models 35 43 44 45 nor a role for RGC dysfunction 44 46 47 was tested, the nonlinear structure–function relationship for SAP seems sound. On the other hand, small deviations in the parameters for estimating axon numbers from RNFL thickness would affect only the magnitude and not the form of the relationships presented in the present study. 
The normal aging-related losses of neurons in the inner retina also may help to explain why age is a significant risk factor for the development of visual defects associated with glaucomatous neuropathy. If the occurrence of clinically significant visual field defects represent the time when neuronal losses reach a critical number, then a visual defect will occur when the pathologic and nonpathologic losses sum to that number. Consequently, a young person will have a neural reserve that is depleted with age, making the elderly patient more susceptible to the development of visual field defects from pathologic losses. For example, the functions presented in Figures 2C and 2Dshow that, on average, the retina of a 25-year-old has twice as many RGCs as that of a 95-year-old patient. Similarly, although regional or eccentricity variations of age-dependent losses of RGCs have not been defined, the large neural reserve for the central retina may partially explain why the central defects occur at later stages of glaucoma than do peripheral visual field defects. 
It is implicit in the proposed model of age-related thinning of the RNFL (Fig. 1)that remodeling of the nerve fiber layer, to compensate partially for axonal loss, is a direct response to axon loss. Further, if this form of remodeling is a general response to axonal loss, then it is likely that the pathologic loss of RGC axons in glaucoma would also initiate an increase in the non-neuronal components of the RNFL. An increase in non-neuronal support tissue has been demonstrated histologically by increased glial cell density in human donor eyes with glaucoma, compared with control eyes. 27 48 49 Also, preliminary data from patients with glaucoma, obtained by using the procedures described in this study, have suggested a proportional decrease in axonal density in the RNFL as a function of visual field defect depth (Wheat JL, et al. IOVS 2007;48:ARVO E-Abstract 491). It is likely, therefore, that the residual RNFL thickness for OCT measurements in eyes with advanced or end-stage glaucoma is a reflection of this process, and the residual thickness is actually a greater amount of glial tissue than would be present in a normal healthy eye. For this reason, RNFL thickness measurements on eyes that are blind from either glaucomatous 13 35 50 or nonglaucomatous 51 neuropathy may retain approximately 50% of the thickness of a healthy eye in some regions around the optic nerve. However, if it is determined that remodeling in glaucoma is systematic, as with aging, then the neuronal and non-neuronal components of total thickness can be separated computationally. 
In conclusion, the study of the normal age-related losses of RGCs and their axons provided additional support for a model proposing that the proportion of RNFL that is composed of RGC axons is not constant across ages, but rather, greater proportions of the total thickness are non-neuronal tissue in older patients. 30 The present analysis of empiric data from normal eyes of subjects between 18 and 80 years of age strongly supported the model of age-dependent thinning of the RNFL that had been developed with normative SAP and OCT data and, together, the results imply that the dynamic range for RNFL thickness measurements decreases with age. Further, because glaucoma can be considered as accelerated aging, 52 a similar remodeling of RNFL thickness should occur with glaucomatous neuropathy and, thus, stage-dependent corrections are needed to translate OCT measurements to neuronal components in addition to the age-dependent corrections derived in the present study. 
 
Figure 1.
 
The model for the age-related thinning of the RNFL proposed by Harwerth and Wheat. 30 The RNFL thickness at each age (solid line), represents the sum of two components of the total thickness: the age-dependent loss of neuronal tissue (dashed line) and a compensating increase of nonneuronal tissue (dot-dash line).
Figure 1.
 
The model for the age-related thinning of the RNFL proposed by Harwerth and Wheat. 30 The RNFL thickness at each age (solid line), represents the sum of two components of the total thickness: the age-dependent loss of neuronal tissue (dashed line) and a compensating increase of nonneuronal tissue (dot-dash line).
Table 1.
 
Characteristics of the Subject Population
Table 1.
 
Characteristics of the Subject Population
Age (Decades) n Age ± SD (y) Sex (M/F) Eye (OD/OS) RNFL ± SD (dB) MD ± SD (dB) PSD ± SD (dB)
<3 2 18.8 ± 1.6 2/0 1/1 94.2 ± 13.3 0.58 ± 1.4 1.47 ± 0.4
3 13 26.4 ± 3.2 6/7 7/6 104.4 ± 7.6 0.01 ± 0.6 1.30 ± 0.2
4 10 33.3 ± 2.6 6/4 6/4 100.3 ± 9.4 −0.17 ± 0.9 1.31 ± 0.2
5 10 43.9 ± 2.9 4/6 4/6 98.6 ± 8.8 −0.10 ± 0.7 1.40 ± 0.2
6 7 56.5 ± 2.8 3/4 4/3 94.1 ± 5.2 0.00 ± 0.6 1.51 ± 0.3
7 9 65.4 ± 2.9 5/4 4/5 89.5 ± 7.5 0.21 ± 1.2 1.45 ± 0.3
>7 4 76.5 ± 4.2 1/3 1/3 82.6 ± 16.1 −0.28 ± 0.6 1.60 ± 0.4
Mean ± SD Total 55 44.5 ± 17.3 27/28 25/30 97.2 ± 10.2 −0.02 ± 0.8 1.39 ± 0.3
Figure 2.
 
Age-dependent functions for RNFL thickness and total number of RGCs and axons. Solid line: the results of a linear regression analysis of the data; inset: parameters. (A) OCT measurements of the age-related thinning of the RNFL in 61 normal subjects. (B) The total number of axons in the RNFL derived from the OCT measurements using a constant axon density of 1.23 axons/μm2 for each subject, to translate the area of the RNFL to the number of axons. (C) The number of RGCs derived from SAP measurements of visual sensitivity according to published methods. 31 (D) The total number of axons in the RNFL derived from the OCT measurements by using an axon density that varied with age (density = 0.007[age] + 1.4) to translate the area of the RNFL to the number of axons.
Figure 2.
 
Age-dependent functions for RNFL thickness and total number of RGCs and axons. Solid line: the results of a linear regression analysis of the data; inset: parameters. (A) OCT measurements of the age-related thinning of the RNFL in 61 normal subjects. (B) The total number of axons in the RNFL derived from the OCT measurements using a constant axon density of 1.23 axons/μm2 for each subject, to translate the area of the RNFL to the number of axons. (C) The number of RGCs derived from SAP measurements of visual sensitivity according to published methods. 31 (D) The total number of axons in the RNFL derived from the OCT measurements by using an axon density that varied with age (density = 0.007[age] + 1.4) to translate the area of the RNFL to the number of axons.
Figure 3.
 
Examples of the translation of SAP and OCT measurements to common parameters of neuron numbers in two subjects. Subjects in (A) the youngest decade and (B) the oldest decade of the subjects in the study.
Figure 3.
 
Examples of the translation of SAP and OCT measurements to common parameters of neuron numbers in two subjects. Subjects in (A) the youngest decade and (B) the oldest decade of the subjects in the study.
Figure 4.
 
The relationship between SAP and OCT estimates of RGCs and axons in all the subjects. The data represent the correlation of estimates of neurons, in decibels, from OCT and SAP measurements in each of the 10 OCT sectors of the RNFL scan and the corresponding SAP test locations (A) or summed across the whole RNFL scan and all visual field locations (B). Inset: goodness-of-fit statistics, which are the mean absolute deviation (MAD) with respect to a unity relationship and the coefficient of determination (r 2) for the 1:1 relationship.
Figure 4.
 
The relationship between SAP and OCT estimates of RGCs and axons in all the subjects. The data represent the correlation of estimates of neurons, in decibels, from OCT and SAP measurements in each of the 10 OCT sectors of the RNFL scan and the corresponding SAP test locations (A) or summed across the whole RNFL scan and all visual field locations (B). Inset: goodness-of-fit statistics, which are the mean absolute deviation (MAD) with respect to a unity relationship and the coefficient of determination (r 2) for the 1:1 relationship.
Figure 5.
 
The analyses of subregions of the RNFL scan and SAP test locations. (AC) Data in three sectors of the RNFL scan. (A) The temporal ONH, representing the papillomacular RNFL bundles; (B) the superior ONH, representing the arcuate bundles of the superior retina; and (C) the inferior ONH, representing the arcuate bundles from the inferior retina. The statistics for a unity correlation between axons and cell bodies for each sector were analyzed by the same statistics as the composite data in Figure 4 .
Figure 5.
 
The analyses of subregions of the RNFL scan and SAP test locations. (AC) Data in three sectors of the RNFL scan. (A) The temporal ONH, representing the papillomacular RNFL bundles; (B) the superior ONH, representing the arcuate bundles of the superior retina; and (C) the inferior ONH, representing the arcuate bundles from the inferior retina. The statistics for a unity correlation between axons and cell bodies for each sector were analyzed by the same statistics as the composite data in Figure 4 .
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Figure 1.
 
The model for the age-related thinning of the RNFL proposed by Harwerth and Wheat. 30 The RNFL thickness at each age (solid line), represents the sum of two components of the total thickness: the age-dependent loss of neuronal tissue (dashed line) and a compensating increase of nonneuronal tissue (dot-dash line).
Figure 1.
 
The model for the age-related thinning of the RNFL proposed by Harwerth and Wheat. 30 The RNFL thickness at each age (solid line), represents the sum of two components of the total thickness: the age-dependent loss of neuronal tissue (dashed line) and a compensating increase of nonneuronal tissue (dot-dash line).
Figure 2.
 
Age-dependent functions for RNFL thickness and total number of RGCs and axons. Solid line: the results of a linear regression analysis of the data; inset: parameters. (A) OCT measurements of the age-related thinning of the RNFL in 61 normal subjects. (B) The total number of axons in the RNFL derived from the OCT measurements using a constant axon density of 1.23 axons/μm2 for each subject, to translate the area of the RNFL to the number of axons. (C) The number of RGCs derived from SAP measurements of visual sensitivity according to published methods. 31 (D) The total number of axons in the RNFL derived from the OCT measurements by using an axon density that varied with age (density = 0.007[age] + 1.4) to translate the area of the RNFL to the number of axons.
Figure 2.
 
Age-dependent functions for RNFL thickness and total number of RGCs and axons. Solid line: the results of a linear regression analysis of the data; inset: parameters. (A) OCT measurements of the age-related thinning of the RNFL in 61 normal subjects. (B) The total number of axons in the RNFL derived from the OCT measurements using a constant axon density of 1.23 axons/μm2 for each subject, to translate the area of the RNFL to the number of axons. (C) The number of RGCs derived from SAP measurements of visual sensitivity according to published methods. 31 (D) The total number of axons in the RNFL derived from the OCT measurements by using an axon density that varied with age (density = 0.007[age] + 1.4) to translate the area of the RNFL to the number of axons.
Figure 3.
 
Examples of the translation of SAP and OCT measurements to common parameters of neuron numbers in two subjects. Subjects in (A) the youngest decade and (B) the oldest decade of the subjects in the study.
Figure 3.
 
Examples of the translation of SAP and OCT measurements to common parameters of neuron numbers in two subjects. Subjects in (A) the youngest decade and (B) the oldest decade of the subjects in the study.
Figure 4.
 
The relationship between SAP and OCT estimates of RGCs and axons in all the subjects. The data represent the correlation of estimates of neurons, in decibels, from OCT and SAP measurements in each of the 10 OCT sectors of the RNFL scan and the corresponding SAP test locations (A) or summed across the whole RNFL scan and all visual field locations (B). Inset: goodness-of-fit statistics, which are the mean absolute deviation (MAD) with respect to a unity relationship and the coefficient of determination (r 2) for the 1:1 relationship.
Figure 4.
 
The relationship between SAP and OCT estimates of RGCs and axons in all the subjects. The data represent the correlation of estimates of neurons, in decibels, from OCT and SAP measurements in each of the 10 OCT sectors of the RNFL scan and the corresponding SAP test locations (A) or summed across the whole RNFL scan and all visual field locations (B). Inset: goodness-of-fit statistics, which are the mean absolute deviation (MAD) with respect to a unity relationship and the coefficient of determination (r 2) for the 1:1 relationship.
Figure 5.
 
The analyses of subregions of the RNFL scan and SAP test locations. (AC) Data in three sectors of the RNFL scan. (A) The temporal ONH, representing the papillomacular RNFL bundles; (B) the superior ONH, representing the arcuate bundles of the superior retina; and (C) the inferior ONH, representing the arcuate bundles from the inferior retina. The statistics for a unity correlation between axons and cell bodies for each sector were analyzed by the same statistics as the composite data in Figure 4 .
Figure 5.
 
The analyses of subregions of the RNFL scan and SAP test locations. (AC) Data in three sectors of the RNFL scan. (A) The temporal ONH, representing the papillomacular RNFL bundles; (B) the superior ONH, representing the arcuate bundles of the superior retina; and (C) the inferior ONH, representing the arcuate bundles from the inferior retina. The statistics for a unity correlation between axons and cell bodies for each sector were analyzed by the same statistics as the composite data in Figure 4 .
Table 1.
 
Characteristics of the Subject Population
Table 1.
 
Characteristics of the Subject Population
Age (Decades) n Age ± SD (y) Sex (M/F) Eye (OD/OS) RNFL ± SD (dB) MD ± SD (dB) PSD ± SD (dB)
<3 2 18.8 ± 1.6 2/0 1/1 94.2 ± 13.3 0.58 ± 1.4 1.47 ± 0.4
3 13 26.4 ± 3.2 6/7 7/6 104.4 ± 7.6 0.01 ± 0.6 1.30 ± 0.2
4 10 33.3 ± 2.6 6/4 6/4 100.3 ± 9.4 −0.17 ± 0.9 1.31 ± 0.2
5 10 43.9 ± 2.9 4/6 4/6 98.6 ± 8.8 −0.10 ± 0.7 1.40 ± 0.2
6 7 56.5 ± 2.8 3/4 4/3 94.1 ± 5.2 0.00 ± 0.6 1.51 ± 0.3
7 9 65.4 ± 2.9 5/4 4/5 89.5 ± 7.5 0.21 ± 1.2 1.45 ± 0.3
>7 4 76.5 ± 4.2 1/3 1/3 82.6 ± 16.1 −0.28 ± 0.6 1.60 ± 0.4
Mean ± SD Total 55 44.5 ± 17.3 27/28 25/30 97.2 ± 10.2 −0.02 ± 0.8 1.39 ± 0.3
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