The Investigation of Autofluorescence in Myopes and Emmetropes

A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy (PhD) in the Faculty of Biology, Medicine and Health

2018

Teresa S L Tee

School of Biological Sciences / Division of Neuroscience & Experimental Psychology

Contents

List of Figures ...... 6

List of Tables ...... 14

List of Abbreviations ...... 16

Abstract ...... 18

1. Fundus Autofluorescence (FAF) and Myopia 1.1. Retinal Pigment Epithelium (RPE) Lipofuscin ...... 21 1.1.1. The Formation of RPE Lipofuscin ...... 23 1.1.2. Autofluorescent Properties of RPE Lipofuscin...... 27 1.1.3. Biochemical Properties of RPE Lipofuscin ...... 28 1.1.4. RPE Lipofuscin and Aging ...... 29 1.1.5. RPE Lipofuscin in Disease ...... 29 1.2. FAF Imaging ...... 30 1.2.1. Confocal Scanning Laser Ophthalmoscope (cSLO) ...... 32 1.2.2. Quantitative Assessment of FAF Images ...... 34 1.2.3. Sources of Error and Asymmetry in FAF imaging ...... 37 1.2.4. Patient-based Factors Affecting the Autofluorescence Image...... 38 1.2.5. Autofluorescence of the Normal Fundus ...... 39 1.2.6. Interpretation of the FAF Image ...... 42 1.2.7. Applications of FAF Imaging ...... 42 1.2.7.1. Disease Detection and Diagnosis ...... 42 1.2.7.2. Patient Management ...... 45 1.2.7.3. Visual Function...... 48 1.2.8. Advantages of FAF Imaging ...... 49 1.2.9. Limitations of FAF Imaging ...... 50 1.3. Myopia ...... 50 1.3.1. Physical Dimensions of the Eye in Myopia ...... 51 1.3.2. Pathological Myopia and FAF ...... 52 1.3.3. Autofluorescence Around the Optic Disc in High Myopia ...... 54 1.3.4. Eye Diseases Associated with Oxidative Stress and High Myopia ... 55 1.4. Contrast Sensitivity ...... 56 1.4.1. Contrast Sensitivity with Age ...... 58 1.4.2. Contrast Sensitivity in Myopia ...... 60 1.5. Objective of the Thesis ...... 62 1.6. References ...... 63

2. General Methodology 2.1. Subject Recruitment ...... 73 2.2. Clinical Procedures ...... 75 2.2.1. Refraction and Keratometry ...... 75 2.2.2. Axial Length Measurement ...... 75 2.2.3. Autofluorescence Imaging ...... 75 2.2.4. Optical Coherence Tomography (OCT) ...... 76 2.2.5. Contrast Sensitivity ...... 76 2.3. Determination of Autofluorescence Image Quality ...... 77 2.3.1. FAF Image Quality Analysis ...... 78 2.3.2. Parapapillary Autofluorescence (PAF) Image Quality Analysis ...... 83 2.4. Statistical Analysis ...... 86 2.5. References ...... 88

3. Effect Of Image Capture Settings on the Autofluorescence Signal 3.1. Abstract ...... 90 3.2. Introduction...... 93 3.3. Part A - Effect of Image Averaging (Automatic Real-time Averaging) on FAF Signal ...... 95 3.3.1. Methods ...... 95 3.3.1.1. Ethics and Subjects ...... 95 3.3.1.2. Autofluorescence Imaging ...... 95 3.3.1.3. Image Sampling ...... 96 3.3.1.4. Statistical Analysis ...... 98 3.3.2. Results ...... 98 3.3.2.1. Average Intensity of Sampled FAF from 55° and 30° Images ...... 98 3.3.2.2. Standard Deviation (SD) of Sampled FAF from 55° and 30° Images ...... 100 3.4. Part B - Optimising Detector Sensitivity Level for 55° and 30° Images ...... 103 3.4.1. Methods ...... 103 3.4.1.1. Ethics and Subjects ...... 103 3.4.1.2. Autofluorescence Imaging ...... 103 3.4.2. Results ...... 104 3.4.2.1. Autofluorescence Intensity ...... 104 3.4.2.2. Caucasian vs Asian Eyes ...... 105 3.4.2.3. Effect of Detector Sensitivity on FAF Profile...... 109 3.5. Part C – Effect of Photobleaching on FAF ...... 112 3.5.1. Methods ...... 112 3.5.1.1. Ethics and Subjects ...... 112 3.5.1.2. Autofluorescence Imaging ...... 112 3.5.1.3. Image Analysis...... 112 3.5.1.4. Statistical Analysis ...... 113 3.5.2. Results ...... 114 3.5.2.1. FAF Profile ...... 114 3.5.2.2. Autofluorescence Intensity over Time ...... 116 3.5.2.3. FAF Peak Location ...... 117 3.6. Discussion and Conclusion ...... 118 3.6.1. Part A – Effect of Image Averaging (Automatic Real-time Averaging) on FAF Signal ...... 118 3.6.2. Part B – Effect of Detector Sensitivity on FAF ...... 118 3.6.3. Part C – Effect of Photobleaching on FAF ...... 119 3.7. References ...... 120

4. The Spatial Profile Of Fundus Autofluorescence (FAF) In Young Healthy Eyes 4.1. Abstract ...... 123 4.2. Introduction...... 124 4.3. Methods ...... 126 4.3.1. Ethics and Subjects ...... 126 4.3.2. Procedure ...... 127 4.3.3. Image Analysis...... 127 4.3.4. Statistical Analysis ...... 128 4.4. Results ...... 130 4.4.1. Intra-session Coefficient of Repeatability (COR) of FAF Peak Location and Intensity ...... 130 4.4.2. Autofluorescence Intensity Profiles ...... 132 4.4.3. Location of FAF Peaks ...... 134 4.5. Discussion ...... 137

4.5.1. Spatial Distribution of FAF Peak Intensity ...... 137 4.5.2. FAF Peaks ...... 138 4.6. Conclusion ...... 141 4.7. References ...... 142

5. Fundus Autofluorescence (FAF) and Contrast Sensitivity in Emmetropes and High Myopes 5.1. Abstract ...... 145 5.2. Introduction...... 147 5.3. Methods ...... 148 5.3.1. Ethics and Subjects ...... 148 5.3.2. FAF Imaging ...... 148 5.3.3. Contrast Sensitivity ...... 150 5.3.4. Statistical Analysis ...... 151 5.4. Results ...... 152 5.4.1. Subjects ...... 152 5.4.2. FAF Peaks ...... 153 5.4.3. FAF Intensity ...... 154 5.4.4. Contrast Sensitivity ...... 155 5.5. Discussion ...... 159 5.5.1. FAF Peaks in the Posterior Pole ...... 159 5.5.2. Age-related Increase in FAF ...... 160 5.5.3. Contrast Sensitivity in Emmetropes and High Myopes ...... 162 5.5.4. Contrast Sensitivity and Age ...... 164 5.5.5. Contrast Sensitivity and Perifoveal Autofluorescence ...... 165 5.6. Limitations ...... 166 5.7. Future Work ...... 166 5.8. Conclusion ...... 167 5.9. References ...... 168

6. Parapapillary Autofluorescence (PAF) and the Retinal Nerve Fibre Layer (RNFL) in Emmetropes and High Myopes 6.1. Abstract ...... 172 6.2. Introduction...... 174 6.3. Methods ...... 176 6.3.1. Ethics and Subjects ...... 176 6.3.2. Refraction and Biometry ...... 176 6.3.3. Optical Coherence Tomography (OCT)...... 177 6.3.4. Autofluorescence Imaging ...... 177 6.3.5. Sampling PAF and RNFL Thickness ...... 178 6.3.6. Statistical Analysis ...... 181 6.4. Results ...... 183 6.4.1. Subjects ...... 183 6.4.2. RNFL Thickness Profile ...... 183 6.4.3. Repeatability of PAF Intensity using Different Sampling Methods .. 187 6.4.4. Consistency of the Correlation Analysis ...... 189 6.4.5. Correlation between PAF Intensity and RNFL Thickness using Different Sampling Methods ...... 189 6.4.6. Correlation between PAF Intensity and RNFL Thickness using Arc Segment Method ...... 190 6.4.7. Attenuation of PAF Intensity by RNFL ...... 192 6.5. Discussion ...... 193 6.5.1. RNFL Thickness between Emmetropes and High Myopes ...... 193 6.5.2. RNFL Thickness with Age ...... 194

6.5.3. Correlation between PAF Intensity and RNFL Thickness ...... 194 6.6. Limitations of the Correlation Factor Between PAF Intensity and RNFL Thickness ...... 196 6.7. Conclusion ...... 196 6.8. References ...... 197

7. Parapapillary Autofluorescence (PAF) in in Emmetropia and High Myopia 7.1. Abstract ...... 201 7.2. Introduction...... 203 7.3. Methods ...... 205 7.3.1. Ethics and Subjects ...... 205 7.3.2. Refraction and Biometry ...... 205 7.3.3. Autofluorescence Imaging ...... 205 7.3.4. Optical Coherence Tomography (OCT)...... 206 7.3.5. Sampling PAF ...... 207 7.3.6. Sampling Perifoveal Autofluorescence ...... 209 7.3.7. Statistical Analysis ...... 210 7.4. Results ...... 210 7.4.1. Subjects ...... 210 7.4.2. PAF Profile ...... 211 7.4.3. Age-related Change in PAF ...... 213 7.4.4. Comparison with Perifoveal Autofluorescence ...... 214 7.5. Discussion ...... 215 7.5.1. PAF Profile ...... 215 7.5.2. Age-related Increase in PAF ...... 216 7.5.3. Rate of PAF Increase in Emmetropes and High Myopes ...... 217 7.6. Limitations ...... 218 7.7. Future Work and Conclusion ...... 218 7.8. References ...... 220

8. Overall Discussion and Concluding Comments 8.1. Overall Discussion and Concluding Comments ...... 223 8.2. References ...... 226

Annexes ...... 227

Word count: 47 641

List of Figures

1.1. Autofluorescence image of RPE flat mount from a 40-year-old eye showing yellow-green lipofuscin granules. Excitation wavelength was 460 - 490nm and emission was recorded at wavelengths > 505nm. Figure modified from Ach et al. (2014) ...... 22

1.2. The classic visual cycle in the human eye. Figure modified from Wang et al. (2012) ...... 24

1.3. Emission spectra of whole RPE lipofuscin granules when irradiated with 330nm, 436nm, 480nm and 545nm light. As the wavelength of the excitation light increases, the peak emission also shifts towards longer wavelengths. The numbers above each curve are the peak emission wavelengths for each of the excitation wavelengths used. Figure modified from Sparrow et al. (2010) ...... 28

1.4. RPE lipofuscin autofluorescence and rod photoreceptor distribution along the vertical meridian is plotted for young and old eyes. Dotted vertical lines mark the macula. The arrows mark locations where the largest increase/decrease in autofluorescence intensity and rod loss occurs. RPE autofluorescence increases the most near the perifoveal rod ring and the foveal cone peak. Maximal rod loss corresponds to an area where there is most accumulation of lipoproteins in the Bruch’s membrane (yellow). Figure from Ach et al. (2014) ...... 30

1.5. Schematic diagram of confocal scanning laser ophthalmoscope. Image modified from Delori et al. (2011) ...... 32

1.6. FAF image and corresponding image histogram of a A) normalised and B) non-normalised HRA+OCT autofluorescence image of the same eye. In the normalised image, the blood vessels and optic disc appear darker than in the non-normalised image. Both histograms show a small plateau in the lower GL region followed by a peak in the vicinity of 100GL. However, the peak from the non-normalised image is higher than in the normalised image. The range of grey level intensities is narrower in the non-normalised image compared to normalised image ...... 34

1.7. Macula 30° FAF image from Delori et al. (2011). The rectangular white bar at the top margin of the image is the internal fluorescent reference. It is used to adjust for image capture setting variations in order to obtain quantified autofluorescence (qAF) values. The white lines indicate the regions of the image analysed in their study. The cross (+) and square (□) represent the maximum and minimum intensities in the image...... 35

1.8. Optic-disc centred FAF image showing an oval artefact superior- nasal to the optic nerve caused by a Weiss ring (red arrows) ...... 38

1.9. Autofluorescence and colour photographs of an optic disc with (A) shallow and (B) deep cupping. Autofluorescence intensity in the optic disc is not uniform, with mild autofluorescence visible in discs with deep cups and intense hypoautofluorescence corresponding to

blood vessels ...... 40

1.10. Right eye 30° FAF image from a 15-year-old Asian male. FAF of the retina inferior to the optic disc shows a demarcation line (white arrow) believed to represent the closed optic fissure. Image modified from Duncker et al. (2012) ...... 41

1.11. Fundus photograph (A, C) and FAF images (B, D) of a 49-year-old woman with central serous chorioretinopathy (CSCR). Fundus photographs (A, C) show pigmentary changes. FAF images (B, D) show the CSCR lesions in high contrast. Older lesions may exhibit descending tract appearance, as seen in (B) over the parafovea and optic disc. Hypoautofluorescence involving the macula was associated with poor visual acuity. Image from Imamura et al. (2011) ...... 44

1.12. Colour photograph and FAF image of the optic disc in a patient with ocular hypertension. Larger areas of hyperautofluorescence around the optic disc have been noted in ocular hypertensive compared to normal eyes. Image modified from Laemmer et al. (2007) ...... 47

1.13. Classification of posterior staphyloma based on location and extent. Image from Ohno-matsui et al. (2014) ...... 54

1.14. Autofluorescence image of the optic disc (A) and the corresponding colour photograph (B) from a -12D myope. Parapapillary atrophy (PPA) is intensely hypoautofluorescent except in the areas where the sclera is exposed. The borders of the PPA are clearly demarcated in the autofluorescence image, which provide good contrast between intact retina and areas of atrophy ...... 55

1.15 Human contrast sensitivity function showing peak sensitivity at 4 cycles/degree. The shaded area under the curve can be resolved by the eye whereas regions above the curve cannot be resolved. The arrow on the x-axis is the high frequency cut off and represents the resolution limit of the human eye. Figure from Schwartz (2017) ...... 57

1.16. Illustration of a grating stimulus falling on the receptive field of a ganglion cell. A grating with spatial frequency that matches the excitatory centre of the receptive field elicits optimal response from the ganglion cell (A). If a grating with low spatial frequency has bright bars that fall on the excitatory centre and inhibitory surround of the receptive field, the result is lateral inhibition (B). Figure from Schwartz (2017) ...... 58

2.1. Set up for contrast sensitivity testing. The stimuli were sine wave gratings modulated at 0.5Hz (A). The subject was seated 114cm from the computer screen with the non-study eye patched. Refractive error compensated for the working distance was corrected using trial frame and lenses. Using keyboard up/down arrow keys, the subject reduced the intensity of the stimulus until he could just see the grating (B) ...... 77

2.2. Histogram of an FAF image with the central 90% intensity range (shaded region) used as an index of image contrast. In this example,

the small shoulder between 50 – 68GL was confirmed to comprise pixels from the fovea, optic disc and retinal vasculature using the threshold tool in Photoshop. Above 68GL, pixel intensities come from retinal autofluorescence ...... 79

2.3. Cropped annulus overlaid on the original FAF image. The annulus has an inner and outer radius of 5.2mm and 5.6mm and is centred on the FAF image ...... 79

2.4. Flowchart showing the process of identifying good and poor quality FAF images. The contrast and brightness of the FAF image is determined, then an annulus centred on the image is isolated. Depending on the contrast and brightness of the original image, the annulus is thresholded at 0.28 or 0.30, or rejected ...... 80

2.5. FAF image from subject 32t. The annulus (after thresholding) is superimposed on the FAF image and highlights the shadows in the inferior periphery ...... 81

2.6. Alignment and cropping of PAF annulus. (A) The infra-red image from the deviation plot of the OCT report was aligned to the PAF image to identify the centre of the optic disc. (B) An annulus with an inner and outer radius of 1.7mm and 2.8mm was cropped from the PAF image for analysis of image quality ...... 83

2.7. Flowchart of qualification process for PAF images. An annulus with an inner and outer radius of 1.7mm and 2.8mm is cropped from the PAF image, and the contrast and brightness parameters obtained. Based on the brightness of the annulus, the annulus is thresholded at either 0.2 or 0.3. The binary images that contain ≥ 7541 black pixels is considered to be of poor quality ...... 84

3.1. Location of FAF sampling in (A) 55° and (B) 30° images. Each box is 5x5 pixels large. FAF in the fovea and optic disc were not sampled. If the sampled location contained a blood vessel, an adjacent location along the meridian (200 - 600µm away) was sampled instead. Images were resized and aligned to a scale of 20µm/pixel before FAF was sampled ...... 97

3.2. Autofluorescence intensity (median and interquartile range) extracted from 55° (A, C, E, G) and 30° (B, D, F, H) FAF images averaged from 25, 36 and 49 scans. Each bar represents average FAF signal from 23 points as shown in Figure 3.1. Autofluorescence intensities from 55° images were similar among the 3 ART levels for every subject (One-way ANOVA, F(2, 66) ≤ 2.37, p ≥ 0.101). For 30° data, autofluorescence intensities were similar among the 3 ART levels for every subject except subject 2, who demonstrated lower autofluorescence intensity in ART 49 image than the other 2 (One- way ANOVA, F(2, 66) = 6.59, p = 0.003) ...... 99

3.3. Standard deviation of pixel intensities extracted from 55° (A, C, E, G) and 30° (B, D, F, H) FAF images captured at ART 25, 36 and 49 images for each subject. For 55° images, the standard deviation of the intensity pixels within each sampling square was lower in ART 49 images compared to ART 25 and 36 (Kruskall-Wallis test, H(2) =

20.38, p < 0.0001) (A). Analysis of the subjects’ data separately reveal that there is a trend for ART 49 to give lower standard deviation, and this effect was larger in subjects 2 (Kruskall-Wallis test, H(2) = 10.60, p = 0.005) and subject 3 (H(2) = 17.3, p = 0.0002)(E, G). For 30° images, standard deviation of autofluorescence intensities was lower in ART 49 image compared to ART 36 but not ART 25 (B) (Kruskall-Wallis test, H(2) = 15.7, p = 0.0004). Separate analysis by subject revealed that this observation was primarily due to data from subject 2 (F) (Kruskall-Wallis test, H(2) = 10.14, p = 0.006). Kruskall-Wallis test for subject 1 returned statistical significance (H(2) = 6.43, p = 0.04) but Dunn’s multiple comparisons yielded no significant results (D). The bar graphs show the median and interquartile range ...... 101

3.4. Sampled regions for studying the effects of detector sensitivity on pixel intensity. Pixel intensities in 5x5 pixel squares located at the fovea, optic disc (4000µm eccentricity), and 2400µm eccentricities along the superior, temporal and inferior meridians were sampled .... 104

3.5. Plot of autofluorescence intensity from image of lower detector sensitivity level against intensity from higher detector sensitivity level for 55° (n = 7) and 30° (n = 7) images. Dotted line indicates initial intensity of 175GL. A line is fitted to data with initial intensity below 175GL ...... 105

3.6. Autofluorescence intensity sampled from 5 locations on the retina. In 55° images, the pixel intensities exceed 175GL (dotted line) at detector sensitivity of 105 units for Caucasians and just approach 175GL for Asians at the same detector sensitivity. In 30° images of Caucasian eyes, some of the intensities of the data points exceed 175GL when detector sensitivity is 90 and above, whereas this occurs at detector sensitivity of 100 for Asian eyes...... 107

3.7. Horizontal FAF profile of 1 Asian and 1 Caucasian subject extracted from 30° (A, B) and 55° (C, D) FAF images captured at detector sensitivity levels of 80, 85, 90, 95, 100 and 105 units. OD denotes the optic disc. Dotted line indicates 175GL, which is the maximum GL at which the HRA detector operates linearly (Delori et al., 2011). Despite parts of the FAF profile exceeding 175GL at sensitivity of 105 units (A, B, C) and at 95 units in (D), there was no flattening of the peaks relative to the rest of the profile and the difference in intensity between fovea and peak was enhanced relative to lower sensitivity level. The only exceptions were the profiles captured at sensitivity 100 and sensitivity 105 units in (D), where parts of the profile exceeded 200GL...... 110

3.8. Locations of sampling grid in one FAF image. Eight FAF images were manually aligned prior to insertion of sampling grid. FAF intensities were sampled from thirty-eight 5x5 pixel squares (white squares) spaced at 10 pixel intervals along the horizontal and vertical meridians. FAF profiles along the sampled meridians were compared among the 1st, 3rd, 5th and 7th FAF images ...... 113

3.9. FAF profile across the horizontal, vertical and 2 oblique meridians extracted from the 1st, 3rd, 5th and 7th images. These images

capture the autofluorescence intensity after 0 – 6, 13 – 18, 25 – 30 and 37 – 42 seconds of photobleaching, and demonstrate the change in FAF intensity at different amounts of photobleaching. The corresponding strip of retina from the FAF image is shown below each profile. Regions 1 and 5 span the outer > 5000µm to 7200µm, while the central > 2000µm to 5000µm are denoted as regions 2 and 4 and the central 2000µm is the fovea. (* indicates retinal capillaries, ♦ indicates larger retinal vessels) ...... 115

3.10. The change in mean FAF intensity from 8 consecutively-captured FAF images of 1 subject. Exponential curve was fitted to the data. FAF intensity increased by about 10GL from the 1st to 8th image. The exponential curve indicates that for the FAF to increase to 99% of its maximum intensity, the retina would need to be bleached for 45 seconds. A 20-second bleach would be adequate to bring the FAF to 96.5% (within 5GL) of its maximum intensity...... 116

3.11. FAF peak locations identified from 8 consecutive images of one eye. (T = temporal, ST = superior-temporal, S = superior, SN = superior- nasal, N = nasal before the optic disc, N2 = nasal beyond the optic disc, IN = inferior-nasal, IT = inferior-temporal) ...... 117

4.1. (A) FAF was sampled along 8 meridians of the fundus, with the fovea in the centre. (B) Magnified view of the sampling boxes. Grey level intensities were sampled from 5 x 5 pixel squares sampled at 10 pixel intervals ...... 129

4.2. FAF profiles across the (A) horizontal, (B, D) oblique, and (C) vertical meridians. FAF profiles have been shifted vertically for ease of viewing. Subject IDs are labelled next to the respective FAF profiles. Data points overlapping major blood vessels have been excluded. Small peaks and dips in the profiles are due to localized variations in FAF intensity as well as the presence of capillaries. The fovea at zero eccentricity shows a dip in FAF due to macular pigment masking. (A) The horizontal profile cuts across the optic disc at approximately 13.6º from fovea. (B, C) Autofluorescence distribution gradually decreases with eccentricity beyond ± 13.6˚. (d) The superior-nasal peak is found at a relatively more eccentric location than the other meridians (*) in all subjects except 4 ...... 132

4.3. The typical FAF image of a healthy retina taken using cSLO with 55° lens. The symbols on the image represent the mean and SD of the location of FAF peaks (n = 17). (T = temporal, ST = superior- temporal, S = superior, SN = superior-nasal, N1 = nasal before the optic disc, N2 = nasal beyond the optic disc, IN = inferior-nasal, I = inferior, IT = inferior-temporal) ...... 135

4.4. (A) Rod photoreceptor distribution map from Curcio et al. (1990) showing the rod ring near the eccentricity of the optic nerve head and rod hot spot superior to the optic nerve head and fovea. (B) Composite autofluorescence map of the RPE autofluorescence in donor eyes ≤ 51 years old. The symbols superimposed over these 2 maps are the mean and standard deviations of the FAF peak locations in our study along the 8 meridians (n = 17). Rod distribution and autofluorescence maps are adapted from Ach and 10

co-workers (2014) ...... 140

5.1. Location of FAF sampling. FAF intensities were sampled from four 5x5 pixel squares positioned along 8 cardinal meridians of the eyes. FAF peaks were identified along the 8 meridians. To study age- related change in autofluorescence, perifoveal grey level (GL) intensities were sampled in an annulus spanning the central 2000 – 3000µm of the retina...... 150

5.2. Scatterplot of spherical equivalent against axial length. Axial length increased as spherical equivalent decreased (Spearman’s ρ = -0.801, p < 0.0001) ...... 152

5.3. Age-related increase in perifoveal autofluorescence intensity of emmetropes (n = 30) and myopes (n = 31). FAF intensity was positively correlated with age and increased at similar rates in both emmetropes and myopes. The solid lines are exponential curve fits to the data ...... 154

5.4. Contrast sensitivity in (A) emmetropes and myopes, (B) short emmetropic (< 24mm) and long myopic (> 27mm) eyes, and (C) young and old eyes. (A) Myopes exhibited similar contrast sensitivity as emmetropes as spatial frequency increased (Dunn’s multiple comparisons test, p < 0.9). (B) Long myopic eyes had slightly poorer contrast sensitivity at all spatial frequencies tested compared to short emmetropic eyes, with the largest differences at 2 and 4cpd (Holm-Sidak’s multiple comparisons test, p > 0.2). (C) The youngest (18 – 20 years old) and oldest subjects (45 – 58 years old) had the greatest difference in sensitivity at 2cpd but no statistically significant differences were found at any of the spatial frequencies tested (Dunn’s multiple comparisons test, p > 0.9). The symbols and error bars represent the median and interquartile range ...... 156

5.5. Comparison of age-related increase in perifoveal autofluorescence with other studies ...... 161

6.1. Flow chart of number of participants recruited and with images available for analysis. *Data from 9 myopes were excluded from analysis due to poor image quality (n = 4), poor OCT scan quality (n = 1), poor fixation and photophobia to the excitation laser (n = 4) ... 178

6.2. Method of PAF sampling. Infra-red image from optic disc OCT scan (E) was aligned with the PAF image and 12 clockhour segment RNFL chart (F). (A) For the small square method, 2 to 4 sampling squares 5x5 pixel (59x59µm) in size were manually positioned on the autofluorescence image at the circumference of the RNFL scan circle in each segment. Major vasculatures were avoided as they were devoid of RPE autofluorescence. The medium (B) and large (C) square methods sampled PAF intensity using 31x31 and 51x51 pixels squares respectively. For these 2 methods, only 1 PAF sampling area was recorded for each of the 12 segments. (D) For the arc segment method, PAF intensities within each segment of an annulus around the optic disc were recorded. The outer border of the arc segment corresponded to the circumference of the scan circle (1.7mm radius) while the inner border was 1.1mm in radius. For the small square

sampling method, the corresponding RNFL thickness was determined from the TSNIT graph (G). For the other sampling methods, RNFL values from the clockhour segment RNFL graph were used (F) ...... 180

6.3. Example scatterplot from subject 63E showing correlation between PAF intensity and RNFL thickness. The numbers (1 - 12) beside each data point represent the RNFL clockhour. The arc segment method was used to obtain the autofluorescence signal in this example ...... 182

6.4. RNFL thickness profile in emmetropes and myopes along the clockhour segments. RNFL is thickest at the 7 and 11 o’clock segments and thinnest at the 3 and 9 o’clock segments. Myope RNFL was significantly thinner at 12, 1, 2, 5 and 6 o’clock segments compared to emmetrope (Kruskall-Wallis test, H(23) = 794.8, p < 0.0001). Symbols and error bars represent the median and interquartile range. Asterisks indicate statistical significance using Dunn’s multiple comparisons test (Table 6.3) ...... 184

6.5. Scatterplot of average RNFL thickness against axial length. RNFL decreases with increasing axial length (Spearman’s rho = -0.510, p < 0.0001) ...... 185

6.6. Scatterplots showing correlation between (A) average, (B) superior, (C) inferior, (D) nasal and (E) temporal RNFL thickness and age. An age-related decrease in RNFL thickness was observed in the superior, inferior and nasal regions, but not in the temporal one ...... 186

6.7. Coefficient of repeatability of PAF intensity sampled using different methods (n = 54). Bar and error bars show median and interquartile range. Kruskall-Wallis test returned significant differences in repeatability among the sampling methods (H(3) = 79.26, p < 0.0001). * indicates statistical significance ...... 187

6.8. Percentage of eyes showing significant correlation between PAF intensity and RNFL thickness using each sampling method (n = 39 emmetropes and 50 myopes, referred to as ‘Emm’ and ‘My’ in the bar graph) ...... 188

7.1. Flow chart of number of participants recruited and with images available for analysis ...... 207

7.2. Method of PAF sampling. (A) The original PAF image of subject 70E. (B) The infrared image from OCT scan, with the centre of the optic disc and 12 clockhour segments identified is superimposed over the PAF image in (A). (C) PAF intensities are sampled from 12 arc segments ( ) with an inner and outer radius of 1.1mm and 1.7mm 208

7.3. Method of perifoveal autofluorescence sampling. Perifoveal autofluorescence intensities were sampled in an annulus between 2 – 3mm eccentricity. Pixels corresponding to blood vessels > 80µm were excluded. The white cross in the centre represents the fovea ... 209

7.4. PAF profile of emmetropic and highly myopic eyes captured at detector sensitivity of 95 units before adjustment (A) and after adjustment (B) for RNFL attenuation. In the unadjusted PAF profile

of both emmetropic and myopic eyes (A), PAF intensity is lowest inferiorly-temporally (7 o’clock position) and highest nasally (3 o’clock position). There was no statistically significant difference in PAF intensity between emmetropes and myopes (Dunn’s multiple comparisons test, p > 0.5). After adjustment for RNFL attenuation (B), the compensated PAF profile is similar between emmetropes and myopes (Dunn’s multiple comparisons test, p > 0.9) and shows uniform intensity around the optic disc. Symbols and error bars represent the median and interquartile range. The orientation of the clockhour segments is showed as in a right eye ...... 212

7.5. Average PAF intensity at young (≤ 25 years), middle (26 – 39 years) and old (≥ 40 years) age. Emmetropes and myopes of the same age group had similar PAF intensities. Old emmetropes and old myopes had stronger PAF than their young counterparts (One-way ANOVA,

F(5,67) = 5.588, p = 0.0002). Bar and error bars represent the mean and standard deviation. * indicates statistically significant post-hoc Holm-Sidak’s multiple comparisons test...... 213

7.6. Age-related PAF increase in emmetropes (n = 35) and myopes (n = 38). Emmetrope and myope PAF increased at similar rates with age (z = -1.18, p = 0.238) ...... 214

7.7. Age-related increase in perifoveal and RNFL-corrected PAF intensity of the same subjects (n = 56; 28 emmetropes and 28 myopes). Rate of autofluorescence increase was greater in the perifovea than in the parapapillary region (z = -2.71, p = 0.007) ...... 215

List of Tables

1.1. Functions of the retinal pigment epithelium (Boulton and Dayhaw- Barker, 2001) ...... 22

1.2. Classification of myopic maculopathy proposed by the META-PM study (Ohno-Matsui et al., 2015) ...... 53

2.1. Preliminary data for the calculation of sample size used in chapters 5, 6 and 7 ...... 74

2.2 Inclusion and exclusion criteria for experimental work in chapters 5, 6 and 7 ...... 74

3.1. Demographic data of 7 subjects involved in this study. The data are arranged according to the magnitude of spectacle spherical equivalent . 103

4.1. Demographic data of subjects involved in this study. The data are arranged according to ethnicity. A total of 10 subjects (7 Chinese, 3 Indians) were imaged in Khoo Teck Puat Hospital (KTPH) and 7 in Manchester Royal Eye Hospital (MREH) (3 Chinese, 3 Caucasian, and 1 Pakistani). Asterisk * denotes subjects imaged at KTPH ...... 127

4.2. Average, standard deviation and coefficient of variation (CV) of FAF peak location and intensity obtained from 6 subjects. FAF peak locations are rounded to the nearest 50µm ...... 130

4.3. Location and intensity of FAF peaks at each meridian (n = 17). Data are expressed in mean and standard deviation, and rounded to the nearest 50µm. (T = temporal, ST = superior-temporal, S = superior, SN = superior-nasal, N1 = nasal before the optic disc, N2 = nasal beyond the optic disc, IN = inferior-nasal, I = inferior, IT = inferior- temporal) ...... 136

4.4. Location (mean ± SE) of FAF peaks identified in our study compared to results from fundus spectrophotometer (Delori et al., 2001) ...... 141

5.1. Median (range) age, axial length and spherical equivalent of the subjects involved in this study. There was no significant difference in age between the emmetropic and myopic groups ...... 152

5.2. Median (interquartile range) of FAF peak location (eccentricity rounded to the nearest 50µm) in emmetropic and myopic eyes. The FAF peak locations were similar in both refractive groups (Dunn’s multiple comparisons test, p > 0.9) ...... 153

5.3. Correlation analysis between axial length, spherical equivalent, age, perifoveal autofluorescence and contrast sensitivity at each spatial frequency (n = 61). No statistically significant results were found after Bonferroni correction ...... 158

6.1. List of inclusion and exclusion criteria ...... 176

6.2. Median (range) age, spherical equivalent and axial length of the

emmetropes and myopes ...... 183

6.3. Results of Dunn’s multiple comparisons test comparing emmetrope and myope RNFL thickness...... 184

6.4. Correlation coefficients using Spearman and Pearson correlation for subjects with non-normally distributed data. There was no marked difference in correlation coefficient for subjects 77M, 82M and 86M, with differences between the 2 analysis being < 0.05. Correlation coefficient increased by > 0.1 for 52M and 25M. However there was no difference in the conclusion of the analyses (whether a significant correlation exists or not) for all subjects except 24M. For subject 24M, significant negative correlation was found using Pearson correlation but not Spearman correlation ...... 191

6.5. Six myopic eyes and 1 emmetropic eye did not show correlation between PAF intensity and RNFL thickness using the arc segment method. The reasons for the lack of correlation could be attributed to Weiss ring (97M), parapapillary atrophy (53M, 88M), exceptionally thick inferior RNFL (24M) and RNFL thinning (52M). 67E and 89M did not exhibit any of those features, however superior PAF was unusually bright for the given RNFL thickness ...... 192

7.1. Study inclusion and exclusion criteria ...... 205

7.2. Median (range) age, spherical equivalent and axial length of the emmetropes and myopes. There was no significant difference in age between the emmetropic and myopic groups (Mann-Whitney test, p = 0.573). The myopic group had more negative spherical equivalent (Mann-Whitney test, p < 0.001) and longer axial length than emmetropic group (unpaired t-test, p < 0.001) ...... 211

List of Abbreviations

11-cis RDH 11-cis retinol dehydrogenase A2E N-retinylidene-N-retinylethanolamine ABCR Adenosine triphosphate binding cassette transporter AGEs Advanced glycation end products AMD Age-related macular degeneration ART Automatic real-time averaging AULCSF Area under the log contrast sensitivity function CNV Choroidal neovascularisation COR Coefficient of repeatability cpd Cycles per degree CRALBP Cellular retinaldyhyde binding protein CRBP Cellular retinoid binding protein CSCR Central serous chorioretinopathy cSLO Confocal scanning laser ophthalmoscope dB Decibels DHA Docosahexanoic acid FAF Fundus autofluorescence FLIO Fluorescence lifetime imaging ophthalmoscopy Fs Fuch's spot GL Grey level HRA Heidelberg retinal angiograph I Inferior IN Inferior-nasal IQR Interquartile range IR Infra-red IRPB Interphotoreceptor retinoid binding protein IS/OS Inner segment / outer segment IT Inferior-temporal KTPH Khoo Teck Puat hospital Lc Lacquer crack LRAT Lecithin retinol acyl transferase MREH Manchester royal eye hospital N Nasal NRPE N-retinylidine-phosphatidylethanolamine OCT Optical coherence tomography PAF Parapapillary autofluorescence PEDF Pigment epithelium-derived growth factor PPA Parapapillary atrophy qAF Quantified autofluorescence RNFL Retinal nerve fibre layer RPE Retinal pigment epithelium

S Superior SD Standard deviation SN Superior-nasal ST Superior-temporal T Temporal UWF Ultrawide-field VEGF Vascular endothelial growth factor

Abstract

Thesis Title: The Investigation of Autofluorescence in Myopes and Emmetropes

Author: Teresa SL Tee Year: 2018

Fundus autofluorescence (FAF) is a retinal imaging modality that captures autofluorescence signals from the posterior segment of the eye, particularly from the retinal pigment epithelium (RPE) and the associated lipofuscin. FAF signal can be obtained from all eyes but departures from normal intensity and distribution have been linked to dysfunction or atrophy of the RPE and photoreceptors. High myopia involves structural and functional changes in the retina and it was hypothesised that the axial elongation in high myopia would induce abnormalities in FAF. In this thesis, I set out to study the FAF signals in high myopia and compare it with emmetropia.

The thesis has 3 main aims. First, to understand how the FAF signal captured from young healthy eyes using confocal scanning laser ophthalmoscope (cSLO) is affected by different image acquisition settings and to document FAF distribution in young and healthy eyes. Second, to document/characterise FAF in emmetropes and high myopes, and relate it with contrast sensitivity. Third, to study the link between retinal nerve fibre layer (RNFL) and parapapillary autofluorescence (PAF) intensity and compare PAF between emmetropes and high myopes.

Preliminary experiments found that young emmetropic subjects with good fixation required as few as 25 scans for signal averaging during FAF imaging. There was a trend for standard deviation of sampled pixel intensities to decrease when 49 scans were used for averaging, especially for 55˚ images. The locations of FAF peaks were not affected by increasing the number of scans for signal averaging or when no photobleaching period was incorporated into the imaging protocol.

FAF along 8 cardinal meridians of the young and normal eye was described using 55˚ FAF images. FAF peaks were identified 2100 - 6000µm from the fovea. Data from the superior-nasal and nasal meridians confirmed the close spatial correspondence between FAF intensity and published data on rod photoreceptor and RPE lipofuscin distribution. FAF intensity and contrast sensitivity at low and moderate spatial frequencies were similar between emmetropes and high myopes. No correlation was found between FAF intensity and contrast sensitivity.

A method of sampling PAF was developed. Using this method, we found that RNFL significantly attenuated PAF signal. The PAF profile, adjusted for RNFL attenuation, was similar between emmetropes and high myopes. The rate of age-related increase in PAF was slower than perifoveal autofluorescence and this difference matched the lower phagocytic load of parapapillary RPE cells compared to perifoveal RPE cells.

The work presented in this thesis confirms the close spatial relationship between autofluorescence and rod distribution and describes a method to analyse PAF. This may ultimately open up more avenues of research and clinical applications of autofluorescence imaging in high myopia and optic neuropathies in the parapapillary region.

Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualiﬁcation of this or any other university or other institute of learning.

The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=24420), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The University’s policy on Presentation of Theses.

Acknowledgements

I would like to thank my supervisors, Dr Ivan Leung and Dr Ian Murray for their supervision and mentorship. Both have given me sincere feedback, encouragement and guidance. I am very grateful and consider myself very lucky to have such good mentors whom I can trust to give excellent advice for my professional and personal growth. I am also very thankful to Dr Tim Lam for his critical feedback and advice on my writing.

I must thank my collaborators at the University of Manchester, Manchester Royal Eye Hospital (MREH), Khoo Teck Puat Hospital (KTPH; Singapore) and Singapore Polytechnic. The research would not be possible without support from the staff at these institutions: Dr Jeremiah Kelly at the University of Manchester, for helping to develop the basic MATLAB code; the retinal imaging team at MREH for teaching me to use the HRA+OCT; Ophthalmologists (Dr Lekha Gopal and Dr Kumari Neelam), optometrists (Joanna, Caryn, Siqi, Iris, Jasmine, Sebastian) and imaging technician (Albert) at Khoo Teck Puat Hospital for their guidance, patience and company; my optometry colleagues and students at Singapore Polytechnic who helped recruit many of the research participants and were essential to the completion of the research. I am also very thankful to the participants, who endured uncomfortable pupillary dilation, the glare from autofluorescence imaging, and the frustration of contrast sensitivity measurement.

Last but not least, I thank my family and Fiancé, Brian, for this opportunity to grow and for their unwavering support during these challenging years. Words cannot express my gratitude and appreciation for all they have done for me.

Chapter 1. Fundus Autofluorescence (FAF) and Myopia

Fundus autofluorescence (FAF) originates from retinal pigment epithelium (RPE) lipofuscin. The intensity of the autofluorescence signal is closely related to photoreceptor and RPE health. This makes FAF imaging a useful imaging modality for evaluating retinal health and, to an extent, retinal function. Although there are many studies describing the use of FAF in diseased eyes, most of them were qualitative in nature (Oishi et al., 2013, Dinc et al., 2011, Asli Dinc et al., 2009, Bindewald et al., 2005, Cohen et al., 2016).

High myopia is a healthcare concern because of the increased risk of blindness in myopic degeneration. Yet FAF imaging of highly myopic eyes before the onset of pathological changes has not been undertaken. Knowledge of autofluorescence in high myopes may help us understand the pathophysiology of myopic degeneration.

In this chapter, we will discuss the origin of RPE lipofuscin, FAF imaging, and the clinical applications of FAF imaging in ocular disease. We will also discuss pathological myopia and its associated FAF changes, as well as contrast sensitivity in aging and high myopia.

1.1. Retinal Pigment Epithelium (RPE) Lipofuscin

The RPE is found between the neurosensory retina and choroid and plays a vital role in maintaining retinal health and supporting vision (Table 1.1) (Strauss, 2005, Boulton and Dayhaw-Barker, 2001), such as by participating in the phagocytosis of shed photoreceptor outer segments and the recycling of visual pigment. Each foveal RPE cell supports 23 cones while each equatorial RPE cell at 13mm eccentricity supports 22 rod photoreceptors (Gao and Hollyfield, 1992). This presents a heavy phagocytic load to each RPE cell. In the process of performing these vital functions for vision, lipofuscin is formed within the RPE cell.

Functions of the Retinal Pigment Epithelium (RPE)  Regulate the transport of ions, fluids and metabolites between the retina and choroid by forming the blood-retina barrier  Participate in the visual cycle by transporting, storing and recycling retinoids from the photoreceptors  Phagocytose photoreceptor outer segment tips  Decrease oxidative damage to the retina by reducing incident light energy through melanin pigment screening and by providing antioxidants to neutralise reactive oxygen species  Absorb stray light in the retina  Maintain choroid and retina health by secreting various growth factors (e.g. pigment epithelium-derived factor (PEDF) and vascular endothelial growth factor (VEGF)

Table 1.1. Functions of the retinal pigment epithelium (Boulton and Dayhaw-Barker, 2001).

Lipofuscin is found in a variety of postmitotic cells including cardiac myocytes, neurons and hepatocytes (Wing et al., 1978, Brunk and Terman, 2002).

Figure 1.1. Autofluorescence image of RPE flat mount from a 40-year-old eye showing yellow-green lipofuscin granules. Excitation wavelength was 460 - 490nm and emission was recorded at wavelengths > 505nm. Figure modified from Ach et al. (2014).

Lipofuscin in the RPE exists as yellow-brown deposits 1 - 2µm in diameter (Figure 1) and may rarely aggregate into larger granules in older eyes (Han et al., 2006). RPE lipofuscin granules are encapsulated within the lysosomal system of the cell (Sparrow

22 and Boulton, 2005) and located in the basal and central parts of the cytoplasm (Weiter et al., 1986). As the volume of RPE lipofuscin increases, the granules gradually occupy the apical portions of the cell as well (Wing et al., 1978, Roth et al., 2005).

1.1.1. The Formation of RPE Lipofuscin

Non-retinal sources of lipofuscin are primarily derived from autophagy of damaged macromolecules and cellular organelles (Kennedy et al., 1995, Terman et al., 2007). RPE lipofuscinogenesis is similar, but with photoreceptor outer segment phagocytosis being a major contributor to the formation of lipofuscin.

The Visual Cycle

Light perception begins at the photoreceptors, where photopigments convert light energy into electrical signals in a process known as phototransduction. Photopigments are 11-cis-retinal molecules covalently bonded to opsin, which is a signalling protein on the photoreceptor disc membrane. Photopigments in rods and cones are known as ‘rhodopsin’ and ‘cone pigments’ respectively. The visual cycle refers to the recycling of all-trans retinal, released from photopigment when a photon of light is absorbed, to 11-cis retinal, which is required for photopigment regeneration (Figure 1.2) (Saari, 2000).

When a molecule of photopigment absorbs a photon, it is photoisomerized into all- trans-retinal. All-trans retinal is released from opsin and binds with phosphatidylethanolamine to form N-retinylidine-phosphatidylethanolamine (NRPE). Adenosine triphosphate binding cassette transporter (ABCR) transports NRPE to the cytoplasmic disc and releases NRPE into the cytoplasm as all-trans retinal. In the cytoplasm, all-trans retinol dehydrogenase reduces all-trans-retinal to all-trans-retinol (vitamin A). All-trans retinol then leaves the photoreceptor outer segment, binds to interphotoreceptor retinoid binding protein (IRBP), and crosses the interphotoreceptor space to the RPE (Saari, 2000).

In the RPE, 3 enzymes catalyse the conversion of all-trans retinol back to 11-cis retinal (Wang et al., 2012). When 11-cis retinol enters the RPE cell, it is taken to lecithin retinol acyl transferase (LRAT) by cellular retinoid binding protein (CRBP). Lecithin retinol acyl transferase (LRAT) esterifies all-trans retinol with phosphatidyl choline in 23 the membrane to generate all-trans retinyl esters. Next, RPE65 hydrolyses and isomerizes all-trans retinyl esters into 11-cis retinol. 11-cis retinol binds with cellular retinaldyhyde binding protein (CRALBP), and is taken to 11-cis retinol dehydrogenase (11-cis RDH). 11-cis RDH oxidizes 11-cis retinol to 11-cis retinal, which is then transported by IRBP back to the photoreceptor outer segment. 11-cis-retinal binds with opsin to reform visual pigment (Saari, 2000).

Figure 1.2. The classic visual cycle in the human eye. Figure modified from Wang et al. (2012).

There is also evidence of a cone-specific visual cycle (Wang and Kefalov, 2011). However because it does not involve the RPE, it does not contribute to lipofuscinogenesis. It is known that cone photoreceptors regain sensitivity after photobleaching at a much faster rate than rods. Visual pigment recycling via the RPE is too slow to be responsible for the quick recovery of cone sensitivity. A series of experiments revealed that Muller cells were capable of recycling the cone visual pigment, although all the enzymes involved in this process have yet to be determined. Wang and Kefalov (2011) summarised the cone-specific visual cycle as such: light exposure reduces all-trans retinal to all-trans retinol, which then moves away from the cone photoreceptors and enters Muller cells. In the Muller cells, all-trans retinol binds with CRBP and is isomerised into 11-cis retinol by Isomerase II. 11-cis retinol is returned to the cone photoreceptor. Within the cone photoreceptor, 11-cis retinol is oxidised to 11-cis retinal, which then combines with opsin to form visual pigment.

A number of studies have shown that the visual cycle contributes to lipofuscinogenesis. Katz and colleagues (1986a) observed that rats that suffered from early retinal degeneration accumulated less RPE lipofuscin with age compared to normal rats. Thin-layer chromatographic analysis of the degenerated photoreceptors revealed that they contained autofluorescent compounds similar to those found in age-related RPE lipofuscin of the normal rats. This implied that some of the components of RPE lipofuscin must have originated from the photoreceptors. In addition, the degeneration of photoreceptors meant a reduced phagocytic load for the RPE and hence reduced lipofuscin formation.

Katz and co-workers (1986b) also tested a different approach to the study of lipofuscinogenesis by dietary manipulation of albino rats. The rats were fed a diet that resulted in retinoid deficiency. As a result, photoreceptor outer segment production and subsequent phagocytosis was decreased (Katz et al., 1992) thereby reducing lipofuscinogenesis.

Besides manipulating the rate of phagocytosis, disrupting the normal lysosomal digestion of the phagosome using intravitreal injections of lysosomal protease inhibitor also resulted in accelerated accumulation of lipofuscin-like granules in the RPE of albino rats (Katz and Shanker, 1989).

Wassell and Boulton (1997) incubated bovine rod outer segments with either vitamin A (retinol) or retinal. They found that chloroform-soluble fluorophores comparable to those extracted from human lipofuscin were formed only in the presence of both lipids and retinal but not with retinol. The long-wavelength emitting fluorophores similar to lipofuscin only formed under neutral pH conditions. Since lipofuscin is encapsulated in the acidic environment of lysosomes, the fluorophores of lipofuscin must be present with the other components of lipofuscin before the phagosomes fused with lysosomes in the RPE.

Studying the spectral properties of lipofuscin, Boyer and colleagues found that 11-cis– retinal in photoreceptors was a key compound in lipofuscin, as the characteristic orange autofluorescence of lipofuscin (610nm) was only detected in mice with 11-cis- retinal (Boyer et al., 2012). In comparison, autofluorescence from RPE65-/- mice, which did not contain 11-cis-retinal, had shorter emission wavelength of 550nm. These experiments supported the role of photoreceptor and the visual cycle in the formation of lipofuscin.

Cellular Autophagy

Aside from the recycling of visual pigment, autophagy of cellular organelles and macromolecules also play a small role in lipofuscinogenesis. Autophagy is the process by which cells and cell components are delivered to lysosomes for degradation (Levine et al., 2011). Human RPE cells maintained in culture for up to 2 years accumulated lipofuscin even though no phagocytosis of photoreceptor outer segment took place. Although lipofuscin was observed, the autofluorescence of these autophagy-derived lipofuscin differed from those obtained from donor eyes. It was likely that autophagy- derived lipofuscin comprised damaged cellular organelles such as mitochondria, whose spectral characteristics differed from photoreceptor retinoids involved in ‘normal’ lipofuscinogenesis (Burke and Skumatz, 1998).

Oxidative Damage

The photoreceptor outer segment contains abundant polyunsaturated fatty acids. Exposure to bright light induces oxidative damage in photoreceptor outer segments by causing build-up of all-trans-retinal. Excessive all-trans-retinal may be detrimental to the retina because it is a potent photosensitizer and generator of free radicals (Chen et al., 2012). Products of lipid peroxidation, such as malondialdehyde, 4-hydroxynonenal (Kaemmerer et al., 2007), and advanced glycation end-products (Shamsi and Boulton, 2001, Wassell et al., 1999, Schutt et al., 2003) have been found in RPE lipofuscin. These products bind with proteins and ultimately inhibit protein degradation, leading to lipofuscin accumulation.

Strong evidence between oxidation and lipofuscinogenesis was also presented in animal models where oxygen and antioxidant levels were manipulated. When rabbit and bovine RPE cell cultures fed photoreceptor outer segments were kept in 40% oxygen-rich environments, lipofuscinogenesis was enhanced compared to cell cultures kept in 8% oxygen environment (Nilsson et al., 2003). Addition of antioxidant alpha- tocopherol (vitamin E) to the cell cultures reduced lipofuscinogenesis by 67% and 41% in rabbit and bovine RPE respectively by scavenging free radicals.

Katz et al. (1984) compared RPE lipofuscin from albino rats fed vitamin E-deficient diets with control rats. The study showed that vitamin E deficiency increased lipofuscin accumulation in the RPE compared to controls. The spectral properties of RPE

26 lipofuscin from the vitamin E-deficient rats were comparable to the control rats, suggesting that they may have similar composition as lipofuscin from control rats.

Zinc is found in melanosomes and participates in antioxidant enzymatic reactions, visual cycle, and the digestion of photoreceptor outer segment in the RPE. Zinc deficiency was observed to increase lipofuscinogenesis in adult Long Evans rats (Julien et al., 2011). The authors hypothesized that the cause of increased lipofuscin zinc deficient rats was due to increased oxidative damage and subsequent inhibition of lysosomal digestion of phagosomes in the RPE. More recently, elevated RPE lipofuscin was reported in rhesus monkeys fed diets deficient in the antioxidants lutein and zeaxanthin (McGill et al., 2016).

1.1.2. Autofluorescent Properties of RPE Lipofuscin

RPE lipofuscin has autofluorescent properties, which means that when it is irradiated by short wavelength light, it absorbs the energy and releases it as fluorescence of a longer wavelength. RPE lipofuscin will absorb wavelengths between 300 – 600nm (Figure 1.3) (Sparrow et al., 2010). With longer excitation wavelengths, the emitted autofluorescence shifts towards longer wavelengths and decreases in intensity. This broad emission spectrum reflects the diverse composition of RPE lipofuscin; ten fluorophores with emission wavelengths ranging from short to long have been detected in chloroform-soluble fractions of RPE lipofuscin (Eldred and Katz, 1988).

Figure 1.3. Emission spectra of whole RPE lipofuscin granules when irradiated with 330nm, 436nm, 480nm and 545nm light. As the wavelength of the excitation light increases, the peak emission also shifts towards longer wavelengths. The numbers above each curve are the peak emission wavelengths for each of the excitation wavelengths used. Figure modified from Sparrow et al. (2010).

1.1.3. Biochemical Properties of RPE Lipofuscin

Isolated lipofuscin granules generate singlet oxygen, superoxide anion, hydrogen peroxide, and lipid hydroperoxides when exposed to light (Rózanowska et al., 2002, Gaillard et al., 1995, Rózanowska et al., 1995). The generation of free radicals is enhanced with light exposure from 400 - 520nm (Boulton et al., 1993). Free radicals damage mitochondrial DNA and reduce the mitochondria’s ability to carry out oxidative phosphorylation (Terman et al., 2007).

Lipofuscin can promote docosahexaenoic acid (DHA) peroxidation in the dark, demonstrating an alternative mechanism of damage other than by a photochemical process (Dontsov et al., 1999). The authors suggested that catalysts of DHA peroxidation, such as labile hydroperoxides and metal ions within lipofuscin, are responsible for this dark reactivity.

N-retinylidene-N-retinylethanolamine (A2E) is a fluorophore in RPE lipofuscin with several potentially cytotoxic properties. A2E has a detergent-like effect on cell membranes and disrupts the proton gradient over the lysosomal membrane (Sparrow et al., 1999), leading to alkalinisation of lysosomes and deactivation of acid hydrolases 28

(Holz et al., 1999, Eldred and Lasky, 1993). Loss of the proton gradient over the lysosomal membrane leads to plasma membrane blebbing and leakage of lysosomal enzymes. Enzyme leakage is a potentially fatal event for the cell (Wihlmark et al., 1997).

1.1.4. RPE Lipofuscin and Aging

Lipofuscin granules have been observed in the RPE as young as 16 months of age. By age 30, the lipofuscin granules clump together and occupy the cytoplasm throughout the cell. By the eighth decade of life, lipofuscin granules may occupy one fifth of the RPE cell’s cytoplasmic volume (Feeney-Burns et al, 1984).

Quantification by fluorescence microscopy and microfluorometry confirmed that the lipofuscin content of RPE cells increases with age (Wing et al, 1978). Ex vivo analysis of RPE lipofuscin autofluorescence also support this (Ach et al., 2014). In vivo autofluorescence imaging revealed a linear increase in autofluorescence up to age 70 (Delori et al., 2001, Greenberg et al., 2013). Between 70 - 80 years, the autofluorescence intensity decreases. This reduction in FAF is hypothesized to be due to photoreceptor dropout starting in the sixth decade of life, thereby reducing RPE phagocytic load and decreasing the rate of lipofuscin accumulation (Okubo, 1999).

Age-related increase in RPE lipofuscin is most significant around the macula where peak autofluorescence is found (Ach et al., 2014). Delori et al. (2001) reported greatest rates of autofluorescence increase temporally and superiorly, with slowest rates inferiorly. The increase in autofluorescence takes place in the absence of significant change in RPE cell density (Ach et al., 2014).

1.1.5. RPE Lipofuscin in Disease

RPE lipofuscin is frequently seen preceding or in conjunction with the onset of retinal disease. Elevated levels of RPE lipofuscin have been reported in diseases such as age- related macular degeneration (AMD), Stargardt’s, and Best’s disease. Abnormal FAF is associated with visual dysfunction as reported in various clinical studies (see section 1.2.7. Applications of FAF Imaging). The close spatial and temporal relationship between retinal degeneration and the appearance of lipofuscin had led some scientists to believe that RPE lipofuscin may have a role in the pathogenesis of retinal diseases

(Kennedy et al., 1995, Chen et al., 2012). The biochemical properties highlighted above suggested that lipofuscin may harm the retina. As a result, there has been research into therapeutic interventions to reduce lipofuscin accumulation and to remove lipofuscin from the retina (Nociari et al., 2017).

Despite the collection of papers that lend support for the damaging role of lipofuscin and A2E in the retina, most of them were in vitro studies. Recent studies suggest that they may not be as consequential as initially believed. In vivo and histopathologic studies found poor correspondence between the location of maximal age-related increase of autofluorescence and the location of maximal age-related loss of rod photoreceptors (Figure 1.4) (Delori et al., 2001, Ach et al., 2014). Rod loss was concentrated in an area just adjacent to the fovea whereas the peak of age-related increase in lipofuscin occurred at a more eccentric location. Instead, Ach et al. hypothesized that the accumulation of lipoproteins in the Bruch’s membrane under the macula was more likely to harm the retina by inhibiting the transport of materials between the RPE and choroid.

Figure 1.4. RPE lipofuscin autofluorescence and rod photoreceptor distribution along the vertical meridian is plotted for young and old eyes. Dotted vertical lines mark the macula. The arrows mark locations where the largest increase/decrease in autofluorescence intensity and rod loss occurs. RPE autofluorescence increases the most near the perifoveal rod ring and the foveal cone peak. Maximal rod loss corresponds to an area where there is most accumulation of lipoproteins in the Bruch’s membrane (yellow). Figure from Ach et al. (2014).

Studies on A2E, one of the components obtained from RPE lipofuscin, also suggest that it may not be as harmful in vivo as the in vitro studies imply. First, the spatial distribution of A2E in the retina does not match RPE autofluorescence. A2E levels peak in the retinal periphery and decrease towards the fovea (Ablonczy et al., 2013, Adler et al., 2015) whereas RPE autofluorescence peaks in the macula and decreases with eccentricity. There is also evidence that A2E, being contained in the lysosomal compartment of the cell, is not able to cause oxidative damage to the RPE. In fact, A2E may play a role in regulating lipofuscin levels by triggering autophagy (Saadat et al., 2014, Zhang et al., 2015, Perusek et al., 2015). Saadat et al. (2014), Perusek et al. (2015) and Zhang et al. (2015) incubated human RPE cell culture in low levels of A2E (comparable to in vivo levels). All 3 studies reported that short-term exposure to A2E stimulated autophagy in human RPE cell culture. Autophagy may protect RPE cells by reducing the inflammatory reaction that would otherwise occur in the presence of A2E (Zhang et al., 2015).

In a clinical study of AMD, Gliem et al. (2016) found that instead of increased autofluorescence, early and intermediate AMD patients had normal to subnormal FAF compared to normal controls (Gliem et al., 2016). These findings suggest that RPE lipofuscin may not be a major factor in AMD.

1.2. FAF Imaging

FAF imaging is a relatively new imaging modality that captures autofluorescence signals from RPE lipofuscin. It is also known as short-wavelength autofluorescence because the excitation wavelengths used for image capture are in the 400 – 600nm region. FAF can be captured clinically using a confocal scanning laser ophthalmoscope (cSLO) or fundus camera equipped with an FAF filter.

Autofluorescence from the fundus can also be studied using fluorescence lifetime imaging ophthalmoscopy (FLIO). FLIO provides information on how long a fluorophore remains in the excited state once it is exposed to excitation light (i.e. the time from absorbance of excitation light to the time it releases the energy in the form of autofluorescence) (Dysli et al., 2017). FLIO is different from conventional FAF imaging because the autofluorescence data is time and space resolved. This allows different fluorophores with overlapping autofluorescence spectra to be identified based on their fluorescence lifetimes.

1.2.1. Confocal Scanning Laser Ophthalmoscope (cSLO)

The Heidelberg retinal angiograph (HRA and Spectralis HRA+OCT) (Heidelberg Engineering, Germany) is a cSLO frequently used for FAF imaging. A low-powered 488nm excitation laser is scanned across the fundus. The scan pupil is 3mm in diameter while the detection pupil, where autofluorescence from the fundus is captured, is 6mm in diameter. Autofluorescence signal from the retina passes through a barrier filter, which transmits light from 500 – 680nm, and the confocal pinhole to minimise autofluorescence from non-retinal sources like the crystalline lens before finally reaching the detector (Figure 1.5).

Figure 1.5. Schematic diagram of confocal scanning laser ophthalmoscope. Image modified from Delori et al. (2011) .

FAF is 2 orders of magnitude less intense than fundus fluorescein angiography (Schmitz-Valckenverg and Fitzke, 2009). The HRA+OCT adopts 2 methods to obtain a clinically interpretable image. Firstly, detector gain (or detector sensitivity, as it is known in the Heidelberg cSLO) is set at an adequately high enough level to boost the detected autofluorescence signal. Detector sensitivity can be adjusted manually, but the operator must be careful not to set the sensitivity too high as that will saturate the pixels in the image and give the image a ‘washed out’ appearance. Pixel intensity should ideally be kept below 175GL in order to remain within the linear operating range of the detector (Delori et al., 2011). Signal-to-noise ratio can be increased by signal averaging. The HRA+OCT can perform automatic real-time (ART) averaging to average 2 - 100 individual FAF scans to form a single image.

The HRA+OCT has 2 acquisition modes: high-resolution and high speed. High- resolution scanning speed is 4.7 frames/second and provides a resolution of 5µm/pixel. High-speed scanning is performed at 8.8 frames/second at a resolution of 10µm/pixel. High-resolution images, with dimensions of 1536x1536 pixels, are considerably larger than high-speed images (768x768 pixels). The advantage of using high-speed mode is the shorter acquisition time. Imaging duration is an important 32 factor in FAF imaging as the excitation laser is bright and patients may struggle to maintain fixation for extended periods of time.

The HRA+OCT has several objective lenses for imaging. Non-contact objective lenses offer 30°, 55° or 105° fields of view. As field of view increases, the distance between the cSLO and the patient’s cornea decreases. Another accessory lens, the Staurenghi contact lens, permits imaging fields of 150°.

Normalization is the process of changing the pixel intensity range of an image (histogram stretching). Normalisation is usually performed to improve contrast of the images (Figure 1.6), especially when signal strength is very low. However, normalisation means that the autofluorescence intensities from different retinal areas of the same image cannot be compared directly.

Another instrument for FAF imaging is the ultra-widefield (UWF) scanning laser ophthalmoscope (such as the Daytona by Optos PLC, UK). The UWF uses 532nm laser for excitation and detects autofluorescence signals between 570 – 780nm (Nagiel et al., 2016). The main advantage of UWF imaging for FAF is the large field-of-view (200˚) acquired within half a second, reduced absorbance of excitation laser by nuclear sclerosis (Spaide, 2003), and reduced macular pigment masking of foveal autofluorescence due to the longer excitation wavelength used for imaging. This allows foveal FAF changes, which would otherwise be masked by macular pigment in 488nm imaging, to be detected. The trade-off is the relatively lower resolution of the images of 20µm (or 15µm using ResMax imaging). UWF imaging has lower signal-to-noise ratio as signal averaging is not performed. These can reduce the detection ability of small or subtle FAF changes from UWF images. The periphery and mid-periphery of the UWF images are also prone to imaging artefacts, especially from the eye lid and lashes.

Figure 1.6. FAF image and corresponding image histogram of a A) normalised and B) non-normalised HRA+OCT autofluorescence image of the same eye. In the normalised image, the blood vessels and optic disc appear darker than in the non-normalised image. Both histograms show a small plateau in the lower grey level region followed by a peak in the vicinity of 100GL. However, the peak from the non-normalised image is higher than in the normalised image. The range of grey level intensities is narrower in the non-normalised image compared to normalised image.

1.2.2. Quantitative Assessment of FAF Images

Most studies have focused on reporting FAF qualitatively due to the lack of commercial instruments that allow quantitative FAF imaging until recently. These studies described autofluorescence as increased/hyper, decreased /hypo or normal/isoautofluorescent (see section on Applications of FAF Imaging). The problem with purely qualitative analysis of FAF is that 1) it is susceptible to error due to limitations of human visual perception and 2) it may also lack objectivity.

Delori et al. (2011) developed quantitative FAF imaging by modifying a HRA by inserting a calibration reference on the field stop inside the cSLO. This fluorescent 34 reference appears at the top edge of the image (Figure 1.7). The reference allows GL intensities to be adjusted accordingly for variations in image capture settings, thereby allowing comparison of absolute/quantified autofluorescence (qAF) levels between FAF images.

Figure 1.7. Macula 30° FAF image from Delori et al. (2011). The rectangular white bar at the top margin of the image is the internal fluorescent reference. It is used to adjust for image capture setting variations in order to obtain quantified autofluorescence (qAF) values. The white lines indicate the regions of the image analysed in their study. The cross (+) and square (□) represent the maximum and minimum intensities in the image.

Quantified autofluorescence is not available for imaging using wide-field lenses like 55° or 150°. The HRA with internal autofluorescence reference was only available commercially in 2016.

Prior to the development of a true quantitative method to measure autofluorescence (Delori et al., 2011), researchers would attempt to obtain a degree of quantitative analyses by normalising FAF intensities to an area where FAF is expected to be absent (such as the optic disc), or by expressing FAF intensities as ratios relative to another part of the image, as described below.

Lois et al. (1999) performed semi-quantitative analysis of FAF in normal and diseased eyes. Using a Zeiss cSLO, they recorded FAF images by averaging 32 scans to an image. They compared relative levels of autofluorescence by subtracting the GL intensity at a 16x16 pixel square located 7 - 15° temporal to the fovea (‘region of interest’) from the darkest pixel intensity within the optic disc (‘background’ AF

35 intensity). Intraoperator repeatability was high; however interoperator repeatability was comparatively poorer, since the region of interest was not a fixed location and varied between operators.

Lois et al. (2000) later quantified autofluorescence along the horizontal meridian of the macula using the same cSLO. Grey level intensities were extracted from a 10-pixel tall by 750-pixel wide area of the image to generate an FAF profile after normalising the GL intensities to the optic disc. A similar approach was adopted by Huang and co- workers (2015 ARVO ABSTRACT IOVS June 2015, Vol.56, 590), who normalised the autofluorescence signal over the optic nerve (including the myopic crescent) to 0 and adjusting a small area of retina temporal to the optic nerve accordingly. They found this method to be reliable and repeatable for comparing FAF intensities from a preselected zone between images captured using fundus camera. The disadvantage of normalising autofluorescence intensities to the optic disc is that autofluorescence within the optic disc is not uniform. Pathological changes and floaters over the optic disc (e.g. Weiss ring or Bergmeister’s papilla) could also alter autofluorescence intensity. In addition to variation in autofluorescence intensity as a result of optic disc cupping and blood vessels, presence of optic disc drusen, pigmentary changes, parapapillary atrophy (PPA) and myopic crescents would usually be hypoautofluorescent.

Shin et al. (2016) measured FAF intensity in central serous chorioretinopathy (CSCR) with serous retinal detachment. They calculated the amount of hyperautofluorescence associated with the serous retinal detachment by comparing it with the autofluorescence intensity from the fellow normal eye. They expressed the hyperautofluorescence intensity of the eye with CSCR using the formula:

퐹퐴퐹 표푣푒푟 푠푒푟표푢푠 푟푒푡𝑖푛푎푙 푑푒푡푎푐ℎ푚푒푛푡 ( 퐹퐴퐹 표푓 ℎ푒푎푙푡ℎ푦 푟푒푡𝑖푛푎 푎푡 10° 푓푟표푚 푓표푣푒푎 퐹퐴퐹 𝑖푛 푡ℎ푒 푠푎푚푒 푟푒푔𝑖표푛 표푓 푡ℎ푒 푓푒푙푙표푤 푒푦푒 − ) 푥 100 퐹퐴퐹 표푓 ℎ푒푎푙푡ℎ푦 푟푒푡𝑖푛푎 푎푡 10° 푓푟표푚 푓표푣푒푎 표푓 푓푒푙푙표푤 푒푦푒

This formula assumes that autofluorescence intensities are symmetrical between the 2 eyes. The authors avoided direct comparison of the actual intensity ratios by categorising the FAF intensity changes into ‘reduced’ (< -10GL difference), ‘iso’ (-10 to +10GL difference), ‘mildly increased’ (> +10 to +90GL) or ‘intensely increased’ (> 90GL) for evaluation. In this way, they reduced the subjectivity of categorising the FAF intensity. However, the limitation of this method is that a high level of operator skill is

36 needed so that images of the fellow eye are consistently captured, especially when studying eyes with mild autofluorescence changes.

Bellmann et al. (2003) quantified FAF intensities by normalising them to an external fluorescent reference in order to compare FAF intensities among different cSLOs (Bellmann et al., 2003). However, they acknowledged that this method would not allow comparison of FAF intensities between images if the image capture settings were inconsistent. This method of quantification therefore did not allow autofluorescence intensities to be compared with other studies.

1.2.3. Sources of Error and Asymmetry in FAF Imaging

Small amounts of non-uniformity in image intensities have been documented in 30° FAF images captured by the HRA (Greenberg et al., 2013). These asymmetries in the intensity of the FAF image were smallest centrally and increase towards the periphery. They may be caused by asymmetry in the optics of the eye, curvature of the retina / image plane, or minor misalignment of camera optics (Delori et al., 2011). An experienced operator should be sensitive to these sources of error to minimize asymmetry. In qualitative analysis of the images, these non-uniformities are unlikely to affect the interpretation of the FAF image. However, non-uniformity has not been studied in images with larger fields of view such as 55° images.

Asymmetry in the FAF intensities may also be induced by poor tear film, camera focus and camera alignment with the eye (Delori et al., 2011). Accurate camera focus is essential in FAF imaging due to the confocal optics of the cSLO, which cut off light originating from outside of the focus plane. Camera defocus decreases recorded autofluorescence intensity (Greenberg et al., 2013), although we can expect the reduction to be equal along the image plane. Lastly, the cSLO must be positioned well to avoid being blocked by the iris and minimise asymmetries in image brightness. Adequate pupillary dilation is needed to ensure the patient’s own pupil does not block the autofluorescence signal from the eye. Larger pupil size is associated with brighter recorded autofluorescence (Greenberg et al., 2013). The camera should also be positioned at a distance from the eye that minimises vignetting (darkening of the image towards the periphery).

1.2.4. Patient-based Factors Affecting the Autofluorescence Image

Unbleached photopigment reduces the intensity of captured autofluorescence by absorbing excitation light of 488nm and 532nm. Delori et al. (2011) recommended a bleaching period of 20 to 30 seconds for rhodopsin when using the HRA cSLO to capture 30° images for quantitative FAF analysis. For wider angle images, a longer bleaching period may be necessary because the energy from the laser is scanned across a larger retinal area. As a result, the amount of laser energy that bleaches a single photoreceptor is reduced.

The low FAF signal is especially vulnerable to obstruction by media opacities such as corneal scars, cataracts and vitreous floaters (Sepah et al., 2014) (Figure 1.8). Decreased light transmission of the crystalline lens has been reported to significantly affect FAF intensity such that in cSLO-captured FAF images, age-related increase in FAF intensity plateaued at age 50 (Greenberg et al., 2013).

Figure 1.8. Optic-disc centred FAF image showing an oval artefact superior- nasal to the optic nerve caused by a Weiss ring (red arrows).

The relationship between refractive error and image magnification is well-documented; the more myopic the eye, the larger the field-of-view (Parthasarathy and Bhende, 2015, Littmann, 1992). In a study using HRA-captured FAF images, axial length was negatively correlated with FAF intensity because the incident laser energy was spread over a larger area of retina (Delori et al., 2011). Refractive error and keratometry data should be entered before image capture so that the images are accurately scaled for quantitative study, such as the monitoring of lesion size over time.

Ethnicity affects the intensity of FAF (Greenberg et al., 2013). Whites exhibited strongest autofluorescence, followed by Indians, Hispanics, blacks and Asians. The reason for this is yet unknown, but the authors did not believe the difference was related to the optical screening properties of melanin. The authors hypothesised that genetic or nutritional factors may play a part. The role of genetics in the racial difference in RPE lipofuscin has not been studied.

Female gender was associated with stronger FAF than males (Greenberg et al., 2013), as was smoking habit. Smokers may have stronger FAF than non-smokers (Wang Y., et al., IOVS 2017;58:ARVO E-Abstract 4853). Greenberg et al. (2013) failed to find a significant difference but this could possibly be due to the small number of smokers in their study. Stronger FAF in smokers may indicate greater oxidative damage in the retina, resulting in a higher rate of lipofuscinogenesis and stronger FAF.

As FAF images captured using cSLO undergo signal averaging, stable fixation is necessary during image capture. Poor fixation stability is associated with weaker recorded FAF (Greenberg et al., 2013).

1.2.5. Autofluorescence of the Normal Fundus

There are many endogenous fluorophores in the cellular structures of the eye, such as advanced glycation end products (AGEs), oxidized flavinadenindinucleotide and collagen type 2 (Schweitzer et al., 2007). When these fluorophores are excited by 470nm laser, peak emission wavelengths are in the 523nm region so they are potential contributors of autofluorescence in FAF images. Melanin is another fluorophore but it has a peak excitation wavelength at 787nm (near infra-red). It therefore contributes little to the observed autofluorescence during FAF imaging. In fact, melanin pigments found in the apex of RPE cells absorb excitation laser so that underlying RPE lipofuscin receives less energy (Delori et al., 2001).

Regardless of the variables affecting the FAF image, FAF in the normal eye will exhibit the following characteristics: Hypoautofluorescence is seen over areas where RPE lipofuscin is absent or when an opacity blocks the FAF signal from reaching the detector. In the eye, blood vessels and blood appear strongly hypoautofluorescent because they block excitation light from reaching the underlying RPE lipofuscin. The optic disc is hypoautofluorescent because there is no RPE and hence no lipofuscin. However, connective tissue such as collagen

39 and elastin may exhibit mild autofluorescence within or around the disc (Schweitzer et al., 2007, Laemmer et al., 2007). This results in different levels of autofluorescence intensity within the disc itself (Figure 1.9).

Figure 1.9. Autofluorescence and colour photographs of an optic disc with (A) shallow and (B) deep cupping. Autofluorescence intensity in the optics disc is not uniform, with mild autofluorescence visible in discs with deep cups and intense hypoautofluorescence corresponding to blood vessels.

The fovea has less RPE lipofuscin than surrounding retina and displays weaker autofluorescence (Wing et al., 1978, von Rückmann et al., 1995, Delori et al., 1995, Ach et al., 2014, Delori et al., 2001). The foveal autofluorescence profile (within 4.5° eccentricity) may show a sharp dip or a gentle slope among individuals (Delori et al., 2001). Delori et al. (2001) measured foveal autofluorescence to be 39% weaker than at 7° temporal and also reported slower rate increase in autofluorescence in the fovea than at the temporal location (Delori et al., 2001). The lower foveal autofluorescence may, in part, be contributed by melanin pigment screening. Melanin distribution peaks in the fovea and decreases with eccentricity (Weiter et al., 1986), and therefore could attenuate autofluorescence relatively more in the fovea than elsewhere in the macula (Delori et al., 2001). Aside from melanin pigment screening, the lower lipofuscin load in foveal RPE may be due to an alternative route for cone photopigment recycling compared to rod photopigment and antioxidant protection from macular pigment and RPE melanin (Ach et al., 2014, Weiter et al., 1986). 40

Autofluorescence distribution in the remaining parts of the retina is not uniform. The temporal distribution of RPE lipofuscin closely follows rod photoreceptor topography (Ach et al., 2014; Delori et al., 2001). Both histological and in vivo imaging studies found that autofluorescence is maximal in a ring 2 - 4 mm (approximately 7 - 14°) from the fovea (Ach et al., 2014; Delori et al., 2001; Tee T., et al. IOVS 2014;55:ARVO E-Abstract 3428). RPE within the rod-free fovea also contain lipofuscin, indicating that cones contribute to lipofuscinogenesis. Extending inferiorly from the optic disc, a demarcation line exhibiting stronger autofluorescence temporally and weaker autofluorescence nasally may be seen. This line is believed to represent the closed optic fissure (Figure 1.10) (Duncker et al., 2012).

Figure 1.10. Right eye 30° FAF image from a 15-year-old Asian male. FAF of the retina inferior to the optic disc shows a demarcation line (white arrow) believed to represent the closed optic fissure. Image modified from Duncker et al. (2012).

Delori and co-workers documented autofluorescence along the horizontal and vertical meridians using in vivo fundus spectrophotometry (Delori et al., 2001). Strongest autofluorescence was observed between 9 - 14° temporally, 10 – 19° superiorly, 7° nasally and 6 - 11° inferiorly. When measured at a fixed eccentricity of 7°, temporal autofluorescence was strongest, followed by nasal, superior and inferior meridians. Such a pattern of autofluorescence intensity (highest temporally and lowest inferiorly) was observed from the foveal to mid-peripheral retina (< 20° eccentricity). Beyond 20° eccentricity, autofluorescence along the superior and nasal meridians are stronger than the temporal and inferior meridians. Using quantified autofluorescence intensities in the temporal and nasal parafovea, Greenberg et al. (2013) found that asymmetry of FAF distribution in the parafovea was negatively correlated with refractive error (i.e.

41 temporal qAF was stronger than nasal qAF, and the difference increased with myopic refractive error), although they gave no further explanation as to why that was so aside from the presence of peripapillary crescent found nasally in some eyes.

1.2.6. Interpretation of the FAF Image

In the normal eye, autofluorescence comes from RPE lipofuscin. Altered autofluorescence may be observed in active stages of retinal disease such as retinal detachment, oedema, or haemorrhage, (discussed in Applications of FAF Imaging). The normal FAF pattern is disrupted when there is 1) disturbance to normal metabolism in the outer retina, 2) distortion of the normal structure of the retina, or 3) the presence of substances that exhibit or block autofluorescence. Since FAF images are presented in grayscale, abnormalities can appear hyper- , hypo- or isoautofluorescent with respect to normal background autofluorescence (Lois et al., 2002). Interpretation of the FAF image therefore requires the clinician to have some basic understanding of retinal anatomy and retinal disease processes. For instance, hypoautofluorescence corresponding to drusen may or may not come with a hyperautofluorescent border (Delori et al., 2000). The hyperautofluorescent border is thought to be due to the peripheral displacement of RPE or RPE lipofuscin.

It is important to keep in mind that the FAF signal comes from the metabolic processes at the photoreceptor-RPE complex. The FAF image therefore presents a dynamic picture of retinal disease.

1.2.7. Applications of FAF Imaging

FAF images provide valuable insight into retinal disease. It can be used in disease detection and diagnosis, to aid patient management and to predict visual function, as described in the following sections.

1.2.7.1. Disease Detection and Diagnosis

An example of FAF imaging in facilitating diagnosis is in optic disc drusen. Optic disc drusen are calcified deposits that form around the optic nerve head as a consequence

42 of abnormal axonal metabolism (Tso, 1981). Optic disc drusen can be found on the surface of the optic disc or buried under the neuronal tissue, giving rise to the appearance of optic disc swelling (Auw-Haedrich et al., 2002). FAF imaging has high sensitivity for detecting optic disc drusen, which appear as hyperautofluorescent nodules within the disc (Asli Dinc et al., 2009). In contrast, true papilledema demonstrates reduced autofluorescence within and around the optic disc, corresponding to the area of swelling (Chiang et al., 2015). In a study involving paediatric patients with pseudopapilledema due to optic disc drusen, FAF imaging using fundus camera was able to detect optic disc drusen in 30 out of 32 eyes, of which 24 eyes had buried disc drusen (Gili et al., 2013a). Gili et al. also conducted a study on 66 adult patients with optic disc drusen (38 superficial and 28 buried disc drusen), 31 patients with true disc swelling and 70 controls, and found that FAF imaging using a fundus camera had 88% sensitivity and 100% specificity for detecting optic disc drusen (Gili et al., 2013b). On the other hand, another small study (n = 19 children) reported more limited ability of FAF imaging in detecting buried disc drusen. In that study, there were 10 eyes with true papilledema and 25 eyes with optic disc drusen (6 superficial and 19 buried disc drusen). FAF imaging correctly identified optic disc drusen in 100% of eyes with superficial disc drusen but only 56% of the eyes with buried disc drusen (Chang et al., 2017). The difference in sensitivity between Chang et al. (2017) and Gili et al. (2013) may be in their methodology. Gili et al. (2013b) took multiple FAF photographs but at low flash intensity so that background autofluorescence was barely imaged. They believed that using this methodology, autofluorescence from even buried disc drusen could be more easily detected by the clinician. Chang et al. (2017) used a Zeiss FF450 plus infrared fundus camera with fluorescein angiogram filters for FAF imaging. However, the optic disc autofluorescence images did not appear very dark even in cases of true papilledema. This would have hampered the detection of autofluorescence from optic disc drusen, especially if the disc drusen were buried.

Choroidal nevi are benign melanocytic lesion that must be differentiated from cancerous choroidal melanomas. Conventionally, choroidal nevi are examined by biomicroscopy and ultrasound to monitor size and thickness of the mass. However, some lesions are difficult to differentiate using these methods. Recently, FAF imaging was found to differentiate choroidal nevus from choroidal melanoma with a specificity of 97% but sensitivity of 7% using ultrawide-field autofluorescence imaging (Reznicek et al., 2014). Choroidal melanomas were much more hypoautofluorescent than choroidal nevi, with an average grey level intensity of 14.24 ± 7.89 and 22.22 ± 20.75

43 respectively (p < 0.0001). While it cannot be used as the primary tool for differentiating choroidal nevi from melanomas, it can be an adjunct tool to improve the accuracy and confidence of the clinician when making clinical judgement. However, more research is needed before FAF imaging can become an established imaging modality in this disease. Ultrawide-field imaging is susceptible to imaging artefacts and the repeatability of FAF intensity has not been studied.

FAF imaging can detect active lesions that do not display characteristic blister-like appearance of serous retinal detachment in CSCR. These lesions may appear as faint areas of pigmentary changes in fundus photographs but appear hyperautofluorescent in FAF imaging (Figure 1.11).

Figure 1.11. Fundus photograph (A, C) and FAF images (B, D) of a 49-year- old woman with central serous chorioretinopathy (CSCR). Fundus photographs (A, C) show pigmentary changes. FAF images (B, D) show the CSCR lesions in high contrast. Older lesions may exhibit descending tract appearance, as seen in (B) over the parafovea and optic disc. Hypoautofluorescence involving the macula was associated with poor visual acuity. Image from Imamura et al. (2011).

FAF images can clearly delineate normal from abnormal retina and provide information about which specific retinal layer(s) are involved in disease. For example, hypoautofluorescence is seen over areas of retinal detachment in myopic macular detachment. Hyperautofluorescent spots within the lesion indicate subretinal precipitates/deposits (Hiraoka et al., 2010). FAF imaging is also useful for detecting the presence and extent of abnormalities such as choroidal neovascularisation against the tigroid fundus of high myopia (Jabbarpoor Bonyadi, 2016).

In multifocal choroiditis and panuveitis, FAF imaging can show changes before ophthalmoscopy. In this disease, multiple hypoautofluorescent lesions are seen in FAF images. These lesions subsequently develop into ophthalmoscopically-visible chorioretinal scars (Haen and Spaide, 2008). The authors hypothesized that the small hypoautofluorescent lesions not corresponding to chorioretinal scars may represent early RPE damage.

FAF imaging was recently added to the list of optional imaging modalities for screening for chloroquine and hydroxychloroquine retinopathy as it can pick up RPE and photoreceptor alterations even in the absence of obvious fundus or visual changes (Marmor et al., 2011). FAF imaging remains an optional imaging modality in this disease because the sensitivity of detecting abnormalities has not been evaluated on a large scale. One of the challenges is the need for an objective evaluation of the FAF images and the precise measurement of FAF intensity changes. A quantitative method of evaluating FAF could enhance the sensitivity and accuracy of detecting retinal toxicity early.

1.2.7.2. Patient Management

FAF imaging has been used to document different phenotypes of a disease and provide prognostic information. One example is age-related macular degeneration (AMD). In early AMD, 8 patterns of FAF have been observed including normal, minimal change, focal increased, patchy, linear, lacelike, reticular, focal plaque-like, and speckled (Bindewald et al., 2005). These autofluorescence patterns do not consistently correspond to fundus features such as drusen (small drusen may not cause FAF changes) or pigmentary changes. Longitudinal studies revealed poorer visual prognosis associated with patchy, focal plaque-like and reticular autofluorescence patterns. In contrast, no eyes that exhibited minimal change, linear, lace-like or speckled FAF at baseline developed wet AMD. Patchy FAF was associated with severe visual loss of 45 more than 6 ETDRS lines. Hyperautofluorescence at the border of geographic atrophy in AMD has also been associated with geographic atrophy progression (Biarnés et al., 2014, Bearelly et al., 2011). Measurements of geographic atrophy using FAF images have been found to be highly reproducible (Domalpally et al., 2016). Compared to fundus photographs, inter-grader agreement of atrophy sizes can be higher in FAF images compared to colour photographs (Khanifar et al., 2012) and this is likely due to the higher contrast of geographic atrophy borders against intact retina in FAF images.

Stargardt’s disease is a retinal dystrophy with central vision loss secondary to atrophic macular lesions. FAF imaging of the atrophic macular lesions reveal dense hypoautofluorescence. Larger areas of atrophy at baseline have been associated with faster disease progression (Strauss et al., 2017). Three categories of FAF in Stargardt’s disease have been described with different progression rates (Fujinami et al., 2013). Type 1 is characterised by “localized low autofluorescence (AF) signal at the fovea surrounded by a homogeneous background, with/without perifoveal foci of high or low AF signal,” Type 2 is described as “localized low autofluorescence signal at the macula surrounded by a heterogeneous background, and widespread foci of high or low autofluorescence signal extending anterior to the vascular arcades” and Type 3 demonstrates “multiple areas of hypoautofluorescence at the posterior pole with a heterogeneous background, with/without foci of high or low AF signal”. Rates of atrophy progression were significantly slower in Type 1 and faster in Type 3 autofluorescence pattern. Mutations associated with milder forms of Stargardt’s disease were more frequently seen in patients with Type 1 autofluorescence whereas mutations associated with more severe Stargardt’s disease were found in patients with Type 3 autofluorescence. The appearances of the autofluorescence patterns are helpful during patient counselling on visual prognosis.

The use of FAF imaging in glaucoma and ocular hypertension is relatively poorly studied (Laemmer et al., 2007, Reznicek et al., 2013) possibly because it does not involve the outer retina, which has greater influence on autofluorescence. Hyperautofluorescent regions in parapapillary region have been reported to be larger in primary open angle glaucoma eyes than in ocular hypertensive and normal eyes (Viestenz A, et al. IOVS 2006;47 ARVO E-Abstract 3656). In a study comparing ocular hypertensive and normal eyes, the size of the hyperautofluorescent region was positively correlated with increased peak latency on blue on yellow pattern visual evoked potential, which has been associated with glaucomatous damage (Figure 13) (Laemmer et al., 2007). The source of hyperautofluorescence in the parapapillary region is an unusually high concentration of RPE lipofuscin and could indicate 46 extension of parapapillary atrophy, which has also been associated with glaucoma progression (Laemmer et al., 2007). However, since hyperautofluorescence adjacent to the optic disc is also seen in normal eyes, more research is needed to explain the hyperautofluorescence in normal eyes and to differentiate ‘normal’ hyperautofluorescence from those that indicate disease. A longitudinal study would be useful to investigate whether FAF imaging can indicate prognosis in glaucoma and ocular hypertension.

Figure 1.12. Colour photograph and FAF image of the optic disc in a patient with ocular hypertension. Larger areas of hyperautofluorescence around the optic disc have been noted in ocular hypertensive compared to normal eyes. Image modified from Laemmer et al. (2007).

In eyes with primary open angle glaucoma, FAF was segmented into different retinal regions and the FAF intensity of each region was correlated with the corresponding sectoral retinal nerve fibre layer (RNFL) thickness (Reznicek et al., 2013). A weak positive correlation was found for the nasal and inferior-temporal sectors. However, the study by Reznicek and associates did not include a control group. FAF was captured using Optos Optomap, an ultra-widefield (UWF) scanning laser ophthalmoscope that uses a 532nm laser for excitation. While the Optomap has a large field of view, image artefacts are common and contrast has been shown to be affected by (lack of) pupil dilation (Oishi et al., 2014). The repeatability of FAF intensity and the uniformity of the UWF-FAF images were not addressed in the study.

FAF imaging allows the differentiation of acute lesions from chronic lesions in CSCR. Resolved/chronic lesions exhibit granular hypoautofluorescence and may take on a ‘descending tract’ appearance (Figure 1.11) (Imamura et al., 2011). This is useful in patient management where treatment is indicated for the chronic form of the disease.

1.2.7.3. Visual Function

Alterations in FAF have been associated with reduced retinal sensitivity. For example, pronounced hypoautofluorescence frequently indicates loss of retinal sensitivity, such as in areas of geographic atrophy with overlying photoreceptor loss. In CSCR, microperimetry revealed reduced retinal sensitivity within areas of hyperautofluorescence, and pronounced loss of sensitivity within areas of hypoautofluorescence (Eandi et al., 2015). Granular hypoautofluorescence corresponding to diffuse epitheliopathy and reduction in vision may be seen in CSCR (Figure 12) (Nicholson et al., 2013, Eandi et al., 2015). Foveal hypoautofluorescence in myopic macular hole indicates foveal involvement and therefore poorer vision and visual recovery (Arias et al., 2015). In retinitis pigmentosa, a hyperautofluorescent ring in the macula is sometimes visible and demarcates the border between functional and dysfunctional retina (Robson et al., 2011). The extent of peripheral FAF abnormality in retinitis pigmentosa was strongly correlated with the area of visual field defect (Ogura et al., 2014). A macular hyperautofluorescent ring , frequently seen in eyes with retinitis pigmentosa, corresponded to the absence of the photoreceptor inner segment/outer segment (IS/OS) line in OCT imaging (Wakabayashi et al., 2010). Macula with normal FAF corresponded to an intact IS/OS line and normal retinal sensitivity as tested using microperimetry. The correlation between FAF and visual field defects / retinal sensitivity suggests that FAF imaging may be used as a monitoring tool for visual field loss in RP.

FAF over macular holes is increased due to the absence of overlying retinal layers and macular pigment. One study of FAF and vision after macular hole surgery found that the recovery of normal autofluorescence (i.e. resolution of increased autofluorescence) was associated with better VA (Zhang et al., 2017). It was believed that the normalisation of FAF signal over the repaired macular hole could represent recovery of normal retinal function and subsequent accumulation of macular pigment.

In Best’s disease, autofluorescence signal increases then reduces in the macula as the disease progresses. Microperimetry revealed reduction in retinal sensitivity corresponding to hypoautofluorescence (Jarc-Vidmar, Popovic, & Hawlina, 2006). A hyperautofluorescent border around the hypoautofluorescent lesion demonstrated normal to reduced retinal sensitivity while areas of normal autofluorescence beyond the lesion had normal retinal sensitivity.

On the other hand, there are some exceptions where reduced retinal sensitivity was observed in the presence of normal FAF. Wakabayashi et al. (2010) observed normal FAF in 4 eyes from 2 retinitis pigmentosa patients. Similar findings were reported by Fleckenstein et al. (2008) in pigmented paravenous retinochoroidal atrophy. These areas of normal FAF were located within regions of photoreceptor degeneration. Fleckenstein et al. (2008) hypothesised that the FAF may arise from lipofuscin that had accumulated in the RPE prior to photoreceptor degeneration. In addition, the presence of normal FAF suggested that an active photoreceptor outer segment turnover and phagocytosis by the RPE was not necessary to maintain normal RPE autofluorescence. Another study of persistent hyperautofluorescence in CSCR appears to support this hypothesis (Pang et al., 2014). In that study, hyperautofluorescence as observed up to 8 years after the resolution of subretinal fluid. The hyperautofluorescent areas were associated with retinal thinning and outer retinal atrophy, which unmasked autofluorescence from the underlying RPE.

The size of the FAF alteration also does not always match the results of functional testing precisely. For example in birdshot chorioretinopathy, FAF alterations were larger than the associated scotomas mapped on Goldmann perimetry (Jack et al., 2016). FAF alterations were also observed in the absence of visual field defects. The authors hypothesised that FAF may allow earlier detection of structural changes before functional changes could be detected with Goldmann perimetry. Optical coherence tomography (OCT) of the retina was not performed so it was not possible to compare the FAF changes with morphological changes at the retinal level. FAF may be more sensitive than Goldmann perimetry because birdshot chorioretinopathy is an inflammatory disease involving the outer retina.

1.2.8. Advantages of FAF Imaging

As discussed above, FAF imaging has many useful clinical applications in disease screening/detection, diagnosis, management and monitoring. In some diseases, FAF can detect more areas of abnormalities than ophthalmoscopy and even predict future areas of disease. FAF imaging can give an indication of visual function as another parameter in patient monitoring (especially in patients who are unable to perform visual function tests). FAF imaging can help us to understand different disease processes in vivo. It has an added advantage of being a non-invasive, objective, clinical tool with minimal risk of adverse reaction to the patient.

1.2.9. Limitations of FAF Imaging

FAF imaging remains an ancillary tool in patient examination. Other imaging modalities such as colour photography, optical coherence tomography, fundus fluorescein angiography and indocyanine green angiography may be needed to confirm findings of altered autofluorescence, as hyper- and hypoautofluorescence can be induced by different mechanisms of the disease processes. The appearance of FAF can vary depending on the wavelength of the excitation light/laser and the characteristics of the barrier filter so any monitoring should be done using the same type of FAF instrument. For example, there is greater macular pigment masking when autofluorescence images are captured using shorter (488nm) excitation laser compared to longer (512nm or 532nm) wavelength. It is also possible for some retinal changes to have inconsistent, minimal or no change in autofluorescence. For example, drusen have variable autofluorescence depending on their size and type (Delori et al., 2000, Deli et al., 2013). Diseased dysfunctional retina may even exhibit normal autofluorescence (Bindewald et al., 2005). As our understanding of the disease process improves, we might be able to explain these inconsistencies. At this point in time, it appears that the presence of FAF changes may indicate involvement of the RPE in the disease process, a more severe disease stage and/or poorer visual prognosis.

1.3. Myopia

Myopia is defined as ‘an optical mismatch between the refractive surfaces of the eye and the position of the macula as determined by axial length whereby for distant targets and relaxed accommodation, the optical image plane is anterior to the retina’ (Flitcroft, 2008). The prevalence of myopia has increased globally, with the most dramatic increases in East Asian countries due to a combination of genetic and environmental factors (Holden et al., 2016, Jones and Luensmann, 2012).

High myopia is a global healthcare concern because it is associated with a variety of retinal changes, degenerations and disease. There is no consensus on what the definition of high myopia is. High myopia has frequently been defined as a refractive error of at least -5D (Wang et al., 2018, Wu et al., 2015), -6D (Mäntyjärvi and Tuppurainen, 1995, Kim et al., 2011, Leung et al., 2006) or -8D (Xu et al., 2007, Jonas et al., 1988). The use of at least -5D as the definition of high myopia are frequently adopted by studies as the uncorrected visual acuity of an uncorrected myope with those magnitudes of refractive error would be worse than the threshold for blindness 50

(worse than 6/120). The threshold of -6D has been associated with an increased prevalence of glaucoma (Xu et al., 2007). In another study, a minimum of -8D was proposed as the definition of high myopia because larger optic disc size was found at - 8D and greater (Wang et al., 2006). High myopia has also been defined based on axial length ≥26.0mm (Praveen et al., 2008) or ≥26.5mm as this value better reflected the increased incidence of retinal detachment after cataract surgery (Percival, 1987). This section discusses myopic eye growth and the myopic retinal changes that have the potential to affect FAF.

1.3.1. Physical Dimensions of the Eye in Myopia

Myopia is characterised by the growth of the eye ball (Morgan et al., 2012, Atchison et al., 2004, Ehsaei et al., 2013). The expansion of the globe occurs along 3 axes. It is greatest axially followed by vertically and horizontally such that the equivalent growth per dioptre of myopia is 0.35 mm/D, 0.19 mm/D and 0.10mm/D respectively. It is believed that the growth is less along the horizontal or vertical axes due to the physical restrictions of the orbit (Atchison et al., 2004, Pope et al., 2017).

Using magnetic resonance imaging, Pope et al. (2017) documented the general eye shape in emmetropia and myopia (Pope et al., 2017). They reported oblate eye shape in emmetropes and a trend towards prolate eye shape with increasing myopia, so that the eye was almost spherical at -7 to -8D of myopia. Pope et al. found that females had average 0.5mm smaller eye balls than males. They also reported no effect of race (East Asians versus Caucasians) on the eye ball dimensions. However, their study involved only 30 East Asians and 25 Caucasian participants with average refractive error of -2D (range +0.37D to -8.15D) and -1D (+0.75D to -4.18D). Data from more high myopes in each ethnic group may be needed in order to detect possible differences in eye growth between Asians and Caucasians.

Kang et al. measured peripheral refraction along the horizontal meridian of emmetropic to moderately myopic eyes up to an eccentricity of 35° (Kang et al., 2010). Similar to the MRI study by Pope et al., they also found emmetropes to have myopic peripheral refraction (implying oblate eye shape) and a shift towards hyperopic peripheral refraction (implying prolate eye shape) with increasing myopia. Unlike the study by Pope, however, Kang et al. found that Asians had more hyperopic peripheral refraction (prolate eye shape) compared to Caucasians of similar refractive error at 25° eccentricities and further. 51

Using measurements by off-axis partial coherence interferometry (IOLMaster; Carl Zeiss Meditec), Ehsaei et al. (2013) reported nasal-temporal asymmetry in myopic eye shape that was statistically significant at 30° eccentricity but not at 10° or 20° eccentricities (Ehsaei et al., 2013). A similar finding was also reported by Kang et al. (2010), suggesting greater myopic eye growth nasally than temporally.

1.3.2. Pathological Myopia and FAF

Most autofluorescence papers dealing with myopia were qualitative studies describing autofluorescence of various degenerative/pathologic changes, as described below.

Myopic maculopathy can be classified into 1 of 5 categories with or without lacquer cracks, choroidal neovascularisation, Fuch’s spot and posterior staphyloma (Table 1.2). The classification of myopic maculopathy outlines the progression from normal retina to chorioretinal atrophy. Category 1 is tessellated fundus. Tessellated fundus is the enhanced visibility of the choroidal vessels due to depigmentation or hypoplasia of the RPE (Tokoro, 1998). It is seen in both aged and highly myopic eyes and has no consequence on visual acuity. Fundus tessellation has been most frequently observed around the optic disc (Yoshihara et al., 2014). Since there is no disruption of RPE cells, FAF is normal in tessellated fundus. The examination of a tessellated fundus can be impaired due to the increased visibility of underlying choroidal vessels. Since FAF is unchanged in the presence of tigroid fundus, it (at least) gives the clinician a ‘clear’ view of the state of the RPE.

In areas of chorioretinal atrophy, there is degeneration and loss of RPE cells resulting in hypoautofluorescence. Choroidal vessels can be seen within atrophic lesions exhibiting mild autofluorescence from elastin (Hwang et al., 2010). FAF can detect islands of preserved RPE that are not visible in ophthalmoscopy.

Category Fundus appearance 0: No myopic retinal - changes 1: Tessellated fundus Visible choroidal vessels around the fovea and vascular arcades 2: Diffuse The posterior pole takes on a more yellowish-white chorioretinal atrophy appearance. 3: Patchy Well-defined greyish-white lesions of variable size chorioretinal atrophy 4: Macular atrophy Well-defined, round chorioretinal atrophic lesion that is greyish white or whitish around a regressed fibrovascular membrane that enlarges with time. Generally macular atrophy is centred on the central fovea. + signs - (Posterior staphyloma) +Lc (lacquer cracks) +Fs (Fuch’s spot) +CNV (choroidal neovascularisation)

Table 1.2. Classification of myopic maculopathy proposed by the META-PM study (Ohno-Matsui et al., 2015).

Posterior staphyloma is a structural deformity characterised by out pouching of the eye (Ohno-Matsui et al., 2016). There are 6 classifications of posterior staphyloma (Figure 1.13). Depigmented borders of posterior staphyloma may exhibit hyper- or hypoautofluorescence; pigmented borders are hypoautofluorescent (Ohno-Matsui, 2014). Chorioretinal folds associated with posterior staphyloma are similarly associated with hyper- and hypoautofluorescence (Ishida et al., 2015). Lacquer cracks are breaks in the Bruch’s membrane and may appear hypoautofluorescent (Suga et al., 2017).

Fuch’s spots are pigmented spots from scarred choroidal neovascularisation and also do not exhibit autofluorescence (Liang et al., 2015). FAF in choroidal neovascularisation may have hyperautofluorescence with or without spots of hypoautofluorescence (Parodi et al., 2009).

Figure 1.13. Classification of posterior staphyloma based on location and extent. Image from Ohno-matsui et al. (2014).

Other complications include myopic macular retinoschisis, foveoschisis, and macular hole retinal detachment. In retinoschisis, FAF is diminished as the layers of the inner retina that have split off block FAF signal from the RPE (Sayanagi et al., 2007, Hiraoka et al., 2010). Autofluorescence of foveoschisis was variable and could appear normal, mildly hypoautofluorescent, or mottled hyperautofluorescent. The hypoautofluorescence associated with retinal detachment was hypothesised to be due to the presence of viscous subretinal fluid, which blocked FAF signal from the RPE. Macular hole retinal detachment was associated with hypoautofluorescence corresponding to the area of retinal detachment. Macular hole is associated with enhanced FAF (von Rückmann et al., 1998) due to the absence of overlying neurosensory retina and macular pigment.

1.3.3. Autofluorescence around the Optic Disc in High Myopia

As the eye ball elongates, the optic disc moves to a relatively more nasal location and appears more oval in shape (Jonas and Xu, 2014). This frequently causes a protrusion (‘overhang’) of the Bruch’s membrane beyond the inner border tissue on the nasal side of the optic disc (Reis et al., 2012). On the temporal side, retinal tissue is pulled away

54 from the optic disc margin to expose the underlying choroid and/or sclera (Jonas and Xu, 2014). This forms the frequently-observed parapapillary atrophy (PPA). PPA can be categorised into 2 major types according to their histological appearance: alpha (pigmentary irregularities) and beta (RPE and choriocapillary atrophy) (Jonas et al., 1989). More recently, parapapillary gamma and delta zones have been reported (Jonas et al., 2012). Gamma zone corresponds histologically to sclera with no overlaying choroid, Bruch’s membrane, RPE or neurosensory retina, and delta zone corresponds to the region within gamma zone that is devoid of vasculature >50µm. Gamma and delta zones are correlated with axial length but beta zone is associated with glaucoma. In these areas of PPA, there is minimal autofluorescence (Figure 1.14). Exposure of the sclera reveals weak autofluorescence.

Figure 1.14. Autofluorescence image of the optic disc (A) and the corresponding colour photograph (B) from a -12D myope. Parapapillary atrophy (PPA) is intensely hypoautofluorescent except in the areas where the sclera is exposed. The borders of the PPA are clearly demarcated in the autofluorescence image, which provide good contrast between intact retina and areas of atrophy.

However, it is unknown what factors affect autofluorescence around the optic disc. The RNFL converges at the optic disc and may block FAF, but the extent to which the RNFL attenuates autofluorescence around the optic disc has not been studied.

1.3.4. Eye Diseases Associated with Oxidative Stress and High Myopia

Aside from the retinal and optic disc changes, high myopia is associated with cataracts and primary open angle glaucoma (Francisco et al., 2015, Pan et al., 2013b). In

55 particular, high myopia is associated with an increased prevalence of posterior subcapsular cataract (Pan et al., 2013a).

Higher levels of malondialdehyde have been found in myopia-related cataracts compared to senile cataracts (Simonelli et al., 1989, Bhatia et al., 2006), suggesting higher levels of oxidative stress in the myopic eye that plays a role in cataractogenesis (Micelli-Ferrari et al., 1996). It has also been hypothesised that the longer axial length reduces antioxidant diffusion of nutrients from the retina to the crystalline lens, thereby compromising the antioxidant defences in the lens (Richter et al., 2012). An alternate hypothesis suggests that vitreous liquefaction in long eyes allows greater diffusion of oxygen to the crystalline lens, thereby promoting oxidation and formation of cataract (Holekamp et al., 2005).

Population studies have found increased risk of glaucoma in myopia. The proposed mechanisms are oxidative damage and ischemia secondary to suboptimal blood supply (Yokoyama and Nakazawa, 2015) as well as structural deformations at the optic disc that increase susceptibility of ganglion cell axons to intraocular pressure (Jonas et al., 2004). For example, the circle of Haller-Zinn is further from the optic disc in highly myopic eyes (Jonas et al., 2013) and the peripapillary choroid is thinner than normal, implying that blood supply to the optic disc may also be reduced. In highly myopic eyes, the lamina cribosa is thinned, which increases the pressure gradient between the intraocular space and the retrobulbar cerebrospinal fluid space. This may increase the susceptibility of the nerve fibre axons to damage from intraocular pressure (Jonas and Xu, 2014).

1.4. Contrast Sensitivity

Contrast of an object refers to the relative difference in luminance of the object against its background. Measurement of a person’s contrast sensitivity tells us how well he can perceive small differences in luminance of a visual stimulus. Contrast sensitivity testing is regarded as a test that more accurately reflects a person’s quality of vision compared to high contrast visual acuity as the environment in real life is seldom of 100% contrast. Contrast sensitivity measurement is also more sensitive than visual acuity, which simply measures the resolution of the eye at high contrast, as eye diseases can cause reduction in contrast sensitivity before loss of visual acuity is detected.

Contrast sensitivity is frequently tested using sine wave gratings, where the contrast level is expressed in Michelson contrast 퐿푚푎푥−퐿푚푖푛 , with Lmax and Lmin being the (퐿푚푎푥+퐿푚푖푛) maximum and minimum luminance of the grating (Elliott, 2006). Contrast sensitivity can also be tested using letters of different contrast levels, however such tests require the patient to recognise the letter and this poses a more difficult visual task than simple detection of sine wave grating.

In the normal human eye, contrast sensitivity peaks at intermediate spatial frequencies of 2 – 6 cycles / degree (cpd) (Ross et al., 1985) as shown in Figure 1.15, and varies depending on the test conditions. For example, contrast sensitivity peaks at 4cpd and higher when luminance is high and shifts towards lower spatial frequencies at lower luminance levels (De Valois et al., 1974, Karatepe et al., 2017, Comerford et al., 1987). Contrast sensitivity reduces at low and high spatial frequencies. Sensitivity at low spatial frequencies is limited by lateral inhibition in the neural system (Figure 1.16) whereas at high spatial frequencies, it is limited by optical quality and cone photoreceptor sampling (Elliott, 2006).

Figure 1.15. Human contrast sensitivity function showing peak sensitivity at 4 cycles/degree. The shaded area under the curve can be resolved by the eye whereas regions above the curve cannot be resolved. The arrow on the x-axis is the high frequency cut off and represents the resolution limit of the human eye. Figure from Schwartz (2017).

Figure 1.16. Illustration of a grating stimulus falling on the receptive field of a ganglion cell. A grating with spatial frequency that matches the excitatory centre of the receptive field elicits optimal response from the ganglion cell (A). If a grating with low spatial frequency has bright bars that fall on the excitatory centre and inhibitory surround of the receptive field, the result is lateral inhibition (B). Figure from Schwartz (2017).

1.4.1. Contrast Sensitivity with Age

Contrast sensitivity has been reported to decrease with age (Sloane et al., 1988). The reason for this decline can be broadly attributed to increased noise and decreased neural efficiency in retinal and/or cortical processing (Allard et al., 2013) .

With aging, pupil size and crystalline lens transparency decrease (Birren et al., 1950). This leads to an overall reduction in retinal illumination, which reduces contrast sensitivity at high spatial frequencies (Owsley et al., 1983). Intraocular light scatter also increases with age due to inhomogeneity in crystalline lens density, which could affect contrast sensitivity across all spatial frequencies (Elliott, 1987).

Age-related changes in the retina include increased RPE lipofuscin, drusen, loss of photoreceptors and ganglion cells (Ach et al., 2014, Curcio, 2001, Calkins, 2013). The presence of RPE lipofuscin and drusen may impair physiological functions of the outer retina, while loss of photoreceptors and ganglion cells may directly affect visual perception.

Studies have reported differing results as to which spatial frequencies are affected by aging. In 1983, Owsley and co-workers measured contrast sensitivity in 91 subjects (age 19 – 87) at 0.5, 1, 2, 4, 8, 16cpd (Owsley et al., 1983). They found that in subjects aged 40 and older, contrast sensitivity at 2cpd and higher spatial frequencies declined with age. They also simulated the reduced retinal illuminance of aged eyes in 58

7 young participants, and repeated contrast sensitivity testing. The results indicated that reduced retinal illuminance causes decreased contrast sensitivity at intermediate and high spatial frequencies, similar to the performance of the elderly participants in their study. The observation of reduced sensitivity in the older eyes with poorer visual acuity could be due to the inclusion of participants with mild ocular disease.

A study involving 70 subjects aged 20 – 87 revealed poorer contrast sensitivity in older subjects (50 – 87 years old, n = 53) compared to younger subjects (20 – 30 years old, n = 17) at low, intermediate and high spatial frequencies tested (0.4 - 19.25cpd) (Ross et al., 1985). The correlation between contrast sensitivity and age of the elderly group was significant at 2.88, 6.73, 12.70 and 19.25cpd, with strongest correlation at 2.88cpd.

Sloane et al. (1988) measured contrast sensitivity in young and old subjects, and found poorer contrast sensitivity from low to high spatial frequencies (0.5, 1, 2, 4, 8 and 11.4cpd) in the older group. The age-related differences increased with spatial frequency. They further investigated the effect of senile miosis on contrast sensitivity. Contrast sensitivity of 7 elderly subjects aged 67 – 79 was measured with natural pupils and with pupils dilated to different diameters. Each participant underwent contrast sensitivity testing 2 to 4 times. They found that contrast sensitivity did not improve with pupil dilation (Sloane et al., 1988). They concluded that senile miosis did not affect contrast sensitivity in aged eyes. Instead, increased light scatter and decreased retinal illuminance due to age-related changes to the crystalline lens play a comparatively larger role. In addition, they hypothesised that age-related changes in neural mechanisms may be able to explain more of the contrast sensitivity deficits observed in aged eyes.

A study on emmetropes aged 7 to 65 revealed a reduction in sensitivity at 12 and 24cpd, but not lower spatial frequencies, which the authors attributed in part to senile miosis (Karatepe et al., 2017).

In contrast, another study of 201 myopic participants (-1.25D to -8.25D) found no correlation between age and area under the log contrast sensitivity function (AULCSF) from 1.5 to 18cpd (Kamiya et al., 2014). However, the participants were young (18 – 53 years old) and the use of AULCSF for statistical analysis may have masked the effect of aging if sensitivity loss varied across spatial frequencies.

Despite the different results, the general consensus is that loss of sensitivity at the higher spatial frequencies is associated with aging. Optical quality such as senile miosis 59 and light scatter play a role, but it is likely that neural factors have a bigger impact on age-related contrast sensitivity loss.

1.4.2. Contrast Sensitivity in Myopia

The effect of myopia on contrast sensitivity has been investigated and produced mixed results. Minification of the stimulus due to high minus lenses used to correct myopia, reduction in retinal illumination due to reflection and scatter of light at the lens interface, increased ocular aberrations (Kamiya et al., 2014, Collins and Carney, 1990), and retinal dysfunction (Liou and Chiu, 2001, Stoimenova, 2007) secondary to axial elongation have all been proposed as possible reasons for the reduced contrast sensitivity in myopes compared to emmetropes.

Fiorentini and Maffei (1976) measured monocular contrast sensitivity to sine wave gratings at 10cd/m2 in a small number of young high myopes (-5.50D to -14D, n = 10) and compared the results to emmetropes (n = 3). Reduced contrast sensitivity was found at all spatial frequencies (0.5 to approx. 25cpd) in high myopes compared to emmetropes. However, 4 of the subjects had reduced visual acuity (6/12) and this likely contributed to the poor performance at high spatial frequencies.

Thorn et al. (1986) investigated photopic (100cd/m2) contrast sensitivity in 13 young, high myopes (-6D to -9.75D) and found that the myopes’ sensitivity was similar to emmetropes at low to high spatial frequencies, except at 22.8cpd. However the small sample size and the repeated statistical testing could have increased the chance of a type I (false positive) error.

Collins and Carney (1990) measured contrast sensitivity in 16 young myopes with good acuity and no ocular disease. The subjects were grouped into ‘low degree of myopia’ (between -2D to -7D of myopia) and ‘high degree of myopia’ (greater than -7D). Photopic contrast sensitivity was measured using sine wave gratings at 1, 2, 4, 12 and 24cpd. Contrast sensitivity for each subject was measured using spectacle correction and contact lenses. When subjects were corrected using spectacles, the high myopes demonstrated significantly poorer contrast sensitivity at the 2 highest spatial frequencies (12 and 24cpd). This difference was eliminated when subjects were corrected using contact lenses. The authors hypothesised that the high-powered spectacle lenses worn by high myopes introduced significant amounts of aberrations

60 that resulted in poorer resolution (visual acuity) and therefore reduced contrast sensitivity at 12 and 24cpd compared to low myopes.

In 2001, Liou and Chiu measured contrast sensitivity in 53 young myopes with low (- 1D to -3D), medium (-3.25D to -6D), high (-6.25D to -12D) and severe myopia (> - 12D) (Liou and Chiu, 2001). Contrast sensitivity was tested with spectacle and contact lens correction. Low and medium myopes’ contrast sensitivities were similar to emmetropes’. High myopes demonstrated reduced contrast sensitivity at 12 and 18cpd when corrected with spectacles but not contact lenses. Severe myopes also demonstrated reduced contrast sensitivity at all spatial frequencies when corrected with spectacles, and reduced sensitivity at 6, 12 and 18cpd when corrected with contact lenses. In the high and severe myopes, the poorer sensitivity when corrected with spectacles might be attributed to optical degradation from aberrations. However, the reduced sensitivity in severe myopes corrected with contact lenses implied ‘myopic retinal changes or very mild amblyopia’. The authors hypothesised that the stretching of the retina in axial elongation of severely myopic eyes could compromise visual function.

Stoimenova (2007) measured contrast sensitivity using letter targets subtending 6cpd, under several photopic and mesopic luminance levels. Sixty young healthy myopes (18 to 31 years old, spherical equivalent -1D to -8D) demonstrated poorer contrast sensitivity compared to emmetropes (n = 20) under all luminance levels. The author attributed the reduction to aberrations inherent to myopic eyes, aberrations introduced by optical correction, and possible functional or morphological changes in myopia.

1.5. Objectives of the Thesis

Studies have suggested that oxidative stress in the high myopic eye is higher than in non-myopic eyes. This could lead to changes in autofluorescence in the eye. The aims of this study are to:

1. Understand FAF in normal eyes and to study the effect of autofluorescence image capture settings on the FAF signal using autofluorescence images captured using cSLO without quantitative autofluorescence capabilities. 2. Document FAF in young and healthy eyes of emmetropes and myopes and to compare FAF with contrast sensitivity, 3. Document parapapillary autofluorescence (PAF) in emmetropes and high myopes without ocular disease.

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Chapter 2. General Methodology

This chapter describes the subject recruitment and data collection for clinical procedures such as refraction, ocular biometry, autofluorescence imaging, optical coherence tomography (OCT) and contrast sensitivity measurement. A comprehensive objective method was developed to determine the quality of autofluorescence images. The statistical analyses used in each chapter are stated at the end of this chapter.

2.1. Subject Recruitment

Ethics approval was obtained from the 3 study sites: Manchester Royal Eye Hospital (Manchester, UK), Khoo Teck Puat Hospital (Singapore) and Singapore Polytechnic (Singapore). The experiments were carried out in accordance with the tenets of the Declaration of Helsinki (World Medical Association: 1964 – 2008). Informed consent was obtained from all subjects prior to participation. All subjects recruited were healthy individuals without ocular disease.

Sample size calculation was not performed for experiments in chapters 2, 3 and 4 as they were preliminary studies to explore the effect of image acquisition settings on the FAF signals.

In chapter 2, healthy subjects aged 18 to 58 years old were recruited in Singapore for image quality analysis. FAF images from 65 subjects and PAF images from 46 subjects were used. Refractive errors of the subjects ranged from +0.83D to -8.63D.

In chapters 3 and 4, young and healthy subjects were recruited in the United Kingdom and Singapore to study the effect of image acquisition settings on the detected FAF signal. The subjects ranged from 21 to 34 years old and refractive errors from +0.63 to -5.50D. See Chapter 3 Methods for further details. Data from 17 subjects were used in Chapter 4.

For experiments in chapters 5, 6 and 7, sample size was calculated using preliminary data from 10 subjects. The calculation of sample size was to determine how many subjects needed to be studied in order to detect statistically significant differences in FAF peak location between low and high myopes. Subjects were put into 1 of 2 groups: group 1 had refractive error between +0.63D to -1.00D and group 2 had 73 refractive error between -4.75D to -8.00D. Temporal and nasal FAF peak locations were identified from the 10 eyes:

FAF Peak Location (µm) Group 1 (n = 4) Group 2 (n = 6) Temporal 3091 ± 600 3636 ± 575 Nasal 2200 ± 551 2182 ± 257 2nd nasal (beyond optic disc) 5933 ± 206 6272 ± 525

Table 2.1. Preliminary data for the calculation of sample size used in Chapters 5, 6 and 7.

Taking the FAF peak location difference between the 2 groups to be 301µm (averaged from locations of the temporal, nasal and 2nd nasal peak) and the average SD to be 452µm, a sample size of 37 is needed in each group to detect statistical significance with a power of 80% and significance level (alpha) of 0.05 (two-tailed). The sample size was increased by 35% for a total of 50 subjects per group in order to account for subject drop out, poor fixation or unexpected poor-quality images in some subjects.

Emmetropic and highly myopic subjects aged between 18 and 60 years old were recruited at Singapore Polytechnic for experimental work in chapter 5, 6 and 7. Emmetropia was defined as a spherical refractive error within ±0.75D while high myopia was defined as a spherical refractive error of -6.00D and above. All participants underwent a vision screening to ensure eligibility (Table 2.1).

Inclusion criteria Exclusion criteria Aged 18 – 60 years Best-corrected visual acuity worse than 6/12 Emmetropia (±0.75D), or Astigmatism more than -2.00D High myopia (-6.00D or History of macular surgery or laser refractive surgery greater) Diagnosed with ocular disease (excluding myopia- related retinal changes) Presence of significant cataract Contraindication to pupil dilation Use of medication with known ocular side effects Inability to fixate steadily Sensitivity to bright light Diabetes mellitus Inability to provide informed consent

Table 2.2. Inclusion and exclusion criteria for experimental work in chapters 5, 6 and 7. 74

2.2. Clinical Procedures

2.2.1. Refraction and Keratometry

Autorefraction and keratometry was performed on all participants using the Nidek Tonoref II autorefractor (Nidek Co. Ltd., Japan) or Topcon TRK-2P autorefracto- keratometer (Topcon Corporation, Japan). Refractive error was refined from the autorefractor reading using subjective refraction without cycloplegia. Refractive error was subsequently expressed as spherical equivalent, which was defined as the sum of the sphere power plus half of the astigmatic power.

2.2.2. Axial Length Measurement

Axial length measurement of the study eye was performed using IOLMaster 500 (Carl Zeiss Meditec., Germany) according to manufacturer’s instructions. The average of 5 reliable measurements was used.

2.2.3. Autofluorescence Imaging

The study eye was dilated using 1% tropicamide (Alcon Laboratories, USA). An additional drop of 2.5% phenylephrine (Alcon Laboratories, USA) was instilled if the pupil was not mid-dilated after 10 minutes. Autofluorescence imaging commenced when the pupil was > 5mm in diameter. Macula-centred FAF and optic disc-centred PAF images were captured with 55˚ and 30° lenses using HRA+OCT (Heidelberg Engineering, Germany). The HRA+OCT was used in high-speed mode with automatic real-time averaging (ART) at 49 or 100 scans. The exception was for experimental work in Chapter 3, where ART was set at 25, 36 and 49 to study the effect of ART on the autofluorescence signals. FAF and PAF images were captured and saved as 496x496 or 768x768 pixel TIFF files and exported to MATLAB (R2015a version 8.5.0.197613; The Mathworks Inc., USA) or Photoshop CS 6 (version 13; Adobe Systems Inc., San Jose, CA., USA.) for autofluorescence intensity sampling.

2.2.4. Optical Coherence Tomography (OCT)

Optical coherence tomography (OCT) scans of the study eye were captured under pupillary dilation using Cirrus HD-OCT (ver. 5.1.1.6 and 7.0.1.290; Carl Zeiss Meditec, Germany). The optic disc cube (200x200) protocol was used. The subject was instructed to rest his chin and forehead firmly against the headrest, and to fixate on the centre of the internal fixation star in the machine. Once the scanner position and focus was optimised, the subject was instructed to blink once and to hold his eyes open and still for 5 seconds. Scanning was initiated. The Cirrus HD-OCT shows the scan quality on a scale of 0 to 10 at the top of the OCT report. Scans were accepted if the scan quality score was at least 6, as recommended by the manufacturer. The scan was inspected for artefacts due to blinking and eye movement. If such artefacts were present, the scan was repeated. The automatically segmented optic disc margin was also inspected for gross inaccuracies, which may occur in the presence of parapapillary atrophy (PPA) commonly found in highly myopic eyes. If such inaccuracy was detected, the operator manually adjusted the detected optic disc centre so that the software re-performed optic disc segmentation and RNFL thickness measurement.

2.2.5. Contrast Sensitivity

Photopic contrast sensitivity (20 cd/m2) was measured using the Visual Psychophysics Engine (version 1.06; purpose built software written and developed by Dr Neil Parry and Dr Ian Murray; Cambridge Research Systems Ltd, UK). The stimuli were sine wave gratings produced with ViSaGe stimulus generator (Cambridge Research Systems Ltd, UK) and presented on a cathode ray tube monitor that was calibrated using the ColorCal (Cambridge Research Systems Ltd, UK) every 6 months. The monitor was switched on for at least 30 minutes prior to the first measurement of contrast sensitivity. The gratings were modulated at 0.5Hz to avoid the effects of fade out at the lower spatial frequencies. Gratings subtended 10° to the eye were presented in increasing spatial frequency of 0.3, 0.5, 1, 2, 4, 6 and 8 cycles/degree (cpd).

The subject was seated 114cm from the monitor. Sphero-cylindrical refractive error of the study eye was compensated for the working distance (+0.75D) using trial frame and lenses. The non-study eye was patched.

Figure 2.1. Set up for contrast sensitivity testing. The stimuli were sine wave gratings modulated at 0.5Hz (A). The subject was seated 114cm from the computer screen with the non-study eye patched. Refractive error compensated for the working distance was corrected using trial frame and lenses. Using keyboard up/down arrow keys, the subject reduced the intensity of the stimulus until he could just see the grating (B).

The subject was instructed to rest his chin and forehead firmly against a headrest. Next, he was instructed to fixate on a small black cross in the centre of the monitor and to reduce the intensity of the sine wave gratings until he could just see it, using the up/down arrow keys on a keyboard (method of adjustment). The subject was told to blink and confirm the threshold before recording his response. To record a response, the subject would tap the spacebar once. The computer beeped when the response was recorded and the next grating would be displayed on the monitor. After a trial run with 1 – 2 gratings, the room lights were switched off so that the monitor was the only light source in the room and the subject was left to dark adapt for 10 minutes. The average of 3 repeated readings at each spatial frequency was used for data analysis. Data were expressed in decibels (dB).

2.3. Determination of Autofluorescence Image Quality

Autofluorescence imaging captures autofluorescence signals from the retina. The autofluorescence is presented in arbitrary greyscale units between 0 – 255 grey levels (GL). During image capture, eye lashes, eye lid and the pupil margin can block autofluorescence signal reaching the detector. These obstructions result in shadowy

77 patches at the periphery of the autofluorescence image. In order to extract valid autofluorescence data from the whole images, it is essential to ensure that such artefacts (if present) do not involve the region of interest. Since there is no established method to quantitatively determine image quality, we used a preliminary set of 65 fovea-centred fundus autofluorescence (FAF) and 46 optic-disc centred parapapillary autofluorescence (PAF) images to develop a procedure to determine image quality of autofluorescence images.

2.3.1. FAF Image Quality Analysis

The FAF images were opened in MATLAB (R2015a version 8.5.0.197613; The Mathworks Inc., USA) and image histogram was analysed using the imhist function (Figure 2.2). The central 90% of the image pixels were selected to measure the image contrast. The black pixels at 0GL, which corresponded to the black border of the image, were excluded.

For images with contrast ≥ 31GL, we further analysed the image brightness. Image brightness was defined as 푁푢푚푏푒푟 표푓 푝푖푥푒푙푠 x 푝푖푥푒푙 푖푛푡푒푛푠푖푡푦 . Next, the image was resized 푁푢푚푏푒푟 표푓 푝푖푥푒푙푠 푖푛 푡ℎ푒 푤ℎ표푙푒 푖푚푎푔푒 to a scale of 16.7µm/pixel and an annulus with an inner and outer radius of 5.2mm (155 pixels) and 5.6mm (168 pixels) centred on the FAF image (assumed to be the fovea) was cropped out (Figure 2.3). Thresholding of the annulus was needed to determine the final quality of the image.

The process of determining image quality is shown in Figure 2.4. For images with brightness ≤ 65GL, the cropped annuli were thresholded at 0.28. This ratio is equivalent to a cut-off intensity of 72GL. During thresholding, pixels below 72GL were converted to 0GL (black pixels) and the remaining pixels converted to 255GL (white pixels). If the number of black pixels in the thresholded annulus was ≤ 2100, the image was considered good quality. Otherwise, the image was rejected. The cut-off of 2100 black pixels corresponded to 16.5% of the number of pixels in the annulus. For images with brightness between 66 – 79GL, the cropped annuli were thresholded at 0.30 (equivalent to a cut-off intensity of 77GL). The criteria for determination of image quality was the same (≤ 2100 black pixels after thresholding to be considered good quality). Images with brightness ≥ 80GL but contrast between 31 – 39GL were rejected.

Figure 2.2. Histogram of an FAF image with the central 90% intensity range (shaded region) used as an index of image contrast. In this example, the small shoulder between 50 – 68GL was confirmed to comprise pixels from the fovea, optic disc and retinal vasculature using the threshold tool in Photoshop. Above 68GL, pixel intensities come from retinal autofluorescence.

Figure 2.3. Cropped annulus overlaid on the original FAF image. The annulus has an inner and outer radius of 5.2mm and 5.6mm and is centred on the FAF image.

Figure 2.4. Flowchart showing the process of identifying good and poor quality FAF images. The contrast and brightness of the FAF image is determined, then an annulus centred on the image is isolated. Depending on the contrast and brightness of the original image, the annulus is thresholded at 0.28 or 0.30, or rejected.

Results

We applied this objective grading method to 65 FAF images, and 46 images passed the criteria while 19 failed. The original FAF images are shown in Annex 2.1 and 2.2.

We then subjectively graded the images based on the presence of significant shadow encroaching onto the macular region from the image periphery. The subjective grading agreed with the objective grading for the 19 images that failed. Of the 46 images that passed objectively, the subjective grading was in agreement for 45 images but not for 1 image (subject 32t). Image of subject 32t was subjectively graded as poor quality due to the presence of shadows at the inferior periphery. However, it passed the objective grading method because the objective method only considered shadows that encroached within the central 5.2mm to 5.6mm (Figure 2.5) and the number of black pixels corresponding to these shadows did not exceed 2100 pixels.

Figure 2.5. FAF image from subject 32t. The annulus (after thresholding) is superimposed on the FAF image and highlights the shadows in the inferior periphery.

Discussion and Conclusion

The FAF image captures autofluorescence signals from the fundus and displays the signal in grayscale. Poor alignment of the cSLO and exit pupil and presence of eye lashes can obstruct the detected FAF signal. The purpose of this objective grading method is to detect such artefacts (shadows) from the image periphery so that poor images with reduced FAF signal can be identified.

The objective method used image contrast and brightness to determine the threshold level. For images with contrast ≤ 30GL, the images were automatically deemed to be of poor quality as such images were generally dim or contained significant amount of shadow artefact. Images with contrast between 31 – 39GL but brightness ≥ 80GL were also automatically excluded because such images had a hazy appearance where even the blood vessels and optic disc did not appear very dark, suggesting the presence of media opacity.

After the threshold level was determined, an annulus was cropped. The dimensions of the annulus were chosen to identify artefacts in the mid-periphery of the image while avoiding increasing amounts of nonuniformity towards the far periphery of the image. Analysing just an annulus instead of the entire image would be sufficient for us to determine whether artefacts were blocking FAF signals from the macular region.

The objective grading method we have developed agreed well with subjective grading. Disagreement between the two methods arose when the image grader placed greater emphasis on peripheral artefacts (compared to the more mid-peripheral annulus that the objective method uses) such as in the image from subject 32t.

The advantage of this objective method is that there is no need for an image grader to subjectively grade the image quality. There was minimal manipulation of the image other than to crop the annulus out from the image for thresholding. By using an annulus, we also minimised the effect of other retinal features that can produce hypoautofluorescence that ultimately becomes thresholded into black pixels, such as blood vessels, macular pigment and the optic disc.

2.3.2. Parapapillary Autofluorescence (PAF) Image Analysis

For objective qualification, an infra-red image from the Cirrus HD-OCT disc scan was aligned to the PAF image in Photoshop CS 6 (Figure 2.6). The infra-red image contained markers identifying the centre of the optic disc and the RNFL scan circle where RNFL thickness is measured. As the area of interest in the PAF image was in the region of the scan circle, we isolated an annulus with an inner and outer radius of 1.7mm and 2.8mm centred on the optic disc. The annulus was resized to 300x300 pixels and exported to MATLAB (R2015a version 8.5.0.197613; The Mathworks Inc., USA).

Figure 2.6. Alignment and cropping of PAF annulus. (A) The infra-red image from the deviation plot of the OCT report was aligned to the PAF image to identify the centre of the optic disc. (B) An annulus with an inner and outer radius of 1.7mm and 2.8mm was cropped from the PAF image for analysis of image quality.

Similar to FAF images, the thresholding method was also employed for PAF images. First, the histogram of the pixel intensities within the annulus was obtained from MATLAB. If the image brightness was ≤ 104GL, the annulus was thresholded at 0.2. This meant that pixels < 51GL were converted into 0GL (black pixels) while those ≥ 51GL were converted to 255GL (white pixels). If brightness was ≥ 105GL, the threshold level was set at 0.3. During thresholding, pixels < 77GL were converted to 0GL while the remaining pixels were changed to 255GL. Images that returned ≥ 7541 black pixels after thresholding were deemed to be of poor quality while those with ≤ 7540 black pixels were considered to be of good quality (Figure 2.7). The cut-off value of 7540 pixels corresponded to 10% of the total number of pixels in the annulus.

Figure 2.7. Flowchart of qualification process for PAF images. An annulus with an inner and outer radius of 1.7mm and 2.8mm is cropped from the PAF image, and the contrast and brightness parameters obtained. Based on the brightness of the annulus, the annulus is thresholded at either 0.2 or 0.3. The binary images that contain ≥ 7541 black pixels are considered to be of poor quality.

Results

A total of 46 images from 46 eyes of 46 subjects were analysed using the objective method. Forty-four images were deemed to be of good quality and 2 were poor quality. The original PAF images are shown in Annex 2.3 and 2.4.

We also subjectively graded the PAF images based on the presence of shadow artefact within 1.5 disc diameters of the disc margin. The results of the subjective grading were in agreement with the objective grading for all 46 images.

Discussion and Conclusion

The region of interest in PAF images is smaller than in FAF images, hence an annulus was cropped around the optic disc instead of analysing the whole 30˚ image. The size of the annulus was chosen to span the parapapillary region approximately 1 disc diameter from the RNFL scan circle so that any artefact that may potentially affect PAF intensity could be detected. The zone beyond the 2.8mm outer radius of the annulus was assumed to be irrelevant as they were too far from the optic disc. The parapapillary zone within 1.7mm radius of the optic disc was excluded from analysis to avoid sampling the optic disc and PPA. Unlike the process of determining FAF image quality, the use of the image contrast parameter was not necessary for PAF images because the images had good contrast. Therefore, the threshold values of 0.2 and 0.3 were decided based on the annulus brightness.

The objective method described here agreed well with subjective image grading. The strengths of the objective method are that it analyses only the region of interest in the PAF image. This allowed more accurate detection of artefacts such as shadows that are near the optic disc. In fact, there was less poor quality PAF images compared to FAF images because the area of interest in the PAF images was much smaller than in the FAF images. There was minimal manipulation of the PAF image aside from cropping the annulus. In the 46 images tested, none had PPA large enough to extend beyond the 1.7mm inner radius of the annulus. However, in the presence of significant PPA, one would expect that it would be necessary to manually exclude the pixels corresponding to the PPA from the total number of black pixels in the thresholded annulus.

2.4. Statistical Analysis

For all chapters, statistical analyses were performed in Graphpad Prism 7.01 (GraphPad Software Inc., CA, USA). Normality of data distribution was tested using D’Agostino-Pearson omnibus normality test. Statistical significance was set at p < 0.05. A combination of parametric and nonparametric statistical tests may be used within each chapter so that we might have greater power to detect statistical significance. Parametric data were expressed in mean and standard deviation while nonparametric data were expressed in median and interquartile range unless otherwise specified. The specific statistical analyses used in each chapter are described below.

In chapter 3, the intensity and standard deviation of pixels sampled from the autofluorescence images were compared using one-way ANOVA and Kruskall-Wallis tests. The repeatability of the FAF peaks detected using images captured at ART 25, 36 and 49 were calculated from 2 images captured consecutively for 3 subjects. Repeatability of FAF peak locations was calculated using the formula of Bland and Altman (1986):

퐶표푒푓푓𝑖푐𝑖푒푛푡 표푓 푅푒푝푒푎푡푎푏𝑖푙𝑖푡푦 (푙표푐푎푡𝑖표푛)

∑(푑𝑖푓푓푒푟푒푛푐푒 표푓 1푠푡 푎푛푑 2푛푑 푚푒푎푠푢푟푒푚푒푛푡)2 = 1.96 x (√ ) (1) 푛

Repeatability of FAF peak intensities was calculated using a modified formula to normalise the differences in intensity to the average intensity (Delori et al., 2011):

퐶표푒푓푓𝑖푐𝑖푒푛푡 표푓 푅푒푝푒푎푡푎푏𝑖푙𝑖푡푦 (𝑖푛푡푒푛푠𝑖푡푦)

= 1.96 푥 푆퐷 푑푖푓푓푒푟푒푛푐푒 표푓 1푠푡 푎푛푑 2푛푑 푚푒푎푠푢푟푒푚푒푛푡푠 푥 100 (2) ( ) 푎푣푒푟푎푔푒 표푓 2 푚푒푎푠푢푟푒푚푒푛푡푠

An exponential curve was fitted to the data of autofluorescence signal intensity over time in the third experiment of Chapter 3. The locations of FAF peaks identified from the 8 FAF images were compared using Friedman test.

In chapter 4, Mann-Whitney test was used to compare for differences between the age and axial lengths of the subjects from the 2 study sites. Kruskall-Wallis test was used to compare the FAF peak location between the data from 2 study sites. Spearman correlation was used to test for relationship between FAF peak location and refractive

86 error. Coefficient of repeatability for FAF peaks and intensities were calculated using formulae (1) and (2).

In chapter 5, Mann-Whitney test was used to compare for differences between the age and axial lengths of the emmetropic and myopic groups. Unpaired t-test was used to test for differences in spherical equivalent between the emmetropes and myopes.

For comparing contrast sensitivity between emmetropes and myopes and between the youngest and oldest subjects, Kruskall-Wallis and Dunn’s multiple comparisons test was used. For comparing contrast sensitivity between the shortest emmetropic and longest myopic eyes, one-way ANOVA was used. Spearman and Pearson correlation was used to test the relationship between parameters. To compare age-related FAF increase in emmetropes and myopes, exponential curves were fitted to autofluorescence data of each group. The z score was calculated for the rate constant, k, of each curve:

푘 −푘 1 (3) 푧 = 2 2 √푆퐸(푘1) +푆퐸(푘2)

In chapter 6, Mann-Whitney was used to compare the differences in age, spherical equivalent and axial length between the 2 groups. Kruskall-Wallis test was used to compare RNFL thickness between emmetropes and myopes. Spearman correlation was used to correlate RNFL thickness with age and with axial length. Either Pearson or Spearman correlation was used to correlate PAF intensities with RNFL thickness for each of the subjects. Repeatability of PAF intensities was calculated (formula 2). Lastly, linear regression was performed to determine the magnitude by which PAF could be attenuated by the overlying RNFL.

In chapter 7, Mann-Whitney test was used to compare the age and spherical equivalent between emmetropes and myopes. Kruskall-Wallis test was used to compare PAF intensities between emmetropes and myopes. Pearson and Spearman correlation was used to determine the relationship of PAF intensity with age in emmetropes and myopes respectively. To compare age-related PAF increase with age between emmetropes and myopes, the best fit exponential curves was fitted to PAF data of each group. The z score was then calculated for the rate constant, k, of each curve (formula 3).

2.5. References

BLAND, J. M. & ALTMAN, D. G. 1986. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1, 307-10. DELORI, F. C., GREENBERG, J. P., WOODS, R. L., FISCHER, J., DUNCKER, T., SPARROW, J. & SMITH, R. T. 2011. Quantitative measurements of autofluorescence with the scanning laser ophthalmoscope. Invest Ophthalmol Vis Sci, 52, 9379-90.

Chapter 3: Effect of Image Capture Settings on the Autofluorescence Signal

Contribution My work included collaboration in the design of the study together with my supervisor and co-supervisor. Participant recruitment was done by me and 6 Diploma in Optometry students at Singapore Polytechnic. Data collection was done by me. Dr J. M. F. Kelly and I develop the MATLAB code for sampling autofluorescence intensities from the images.

Publication Nil

Conference Presentation Nil

Acknowledgments Supported by the Singapore Polytechnic Final Year Project fund (CLS-15A154, CLS- 16A023).

3.1. Abstract

Purpose

Fundus autofluorescence (FAF) images can be captured clinically using a confocal scanning laser ophthalmoscope (cSLO) such as the Heidelberg Spectralis HRA+OCT, but the autofluorescence signals are weak so different approaches are used to improve signal strength. These include averaging several scans to create a single FAF image (referred to as automatic real-time averaging, ART) and manipulating detector gain (sensitivity) to reduce inherent noise and improve overall performance particularly for the detection of abnormalities. Unbleached photopigment absorbs the excitation laser, thereby reducing the amount of energy that reaches underlying RPE lipofuscin to stimulate autofluorescence. To counter this, a period of photobleaching can be incorporated prior to the recording of the autofluorescence image.

In order to determine how the image acquisition settings affect the FAF images, we studied 1) the effect of ART averaging on the intensity and standard deviation (SD) of pixels sampled, 2) the effect of detector sensitivity level on the autofluorescence intensities, and 3) the change in autofluorescence intensities sampled from consecutive FAF images captured without prior photobleaching.

Methods

All images were captured using the HRA+OCT (Heidelberg Engineering, Germany) after pupillary dilation.

ART level: Three healthy participants were involved. FAF images were captured using both 30° and 55° lenses. Each FAF image was averaged from 25, 36 and 49 scans (referred to as ‘ART 25’, ‘ART 36’ and ‘ART 49’). Images of the same eye were manually aligned in Photoshop CS 6. The images were exported to MATLAB and a custom code was used to sample FAF intensities from 5x5 pixel square areas at 23 locations spread across 8 meridians in the macula. The average intensity and SD of the pixel intensities were calculated across the 3 images captured at different ART levels for each of the participants.

Detector sensitivity level: Seven healthy participants were involved. Thirty degree FAF images were captured at 80, 85, 90, 95, 100 and 105 units. Fifty-five degree images were captured at detector sensitivity levels of 85, 90, 95, 100 and 105 units. All 90 images of the same eye were manually aligned in Photoshop CS 6. Pixel intensities from the FAF images were sampled in 5x5 pixel squares located at the fovea, optic disc (4000µm), and 2400µm eccentricities along the superior, temporal and inferior meridians were sampled. The change in FAF intensity across the detector sensitivity levels was plotted for 30° and 55°.

Photobleaching: Eight consecutive 55˚ FAF images were captured at detector sensitivity of 105 units, ART 49 and without prior photobleaching or dark adaptation for 1 young and healthy emmetropic subject. Since the images were captured consecutively, the subsequent image had a longer duration of exposure to the excitation laser than the preceding image, and the effect of different durations of laser exposure prior to image capture can be studied. FAF intensities were extracted using a custom MATLAB code that sampled grey level intensities from 5x5 pixel squares (spaced 10 pixels apart) along 8 cardinal meridians to obtain the FAF profile and identify FAF peaks. Additionally, FAF intensity sampled from eight 11x11 pixel squares positioned 2000 - 2550µm from the fovea along the 8 cardinal meridians was plotted to study the increase in macular autofluorescence intensity across the 8 consecutive images.

Results

ART level: For 30° and 55° images, average intensity of the sampled FAF were similar among the images captured at ART 25, 36 and 49. In 55° images, 2 of the 3 subjects demonstrated significantly lower SD of the pixel intensities from ART 49 images compared to ART 25 and 36. In 30° images, there was a trend for SD to be lowest in the ART 49 images. The effect of ART on pixel intensity SD was more pronounced in 55° than 30° images.

Detector sensitivity level: Pixel intensities of sampled FAF gradually increased as detector sensitivity is increased. Macular autofluorescence increased the most, followed by the fovea and optic disc. For 30° and 55° images, the pixel intensities exceeded 175 grey levels (GL) when detector sensitivity was ≥ 90 units and 105 units for Caucasians, and > 100 and 105 units for Asians, respectively. FAF profiles plateaued when pixel intensities exceeded 200GL.

Photobleaching: FAF profiles showed gradual increase in intensity from the 1st to 5th images and minimal change in intensity from the 5th image onwards (equivalent to 24 -

30 seconds of total laser exposure). FAF peaks were consistently identified at similar eccentricities across the 8 consecutive images.

Conclusion

ART level: ART level does not affect the intensity of the FAF image or repeatability of the detected FAF peaks. As expected when ART 49 was used there was a trend for SD of sampled pixels to decrease, especially for 55˚ images.

Detector sensitivity level: To optimise contrast, 30° images can be captured at detector sensitivity of 85 – 90 units for Caucasian eyes, and 95 units for Asian eyes, whereas 55° images can be captured between 95 – 100 units for both Caucasians and Asians.

Photobleaching: FAF intensity increases with increasing duration of exposure to the excitation laser and photopigment bleaching. In 55° images, there was negligible increase in autofluorescence intensity after 30 seconds of laser exposure. FAF peak locations were similar among the 8 images, suggesting that the peak locations were not affected by the duration of laser exposure prior to image capture.

3.2. Introduction

Fundus autofluorescence (FAF) imaging uses a low power 488nm laser to excite RPE lipofuscin and elicit autofluorescence. As autofluorescence signal is very weak, a single autofluorescence scan is very noisy. Noise is the random fluctuation of pixel intensity values. The type of noise in FAF images is primarily photon noise and readout noise (Bennett, 2007) . Photon noise is an inherent natural variation in the detection of light (photons) due to the random intervals by which photons reach the detector. Photon noise has a Poisson distribution, so that there is a square root relationship between signal and noise. Photon noise gives the FAF image a grainy appearance. The second type of noise, readout noise, is also known as amplifier noise and is due to the ‘imperfect nature of signal conversion and the amplification process’ (Bennett, 2007). The greater the gain setting, the greater the amount of noise. In a single FAF scan (no signal averaging performed), the optic disc and major retinal vasculature appear as dark silhouettes and the rest of retina appears dark grey with a lot of ‘static’ (noise). In order to obtain an image that can be interpreted clinically, strategies to improve signal-to-noise ratio are employed and these are described below.

Signal averaging can be performed using a function known as ‘automatic real-time (ART) averaging’ on the HRA+OCT to improve signal-to-noise ratio. The ART averaging function aligns and performs signal averaging on up to 100 individual autofluorescence scans to form one image. By doing so, random noise will be minimised while signal, which is systematic, will increase. However, since there is a square root relationship between signal and noise, the improvement in signal-to-noise ratio decreases as the number of scans averaged increases. Increasing the number of scans for signal averaging extends the duration of imaging, and this could paradoxically introduce more variability into the data (Schmitz-Valckenberg and Fitzke, 2012). A balance needs to be struck between signal-to-noise ratio and imaging duration.

The light detector in the HRA+OCT detects autofluorescence signals from the retina. The magnitude of signal amplification can be changed by adjusting the detector sensitivity level. If detector sensitivity is too high, it goes into a nonlinear operating range so that excessively strong signals are underestimated (Delori et al., 2011). The image appears overexposed with little variation in intensity. If detector sensitivity is too low, the image appears dark with poor contrast. Variations in autofluorescence intensity across the fundus cannot be discerned from the images when the contrast is very poor.

Photopigment such as rhodopsin are found in the outer segment of the photoreceptor. Unbleached photopigment absorbs the 488nm excitation laser during autofluorescence image capture. This reduces the amount of laser energy reaching underlying RPE lipofuscin to elicit autofluorescence, and decreases the intensity of the autofluorescence signal that reaches the detector. Delori et al. (2011) recommended a 20- to 30-second photobleaching period (using the standard laser power of 280μW on the Heidelberg cSLO) for 30° autofluorescence imaging to reduce photopigment absorption of the excitation laser to < 5%, but no guideline has been determined for 55° imaging. The amount of photopigment that is bleached by an exposure to light is determined by how much light energy reaches the photoreceptors. This depends on the intensity and duration of the light, the pupil size, and ocular absorption. On the HRA+OCT, laser power is fixed and imaging duration is independent of the area of capture (55˚ or 30˚). Assuming that patient-based factors such as pupil size and ocular absorption are kept constant, then the same amount of laser energy is spread across a larger area of retina during 55˚ compared to 30˚ imaging. Therefore the amount of energy incident on a single RPE cell is lower in 55˚ imaging than 30˚ imaging. Hence, it would take a longer duration of laser exposure to bleach photopigment by 95% when using a 55˚ lens for image capture compared to 30˚ lens.

This chapter comprises 3 parts. Part A studies the effect of ART level on the FAF images. Part B studies how detector sensitivity affects the intensity of the FAF signals, and Part C is an experiment to study how inadequate photobleaching during image capture affects the FAF intensity and the identification of FAF peaks from the images.

3.3. Part A - Effect of Image Averaging (Automatic Real-time Averaging) on FAF Signal

3.3.1. Methods

3.3.1.1. Ethics and Subjects

The study procedures were approved by the respective institutional review boards at Singapore Polytechnic and Khoo Teck Puat Hospital (Singapore). All participants gave their informed consent prior to participation. Three healthy participants were recruited in this part of the study. The 3 subjects were aged 23, 27 and 37, and had spherical equivalent ocular correction between +0.38D to -0.50D.

3.3.1.2. Autofluorescence Imaging

The Spectralis HRA+OCT cSLO (Heidelberg Engineering, Germany) was used to capture FAF images. The HRA+OCT uses a 488nm argon laser for excitation and a barrier filter transmits autofluorescence signals between 500nm and 680nm towards the detector (Delori et al., 2011).

One eye of each subject was dilated with tropicamide 1% (Alcon Laboratories Inc., USA) to ≥ 5mm diameter for imaging. Images were captured in high-speed mode without normalisation in order to facilitate comparison of relative FAF intensities. Image focus and position were first adjusted in infra-red mode before the HRA+OCT was switched to autofluorescence mode.

Macula-centred 55° and 30° FAF images of the 3 emmetropic subjects were captured. Since signal-to-noise ratio increases as the square root of the number of scans averaged, the improvement in signal-to-noise ratio reduces as the number of scans averaged increases. Hence it is not necessary to extend the duration of image capture for the purpose of obtaining as many scans as possible (i.e. 100 scans, the maximum allowed on the HRA+OCT) for ART averaging. In this experiment, the FAF images were captured by averaging 25, 36 and 49 scans (henceforth referred to as ‘ART 25’, ‘ART 36’ and ‘ART 49’). The lower range of 25 was selected as it was close to 16 scans, a value commonly used in manual selection of individual scans for the calculation of mean image. The upper limit of 49 was selected because no further reduction in noise (as indicated from the FAF image graininess) was observed beyond 49 scans. Additionally, extended duration of imaging to study ART above 49 scans may 95 introduce more variability to the results due to poorer fixation stability; durations of laser exposure to capture one ART 25, 36 and 49 image were approximately 3, 4 and 6 seconds.

3.3.1.3. Image Sampling

All images from the same eye were aligned in Photoshop CS 6. The process of aligning the images took 10 – 15 minutes per subject. The ART 49 image was used as the reference image and the ART 25 and 36 images were manually aligned to it. Alignment was achieved by resizing and rotating the images to match the reference image, using the optic disc and retinal vasculature as landmarks for alignment. The images were subsequently scaled to 20µm/pixel. FAF intensities were sampled using MATLAB (R2015a version 8.5.0.197613; The Mathworks Inc., USA). The locations for FAF sampling were chosen to cover the central, midperipheral and peripheral regions of the images (Figure 3.1). In the 55° images, FAF intensities were sampled from 5x5 pixels squares at 2000, 4000 and 6000µm eccentricity along 8 cardinal meridians except nasal region, where FAF was sampled at 2000 and 6000µm eccentricities only to avoid the optic disc. In 30° images, the autofluorescence intensities were sampled at 2000, 3000 and 4000µm eccentricity along 8 cardinal meridians, except nasally. Sampling along the nasal meridian was done at 2000 and 3000µm eccentricity to avoid the significant reduction of FAF intensity within the optic disc. A total of 23 FAF samples were analysed from each image. If the eccentricity that was supposed to be used for sampling corresponded to a blood vessel, an adjacent location along the meridian (200 - 600µm away) was sampled.

Figure 3.1. Location of FAF sampling in (A) 55° and (B) 30° images. Each box is 5x5 pixels large. FAF in the fovea and optic disc were not sampled. If the sampled location contained a blood vessel, an adjacent location along the meridian (200 - 600µm away) was sampled instead. Images were resized and aligned to a scale of 20µm/pixel before FAF was sampled.

FAF peak was identified using custom MATLAB code, which sampled 5x5 pixel squares across the 8 cardinal meridians of the retina. The repeatability of the FAF peaks detected using images captured at ART 25, 36 and 49 were calculated from 2 images captured consecutively for 3 subjects. Repeatability of FAF peak location was calculated using the formula of Bland and Altman (1986):

퐶표푒푓푓𝑖푐𝑖푒푛푡 표푓 푅푒푝푒푎푡푎푏𝑖푙𝑖푡푦

∑(푑푖푓푓푒푟푒푛푐푒 표푓 1푠푡 푎푛푑 2푛푑 푚푒푎푠푢푟푒푚푒푛푡)2 = 1.96 푥 (√ ) (1) 푛

Repeatability of FAF peak intensities was calculated using a formula that normalises the differences in intensity between measurements to the average intensity of the 2 measurements (Delori et al., 2011):

Coefficient of Repeatability

= 1.96 x SD (difference of 1st and 2nd measurements/average of 2 measurements) x 100 (2)

3.3.1.4. Statistical Analysis

Statistical analyses were performed using Prism 7.01 (Graphpad Software, Inc., USA). Normality testing was done using D’Augustino-Pearson omnibus K2 normality test. This normality test calculates skew and kurtosis to determine how much each data point deviates from a Gaussian distribution. P value is calculated from the sum of the deviations. Using MATLAB, the mean and standard deviation (SD) of the grey level (GL) of the 25 pixels sampled was obtained and used as a measure of noise in the image. Data were compared among the ART 25, 36 and 49 images using One-way ANOVA or Kruskall-Wallis test. Statistical significance was set at p < 0.05.

3.3.2. Results

3.3.2.1. Average Intensity of Sampled FAF from 55° and 30° Images

For the purpose of describing the effect of ART level on the FAF signal, data from 55° images and 30° (n = 3) were analysed. Mean intensity sampled from the 55° FAF images (n = 3) captured at ART 25, 36 and 49 are presented in Figures 3.2A, C, E and G. No statistically significant difference in averaged FAF intensity was observed among the 3 ART levels for any of the subjects (One-way ANOVA, F(2, 66) ≤ 2.37, p ≥ 0.101).

The mean intensity sampled from the 30° FAF images (n = 3) captured at ART 25, 36 and 49 are presented in Figure 3.2B, D, F and H. No statistically significant difference in averaged autofluorescence intensity was observed among the 3 ART levels for subjects 1 and 3. Subject 2 demonstrated significantly lower autofluorescence intensity in ART 49 compared to ART 25 and ART 36 (One-way ANOVA test, F(2, 66) = 6.59, p = 0.003).

Figure 3.2. Autofluorescence intensity (median and interquartile range) extracted from 55° (A, C, E, G) and 30° (B, D, F, H) FAF images averaged from 25, 36 and 49 scans. Each bar represents average FAF signal from 23 points as shown in Figure 3.1. Autofluorescence intensities from 55° images were similar among the 3 ART levels for every subject (One-way ANOVA, F(2,

66) ≤ 2.37, p ≥ 0.101). For 30° data, autofluorescence intensities were similar among the 3 ART levels for every subject except subject 2, who demonstrated lower intensity in ART 49 image than the other 2 (One-way

ANOVA, F(2, 66) = 6.59, p = 0.003).

3.3.2.2. Standard Deviation (SD) of Sampled FAF from 55° and 30° Images

For 55˚ images, the SD of the pixel intensities sampled among the 3 ART levels was lower for ART 49 compared to ART 25 and ART 36 (Kruskall-Wallis test, H(2) = 20.38, p < 0.0001) (Figures 3.3A, C , E, G). When analysis was done separately for each subject, this trend for ART 49 to give lower SD was observed in subject 2 (Kruskall- Wallis test, H(2) = 10.60, p = 0.005) and subject 3 (H(2) = 17.3, p = 0.0002).

For 30° images, the SD of the pixel intensities sampled was lower for ART 49 compared to ART 36 (Kruskall-Wallis test, H(2) = 15.7, p = 0.0004) (Figures 3.3B, D, F, H). When the analysis was done separately for each subject, Kruskall-Wallis test for subject 1’s data returned significant results (H(2) = 6.43, p = 0.04) but subsequent post-hoc comparison (Dunn’s multiple comparisons) was not statistically significant. Both subjects 2 and 3 showed similar SD for ART 25 and 36, and lower SD in ART 49. The lower SD for ART 49 was statistically significant for subject 2 but not subject 3 (Kruskall-Wallis test, H(2) = 10.14, p = 0.006 and H(2) = 4.64, p = 0.098 respectively).

We also compared the repeatability of the FAF peak locations and intensities from 55˚ and 30˚ images captured at ART 25, 36 and 49. Repeatability of FAF peak locations and intensities were similar regardless of whether the data were obtained from ART 25, 36 or 49 images.

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Figure 3.3. Standard deviation of pixel intensities extracted from 55° (A, C, E, G) and 30° (B, D, F, H) FAF images captured at ART 25, 36 and 49 images for each subject. For 55° images, the standard deviation of the intensity pixels within each sampling square was lower in ART 49 images compared to ART 25 and 36 (Kruskall-Wallis test, H(2) = 20.38, p < 0.0001) (A). Analysis of the subjects’ data separately reveal that there is a trend for ART 49 to give lower standard deviation, and this effect was larger in subjects 2 (Kruskall-Wallis test, H(2) = 10.60, p = 0.005) and subject 3 (H(2) = 17.3, p = 0.0002)(E, G). For 30° images, standard deviation of autofluorescence intensities was lower in ART 49 image compared to ART 36 but not ART 25 (B) (Kruskall-Wallis test, H(2) = 15.7, p = 0.0004). Separate analysis by subject revealed that this observation was primarily due to data from subject 2 (F) (Kruskall-Wallis test, H(2) = 10.14, p = 0.006). Kruskall-Wallis test for subject 1 returned statistical significance (H(2) = 6.43, p = 0.04) but Dunn’s multiple comparisons yielded no significant results (D). The bar graphs show the median and interquartile range.

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3.4. Part B - Optimising Detector Sensitivity Level for 55° and 30° Images

3.4.1. Methods

3.4.1.1. Ethics and Subjects

Ethical approval was obtained from the Institutional Review Board which provides ethical oversight for the Manchester Royal Eye Hospital (MREH; Manchester, UK). Informed consent was obtained from all participants. The experiment was carried out in accordance with the tenets of the Declaration of Helsinki (World Medical Association: 1964-2008).

Seven normal subjects without ocular disease were recruited from University of Manchester staff and postgraduate student population (Table 3.1). Three of them were Caucasians, 3 were Chinese and 1 was Pakistani.

Age (yr) Spherical equivalent (D) Ethnicity Gender 26 0.00 Caucasian Female 31 -1.00 Caucasian Male 30 -1.25 Pakistani Male 25 -2.00 Chinese Female 29 -3.00 Chinese Male 32 -4.00 Chinese Female 29 -8.00 Caucasian Female

Table 3.1. Demographic data of 7 subjects involved in this study. The data are arranged according to the magnitude of spectacle spherical equivalent.

3.4.1.2. Autofluorescence Imaging

The Spectralis HRA+OCT (Heidelberg Engineering, Germany) cSLO was used to capture FAF images. One eye of each subject was dilated with tropicamide 1% (Alcon Laboratories Inc., USA) to ≥ 5mm diameter for imaging. Images were captured in high-speed mode without normalisation in order to facilitate comparison of relative FAF intensities. Image focus and position were first adjusted in infra-red mode before the HRA+OCT was switched to autofluorescence mode. Thirty degree and 55° macula- centred FAF images were captured with ART averaging at 100 scans and saved as 496x496 pixel TIFF files, giving a resolution of 20µm/pixel. Thirty degree images were

103 captured at 80, 85, 90, 95, 100 and 105 units. Fifty-five degree images were captured at detector sensitivity levels of 85, 90, 95, 100 and 105 units.

All images from the right eye of each subject were aligned. Custom MATLAB (R2015a version 8.5.0.197613; The Mathworks Inc., USA) code was used to sample FAF intensities in a 5x5 pixel square at the fovea, optic disc (sampled approximately 4000µm nasal to the fovea), and the perifovea (approximately 2400µm) in the superior, inferior and temporal retina (Figure 3.4). FAF intensities were expressed in grey level (GL). If the sampled location happened to correspond to retinal vasculature, an adjacent location to it (200 - 400µm away) was used.

Figure 3.4. Sampled regions for studying the effects of detector sensitivity on pixel intensity. Pixel intensities in 5x5 pixel squares located at the fovea, optic disc (4000µm eccentricity), and 2400µm eccentricities along the superior, temporal and inferior meridians were sampled.

3.4.2. Results

3.4.2.1. Autofluorescence Intensity

Figure 3.5 plots the autofluorescence intensity from the initial (lower detector sensitivity) image against that from an image captured at the subsequent (higher) detector sensitivity level. In 55° images, there is a steady increase in intensity as detector sensitivity is increased. In 30° images, there is a steady increase in pixel

104 intensities up until the point where intensity of the initial image exceeds 175GL. When the initial intensities exceeded 175GL, increasing detector sensitivity led to an underestimation of the pixel intensities as the pixels approached saturation at 255GL.

A line was fitted to the data with initial intensity below 175GL. The slope of the best fit line was 1.64 and 1.37 for 55˚ and 30˚ data respectively. The relatively gentler slope in 30˚ data indicates that the 30˚ images saturated (exceeded 175GL) at lower detector sensitivity compared to 55˚ images.

Figure 3.5. Plot of autofluorescence intensity from image of lower detector sensitivity level against intensity from higher detector sensitivity level for 55° (n = 7) and 30° (n = 7) images. Dotted line indicates initial intensity of 175GL. A line is fitted to data with initial intensity below 175GL.

3.4.2.2. Caucasian vs Asian Eyes

Figure 3.6 shows the FAF intensity sampled from 5 retinal locations at different detector sensitivity levels. Intensity at the superior, inferior and temporal locations increased by similar amounts across the different sensitivity levels, and the change in intensity was greater compared to the fovea and optic disc where there was less autofluorescence.

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Figure 3.6 also shows how differences in intensities among the different regions were enhanced by high detector sensitivity, which can help to differentiate regions with subtly higher/lower autofluorescence intensity. For example, in 30° Asian eyes, the intensity differences among the superior, inferior and temporal samples were most apparent in the images captured at detector sensitivity of 100 units while for 30° Caucasian eyes, this was at 95 units. For 55° Caucasian and Asian images, this was at 105 units.

Comparing the graphs for Caucasians and Asians, we can see that Caucasian eyes exceed 175GL at lower detector sensitivity level than Asian eyes. For 55° Caucasian images, the intensities exceeded 175GL when detector sensitivity was 105 units whereas in the Asian images, only some of the data points exceeded 175GL. For 30° Caucasian images, some of the intensities exceeded 175GL at detector sensitivity of 90 units whereas for Asian images, the pixel intensities approached 175GL at detector sensitivity of 100 units. The Caucasian subjects had larger inter-subject variability in autofluorescence intensity compared to Asian subjects

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Figure 3.6. Autofluorescence intensity sampled from 5 locations on the retina. In 55° images, the pixel intensities exceed 175GL (dotted line) at detector sensitivity of 105 units for Caucasians and just approach 175GL for Asians at the same detector sensitivity. In 30° images of Caucasian eyes, some of the intensities of the data points exceed 175GL when detector sensitivity is 90 and above, whereas this occurs at detector sensitivity of 100 for Asian eyes.

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3.4.2.3. Effect of Detector Sensitivity on FAF Profile

The FAF profiles illustrate the effect of detector sensitivity on FAF intensity. If detector sensitivity is too low, the FAF signal from the retina is not well-distinguished from blood vessels and the optic disc. The optimal sensitivity level should maximize the intensity differences of the retina where RPE lipofuscin accumulates, from areas where autofluorescence is low, such as at retinal vasculature, the optic disc and the fovea. Figure 3.7 shows FAF profiles from one Asian and one Caucasian eye captured at various sensitivity levels.

The profiles from 55° images show a large jump in autofluorescence intensity between the images captured at detector sensitivity of 100 and 105 units (Figure 3.7A, B). The profiles from 30° images show a relatively steady increase in autofluorescence intensity as detector sensitivity is increased (Figure 3.7C, D). As detector sensitivity is increased, the difference in intensity between the fovea and the nasal and temporal FAF peaks also increases. The only exception is in the Caucasian eye, for profiles from 30° images captured at sensitivity of 100 and 105 units (Figure 3.7C) due to saturation of pixel intensities. The profile from the image captured at detector sensitivity of 95 units retained a similar shape as that from the lower detector sensitivity image, even though the profile exceeded 175GL in some areas. This shows that even when parts of the profile exceed 175GL, the overall shape of the profile is maintained. It is only at excessively high detector sensitivity when pixel intensities approach 200GL that the FAF profile starts to plateau.

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Figure 3.7. Horizontal FAF profile of 1 Asian and 1 Caucasian subject extracted from 55° (A, B) and 30° (C, D) FAF images captured at detector sensitivity levels of 80, 85, 90, 95, 100 and 105 units. OD denotes the optic disc. Dotted line indicates 175GL, which is the maximum GL at which the HRA detector operates linearly (Delori et al., 2011). Despite parts of the FAF profile exceeding 175GL at sensitivity of 105 units (A, B, C) and at 95 units in (D), there was no flattening of the peaks relative to the rest of the profile and the difference in intensity between fovea and peak was enhanced relative to lower sensitivity level. The only exceptions were the profiles captured at sensitivity 100 and sensitivity 105 units in (D), where parts of the profile exceeded 200GL.

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3.5. Part C - Effect of Photobleaching on FAF

3.5.1. Methods

3.5.1.1. Ethics and Subjects

The study procedures were approved by the respective institutional review boards at Singapore Polytechnic and Khoo Teck Puat Hospital (Singapore). The participant was a 27 year old Chinese male with spherical equivalent +0.38D. He gave his consent prior to participation. He was healthy with no ocular disease.

3.5.1.2. Autofluorescence Imaging

The Spectralis HRA+OCT cSLO (Heidelberg Engineering, Germany) was used to capture FAF images. The right eye was dilated with tropicamide 1% (Alcon Laboratories Inc., USA) to > 5mm diameter for imaging. During pupillary dilation, the subject rested in a well-lit room. There was no dark adaptation period prior to image capture. When the pupil was sufficiently dilated, the subject was instructed to rest his chin and forehead firmly against the headrest of the HRA+OCT. Focus and positioning of the HRA+OCT was made in infra-red mode. Next, detector sensitivity was adjusted to 105 units. Since the purpose of this experiment was to study how FAF intensity changes as the amount of photobleaching increases, recording of the autofluorescence images commenced the moment the HRA+OCT was switched to autofluorescence mode. No photobleaching period was incorporated. Images were captured in high-speed mode without normalisation in order to facilitate comparison of relative FAF intensities. A series of 8 consecutive FAF images were captured. Each image took 6 seconds to capture and all 8 FAF images were captured in 48 seconds. The images were saved as 796x796 pixel TIFF files, giving a resolution of 20µm/pixel.

3.5.1.3 Image Analysis

All 8 FAF images were manually aligned in Photoshop CS 6 as described above using retinal vessels and the optic disc as landmarks. This process took about 3 minutes per image. A custom MATLAB code was used to extract FAF intensity data across 8 cardinal meridians of the images (Figure 3.8) (Annex 3.1). FAF profiles across the 8 meridians of the retina were plotted using the 1st, 3rd, 5th and 7th images to study changes to the FAF profile sampled from images captured at different levels of photobleaching. The average FAF intensity sampled from 11x11 pixel squares at 2000µm to 2550µm eccentricity (avoiding retinal vasculature)

112 along 8 meridians was plotted to study the increase in FAF intensity from the 1st to 8th images. We also identified FAF peaks along 8 cardinal meridians of the 8 FAF images to investigate whether inadequate photobleaching affected the identification of FAF peak location.

Figure 3.8. Locations of sampling grid in one FAF image. Eight FAF images were manually aligned prior to insertion of sampling grid. FAF intensities were sampled from thirty-eight 5x5 pixel squares (white squares) spaced at 10 pixel intervals along the horizontal and vertical meridians. FAF profiles along the sampled meridians were compared among the 1st, 3rd, 5th and 7th FAF images.

3.5.1.4. Statistical Analysis

Statistical analyses were performed in Graphpad Prism 7.01 (GraphPad Software Inc., La Jolla, CA, USA). Normality of data distribution was tested using D’Agostino- Pearson omnibus normality test. Statistical significance was set at p < 0.05. Friedman test was used to compare FAF peaks identified along 8 cardinal meridians from 8 consecutive images.

An exponential curve was fitted to the data of autofluorescence signal intensity over time. The locations of FAF peaks identified from the 8 FAF images were compared using Friedman test.

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3.5.2. Results

3.5.2.1. FAF Profile

Figure 3.9 shows the FAF profile extracted from the 1st, 3rd, 5th and 7th FAF images captured. The duration of laser exposure during image capture for each image is equivalent to 0 – 6 seconds, 13 – 18 seconds, 25 – 30 seconds and 37 - 42 seconds respectively. The profiles have been divided into 5 regions. Regions 1 and 5 span the outer > 5000µm to 7200µm, the central > 2000µm to 5000µm are denoted as regions 2 and 4 and the central 2000µm is the fovea.

Starting from the temporal meridian (Figure 3.9A), FAF profile shows a slight decrease in FAF intensity from the 1st to 7th images captured. This is inconsistent with the effect of photobleaching, since we expect FAF intensity to increase as photopigment becomes bleached. In region 2, there is gradual increase in FAF intensity, with the intensity changes being most apparent between the eccentricities of 2000µm and 4000µm. Region 3 (fovea) shows no consistent or obvious change in FAF intensity. Before the optic disc (region 4), there is increase in FAF intensity. The optic disc itself shows no changes in intensity over time. Beyond the optic disc (region 5), FAF continues to increase, with the change in intensity increasing towards the edge of the image.

Along region 1 of the superior meridian (Figure 3.9B), FAF intensity increases from the 1st to 5th images captured. Intensity between the 5th and 7th images showed little increase. Region 2 shows steady increase in FAF intensity from the 1st to 7th images. The fovea again showed no appreciable increase in FAF intensity. Inferiorly (region 4), FAF increases over time but the increment is most apparent between 2000µm and 4000µm. In region 5 (inferior meridian), FAF intensity increased over time between the 5000µm and 6000µm eccentricities. Beyond 6000µm, FAF intensity appeared stable.

In region 1 of the superior-temporal meridian (Figure 3.9C), FAF intensity did not increase from the 1st to the 7th images. In region 2, there is mild increase in FAF intensity from the 1st to 5th images but not in the 7th image. The fovea showed no appreciable increase in FAF intensity. Along the inferior-nasal meridian (regions 4 and 5), FAF intensity increased from the 1st to 7th images.

In region 1 of superior-nasal meridian (Figure3.9D), FAF intensity increased continuously from the 1st to 7th images. In region 2, FAF intensity plateaued between the 5th and 7th images. The fovea showed no change in FAF intensity over time. In the inferior temporal meridian (region 4), FAF increased by a small

114 amount from the 1st to 7th images. In region 5, FAF intensity demonstrated minimal change over time.

Figure 3.9. FAF profile across the horizontal, vertical and 2 oblique meridians extracted from the 1st, 3rd, 5th and 7th images. These images capture the autofluorescence intensity after 0 – 6, 13 – 18, 25 – 30 and 37 – 42 seconds of photobleaching, and demonstrate the change in FAF intensity at different amounts of photobleaching. The corresponding strip of retina from the FAF image is shown below each profile. Regions 1 and 5 span the outer > 5000µm to 7200µm, while the central > 2000µm to 5000µm are denoted as regions 2 and 4 and the central 2000µm is the fovea. (* indicates retinal capillaries, ♦ indicates larger retinal vessels).

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3.5.2.2. Autofluorescence Intensity over Time

An exponential curve was fitted into the intensity data to show the change in AF intensity with time (Figure 3.10). The exponential curve indicates that for the FAF intensity to reach 99% of its maximum intensity, it would need to be bleached for 45 seconds. Practically, this may be difficult to achieve. A 20-second bleach would bring the FAF to 96.5% (within 5GL) of its maximum intensity. The increase in FAF intensity between 20 seconds and 45 seconds is 4GL.

Figure 3.10. The change in mean FAF intensity from 8 consecutively- captured FAF images of 1 subject. Exponential curve was fitted to the data. FAF intensity increased by about 10GL from the 1st to 8th image. The exponential curve indicates that for the FAF to increase to 99% of its maximum intensity, the retina would need to be bleached for 45 seconds. A 20-second bleach would be adequate to bring the FAF to 96.5% (within 5GL) of its maximum intensity.

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3.5.2.3. FAF Peak Location

Since it may not be practical to fully bleach photopigment for image capture, we compared the FAF peak location identified along the 8 cardinal meridians from the 8 FAF images (Figure 3.11) to see if incomplete photobleaching affected the identification of FAF peak locations.

Figure 3.11. FAF peak locations identified from 8 consecutive images of one eye. (T = temporal, ST = superior-temporal, S = superior, SN = superior-nasal, N = nasal before the optic disc, N2 = nasal beyond the optic disc, IN = inferior-nasal, IT = inferior-temporal.)

Friedman test showed no significant difference in the FAF peak locations among the 8 images for all meridians (p = 0.081). It can be seen from Figure 3.11 that the FAF peaks were consistently identified in all 8 images despite different levels of photobleaching. The nasal FAF peaks (N and N2) were detected at almost exactly the same eccentricity across all 8 images. The temporal, superior-nasal and inferior-temporal peaks were relatively less repeatable and this may be due to the shape of the FAF profile along these meridians. For example, the nasal autofluorescence profile demonstrates sharp, prominent peaks so that the FAF peak location can be detected with a high level of consistency. The remaining meridians have a more gradual FAF profile. This gradual slope in FAF means that detection of FAF peaks is more variable as even 1GL difference in intensity could affect the location of the identified peak. Another reason for the higher variability of the temporal peak is the presence of a shadow on the temporal side of the images. As imaging continued, the alignment of the camera and pupil drifted nasally, resulting in a shadow forming on the temporal part of the image and obscuring FAF signal.

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3.6. Discussion and Conclusion

3.6.1 Part A – Effect of Image Averaging (Automatic Real-time Averaging) on FAF Signal

Signal averaging is performed to improve signal-to-noise ratio. An image with low signal-to-noise ratio will appear ‘grainy’ and fine details in the image may be obscured. Comparing the ART 25, 36 and 49 images, the images averaged from 25 and 36 scans were visibly more grainy (noisy) than the images averaged from 49 scans. The ART 49 images had the least variable pixel intensities, suggesting that ART 49 further reduced noise in the images compared to ART 25 and ART 36, and this is consistent with the observed reduction of ‘graininess’ of the images at ART 49. However, the statistical analyses of average intensity and variability of pixel intensities sampled from various locations in the 55° FAF image indicated that using 25, 36 or 49 scans for signal averaging had no statistically significant influence on the pixel intensities and variability. For 30° images, there was no difference among the ART levels in terms of FAF intensity but there was a trend for ART 49 images to give lower variability in the intensity of the sampled pixels (Figure 3.3). ART level did not affect the location and intensity of identified FAF peaks.

It should be noted that these results were calculated from 3 cooperative, young emmetropic subjects. Young subjects have clear ocular media therefore it is possible to obtain FAF images with low amount of noise. Highly myopic and older eyes have more vitreous floaters and reduced crystalline lens light transmission compared to young and emmetropic eyes. Vitreous floaters and reduced crystalline lens transparency obscure FAF signal, resulting in lower signal-to-noise ratio. In addition, the myopic eye is longer than the emmetropic eye. This means that the laser power is spread over a larger area in the myopic eyes and hence less energy reaches the RPE (inverse square law) (Delori et al., 2011).

In conclusion, ART level may not have a significant effect on the intensity of the autofluorescence image or repeatability of the detected FAF peaks. ART 49 tends to give lower variability in the intensities of the pixels sampled, especially for 55° images.

3.6.2. Part B – Effect of Detector Sensitivity on FAF

The detector in the HRA+OCT starts to underestimate intensities of pixels > 175GL (Delori et al., 2011). However we observed that even when parts of the FAF profile

118 exceeded 175GL, the shapes of the FAF profiles did not distort (Figure 3.7). In fact, the differences between foveal and peak intensities were enhanced. Only when pixel intensities exceeded 200GL did the FAF profile appear to plateau in the areas of highest intensity.

High detector sensitivity gives high contrast image where small differences in intensities within the image can be differentiated. The 55˚ images have a wider field of view. Since laser power is fixed regardless of the size of the imaging field, the same laser power is spread over a larger area when imaging with a 55˚ lens (compared to 30˚ lens) so that the energy that reaches the RPE to excite lipofuscin is less. Subsequently, the intensity of autofluorescence emitted is weaker, and detected autofluorescence intensity is lower. This makes the 55˚ images slower to saturate compared to 30˚ images and allows imaging to be performed at higher detector sensitivity level.

We also observed that Asian eyes can be imaged at higher detector sensitivity level than Caucasian eyes. This could be because Asian eyes have weaker autofluorescence than Caucasian eyes (Greenberg et al., 2013).

In conclusion, 30° images can be captured at detector sensitivity of 85 – 90 units for Caucasian eyes, and 95 units for Asian eyes. For 55° images, the detector sensitivity for image capture can be between 95 – 100 units for both Caucasians and Asians.

3.6.3. Part C – Effect of Photobleaching on FAF

The FAF profiles (Figure 3.9) showed non-uniform increase in FAF intensity as the proportion of photopigment bleaching increased between the 1st to 8th image captured. The regional variation in the increase of autofluorescence intensity across the images may be attributed to 1) the distribution of rod photoreceptors in the fovea, optic disc and macula or 2) the misalignment of the camera and pupil, resulting in darkening of the temporal regions of the image and relative brightening of the nasal region. The absence of rod photoreceptors and RPE in the optic disc means that there was no photopigment and therefore no increase in autofluorescence intensity over the duration of image capture. Similarly, the increase in autofluorescence intensity is greater in the macula than in the fovea due to rod distribution. In the fovea, there is a rod-free zone in the central 350µm and rod density gradually increases with eccentricity to peak at approximately 3000µm (Curcio et al., 1990). The central fovea is populated by cones, which have an alternate route of visual pigment recycling via the Muller cells (Wang and 119

Kefalov, 2011). As a result, cones are less affected by photobleaching with the 488nm laser compared to rods.

Fitting an exponential curve to the average FAF intensity showed that 20 seconds of bleaching would be sufficient to bring the FAF intensity to 95% of its maximum intensity (Figure 3.10). Importantly, our results showed that inadequate photobleaching did not affect the location of the FAF peaks identified when the images were sampled using the 5x5 pixel square method. It is therefore not necessary to have a protracted photobleaching period to bleach rhodopsin to >99% for the purpose of identifying FAF peaks.

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3.7. References

BENNETT, T. J. 2007. The effects of gain and noise in fundus autofluorescence imaging. The Journal of Ophthalmic Photography, 29, 87-92. BLAND, J. M. & ALTMAN, D. G. 1986. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1, 307-10. CURCIO, C. A., SLOAN, K. R., KALINA, R. E. & HENDRICKSON, A. E. 1990. Human photoreceptor topography. J Comp Neurol, 292, 497-523. DELORI, F. C., GREENBERG, J. P., WOODS, R. L., FISCHER, J., DUNCKER, T., SPARROW, J. & SMITH, R. T. 2011. Quantitative measurements of autofluorescence with the scanning laser ophthalmoscope. Invest Ophthalmol Vis Sci, 52, 9379-90. GREENBERG, J. P., DUNCKER, T., WOODS, R. L., SMITH, R. T., SPARROW, J. R. & DELORI, F. C. 2013. Quantitative fundus autofluorescence in healthy eyes. Invest Ophthalmol Vis Sci, 54, 5684-93. SCHMITZ-VALCKENBERG, S. & FITZKE, F. 2012. Imaging techniques of fundus autofluorescence. In: LOIS, N. & FORRESTER, J. (eds.) Fundus Autofluorescence. Philadelphia, USA: Lippincott Williams & Wilkins. p.49- 59. WANG, J. S. & KEFALOV, V. J. 2011. The cone-specific visual cycle. Prog Retin Eye Res, 30, 115-28.

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Chapter 4: The Spatial Profile of Fundus Autofluorescence (FAF) in Young Healthy Eyes

Contribution My work included collaboration in the design of the study together with my supervisor and co-supervisor. Participant recruitment and data collection was done by me with the help of 6 Diploma in Optometry students at Singapore Polytechnic (Singapore) and Ms Joanna Cher, who was conducting her MPhil research. I was solely responsible for data analysis.

Dr J. M. F. Kelly helped to develop the MATLAB code for sampling autofluorescence intensities from the images.

Publication Tee, S.L. Teresa; Cher, H.Y. Joanna; Neelam, Kumari; Murray, Ian J.; Aslam, Tariq M., Leung, Ivan Y.F. (2018) The Spatial Profile of Fundus Autofluorescence in Young Healthy Eyes. Biomedical Optics Express (manuscript ready to submit)

Conference Presentation The Association of Research in Vision and Ophthalmology (ARVO) 2014. Baltimore, US (poster).

Tee, S.L. Teresa; Murray, Ian J., Aslam, Tariq M.; Leung, Ivan Y.F. Detection of peak fundus autofluorescence (FAF) signals and hyperautofluorescent spots using confocal scanning laser ophthalmoscope (cSLO) with 30° and 55° lenses. Invest. Ophthalmol. Vis. Sci. 2014;55(13):3428.

ASEAN Optometric Conference 2014, Singapore, Singapore (talk)

Tee, S.L. Teresa, Cher, H.Y. Joanna, Neelam, Kumari, Murray, Ian J., Aslam, Tariq M., Leung, Ivan Y.F. Fundus autofluorescence distribution in the central 55˚ captured by cSLO.

Acknowledgments Supported by the Enabling grant (KPREF14EG15Z, KPREF14EG15Z) and the Singapore Polytechnic Final Year Project fund (CLS-15A154, CLS-16A023).

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4.1. Abstract

Purpose: Fundus Autofluorescence (FAF) imaging is a widely used method for mapping the accumulation of lipofuscin in the retinal pigment epithelium. The main aims of this study were to document the characteristic FAF spatial profiles in young healthy eyes and to compare these with those previously published using quantitative methods. We also wanted to test the technique by linking FAF profiles with rod density distributions.

Methods: A clinical confocal scanning laser ophthalmoscope (cSLO) was used to capture 55° FAF images from 17 young healthy subjects. Data were collected from two centres by 2 operators. Identical instruments were used in each location. Data were analysed with a customized MATLAB code. Grey level intensities along 8 cardinal meridians of the images were sampled. The technique allowed 38, 5x5 clusters of pixels to be obtained along each meridian to give resolution of 0.35°/sample.

Results: A characteristic pattern of FAF signal was obtained for all subjects. The grey level values showed minima at the fovea (66 ± 15) and at the optic disc (52 ± 11). On the nasal horizontal meridian, distinctive FAF peaks were identified either side of the optic disc at 2100µm (7.1°) and 6000µm (20.3°) from the fovea. The superior-nasal peak had strongest autofluorescence and was detected at a 6000µm (20.3°). The remaining meridians had peaks at around 3000 µm (10.1°).

Conclusions: A clinically available cSLO can be used to obtain relative and semiquantitative FAF spatial profiles that match published ex vivo data of RPE lipofuscin distribution. Wider field autofluorescence imaging allows more comprehensive comparison with rod density distribution. The established protocol allows semi-quantitative analysis of autofluorescence images for clinical use.

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4.2. Introduction

Posterior segment of the eye presented itself with autofluorescence after short wave (~440 nm) or near-infrared (>800nm) excitation because of the presence of fluorophores such as lipofuscin and melanin in the retinal pigment epithelium (RPE). Fundus autofluorescence (FAF) imaging takes advantage of this property to map the content of these fluorophores in the RPE. Short-wave FAF (excitation at ~440 nm) predominantly comes from RPE lipofuscin, which is a complex mixture of fluorophores being mostly by-products of the visual cycle, such as N-retinylidene- N-retinylethanolamine (A2E), and accumulates in the RPE after phagocytosis of photoreceptor outer segments (Sparrow et al., 2012). While grayscale FAF images display the intensity of the autofluorescence signals captured from the retina, fluorescence lifetime imaging ophthalmoscopy (FLIO) measures the time that a fluorophore remains in the excited state before emitting autofluorescence and returning to the ground state (Dysli et al., 2017). FLIO has been used to study various retinal diseases such as age-related macular degeneration (AMD), geographic atrophy and macular hole (Dysli et al., 2017). Due to the close relationship between lipofuscin and the photoreceptor-RPE complex, both FAF and FLIO could be non-invasive means to probe the state of photoreceptors and RPE in vivo. Indeed, the intensity of the autofluorescence signal from RPE lipofuscin is found to be closely related to photoreceptor and RPE health (Yung et al., 2016) While FAF has been studied for many years, FLIO finds limited use because of limited commercially available equipment and its technology being new.

Compared to regular fluorescein angiography, the intensity of the autofluorescence signal in conventional FAF imaging is approximately 2 orders of magnitude less intense (Schmitz-Valckenberg and Fitzke, 2009). Measurement of FAF is feasible in recent years because of the advance of high sensitivity in the detection of fluorescence and signal averaging of multiple fast scans to enhance the signal-to- noise ratio with scanning ophthalmoscopy. RPE lipofuscin autofluorescence increases linearly with age until around age 70 and then declines (Delori et al., 2001) and its alterations is associated with reduced visual function (Scholl et al., 2004, Fleckenstein et al., 2009). FAF imaging has been used to examine various ocular diseases such as AMD (Bindewald et al., 2005), type 2 idiopathic macular telangiectasia (Wong et al., 2009), birdshot chorioretinopathy (Jack et al., 2016), and retinitis pigmentosa (RP) (Robson et al., 2011), and there are reported changes in FAF preceding or in conjunction with the onset of ophthalmoscopic changes in some of these retinal disease. However, FAF imaging in clinical settings has mostly been qualitative or semi-quantitative (Reznicek et al., 2014, Meleth and Sen, 2012, Holz et al., 2007, Jeong et al., 2014, Lois et al., 1999) due to the lack

124 of commercially available equipment for quantitative analysis. These qualitative studies, though provide helpful information, are subjected to the limitations of human visual perception with a lack in objectivity and are difficult to compare among different studies. Fully quantitative analysis was only recently introduced by Delori et al. (2011). Early quantitative FAF by Delori was achieved by the introduction of an internal reference fluorescent standard in each image. Using a similar approach to quantification, Burke et al. (2014) reported that “even when qualitative differences in FAF images are not evident, quantitative FAF can elucidate phenotypic variation” in recessive Stargardt’s disease. Other studies (Marsiglia et al., 2015, Gliem et al., 2016) also demonstrated the various advantages of quantitative FAF. Quantitative FAF equipment with a field lens at 30°was first commercially available in 2016. With that, autofluorescence images can then be easily compared longitudinally on separate sessions, between eyes and between instruments.

With the increasing use of FAF imaging, there is a need to characterize the autofluorescence distribution beyond the central 30° obtained with standard clinical instruments due to the fact that many pathologic changes may start in the peripheral or mid-peripheral retina (Price et al., 2015) such as in RP, Stargardt’s disease and diabetic retinopathy (Oishi et al., 2013, Klufas et al., 2018, Price et al., 2015). A recent study by Guduru et al. (2017) noted peripheral FAF abnormalities in both AMD and control patients with a 200°ultra-widefield FAF instrument suggesting a high sensitivity of peripheral FAF. While ultra-widefield imaging offers information on peripheral FAF that may have clinical implications, one limitation is that the reported instrument does not perform signal averaging and can only provide low resolution images compared to confocal scanning ophthalmoscope (cSLO). The combination of these limitations makes the detection of subtle and small alterations in the current ultra-wide field FAF challenging. Furthermore, nonuniformity of the image and repeatability of these ultra-wide field FAF measurements remain unclear (Oishi et al., 2014). In addition ultra-widefield imaging is susceptible to certain imaging artefacts (Yung et al., 2016).

Our current study takes advantage of a 55˚ Spectralis HRA+OCT confocal scanning laser ophthalmoscope that provides high resolution FAF with a wider field than most instrument currently at 30˚. This field-of-view means the FAF maps can be more effectively compared with the distribution of rod density that is thought to account for much of the accumulation of lipofuscin in normal eyes. Since Curcio et al. (1990) has reported a rod hot spot in the superior/superior-nasal region of the eye and a second peak in rod density nasally, we propose to study the FAF profile (Lois et al., 2000, Delori et al., 2001) along the oblique meridians in addition to the

125 traditional studies profiling the horizontal and vertical meridians. In this study, we explored the method of quantification, and described the spatial characteristics of autofluorescence signal across the 8 cardinal meridians (2 horizontal, 2 vertical and 4 oblique) of the eye using 55° images in a group of normal young individuals. The locations of the peaks are described in detail and the data are compared with measurements obtained with quantitative methods by other researchers using the traditional 30° images, in vivo fundus spectrophotometry, and ex vivo RPE cell imaging.

4.3. Methods

4.3.1. Ethics and Subjects

This study adhered to the tenets of the Declaration of Helsinki 2013. Ethical approval was obtained from the Institutional Review Boards at Manchester Royal Eye Hospital (MREH; Manchester, UK) and Khoo Teck Puat Hospital (KTPH; Singapore), where all the data and images were obtained. Informed consent was obtained from all participants.

Seventeen subjects of 4 ethnic groups (3 Caucasian, 10 Chinese, 3 Indian and 1 Pakistani) (with normal healthy eyes and no reported ocular diseases or symptoms except refractive corrections; screened with ophthalmic examination and fundus photography with no abnormalities or media opacities) were included in the study. Ocular and medical history were self-reported. Three Caucasians and 4 Asians were recruited in Manchester and 10 Asians were recruited in Singapore.

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Subject ID Age (yr) Sph Equiv(D) Ethnicity Gender *019 21 -5.13 Chinese Female *007 23 -2.00 Chinese Female *013 23 -5.50 Chinese Female *048 24 -4.75 Chinese Female *005 25 0.63 Chinese Female *052 25 plano Chinese Female 107 25 -2.00 Chinese Female 104 29 -3.00 Chinese Male 101 32 -4.00 Chinese Female *066 34 -2.25 Chinese Male 105 26 plano Caucasian Female 102 29 -8.00 Caucasian Female 106 31 -1.00 Caucasian Male 103 30 -1.25 Pakistani Male *236 25 -4.75 Indian Male *248 27 -4.00 Indian Female *250 34 -4.88 Indian Female

Table 4.1. Demographic data of subjects involved in this study. The data are arranged according to ethnicity. A total of 10 subjects (7 Chinese, 3 Indians) were imaged in Khoo Teck Puat Hospital (KTPH) and 7 in Manchester Royal Eye Hospital (MREH) (3 Chinese, 3 Caucasian, and 1 Pakistani). Asterisk * denotes subjects imaged at KTPH.

Mean (± SD) subject age was 26.1 ± 4.5 years in KTPH and 28.9 ± 2.6 years at Manchester. Average (± SD) spherical equivalent was -3.26 ± 2.22D and -2.75 ± 2.67D. There was no statistically significant difference in age (Mann-Whitney test, p = 0.09) or spectacle correction (Mann-Whitney test, p = 0.431) between the two groups of subject at the two sites. After informed consent was obtained, refractive error, keratometry and demographic information were collected. These data are presented in Table 1. The subjects’ right eyes were dilated with 1% tropicamide and images were captured using the following procedures when pupil diameter was > 5mm.

4.3.2. Procedure

FAF images were captured using the Heidelberg Spectralis HRA+OCT (Heidelberg Engineering, Germany) with a 55° wide-angle lens. The operators were 2 of the 127 authors (TT and JC) and the image capture settings were identical: scanning was done in high-speed mode with automatic real-time averaging set at 49 or 100 scans. Images were not normalized to allow semi-quantitative analyses of FAF intensity because normalization may change the contrast of the image thereby preventing the comparison of FAF intensities from different parts of the same image. The scan protocol was as follows: the subject was instructed to fixate on the central internal fixation target. Focus was aligned in infra-red (IR) reflectance mode before the HRA+OCT was switched to autofluorescence mode. Focus was refined and detector sensitivity was set between 95 - 105 units. Subjects were exposed to the excitation laser (488nm) for at least 20 seconds to bleach photopigment. Analysis of photo bleaching in 7 eyes indicated that any unbleached photopigment after 20 seconds of laser exposure had minimal effect on the FAF intensities (data not shown). When the real-time averaged image was deemed to be of good quality (well-focused, centred, and with good contrast), the image was saved as 768x768 pixel TIFF file. Typically, 1 to 2 images were captured. A custom MATLAB (2013a, The MathWorks, Inc., Natick, MA, USA) code was used to extract grey level intensities along 8 cardinal meridians centred on the fovea, which was assumed to be the centre of the image (Annex 3.1). The recorded images were re- scaled manually using the formula in Delori et al. (2011).

4.3.3. Image Analysis The MATLAB code returned intensity data from 5x5 pixel squares spaced at 10 pixel intervals along the vertical, horizontal and oblique meridians of the eye illustrated in Figure 4.1. The grey level intensity within each 5x5 pixel square was averaged and taken as a single data point. Using this method, we obtained 39 samples along each of the oblique meridians and 38 points along each of the vertical and horizontal meridians. Depending on magnification effects, this yielded a resolution of approximately 0.35° per sample point with total image sizes covering 49° to 57°. The FAF peak along each meridian was defined by the data points with highest grey level values. Because the data points were sampled from fixed intervals on the image, we could determine the retinal eccentricity at any particular point along the axis. A conversion factor of 296μm/degree was used based on the parameters of the Gullstrand schematic eye (Almeida and Carvalho, 2007).

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Figure. 4.1. (A) FAF was sampled along 8 meridians of the fundus, with the fovea in the centre. (B) Magnified view of the sampling boxes. Grey level intensities were sampled from 5x5 pixel squares sampled at 10 pixel intervals.

4.3.4. Statistical Analysis

Same day intra-session coefficient of repeatability (COR) of the same examiner of the same instrument at MREH was calculated using a formula by Bland and Altman (1986a) for FAF peak location and a formula by Delori and colleagues (2011) for FAF peak intensity:

퐶표푒푓푓𝑖푐𝑖푒푛푡 표푓 푟푒푝푒푎푡푎푏𝑖푙𝑖푡푦 (푙표푐푎푡𝑖표푛)

∑(푑𝑖푓푓푒푟푒푛푐푒 표푓 1푠푡 푎푛푑 2푛푑 푚푒푎푠푢푟푒푚푒푛푡)2 = 1.96 x (√ ) (1) 푛

퐶표푒푓푓𝑖푐𝑖푒푛푡 표푓 푅푒푝푒푎푡푎푏𝑖푙𝑖푡푦 (𝑖푛푡푒푛푠𝑖푡푦)

This analysis was conducted for the examiner and the instrument at MREH on 6 subjects (the same subjects were imaged at MREH, excluding subject 107) using data from all meridians except the nasal meridian (a total of 7 meridians). Nasal data were excluded because the nasal meridian has fewer sampled data points due 129 to the presence of optic disc and pronounced steep peaks compared to the other meridians. COR was assessed in two ways: comparing the FAF peak location and intensity based on the 6 subjects who had 2 FAF images captured on the same visit. The second image was captured within 5 minutes of the first image without the subject moving his head away from the chin/forehead rest.

Statistical analyses were performed using Graphpad Prism 6 (Graphpad Software Inc., La Jolla, CA, USA). Non-parametric Kruskall-Wallis test was used to compare the FAF peak location and intensities between the data from the 2 sites (KTPH and MREH). Statistical significance was set at p < 0.05.

4.4. Results

4.4.1. Intra-session Coefficient of Repeatability (COR) of FAF Peak Location and Intensity

For the 6 subjects at MREH, intra-session COR of FAF peak locations and grey level intensity was 738µm and 3.39% respectively. COR of FAF peak locations was smaller than or equal to the standard deviation of the FAF peak locations. Intra- session COR of FAF intensities was noticeably smaller than the CV of the subjects (Table 4.2). These CORs suggest a reasonable repeatability in peak locations and high repeatability in peak intensity.

FAF Peak (n = 6) Meridian Location (µm) Intensity (GL) Temporal 3700 ± 450 (12.2) 125 ± 28 (22.4) Superior-temporal 3150 ± 550 (17.5) 125 ± 26 (20.8) Superior 4000 ± 1050 (26.3) 126 ± 20 (15.9) Superior- nasal 4850 ± 1050 (21.7) 132 ± 17 (12.9) Inferior-nasal 2400 ± 200 (8.3) 120 ± 20 (16.7) Inferior 2300 ± 450 (19.6) 114 ± 21 (18.4) Inferior-temporal 3000 ± 650 (21.7) 117 ± 23 (19.7)

Table 4.2. Average, standard deviation and coefficient of variation (CV) of FAF peak location and intensity obtained from 6 subjects. FAF peak locations are rounded to the nearest 50µm.

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4.4.2. Autofluorescence Intensity Profiles

Individual subjects’ FAF intensity profiles were plotted for the horizontal, vertical and oblique meridians in Figure 4.2. The autofluorescence profiles exhibited a characteristic shape for each of the vertical and horizontal meridians sampled that were in agreement with those reported (Lois et al., 2000, Delori et al., 2001).

Along all meridians excluding nasally (Figure 4.2B - D), the foveal GL was 60% (range 43.2 – 73.5%) of the average macular intensity in the central 7 - 14° of the cardinal meridians. As eccentricity increased, FAF increased and reached a maximum in the mid-periphery before decreasing again towards the periphery.

Along the horizontal meridian (Figure 4.2A), the FAF signal showed a broad range of maxima in the temporal region between 2200µm - 5400µm (7.4° – 18.2°) eccentricity. On the nasal side, the signal peaked twice. The first nasal peak was close to the margin of the optic disc and the second was nasal to the optic disc. Within the optic disc there were low levels of autofluorescence. In 7 eyes, autofluorescence within the optic cup was relatively brighter compared to the neuroretinal rim. This was represented by small double peaks in the FAF profile inside the optic disc region (e.g. #236 and #250 in Figure 4.2A). Nasal to the optic disc there was a sharp FAF peak, N2.

FAF profile along oblique meridians has not been reported. The superior-nasal FAF was marginally brighter than the inferior-temporal FAF (Figure 4.2D). The profile appeared to have small reduction or plateau in intensity between 10-20˚. The FAF peaks were situated either before or after this feature

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Figure 4.2. FAF profiles across the (A) horizontal, (B, D) oblique, and (C) vertical meridians. FAF profiles have been shifted vertically for ease of viewing. Subject IDs are labelled next to the respective FAF profiles. Data points overlapping major blood vessels have been excluded. Small peaks and dips in the profiles are due to localized variations in FAF intensity as well as the presence of capillaries. The fovea at zero eccentricity shows a dip in FAF due to macular pigment masking. (A) The horizontal profile cuts across the optic disc at approximately 13.6º from fovea. (B, C) Autofluorescence distribution gradually decreases with eccentricity beyond ± 13.6˚. (D) The superior-nasal peak is found at a relatively more eccentric location than the other meridians (*) in all subjects except 4.

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4.4.3. Location of FAF Peaks

Non-parametric Kruskall-Wallis test comparing the FAF peak location and intensity data from the two sites (MREH and KTPH) showed no significant difference between the two sites (Dunn’s multiple comparisons, p > 0.9). Accordingly, subjects from both sites were combined together for subsequent analyses (n = 17).

We observed typical inhomogeneities in the FAF intensity profiles similar to those described in earlier studies (Delori et al., 2001, Lois et al., 2000). Details of the autofluorescence peaks and their grey levels are provided in Table 4.3. Standard deviations and coefficient of variation indicate variability across the subjects. Along the nasal horizontal meridian, typical peaks were identified at 2100 ± 400µm (7.1 ± 1.3°) and at 6000 ± 400µm (20.3 ± 1.4°) from the fovea. These are referred to as N1 (between the fovea and the optic disc) and N2 (nasal to the optic disc). The nasal N2 and superior-nasal (SN) peaks were consistently furthest from the fovea as illustrated in Figure 4.3.

The average of superior (S), temporal (T) and superior-temporal (ST) peaks were found at similar eccentricities (3100 to 3600µm, 10.5° – 12.2°). The average location of nasal N1, inferior (I), inferior-temporal (IT) and inferior-nasal (IN) peaks was < 3000µm from the fovea. The locations of the peaks were highly consistent across the subjects for all meridians. No correlation was found between refractive error and the location of the FAF peaks (p > 0.160).

Figure 4.3 presents the location of the FAF peaks superimposed on an autofluorescence image of a normal retina. In our group of subjects, peaks N1 and N2 showed the least inter-individual variability and high repeatability as shown by a relatively small standard deviation and coefficient of variation (Table 2). The peaks with most inter–individual variability were in the superior-nasal location illustrated by the highest standard deviation (Table 4.3), labelled SN in Figure 4.3.

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Figure 4.3. The typical FAF image of a healthy retina taken using cSLO with 55° lens. The symbols represent the mean and SD of the location of FAF peaks (n = 17). (T = temporal, ST = superior-temporal, S = superior, SN = superior-nasal, N1 = nasal before the optic disc, N2 = nasal beyond the optic disc, IN = inferior-nasal, I = inferior, IT = inferior-temporal).

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T ST S SN N1 N2 IN I IT Average 3600 ± 800 3100 ± 550 3550 ± 950 4450 ± 1250 2100 ± 400 6000 ± 400 2550 ± 1050 2400 ± 550 2800 ± 650 location (µm) (22.2) (17.7) (26.8) (28.1) (19.0) (6.7) (41.2) (22.9) (23.2) Average 12.1 ± 2.7 10.6 ± 1.9 12.0 ± 3.2 15.0 ± 4.3 7.1 ± 1.3 20.3 ± 1.4 8.6 ± 3.6 8.2 ± 1.9 9.5 ± 2.1 location (°) (22.3) (17.9) (26.7) (28.7) (18.3) (6.9) (41.9) (23.2) (22.1) Average 125 ± 27 123 ± 28 122 ± 26 129 ± 27 118 ± 26 115 ± 26 118 ± 24 113 ± 24 117 ± 24 intensity (GL) (21.6) (22.8) (21.3) (20.9) (22.0) (22.6) (20.3) (21.2) (20.5)

Table 4.3. Location and intensity of FAF peaks at each meridian (n = 17). Data are expressed in mean and standard deviation with coefficient of variation in parentheses, and rounded to the nearest 50µm. (T = temporal, ST = superior-temporal, S = superior, SN = superior-nasal, N1 = nasal before the optic disc, N2 = nasal beyond the optic disc, IN = inferior-nasal, I = inferior, IT = inferior-temporal).

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4.5. Discussion

To our knowledge, this is the first study to explore the use of 55˚ FAF images to document FAF intensity along all 8 meridians of the eye where previous studies have used images with smaller field-of-view or focused on the vertical and horizontal meridians only. The consistency of our results and the good agreement of the FAF profiles with earlier studies suggest that our data from 17 healthy eyes are representative of the normal population. In addition, the use of 55˚ images allowed the detection of a new superior-nasal FAF peak that has not been described in clinical studies but matches rod distribution. Our findings reinforce the close relationship between FAF and rod distribution and illustrate the advantages of wider field data collection and the processing of additional meridians.

4.5.1. Spatial Distribution of FAF Peak Intensity

Consistent with earlier findings, our FAF profile shows a sharp minimum at the fovea due to absorption of blue light by macular pigment (Delori et al., 1995). Low autofluorescence in the fovea may also reflect the denser concentration of RPE melanin granules that attenuate the excitation laser and autofluorescence signal travelling to and from the RPE (Delori et al., 2007) and the overall lower levels of lipofuscin in foveal RPE (Ach et al., 2014). Foveal RPE has lower phagocytic load due to the presence of an alternative visual pigment recycling pathway, via the Muller cells (Wang and Kefalov, 2011). From the fovea, FAF intensity than gradually increases with eccentricity to the mid-periphery. This profile of autofluorescence agrees with previous publications and reflects rod distribution in the macula (Delori et al., 2001, Lois et al., 2000).

We observed that the superior-nasal FAF profile exhibited a small localized reduction or plateau between 10-20˚ that was not evident along other meridians. This may be related to the superior vascular arcade and RNFL bundle. The superior vascular arcade is hypoautofluorescent (and excluded when plotting the FAF profile) whereas RNFL may attenuate autofluorescence signal (Tee T., et al. IOVS 2017;58(8):ARVO E- abstract 1886)(Duncker et al., 2012), giving this meridian its characteristic shape.

While the optic disc is devoid of RPE, FAF signals from connective tissue may contribute weak autofluorescence (Schweitzer et al., 2007). In the fundus, localized reduction of FAF intensity has been observed along the superior and inferior retinal

137 nerve fibre bundles (Duncker et al., 2012). RNFL has also been found to attenuate autofluorescence signals around the optic disc (Tee T., et al. IOVS 2017;58(8):ARVO E-abstract 1886). The nerve fibres making up the neuroretinal rim may similarly obscure autofluorescence from non-RPE sources within the optic disc and contribute to its unique autofluorescence pattern.

4.5.2. FAF Peaks

FAF peaks are expected to mirror rod density distribution because RPE lipofuscin is derived from phagocytosed outer segment membranes of photoreceptors. In human eyes, a ‘rod ring’ is found between 3000 – 4000µm from the fovea and a rod ‘hot spot’ 3000 - 5000µm superior-nasal to the fovea is seen in some eyes (Curcio et al., 1990). Our data shows that FAF peaks correspond well to maximum rod density (Figure 4.4A) as described by Curcio et al. (1990). There is a conspicuous peak matching the rod hot spot in more than half of our subjects (Figure 4.2D). This superior-nasal peak has not been identified in previous studies of FAF profile as the oblique meridians have not been studied or the authors used images with smaller field-of-view (Lois et al., 2000, Delori et al., 2001).Our 55° image enabled the shape and extent of the superior-nasal FAF peak to be explored better than the case for 30° FAF images (Tee T., et al. IOVS 2014;55(13):ARVO E-Abstract 3428) despite the increasing instrument related nonuniformity towards the periphery of the images (Delori et al., 2011). The large variation of the superior-nasal peak may also be related to the thicker nerve fibre layer in that area and the presence of the superior vascular arcade that cuts across the superior-nasal meridian where FAF was sampled.

As seen in Figure 4.4A there were slight exceptions to the correspondence between our data and rod density data. These were along the nasal and inferior meridians, where our peaks were more centrally located than the rod density maps (Curcio et al., 1990). The first nasal FAF peak (N1), matched that identified from 30° FAF images captured by the HRA+OCT (Greenberg et al., 2013). Inferiorly, non-uniformities in the instrument optics and/or eyelash interference may have masked the FAF peak at further eccentricities.

Greenberg et al. (2013) reported strongest autofluorescence in the superior-temporal retina. In our data (Table 4.3, last row), the superior nasal peak intensity is stronger by 6GL than that for superior-temporal meridian. The marginal difference could be the result of local variations in autofluorescence intensity or non-uniformities in the image. 138

Data in Greenberg et al. were obtained using the same instrument as used in the present study, but there were a few methodological differences that could easily account for differences in peak intensities. Greenberg et al. used data from 30° FAF images. The superior-nasal peak we found in our study was located at 15° eccentricity. Delori et al. (2011) had previously reported that nonuniformity at the periphery of 30° images reduced FAF intensity by 16%. Given the small difference in FAF intensity between the superior-nasal and superior-temporal location in our study, this amount of nonuniformity at the periphery of the 30° image would likely have reduced the superior-nasal peak intensity below that of the superior-temporal.

Our FAF peak locations corresponded to regions where autofluorescence was reported to be greatest in the RPE of post-mortem eyes (Ach et al., 2014). Macular FAF from younger subjects aged ≤ 51 years is relatively homogenous and shows more autofluorescence in the perifovea, with a wider spread superior-nasally (Figure 4.4B). The central 2000 - 5000µm exhibited strong autofluorescence in the older donor eyes, and this region coincided with the FAF peaks in our subjects.

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Figure 4.4. (A) Rod photoreceptor distribution map from Curcio et al. (1990) showing the rod ring near the eccentricity of the optic nerve head and rod hot spot superior to the optic nerve head and fovea. (B) Composite autofluorescence map of the RPE autofluorescence in donor eyes ≤ 51 years old. The symbols superimposed over these 2 maps are the mean and standard deviations of the FAF peak locations in our study along the 8 meridians (n = 17). Rod distribution and autofluorescence maps are adapted from Ach and co-workers (2014).

The minor differences between our data and the results from morphometric RPE data (Ach et al., 2014) may be due to different methodologies such as excitation wavelength (460 – 490nm) or image analysis. We used a 256 level grayscale, which gave us the precision to distinguish intensity differences down to 1GL among our 5x5 pixel sampling squares, whereas the colour coded maps by Ach and co-workers would have grouped a wider range of intensities into a single category.

Our data also agree well with Delori’s result (2001) (Table 3) who used a fundus spectrophotometer from 14 locations along the vertical and horizontal meridians of the eye. In addition, the new instrument provides high precision as compared to the fundus spectrophotometer.

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Location of FAF Peak from Fovea (°) Meridian Our study (n = 17) Delori et al., 2001 (n = 7) Temporal 12.1 ± 0.7 11.2 ± 2.2 Superior 12.0 ± 0.8 12.7 ± 3.4 Nasal 1 7.1 ± 0.3 6.9 ± 0.4 Nasal 2 20.3 ± 0.3 21.0 ± 2.0 Inferior 8.2 ± 0.5 8.6 ± 2.0

Table 4.4. Location (mean ± SE) of FAF peaks identified in our study compared to results from fundus spectrophotometer (Delori et al., 2001).

4.6. Conclusion

In conclusion, sampling along 8 meridians of the central 55° of the retina using a standard clinically-available cSLO and a rigorous protocol reveals characteristic spatial short wavelength FAF profiles. We found strongest autofluorescence in the superior- nasal meridian that was not detected or reported in 30˚ images. Our results confirm and add on to the literature demonstrating the close relationship between FAF and rod distribution and the advantages of wider field data collection and the processing of additional meridians. In spite of the ethnic variation of the subjects involved, the repeatability of the peak locations, the extent to which these matches previous reports of autofluorescence (Delori et al., 2001, Ach et al., 2014, Greenberg et al., 2013) and the high correspondence with rod density (Curcio et al., 1990) distributions gives confidence as to the veracity of the data.

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4.7. References

ACH, T., HUISINGH, C., MCGWIN, G., MESSINGER, J. D., ZHANG, T., BENTLEY, M. J., GUTIERREZ, D. B., ABLONCZY, Z., SMITH, R. T., SLOAN, K. R. & CURCIO, C. A. 2014. Quantitative autofluorescence and cell density maps of the human retinal pigment epithelium. Invest Ophthalmol Vis Sci, 55, 4832-41. ALMEIDA, M. S. D. & CARVALHO, L. A. 2007. Different schematic eyes and their accuracy to the in vivo eye: a quantitative comparison study. Brazilian Journal of Physics, 37, 378-387. BINDEWALD, A., BIRD, A. C., DANDEKAR, S. S., DOLAR-SZCZASNY, J., DREYHAUPT, J., FITZKE, F. W., EINBOCK, W., HOLZ, F. G., JORZIK, J. J., KEILHAUER, C., LOIS, N., MLYNSKI, J., PAULEIKHOFF, D., STAURENGHI, G. & WOLF, S. 2005. Classification of fundus autofluorescence patterns in early age-related macular disease. Invest Ophthalmol Vis Sci, 46, 3309-14. CURCIO, C. A., SLOAN, K. R., KALINA, R. E. & HENDRICKSON, A. E. 1990. Human photoreceptor topography. J Comp Neurol, 292, 497-523. DELORI, F., KEILHAUER, C., SPARROW, J. & STAURENGHI G. 2007. Origin of Fundus Autofluorescence. In: HOLZ, F., SPAIDE, R., BIRD, A. & SCHMITZ- VALCKENBERG, S. (eds.) Atlas of Fundus Autofluorescence Imaging. Berlin, Heidelberg: Springer Berlin Heidelberg. p.17-29. DELORI, F. C., DOREY, C. K., STAURENGHI, G., AREND, O., GOGER, D. G. & WEITER, J. J. 1995. In vivo fluorescence of the ocular fundus exhibits retinal pigment epithelium lipofuscin characteristics. Invest Ophthalmol Vis Sci, 36, 718-29. DELORI, F. C., GOGER, D. G. & DOREY, C. K. 2001. Age-related accumulation and spatial distribution of lipofuscin in RPE of normal subjects. Invest Ophthalmol Vis Sci, 42, 1855-66. DELORI, F. C., GREENBERG, J. P., WOODS, R. L., FISCHER, J., DUNCKER, T., SPARROW, J. & SMITH, R. T. 2011. Quantitative measurements of autofluorescence with the scanning laser ophthalmoscope. Invest Ophthalmol Vis Sci, 52, 9379-90. DUNCKER, T., GREENBERG, J. P., SPARROW, J. R., SMITH, R. T., QUIGLEY, H. A. & DELORI, F. C. 2012. Visualization of the optic fissure in short-wavelength autofluorescence images of the fundus. Invest Ophthalmol Vis Sci, 53, 6682-6. DYSLI, C., WOLF, S., BEREZIN, M. Y., SAUER, L., HAMMER, M. & ZINKERNAGEL, M. S. 2017. Fluorescence lifetime imaging ophthalmoscopy. Prog Retin Eye Res, 60, 120-143. FLECKENSTEIN, M., CHARBEL ISSA, P., FUCHS, H. A., FINGER, R. P., HELB, H. M., SCHOLL, H. P. & HOLZ, F. G. 2009. Discrete arcs of increased fundus autofluorescence in retinal dystrophies and functional correlate on microperimetry. Eye (Lond), 23, 567-75. GLIEM, M., MÜLLER, P. L., FINGER, R. P., MCGUINNESS, M. B., HOLZ, F. G. & CHARBEL ISSA, P. 2016. Quantitative fundus autofluorescence in early and intermediate age-related macular degeneration. JAMA Ophthalmol, 134, 817- 24. GREENBERG, J. P., DUNCKER, T., WOODS, R. L., SMITH, R. T., SPARROW, J. R. & DELORI, F. C. 2013. Quantitative fundus autofluorescence in healthy eyes. Invest Ophthalmol Vis Sci, 54, 5684-93. GUDURU, A., FLEISCHMAN, D., SHIN, S., ZENG, D., BALDWIN, J. B., HOUGHTON, O. M. & SAY, E. A. 2017. Ultra-widefield fundus autofluorescence in age-related macular degeneration. PLoS One, 12, e0177207. HOLZ, F. G., BINDEWALD-WITTICH, A., FLECKENSTEIN, M., DREYHAUPT, J., SCHOLL, H. P., SCHMITZ-VALCKENBERG, S. & GROUP, F.-S. 2007. Progression of geographic atrophy and impact of fundus autofluorescence patterns in age- related macular degeneration. Am J Ophthalmol, 143, 463-72.

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JACK, L. S., AGARWAL, A., SEPAH, Y. J. & NGUYEN, Q. D. 2016. Spatial agreement between Goldmann visual field defects and fundus autofluorescence in patients with birdshot chorioretinopathy. J Ophthalmic Inflamm Infect, 6, 18. JEONG, Y. J., HONG, I. H., CHUNG, J. K., KIM, K. L., KIM, H. K. & PARK, S. P. 2014. Predictors for the progression of geographic atrophy in patients with age- related macular degeneration: fundus autofluorescence study with modified fundus camera. Eye (Lond), 28, 209-18. LOIS, N., HALFYARD, A. S., BIRD, A. C. & FITZKE, F. W. 2000. Quantitative evaluation of fundus autofluorescence imaged "in vivo" in eyes with retinal disease. Br J Ophthalmol, 84, 741-5. LOIS, N., HALFYARD, A. S., BUNCE, C., BIRD, A. C. & FITZKE, F. W. 1999. Reproducibility of fundus autofluorescence measurements obtained using a confocal scanning laser ophthalmoscope. Br J Ophthalmol, 83, 276-9. MARSIGLIA, M., LEE, W., MAHAJAN, V. B., ZERNANT, J., DELORI, F. C., TSANG, S. H. & SPARROW, J. R. 2015. Quantitative autofluorescence as a clinical tool for expedited differential diagnosis of retinal degeneration. JAMA Ophthalmol, 133, 219-20. MELETH, A. D. & SEN, H. N. 2012. Use of fundus autofluorescence in the diagnosis and management of uveitis. Int Ophthalmol Clin, 52, 45-54. OISHI, A., HIDAKA, J. & YOSHIMURA, N. 2014. Quantification of the image obtained with a wide-field scanning ophthalmoscope. Invest Ophthalmol Vis Sci, 55, 2424-31. PRICE, L. D., AU, S. & CHONG, N. V. 2015. Optomap ultrawide field imaging identifies additional retinal abnormalities in patients with diabetic retinopathy. Clin Ophthalmol, 9, 527-31. REZNICEK, L., STUMPF, C., SEIDENSTICKER, F., KAMPIK, A., NEUBAUER, A. S. & KERNT, M. 2014. Role of wide-field autofluorescence imaging and scanning laser ophthalmoscopy in differentiation of choroidal pigmented lesions. Int J Ophthalmol, 7, 697-703. ROBSON, A. G., TUFAIL, A., FITZKE, F., BIRD, A. C., MOORE, A. T., HOLDER, G. E. & WEBSTER, A. R. 2011. Serial imaging and structure-function correlates of high-density rings of fundus autofluorescence in retinitis pigmentosa. Retina, 31, 1670-9. SCHOLL, H. P., BELLMANN, C., DANDEKAR, S. S., BIRD, A. C. & FITZKE, F. W. 2004. Photopic and scotopic fine matrix mapping of retinal areas of increased fundus autofluorescence in patients with age-related maculopathy. Invest Ophthalmol Vis Sci, 45, 574-83. SCHWEITZER, D., SCHENKE, S., HAMMER, M., SCHWEITZER, F., JENTSCH, S., BIRCKNER, E., BECKER, W. & BERGMANN, A. 2007. Towards metabolic mapping of the human retina. Microsc Res Tech, 70, 410-9. SPARROW, J. R., GREGORY-ROBERTS, E., YAMAMOTO, K., BLONSKA, A., GHOSH, S. K., UEDA, K. & ZHOU, J. 2012. The bisretinoids of retinal pigment epithelium. Prog Retin Eye Res, 31, 121-35. WANG, J. S. & KEFALOV, V. J. 2011. The cone-specific visual cycle. Prog Retin Eye Res, 30, 115-28. WONG, W. T., FOROOGHIAN, F., MAJUMDAR, Z., BONNER, R. F., CUNNINGHAM, D. & CHEW, E. Y. 2009. Fundus autofluorescence in type 2 idiopathic macular telangiectasia: correlation with optical coherence tomography and microperimetry. Am J Ophthalmol, 148, 573-83. YUNG, M., KLUFAS, M. A. & SARRAF, D. 2016. Clinical applications of fundus autofluorescence in retinal disease. Int J Retina Vitreous, 2, 12.

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Chapter 5: Fundus Autofluorescence (FAF) and Contrast Sensitivity in Emmetropes and High Myopes

Contribution My work included collaboration in the design of the study together with my supervisor and co-supervisor. Participant recruitment and data collection was done by me with the help of Diploma in Optometry students at Singapore Polytechnic (Singapore). I was solely responsible for data analysis.

Dr. Jeremiah M. Kelly and I developed the MATLAB code used to sample pixel intensities from the autofluorescence images.

Publication Nil

Conference Presentation Nil

Acknowledgments Supported by the Singapore Polytechnic Final Year Project fund (CLS-15A154, CLS- 16A023).

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5.1. Abstract

Purpose

High myopia is associated with a number of retinal changes that involve the macula, such as tigroid fundus, chorioretinal atrophy, posterior staphyloma and choroidal neovascularisation. These changes can lead to vision loss, making high myopia a growing healthcare concern as more of the world’s population becomes myopic. Fundus autofluorescence (FAF) is a non-invasive retinal imaging modality that may give insight into the functional status of the retina but has not been documented in high myopic eyes. The purpose of this study is to document FAF and contrast sensitivity of highly myopic eyes that are otherwise normal and compare them with emmetropic eyes.

Method

Sixty-one participants were recruited (30 emmetropes and 31 myopes). Fifty-five degree FAF images were captured using Spectralis HRA+OCT (Heidelberg, Germany) at high speed mode with detector sensitivity fixed at 105 units. Autofluorescence intensities were sampled along 8 cardinal meridians of the eye with a custom MATLAB code. The effect of age on macular autofluorescence intensities in an annulus spanning the central 2000 – 3000µm of retina were examined and compared with published data. Contrast sensitivity of the study eye was measured at 0.3, 0.5, 1, 2, 4, 6 and 8 cycles/degree (cpd) using the Visual Psychophysics Engine.

Results

FAF peaks were located in the central 2200 - 6500µm of the retina. Their intensity and location were similar in emmetropes and myopes (p > 0.8). FAF peak location was not correlated with axial length in myopes. Age-related increase in perifoveal autofluorescence was the same in emmetropic and myopic eyes. Contrast sensitivity peaked at 2cpd and reduced at the lower and higher spatial frequencies, as described classically. Overall, there was no difference in contrast sensitivity between emmetropes and myopes (Dunn’s multiple comparisons test, p < 0.9), although we observed a slight reduction in myopes’ contrast sensitivity at the 2 highest spatial frequencies tested (6 and 8cpd) compared to emmetropes’. The longest eyes in our study had 145 marginally poorer contrast sensitivity at almost all spatial frequencies tested compared to the shortest emmetropic eyes, but this did not reach statistical significance (p > 0.2). Lastly, we found a marginally reduced peak at 2cpd in the oldest subjects compared to youngest subjects in our study, although this was also not statistically significant (p > 0.9). Contrast sensitivity was not correlated with age, axial length, spherical equivalent or perifoveal autofluorescence.

Conclusion

Axial elongation in highly myopic eyes does not alter FAF peaks or the rate of age- related increase in FAF compared to emmetropes. High myopia does not reduce contrast sensitivity at spatial frequencies ≤ 8cpd. Contrast sensitivity is not correlated with FAF in healthy, high myopic eyes.

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5.2. Introduction

Fundus autofluorescence (FAF) is an imaging modality that records the fluorescence signal from endogenous ocular fluorophores. In the healthy eye, the primary source of FAF is the retinal pigment epithelium (RPE) lipofuscin (Delori et al., 1995). RPE lipofuscin are yellowish granules formed as a by-product of photoreceptor outer segment phagocytosis and the recycling of photopigment, and accumulates inside the RPE cytoplasm with age (Kennedy et al., 1995). Unusual increase or decrease in FAF is linked to retinal disease and dysfunction, such as in retinitis pigmentosa and central serous chorioretinopathy (Eandi et al., 2015, Robson et al., 2011). FAF imaging has potential to be applied in high myopic eyes to detect changes in the retina that might have functional correlates.

The axial elongation in high myopia is associated with a number of structural and functional changes to the eye. These structural changes include tilted disc, parapapillary atrophy (PPA), myopic maculopathy and posterior staphyloma (Ohno- Matsui et al., 2016, Jonas and Xu, 2014, Chang et al., 2013). Axial elongation that occurs predominantly at the posterior pole cause optic disc tilting and the development of PPA at the temporal optic disc margin (Jonas and Xu, 2014). Posterior staphyloma is an ectasia of the posterior segment of the eye and can cause myopic maculopathy. Myopic maculopathy starts with fundus tessellation, progresses to diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and finally macular atrophy. There may be presence of lacquer cracks, Fuch’s spots, or choroidal neovascularisation (Ohno-Matsui et al., 2015). It is for these reasons that we wanted to study myopic observers.

Contrast of an object refers to the relative difference in luminance of the object against its background. The contrast sensitivity of an observer refers to how small of a difference between the object and background luminance that he/she can perceive. Contrast sensitivity is a more accurate representation of a person’s quality of vision as objects in the real world can be of high or low contrast. It is more sensitive to early functional changes as reductions in contrast sensitivity have been detected before the loss of high contrast visual acuity. In the normal human eye, contrast sensitivity peaks at intermediate spatial frequencies of 2 – 6 cycles / degree (cpd) (Ross et al., 1985). Contrast sensitivity decreases with age (Owsley et al., 1983, Ross et al., 1985, Karatepe et al., 2017) and in the presence of eye disease (Arundale, 1978). Contrast sensitivity has been studied in highly myopic eyes but the results are mixed (Liou and Chiu, 2001, Thorn et al., 1986, Nio et al., 2003, Karatepe et al., 2017).

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Despite the large body of literature on FAF and contrast sensitivity, few have studied highly myopic eyes. With the structural changes occurring in high myopia, we hypothesised that FAF would be affected and a change in visual function may manifest in contrast sensitivity measurements. Therefore in this study, we documented age- related increase in FAF and compared FAF and contrast sensitivity in highly myopic eyes with emmetropic eyes.

5.3. Methods

5.3.1. Ethics and Subjects

This study was approved by the institutional review board of Singapore Polytechnic and Khoo Teck Puat Hospital (Singapore). Informed consent was obtained from 30 emmetropes (within ±0.75D) and 31 high myopes (at least -6.00D). All participants underwent an eye examination to confirm that they were free of ocular disease; slit lamp examination and fundus photography were performed to check for clear ocular media and absence of eye disease. Refraction was performed to measure the optical prescription and that those with more than -2.00D of astigmatism were excluded. All study eyes had best-corrected visual acuity of 6/6 or better. If both eyes met the above criteria, the dominant eye was selected. Refractive error was expressed as spherical equivalent. Axial length was measured using IOLMaster 500 (Carl Zeiss Meditec, Germany).

5.3.2. FAF Imaging

The study eye was dilated using 1% tropicamide until the pupil was > 5mm in diameter. Fifty-five degree macula-centred FAF images were captured using Heidelberg HRA+OCT (Heidelberg Engineering, Germany). Automatic real-time (ART) imaging was enabled at 49 scans without normalisation. The images were captured at high speed mode with detector sensitivity fixed at 105 units. Camera positioning and focus were first adjusted in the infra-red mode and fixating on the internal central target. The HRA+OCT was then switched to autofluorescence mode. Camera focus and positioning was refined to optimise clarity and uniform illuminance of the images. This period of adjustment also served as the photobleaching period for 20 – 30 seconds. FAF images were saved as 768x768 pixel TIFF files. Good quality images

148 were defined as well-focused, good contrast and well-illuminated with minimal shadow artefacts caused by pupil margin or eyelashes (please refer to chapter 2 for details of image quality grading).

The autofluorescence intensities of the images were extracted using a custom MATLAB code (R2015a version 8.5.0.197613; The Mathworks Inc., USA) (Annex 3.1). The MATLAB code sampled the FAF images along 8 cardinal meridians of the eye to identify the FAF peaks. Perifoveal autofluorescence intensities in an annulus within the central 2000 – 3000µm were sampled to plot age-related change in autofluorescence (Figure 5.1). The eccentricities < 2000µm of the fovea were excluded to avoid sampling areas where macular pigment would mask autofluorescence (Delori et al., 1995).

Because the axial length (magnification) of the eye will affect the incident power of the laser, and hence the level of excitation of RPE lipofuscin and resultant autofluorescence intensity, the grey level (GL) intensity data were compensated using the formula below (Delori et al., 2011):

2 푆푐푎푙푒 푓푎푐푡표푟 표푓 푠푢푏푗푒푐푡′푠 푖푚푎푔푒 퐶표푚푝푒푛푠푎푡𝑖표푛 푓푎푐푡표푟 = ( ) (1) 푆푐푎푙푒 푓푎푐푡표푟 표푓 푒푚푚푒푡푟표푝푖푐 푒푦푒

Loss of media transparency is associated with aging. Intensity data were compensated for media transmission accordingly (Delori et al., 2011):

−5 2 푀푒푑𝑖푎 푡푟푎푛푠푚𝑖푠푠𝑖표푛 = 10 5.56푥10 푥(퐴푔푒 −400) (2)

Using formulae 1 and 2, the raw GL intensities extracted from the PAF images were thus compensated using the formula below:

퐶표푚푝푒푛푠푎푡푒푑 𝑖푛푡푒푛푠𝑖푡푦 = (푟푎푤 𝑖푛푡푒푛푠𝑖푡푦 − 푧푒푟표 푟푒푓푒푟푒푛푐푒) 푥 푚푒푑𝑖푎 푡푟푎푛푠푚𝑖푠푠𝑖표푛 푥 푐표푚푝푒푛푠푎푡𝑖표푛 푓푎푐푡표푟 (3)

The zero reference is the GL intensity recorded in the absence of any autofluorescence.

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Figure 5.1. Location of FAF sampling. FAF intensities were sampled from four 5x5 pixel squares positioned along 8 cardinal meridians of the eyes. FAF peaks were identified along the 8 meridians. To study age-related change in autofluorescence, perifoveal grey level (GL) intensities were sampled in an annulus spanning the central 2000 – 3000µm of the retina.

5.3.3. Contrast Sensitivity

Contrast sensitivity of the study eye was measured using the Visual Psychophysics Engine developed by Dr Neil Parry and Dr Ian Murray (software v1.06; Cambridge Research Systems, Rochester, UK). Contrast sensitivity was tested at 20cd/m2. Stimuli were sine wave gratings generated using a ViSage stimulus generator. The stimuli were displayed on a calibrated cathode ray monitor, which was the only source of light in the room. Calibration of the monitor was done using the ColorCal provided by Cambridge Research Systems. The monitor was switched on to warm up for 30 minutes prior to starting contrast sensitivity measurement. The gratings were 10° in diameter and alternating at 0.5Hz to avoid the effects of fade out at the lower spatial frequencies. Stimuli were presented in the following order: 0.3, 0.5, 1, 2, 4, 6 and 8cpd. Higher spatial frequencies were not tested as they may be more vulnerable to optical degradation (Liou and Chiu, 2001, Montés-Micó and Charman, 2001, Collins

150 and Carney, 1990). The refractive error of the study eye, compensated for the test distance of 114cm, was corrected with trial lenses and the non-study eye was patched. Subjects were instructed to maintain fixation on the centre of the fixation cross and to reduce the intensity of the sine wave grating until the grating was just visible against the background. A trial run was conducted for each participant to ensure he/she understood the test instructions. There was a 10-minute adaptation period before testing began. Each spatial frequency was tested 3 times to obtain an average value.

5.3.4. Statistical Analysis

Statistical analyses were performed in Graphpad Prism 7.01 (GraphPad Software Inc., CA, USA). The D’Agostino and Pearson normality test was used to determine normality of data. Where data were normally distributed, parametric tests were used for analysis, while nonparametric tests were used for data that were not normally distributed. Mann-Whitney test was used to compare for differences between the age and axial lengths of the emmetropic and myopic groups. Unpaired t-test was used to test for differences in spherical equivalent between the emmetropes and myopes. For comparing contrast sensitivity between emmetropes and myopes and between the youngest and oldest subjects, Kruskall-Wallis and Dunn’s multiple comparisons test was used. For comparing contrast sensitivity between the shortest emmetropic and longest myopic eyes, one-way ANOVA was used. Spearman and Pearson correlation was used to test the relationship between parameters.

To compare age-related FAF increase in emmetropes and myopes, exponential curves were fitted to autofluorescence data of each group. The z score was then calculated for the rate constant, k, of each curve:

푘 −푘 1 2 (4) 푧 = 2 2 √푆퐸(푘1) +푆퐸(푘2)

Statistical significance was set at p < 0.05 for tests for differences. As multiple correlation analyses was performed for each of the 7 spatial frequency of contrast sensitivity tested, statistical significance was set at p < 0.007 after Bonferroni correction:

1 퐵표푛푓푒푟푟표푛𝑖 푐표푟푟푒푐푡푒푑 푝 − 푣푎푙푢푒 = 1 − (1 − 푎)푡

Where a = critical p value (i.e. 0.05) and t = number of tests (i.e. 7). 151

5.4. Results

5.4.1. Subjects

Table 5.1 shows the average age, axial length and spherical equivalent of the subjects. Myopes had longer axial length (Mann-Whitney test, p < 0.001) and more negative spherical equivalent (unpaired t-test, p < 0.001) than emmetropes. As expected, magnitude of myopia was negatively correlated with axial length (Spearman’s ρ = - 0.801, p < 0.0001) (Figure 5.2).

Emmetropes Myopes p-value Age (years) 31 (18 – 58) 35 (18 – 56) 0.717 Axial length (mm) 23.70 (22.34 – 25.85) 26.74 (24.88 – 28.54) < 0.001 Spherical Plano (+0.63 – -1.50) -7.38 (-6.00 – -14.63) < 0.001 equivalent (D)

Table 5.1. Median (range) age, axial length and spherical equivalent of the subjects involved in this study. There was no significant difference in age between the emmetropic and myopic groups.

C o r r e l a t i o n b e t w e e n S p h e r i c a l E q u i v a l e n t a n d A x i a l L e n g t h

)

D (

- 2

n e

l - 4

a v

i - 6

q e

- 8

a c

i - 1 0

r e

h - 1 2 p

S  = - 0 . 8 0 1 , p < 0 . 0 0 0 1 - 1 4 ( n = 6 1 ) - 1 6 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0

A x i a l l e n g t h ( m m )

Figure 5.2. Scatterplot of spherical equivalent against axial length. Axial length increased as spherical equivalent decreased (Spearman’s ρ = -0.801, p < 0.0001).

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5.4.2. FAF Peaks

FAF peaks were located between 2200µm to 6500µm along the 8 cardinal meridians of the eye. A second nasal peak was found beyond the optic disc at 6200µm - 6500µm eccentricity. Location of the FAF peaks along the 8 cardinal meridians of the eye are shown in Table 5.2. The FAF peak locations were similar between emmetropes and myopes (Dunn’s multiple comparisons test, p > 0.9). Location of FAF peaks was not correlated with axial length in myopes (Spearman’s ρ ranged from -0.225 to 0.324, p ≥ 0.075, n = 31).

Location of FAF peaks (µm)

Emmetropes (n = 30) Myopes (n = 31) Temporal 3750 (3100 - 4500) 3800 (3500 – 4600) Superior-temporal 3550 (2800 – 4150) 3900 (3450 – 4500) Superior 3650 (3250 – 4400) 3700 (3200 – 5600) Superior-nasal 5100 (3500 – 5800) 5900 (5400 – 6750) Nasal 2450 (2000 – 2800) 2600 (2300 – 2800) Nasal beyond optic disc 6700 (6350 – 7350) 7400 (7100 – 7650) Inferior-nasal 2700 (2400 – 3150) 2750 (2450 – 3300) Inferior 2950 (2600 – 3150) 2800 (2300 – 3400) Inferior-temporal 3550 (2850 – 4050) 3300 (2600 – 4300)

Table 5.2. Median (interquartile range) of FAF peak location (eccentricity rounded to the nearest 50µm) in emmetropic and myopic eyes. The FAF peak locations were similar in both refractive groups (Dunn’s multiple comparisons test, p > 0.9).

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5.4.3. FAF Intensity

Average perifoveal autofluorescence intensity of myopic eyes was similar to emmetropic eyes (122 ± 39GL vs 139 ± 41GL respectively; unpaired t-test, t = 1.643, p = 0.106). Perifoveal autofluorescence was positively correlated with age in both emmetropes (Pearson r = 0.872, p < 0.0001) and myopes (Spearman’s ρ = 0.873, p < 0.0001).

Fitting an exponential curve to the data, we obtained the rate constant, k, which describes the rate of FAF increase with age. The rate of change, k, was 0.020 in both emmetropes and myopes (Figure 5.3). Therefore, emmetropes and myopes experienced similar rate of perifoveal autofluorescence increase with age (z = -0.01, p = 0.992).

Figure 5.3. Age-related increase in perifoveal autofluorescence intensity of emmetropes (n = 30) and myopes (n = 31). FAF intensity was positively correlated with age and increased at similar rates in both emmetropes and myopes. The solid lines are exponential curve fits to the data.

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5.4.4. Contrast Sensitivity

Contrast sensitivity of the emmetropes (n = 30) and myopes (n = 31) are shown in Figure 5.4A. Contrast sensitivity peaked at 2cpd and reduced towards the lower and higher spatial frequencies. We observed a reduction in contrast sensitivity at 6 and 8cpd in the myopes compared to emmetropes, but this did not reach statistical significance (Dunn’s multiple comparisons test, p < 0.9).

Comparing contrast sensitivity of the shortest emmetropic eyes (axial length between 22.34mm and 23.46mm, n =12) and longest myopic eyes (axial length between 27.02mm and 28.54mm, n = 13) revealed marginally poorer contrast sensitivity in the longest eyes across all spatial frequencies. One-way ANOVA revealed differences in sensitivity among the spatial frequencies (F 13,161 = 10.67, p < 0.0001) but no difference between emmetropes and myopes within the same spatial frequency (Holm- Sidak’s multiple comparisons test, p > 0.2). This difference between emmetrope and myope was most pronounced in the intermediate spatial frequencies of 2 and 4cpd (Figure 5.4B). Combining emmetropic and myopic data, contrast sensitivity was not correlated with axial length or spherical equivalent (p > 0.007 after Bonferroni correction) (Table 5.4)

As contrast sensitivity between emmetropes and myopes were similar, data from both groups were combined to study the effect of age. Contrast sensitivity was compared between the youngest (18 – 20 years old, n = 15) and oldest subjects (45 – 58 years old, n = 17) (Figure 5.4C). No statistically significant differences were found at any of the spatial frequencies tested (Dunn’s multiple comparisons test, p > 0.9), although we observed a slight dip in sensitivity at 2cpd in the older eyes. A negative correlation was found between 2cpd contrast sensitivity and age (Spearman’s ρ = -0.270, p = 0.035; n = 61) (Table 5.3) however this could be a Type 1 error (p-value to be considered statistically significant was p < 0.007 after Bonferroni correction). Perifoveal autofluorescence intensity was not correlated with contrast sensitivity at any of the spatial frequencies tested (Table 5.4).

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Figure 5.4. Contrast sensitivity in (A) emmetropes and myopes, (B) short emmetropic (< 24mm) and long myopic (> 27mm) eyes, and (C) young and old eyes. (A) Myopes exhibited similar contrast sensitivity as emmetropes as spatial frequency increased (Dunn’s multiple comparisons test, p < 0.9). (B) Long myopic eyes had slightly poorer contrast sensitivity at all spatial frequencies tested compared to short emmetropic eyes, with the largest differences at 2 and 4cpd (Holm-Sidak’s multiple comparisons test, p > 0.2). (C) The youngest (18 – 20 years old) and oldest subjects (45 – 58 years old) had the greatest difference in sensitivity at 2cpd but no statistically significant differences were found at any of the spatial frequencies tested (Dunn’s multiple comparisons test, p > 0.9). The symbols and error bars represent the median and interquartile range.

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Spatial Frequency (cpd) 0.3 0.5 1.0 2.0 4.0 6.0 8.0 Axial length (mm) ρ = -0.159 ρ = 0.001 ρ = -0.144 ρ = -0.158 ρ = -0.216 ρ = -0.177 ρ = -0.122 p = 0.221 p = 0.996 p = 0.267 p = 0.224 p = 0.094 p = 0.173 p = 0.351

Spherical equivalent ρ = 0.003 ρ = -0.133 ρ = 0.001 ρ = 0.056 ρ = 0.090 ρ = 0.183 ρ = 0.135 (D) p = 0.984 p = 0.307 p = 0.997 p = 0.670 p = 0.489 p = 0.157 p = 0.300

Age (years) ρ = -0.140 ρ = -0.174 ρ = -0.220 ρ = -0.270 ρ = -0.203 ρ = -0.145 ρ = -0.143 p = 0.281 p = 0.179 p = 0.089 p = 0.035 p = 0.117 p = 0.267 p = 0.271

Perifoveal r = -0.090 r = -0.098 r = -0.176 r = -0.199 r = -0.086 r = -0.066 r = -0.077 autofluorescence p = 0.490 p = 0.453 p = 0.174 p = 0.125 p = 0.511 p = 0.612 p = 0.555 (GL)

Table 5.3. Correlation analysis between axial length, spherical equivalent, age, perifoveal autofluorescence and contrast sensitivity at each spatial frequency (n = 61). No statistically significant results were found after Bonferroni correction.

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5.5. Discussion

We have found that FAF peak location and rate of age-related increase in FAF was similar between emmetropes and high myopes aged 18 to 60 years old. There was also no significant difference between the 2 groups in terms of contrast sensitivity at low and moderate spatial frequencies. Contrast sensitivity was not correlated with FAF. This is the first study to compare the rate of age-related increase in FAF between these 2 refractive groups and to compare contrast sensitivity with FAF. The lack of differences between emmetropes and high myopes suggest that high myopia in the absence of degenerative retinal changes is unlikely to show significant changes in FAF or in contrast sensitivity that imply outer retinal stress/damage or visual dysfunction.

5.5.1. FAF Peaks in the Posterior Pole

In our 61 subjects, FAF peaks were located between 2200 - 6500µm. The locations of the FAF peaks were closer to the fovea nasally and inferiorly, and further from the fovea temporally to superiorly. The superior nasal and second nasal peaks (beyond optic disc) were the furthest from the fovea. These FAF peak locations matched our earlier data from 17 healthy subjects with emmetropia to high myopia (Chapter 4), as well as rod photoreceptor distribution and spectrophotometric measurements (Curcio et al., 1990, Delori et al., 2001). In this study, we found no difference in the location of FAF peaks between emmetropes and myopes. This suggests that axial elongation in myopic eyes did not significantly alter the photoreceptor distribution in the posterior pole compared to emmetropic eyes, or that autofluorescence peaks may not be a good method to detect FAF changes associated with high myopia. It is possible that early autofluorescence changes in high myopia do not appear in the posterior pole but in the periphery and optic disc. This is because the retinal and parapapillary changes, such as lattice degeneration and PPA, are found at younger age than degenerative macular changes (Curtin and Karlin, 1970, Chang et al., 2013).

Posterior staphyloma is a structural outpouching of the retina that is associated with high myopia. Ohno-matsui et al. (2014) reported that both pigmented and depigmented borders of posterior staphyloma exhibited hypoautofluorescence or hyperautofluorescence using Optos ultrawide-field imaging. Optical coherence tomography (OCT) study of posterior staphyloma revealed thinned inner retinal layers along the staphyloma border (Tanaka et al., 2015). This subsequently develops into patchy chorioretinal atrophy. One can postulate that the FAF peak locations may 159 change in the presence of structural changes such as posterior staphyloma. In our study however, no subjects had posterior staphyloma.

5.5.2. Age-related Increase in FAF

Our finding of an age-related increase in FAF is in agreement with published literature using cSLO (Figure 5.5).

Greenberg et al. (2013) used a HRA+OCT modified with an internal fluorescent reference to quantify FAF in normal eyes (Delori et al., 2011, Greenberg et al., 2013) . An adjustment factor was made to the GL intensities of every image based on the fluorescent reference to obtain quantified autofluorescence (qAF). This conversion from GL to qAF means the autofluorescence intensities are no longer constrained to 0 – 255GL (unlike our data which are expressed in GL). This is likely the reason for the wider spread of the data and relatively weaker correlation coefficient of 0.644 compared to 0.866 in our study. Secondly, it is unclear what is meant by ‘Asian’ ethnicity in Greenberg’s study. The subjects in our study were Chinese (n = 56; 92%), Malay (n = 3; 5%) and Indian (n = 2; 3%), and could be considered a homogenous group, hence the smaller variation in autofluorescence intensities. Our data exhibited similar exponential trend as we employed the same method of compensating autofluorescence intensities for age-related decrease in crystalline lens transmittance and for magnification differences due to refractive error.

Von Ruckmann and co-workers’ (1997) data showed lower autofluorescence intensity and a slower rate of autofluorescence increase with age compared with the present study. A number of methodological differences could account for the difference. Although the excitation wavelength is the same in von Ruckmann’s and our study, the barrier filter in their Zeiss cSLO was restricted to transmit wavelengths between 521nm and 650nm, whereas the HRA+OCT transmits any wavelength greater than 500nm; therefore, the amount of autofluorescence signals collected from our study was greater. To study age-related change in autofluorescence, von Ruckmann et al. sampled small locations (208x208µm) on the retina at the area of peak intensity, which varied between 7° and 15° from the fovea among their subjects, whereas our study sampled autofluorescence from an annulus spanning the central 7 - 10° of the macula. The use of peak autofluorescence to study age-related changes in FAF may not be a good parameter as RPE autofluorescence can vary considerably, reaching very high levels in small localised spots and this would not be representative of the 160 rest of the retina (Ach et al., 2014). Lastly, although von Ruckmann et al. only recruited participants aged 6 – 78 years with ‘clear crystalline lenses’, they made no compensation for age-related decrease in crystalline lens transmittance, which would have reduced the autofluorescence intensities in the older eyes to a greater extent compared to younger eyes and resulted in underestimation of autofluorescence intensity in the older eyes.

Figure 5.5. Comparison of age-related increase in perifoveal autofluorescence with other studies.

Ocular magnification in myopia may potentially affect the perifoveal autofluorescence because the myopic image sizes were reduced and the sample location was moved further from the centre of the fovea. However, the largest change in perifoveal autofluorescence was less than 0.5% when we made the adjustment in 5 of the most myopic eyes. This insignificant effect of ocular magnification on perifoveal autofluorescence is due to the minimal change in autofluorescence signals at the region from 2 to 3mm from the fovea.

Myopes have been reported to have higher levels of oxidative stress than emmetropes (Fedor et al., 2017, Francisco et al., 2015). Since lipofuscinogenesis is increased in oxidative stress (Brunk and Terman, 2002), we hypothesised that autofluorescence 161 intensity and/or age-related increase in autofluorescence could be greater in high myopes. In this study, we found that high myopes had marginally stronger FAF compared to emmetropes, although this was not statistically significant. This implies that our study was not appropriately powered. A difference between the 2 refractive groups may exist, and more research needs to be done into autofluorescence in high myopia.

We also found similar rates of age-related increase in macular autofluorescence between emmetropes and myopes. This is the first study to compare rate of age- related FAF increase between these 2 refractive groups. Our results indicate that axial elongation in high myopia does not affect FAF accumulation rates significantly. This suggests that any possible difference in oxidative stress levels in the rods and RPE between emmetropes and myopes may not be significant enough to accelerate autofluorescence accumulation in the macula.

A longitudinal study would be better suited for studying age-related increase in FAF between emmetropes and myopes. However, such an endeavour would also be challenging as genetic and environmental factors that may affect FAF accumulation remain unknown.

5.5.3. Contrast Sensitivity in Emmetropes and High Myopes

We tested contrast sensitivity from 0.3 - 8cpd at 20cd/m2 to investigate potential contrast sensitivity loses from retinal changes. Higher spatial frequencies were not tested as they may be more vulnerable to optical degradation (Liou and Chiu, 2001, Montés-Micó and Charman, 2001, Collins and Carney, 1990). We found that contrast sensitivity of emmetropes and myopes peaked at 2cpd. Peak at 2cpd was also reported by Nio et al.(2003) and Jaworski et al. (2006), who tested contrast sensitivity at luminance of 21cd/m2 and 19.5cd/m2 respectively. Our results support the conclusion that high myopes do not have reduced contrast sensitivity compared to emmetropes and this is in agreement with previous studies (Kamiya et al., 2014, Comerford et al., 1987, Thorn et al., 1986, Collins and Carney, 1990).

Contrast sensitivity at high spatial frequencies has been reported to be reduced in high myopia (Collins and Carney, 1990, Fiorentini and Maffei, 1976, Stoimenova, 2007, Liou and Chiu, 2001). The reduction is believed to be due to increased aberrations (Feizi

162 and Karimian, 2009, Kamiya et al., 2014, Liou and Chiu, 2001) and changes in neural or retinal function (Stoimenova, 2007, Liou and Chiu, 2001).

Liou and Chiu (2001) measured contrast sensitivity in 28 emmetropes (< 0.5D), 12 low myopes (-1D to -3D), 14 moderate myopes (-3.25D to -6D), 14 high myopes (-6.25D to -12D) and 13 severe high myopes (> -12D) corrected with spectacle lenses using the OPTEC 2000 contrast sensitivity system. The test stimuli were stationary sine wave gratings of different orientations at 1.5, 3, 6, 12 and 18cpd presented at 8 contrast levels. High myopes demonstrated reduced contrast sensitivity at 12 and 18cpd, but not at lower spatial frequencies. This corroborated the findings from an earlier study (1990), which compared contrast sensitivity of low to moderate myopes (-2D to -7D) and high myopes (> -7D). The authors from both studies attributed the loss of sensitivity in the high myopes to optical degradation such as aberrations from the ophthalmic lens correction. We did not test high spatial frequencies, however our findings at intermediate spatial frequencies are in agreement with both of these studies.

Our data contradict some earlier studies. Fiorentini and Maffei (1976) reported reduced contrast sensitivity at all spatial frequencies in high myopes compared to emmetropes. However, the study involved only 10 myopic subjects and 3 emmetropic subjects for comparison. Of the myopic subjects, 4 had a corrected visual acuity of 6/12, another 4 had visual acuity just under 6/6 and only 2 subjects had VA better than 6/6. This indicates that the myopic observers were from a biased population and may have included myopic subjects with amblyopia and/or ocular disease, therefore obtaining substantially reduced contrast sensitivity compared to emmetropic subjects.

Our data also disagree with Stoimenova (2007). In that study, contrast sensitivity was tested using letter targets (at fixed spatial frequency of 6cpd) under photopic and mesopic luminance levels. Young healthy myopes (18 to 31 years old with spherical equivalent ranging from -1D to -8D) demonstrated poorer contrast sensitivity compared to emmetropes regardless of luminance level. This is in contrast to our findings and may be related to the difference in method of contrast sensitivity measurement. The test stimuli were dark or bright Cyrillic letters subtending 30 min arc flashed on a computer screen for 150 milliseconds. Letter targets are not equal to grating targets because letter targets contain complex information of several orientations and spatial frequencies (Moseley and Hill, 1994). Sine wave gratings, such as the ones used in our study, are ‘pure’ targets with only 1 spatial frequency and orientation. The high myopes in Stoimenova’s study were also corrected with contact

163 lenses while the low and moderate myopes were corrected with spectacle lenses. Even though both groups achieved 6/6 visual acuity or better, contact lenses can contribute to higher order aberrations that could reduce contrast sensitivity at high spatial frequencies (Roberts et al., 2006).

Jaworski et al. (2006) tested contrast sensitivity at similar luminance and spatial frequencies as we did. Poorer contrast sensitivity was found in high myopes (n = 10) at 4.2, 7.3 and 9.7cpd compared to emmetropes (n = 10). Aside from their smaller sample size, their myopes were more myopic than our subjects and skewed towards middle age. Contrast sensitivity was also measured under pupillary dilation, which may have induced aberrations.

5.5.4. Contrast Sensitivity and Age

We found no significant difference in contrast sensitivity from 0.3 to 8cpd between the youngest and oldest eyes in our study. Moreover, there was no significant correlation between age and contrast sensitivity. This is in agreement with published studies (Kamiya et al., 2014, Wright and Drasdo, 1985, Karatepe et al., 2017). Karatepe et al. (2017) tested emmetropes aged 7 – 65 found age-related reduction in sensitivity at higher spatial frequencies of 12 and 24cpd, which were above the spatial frequencies tested in our study. Kamiya et al. (2014) tested relatively young subjects aged 18 – 53 years from 1.5 to 18cpd also found no age effect. Wright and Drasdo (1985) measured contrast sensitivity at 1.3, 4, and 12cpd in 70 subjects aged 10 to 79 years. Even with the inclusion of older subjects, they observed no reduction in contrast sensitivity with age at low and intermediate spatial frequencies (1.3 and 4cpd). A steady age-related reduction in contrast sensitivity was only seen at 12cpd, which was also not a spatial frequency tested in our study.

In ageing, aberrations and intraocular light scatter may increase due to increased crystalline lens density (Elliott, 2006, Kamiya et al., 2014, Wright and Drasdo, 1985). Retinal illuminance decreases due to senile pupil miosis and decreased crystalline lens transmittance (Karatepe et al., 2017, Wright and Drasdo, 1985). There may also be a reduction in the number of neural cells, neurotransmitter changes, and higher levels of intracellular lipofuscin that disrupt normal neural processing. The consequences of these age-related changes are a reduction in sampling efficiency and increased noise (Pardhan, 2004), leading to decreased contrast sensitivity. Some papers have reported contrast sensitivity losses at low, intermediate and high spatial frequencies while 164 others only found losses at intermediate and high spatial frequencies, but these studies involved much older subjects (from age 50 years up to 87 years) than those in our study (Sloane et al., 1988, Ross et al., 1985, Pardhan, 2004, Derefeldt et al., 1979, Burton et al., 1993, Elliott et al., 1990), or only had small sample size (n = 5) in the older age group (Arundale, 1978).

Owsley et al. (1983) measured contrast sensitivity of subjects aged 19 to 87. They found reduction in photopic contrast sensitivity at 2, 4, 8 and 16cpd starting from the forties and fifties. However, the older participants also had poorer visual acuity than the younger participants (the subjects in their 80s had minimum angle of resolution [MAR] of 1.82 whereas the subjects in their 20s had MAR 0.68), suggesting that the older subjects may have mild ocular pathology. The poorer visual acuity contributed to the reduced sensitivity at higher spatial frequencies in older subjects. In our study, all participants were corrected to 6/6 (MAR 1.0) or better. The study also did not reveal the post-hoc analysis showing which age groups had significantly poorer contrast sensitivity at which spatial frequency. The mean and SD of contrast sensitivity for the age groups < 60 years of age were similar to the present study at 0.5, 1, 2, 4 and 8cpd. This suggests that the bulk of their observation of age-related loss were likely to come from participants over 60 years old and almost certainly at higher spatial frequencies.

5.5.5. Contrast Sensitivity and Perifoveal Autofluorescence

We found no correlation between perifoveal autofluorescence and contrast sensitivity. Although contrast sensitivity was found to be correlated with FAF patterns in early AMD (Rodrigo-Diaz et al., 2016), our study did not show similar findings in myopic eyes. It has been hypothesised that the mechanical stretching in axial elongation of myopia could alter post-receptoral processing (Jaworski et al., 2006). Our myopic subjects did not have functional changes in contrast sensitivity or metabolic changes that resulted in increased autofluorescence. As mentioned in the introduction, autofluorescence originates from RPE lipofuscin, which forms as a by-product of photoreceptor outer segment phagocytosis (Kennedy et al., 1995). If the photoreceptor or RPE are diseased there may be hyper- or hypoautofluorescence since the normal anatomy and/or physiology of the outer retina is disrupted (Robson et al., 2011, Eandi et al., 2015). In diseased eyes, there could be loss or dysfunction of photoreceptors or other retinal cells, thereby causing loss in contrast sensitivity. Since

165 our subjects did not have any pathological changes in the retina, correlation analysis failed to reveal any significant relationship between macular autofluorescence intensity and contrast sensitivity.

5.6. Limitations

In this chapter, we did not find age-related change in contrast sensitivity from 0.3 to 8cpd. We did not find correlation between contrast sensitivity and FAF. Moreover, we did not find any significant difference in contrast sensitivity between emmetropes and high myopes. These results may be due to the small sample size in our cohort. Significant differences may be seen if higher spatial frequencies, older or more myopic subjects are recruited. However, it may be more challenging to recruit older or more myopic subjects as age-related diseases and reduced ocular medium clarity would affect the visual functions and FAF signals.

The age-related increase in FAF should be interpreted with caution because we did not perform quantified autofluorescence in our study. However, we believe our data are valid because they match Greenberg and colleagues’ (2013) data well, as shown in Figure 5.5. In addition, we ensured that the imaging protocol was standardised for all participants, that all FAF images were captured using the same machine and operator, and only images that have passed through a high quality control process were used.

5.7. Future Work

The results thus far indicate highly myopic eyes are similar to emmetropic eyes in terms of perifoveal autofluorescence and contrast sensitivity. Recruiting and studying eyes with more extreme myopia and/or pathologic myopia may reveal potential correlations between autofluorescence and myopia changes. For example, autofluorescence alterations have been reported in eyes with posterior staphyloma, as discussed earlier. Visual field defects have also been observed in eyes with posterior staphyloma (Ohno-Matsui et al., 2011) . Future work might investigate how FAF changes and visual field defects are correlated in eyes with posterior staphyloma or pathological myopia, and whether FAF imaging can be used to monitor such eyes for progression. In addition, FAF imaging may even shed light on the development and progression of chorioretinal atrophy associated with posterior staphylomas.

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5.8. Conclusion

We did not find any early changes in FAF in highly myopic eyes. FAF peaks are located at similar eccentricities in emmetropes and high myopes. As there was a significant link between myopia and axial length, this indicates that simple axial elongation does not affect the FAF peak locations. It is important to note that, despite axial elongation, perifoveal autofluorescence in myopes undergo similar rates of age-related increase in FAF as emmetropes. Lastly, high myopia does not reduce contrast sensitivity at spatial frequencies ≤ 8cpd. Contrast sensitivity is not correlated with FAF in healthy, high myopic eyes.

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Chapter 6: Parapapillary Autofluorescence (PAF) and the Retinal Nerve Fibre Layer (RNFL) in Emmetropes and High Myopes

Contribution My work included collaboration in the design of the study together with my supervisor and co-supervisor. Participant recruitment was done by me with the help of Diploma in Optometry students at Singapore Polytechnic (Singapore). I was solely responsible for data collection and analysis.

Publication Nil

Conference Presentation The Association of Research in Vision and Ophthalmology (ARVO) 2017. Baltimore, US (poster).

Teresa Tee, Lekha Gopal, Ian J Murray, Ivan Y-F Leung; Comparison of Parapapillary Autofluorescence and Retinal Nerve Fiber Thickness in Emmetropic and High Myopic Eyes. Invest. Ophthalmol. Vis. Sci. 2017;58(8):1886.

Acknowledgments Supported by the Singapore Polytechnic Final Year Project fund (CLS-15A154, CLS- 16A023).

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6.1. Abstract

Background

The link between autofluorescence and retinal nerve fibre layer (RNFL) is interesting because the amplitude of the autofluorescence signal has been observed to reduce as it approaches the optic disc. This effect is important because it would allow the true parapapillary autofluorescence (PAF) intensity to be obtained from normal healthy and from diseased eyes and this would in turn facilitate the study of autofluorescence in optic neuropathies. We hypothesise that the thick RNFL around the optic disc will significantly attenuate PAF intensity. In this study, we assessed 4 methods of PAF sampling to determine a method that would be most appropriate for studying the relationship between PAF intensity and RNFL thickness. Next we used linear regression to quantify the magnitude by which RNFL can be expected to attenuate PAF intensity.

Method

Eighty-nine participants (39 emmetropes and 50 high myopes) underwent autofluorescence imaging after pupillary dilation. Thirty degree optic disc-centred autofluorescence images were captured using the Spectralis HRA+OCT (Heidelberg Engineering, Germany) at detector sensitivity of 90 - 101 units. RNFL thickness was measured using Cirrus HD-OCT (Carl Zeiss Meditec, Germany). PAF was sampled using Photoshop CS 6 (Adobe. Four sampling methods of PAF were employed: small square (5x5 pixel square), medium square (31x31 pixel square), large square (51x51 pixel square) and arc segment (2945 pixel segment). These PAF sampling locations were distributed around the same eccentricity from which RNFL was measured. RNFL thickness was measured from the TSNIT graph (which displays the actual RNFL thickness measured around the optic disc) or the 12 clockhour segments of the OCT report. The 4 PAF sampling methods were evaluated based on the repeatability of the PAF samples and the correlation analysis between PAF intensity and RNFL thickness. Linear regression was performed to determine the magnitude of PAF attenuation by RNFL.

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Results

The arc segment method demonstrated highest repeatability for PAF intensity and consistency for studying the correlation between PAF intensity and RNFL. Myope RNFL was thinner superiorly and inferiorly and thicker temporally compared to emmetropes (Dunn’s multiple comparisons test, p < 0.05). RNFL of the superior (r = -0.453, p < 0.0001), inferior (r = -0.382, p = 0.0002) and nasal (ρ = -0.302, p = 0.004) quadrant demonstrated an age-related decline. Using the arc segment sampling method, 5 myopic eyes and 1 emmetropic eye failed to show a negative correlation between PAF intensity and RNFL. For 4 of these myopic eyes, the result could be attributed to RNFL thinning, the presence of Weiss ring and parapapillary atrophy (PPA) affecting PAF intensity. The remaining myopic eye and emmetropic eye exhibited especially bright autofluorescence in the superior PAF. Every 10µm of RNFL attenuated PAF intensity by an average (± SD) of 3 ± 1GL and this was the same in emmetropes and myopes.

Conclusion

The arc segment sampling method gives most consistent results for studying the correlation between PAF intensity and RNFL thickness. The thick and highly variable RNFL around the optic disc significantly attenuates PAF intensity. We estimate that every 10µm of RNFL attenuated PAF intensity by about 3 ± 1GL. Studies of autofluorescence around the optic disc should take into account the local attenuation of the PAF signal by RNFL.

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6.2. Introduction

Fundus autofluorescence (FAF) is a retinal imaging modality that captures fluorescence signals from endogenous fluorophores in the eye. The FAF signal originates from retinal pigment epithelium (RPE) lipofuscin of the outer retina (Wing et al., 1978). The RPE supports photoreceptors by phagocytosing photoreceptor outer segments and participating in the recycling of visual pigment. In doing so, lipofuscin granules form as a by-product and accumulate within the RPE with age (Wing et al., 1978). When lipofuscin is irradiated with short-wavelength light, it absorbs the energy and releases it in the form of fluorescence of a longer wavelength (Seehafer and Pearce, 2012). Recently, autofluorescence has been used as a marker for ageing and oxidative stress (McGill et al., 2016).

Autofluorescence in the central retina has been documented in healthy eyes. It is relatively low at the fovea and peaks in the macula (Ach et al., 2014, Delori et al., 2001) (Chapter 4). The distribution of FAF reflects rod photoreceptor distribution in the normal eye (Delori et al., 2001, Ach et al., 2014)(Tee et al., manuscript submitted). FAF imaging has been applied to a variety of diseases, such as age-related macular degeneration (AMD), Stargardt’s disease and retinitis pigmentosa (Bindewald et al., 2005, Oishi et al., 2013, Cukras et al., 2012) to understand disease mechanisms and progression.

Autofluorescence in the parapapillary region has received less attention in comparison (Delori et al., 2001, Greenberg et al., 2013, von Rückmann et al., 1995, Bindewald et al., 2005, Cukras et al., 2012, von Rückmann et al., 1997, von Rückmann et al., 1998). A few studies have focused on studying hyperautofluorescent patches or patterns of altered autofluorescence adjacent to the optic disc (Laemmer et al., 2007, Cohen et al., 2016) or within the optic disc (Asli Dinc et al., 2009, Chang et al., 2017). In ocular hypertensive eyes, discrete regions of parapapillary hyperautofluorescence have been correlated with greater peak latency of blue-on-yellow pattern visual evoked potential, which is correlated with glaucomatous damage (Laemmer et al., 2007). In primary open angle glaucoma and normal tension glaucoma eyes, small areas of relative hyperautofluorescence along the optic disc margin have been correlated with thinner retinal nerve fibre layer (RNFL) (Plouznikoff et al. IOVS 2013;54:ARVO E-Abstract 1707; Plouznikoff et al. IOVS 2016;57:ARVO E-Abstract 844). In AMD eyes, 5 types of autofluorescence patterns in the parapapillary region have been documented, although their significance remains to be elucidated (Cohen et al., 2016). These qualitative and semi-quantitative studies have largely ignored the effect of RNFL on the measured

174 autofluorescence intensity, despite the observation that autofluorescence decreases towards the optic disc and regions of mildly reduced autofluorescence have been observed corresponding to the superior and inferior RNFL bundles.

High myopia is a growing health concern globally due to its increasing prevalence and the associated increased risk of blindness from cataract, retinal detachment, retinal degeneration and glaucoma (Holden et al., 2016). Structurally, high myopia is associated with axial elongation and the development of parapapillary atrophy (PPA) and optic disc tilting (Samarawickrama et al., 2011, Curtin and Karlin, 1970). High myopia is also associated with an increased risk of glaucoma, with mechanical and/or oxidative stress possibly causing neuropathy (Ohno-Matsui et al., 2016)

Autofluorescence around the optic disc can be expected to differ from macular autofluorescence due to the different RPE and photoreceptor distribution in the region (Ach et al., 2014). Structural changes around the optic disc, such as PPA and disc tilting in high myopia, could also exhibit different autofluorescence compared to eyes without these structural changes. PAF imaging may therefore have applications in glaucoma and non-glaucomatous visual field loss in high myopia (Ohno-Matsui et al., 2011). Before undertaking further studies of autofluorescence around the optic disc, the normal distribution of PAF and the effect of RNFL on PAF intensity needs to be investigated.

The purpose of the experiments described here is to evaluate 4 methods of PAF sampling to determine which method is most appropriate for studying the relationship between PAF intensity with RNFL thickness. This will be done by assessing the repeatability of the PAF intensities sampled and by comparing the consistency of the correlation analysis using each method. Once we have established an appropriate method for sampling PAF, we will calculate the magnitude by which PAF is attenuated by RNFL using linear regression. This is important because knowing the true autofluorescence intensity would allow more accurate studies of PAF in different diseases to be conducted, and this will help clinicians better understand the disease mechanism or progression.

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6.3. Methods

6.3.1. Ethics and Subjects

This study has received institutional review board approval from Singapore Polytechnic and Khoo Teck Puat Hospital (Singapore). Ninety-nine participants were recruited for the study after they had given informed consent and passed the eligibility screening in Singapore Polytechnic (Table 6.1).

The study eye was the eye that satisfied the inclusion and exclusion criteria. If both eyes met the inclusion criteria, the dominant eye was chosen as the study eye. One myopic participant dropped out after the 1st study visit. In total, 98 participants completed the study (39 emmetropes and 59 myopes).

Inclusion criteria Exclusion criteria Aged 18 – 60 years Best-corrected visual acuity worse than 6/12 Emmetropia (within ±0.75D), or Astigmatism more than -2.00D High myopia (at least -6.00D) History of macular or refractive surgery Diagnosed with diabetes or ocular disease (excluding myopia-related retinal changes) Contraindication to pupil dilation Use of medication with known ocular side effects Sensitivity to bright light Inability to provide informed consent

Table 6.1. List of inclusion and exclusion criteria.

6.3.2. Refraction and Biometry

Keratometry was measured using the Nidek Tonoref II autorefractor (Nidek Co. Ltd., Japan). Subjective refraction was performed and expressed in spherical equivalent by adding half of the astigmatism to the spherical component. Axial length of the study eye was measured using IOLMaster 500 (Carl Zeiss Meditec, Germany). The average of 5 reliable measurements was used.

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6.3.3. Optical Coherence Tomography (OCT)

Optical coherence tomography (OCT) volume scans of the optic disc were captured using Cirrus HD-OCT (ver. 5.1.1.6 and 7.0.1.290; Carl Zeiss Meditec, Germany). All scans had a scan quality of 7 except for subject 10M, who had a scan quality of 6. One subject (33M) was excluded because the scan was inaccurately aligned, resulting in truncation of the RNFL measurements. Each subject’s optic disc report was exported as a PDF file.

6.3.4. Autofluorescence Imaging

Autofluorescence imaging was done at Khoo Teck Puat Hospital, Singapore. The study eye was dilated with one drop of 1% tropicamide (Alcon Laboratories, USA). An additional drop of 2.5% phenylephrine (Alcon Laboratories, USA) was instilled if the pupil was not mid-dilated after 10 minutes. Image capture commenced when the pupil was dilated to > 5mm. Thirty degree optic disc-centred autofluorescence images were captured using Spectralis HRA+OCT (Heidelberg Engineering, Germany). All images were captured with automatic real-time averaging (ART) at 49 scans. Detector sensitivity was fixed at 95 units except for 4 emmetropic and 12 myopic subjects, whose images were captured with sensitivity between 90 and 101 units. In infra-red mode, the external fixation light was used as the fixation target to position the optic disc as close to the centre of the imaging field as possible. The blood vessels on the neuroretinal rim were adjusted in focus. The HRA+OCT was then switched to blue light autofluorescence mode and focus was readjusted. After 20 seconds, the PAF image was recorded and saved as 768x768 pixel TIFF files, giving a scale factor of approximately 12µm/pixel. Autofluorescence images were not captured for 4 subjects as they were too sensitive to the excitation laser to maintain fixation for image capture. Images from another 4 subjects were deemed to be of poor quality and excluded from analysis (Figure 6.1). In total, PAF images and OCT scans from 39 emmetropes and 49 myopes were available for analysis (Figure 6.1).

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Figure 6.1. Flow chart of number of participants recruited and with images available for analysis. *Data from 9 myopes were excluded from analysis due to poor image quality (n = 4), poor OCT scan quality (n = 1), poor fixation and photophobia to the excitation laser (n = 4).

6.3.5. Sampling PAF and RNFL Thickness

PAF images and the PDF of the OCT reports were exported to Photoshop CS 6 (version 13; Adobe Systems Inc., CA, USA). Before sampling PAF intensities, the PAF image of each subject was manually aligned to the infra-red image from the OCT report using Photoshop so that the intensities and RNFL thicknesses were sampled from similar locations (Figure 6.2E).

PAF intensities were sampled in Photoshop using 4 different methods: small (5x5 pixels), medium (31x31 pixels), large (51x51 pixels), and arc segment (2945 pixels) (Figure 6.2A - D). For the small, medium and large square sampling methods, grey level (GL) intensities were sampled at the eccentricity of the RNFL scan circle using the 178 eyedropper tool. For the small square sampling method, 46 to 48 locations were sampled. To ensure even sampling of PAF around the optic disc, the sampling squares were positioned such that each clockhour segment was sampled at 2 to 4 locations (Figure 6.2A). Pixels corresponding to blood vessels were not sampled. For the medium and large square sampling methods, 12 locations (1 for each clock hour segment) were sampled (Figure 6.2B, C). It was not always possible to avoid blood vessels using these sampling methods. The arc segment method of sampling involved placing an annulus with an inner and outer radius of 1.1mm and 1.7mm centred on the optic disc (Figure 6.2D). The annulus was then divided into 12 segments to correspond with the 12 clockhour segment RNFL from OCT. Average PAF intensity of each clockhour segment was obtained from the layer histogram tool.

The clockhour positions are always referenced to the right eye so that the 12 o’clock position is superior, 3 o’clock is nasal, 6 o’clock is inferior and 9 o’clock is temporal. For subjects whose study eyes were the left eyes, the data were flipped horizontally to right eye orientation.

We did not make adjustments to the PAF signal if there were blood vessels present because the OCT algorithm does not exclude blood vessels from RNFL measurements (Hood et al., 2008, Ye et al., 2016).

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Figure 6.2. Method of PAF sampling. Infra-red image from optic disc OCT scan (E) was aligned with the PAF image and 12 clockhour segment RNFL chart (F). (A) For the small square method, 2 to 4 sampling squares 5x5 pixel (59x59µm) in size were manually positioned on the autofluorescence image at the circumference of the RNFL scan circle in each segment. Major vasculatures were avoided as they were devoid of RPE autofluorescence. The medium (B) and large (C) square methods sampled PAF intensity using 31x31 and 51x51 pixels squares respectively. For these 2 methods, only 1 PAF sampling area was recorded for each of the 12 segments. (D) For the arc segment method, PAF intensities within each segment of an annulus around the optic disc were recorded. The outer border of the arc segment corresponded to the circumference of the scan circle (1.7mm radius) while the inner border was 1.1mm in radius. For the small square sampling method, the corresponding RNFL thickness was determined from the TSNIT graph (G). For the other sampling methods, RNFL values from the clockhour segment RNFL graph were used (F).

6.3.6. Statistical Analysis

Statistical analysis was performed with Prism 7.01 (Graphpad Software Inc, La Jolla, CA, USA). Samples were tested for normality using D'Agostino & Pearson normality test (Annex 6.1). Most of the data were not normally distributed so non-parametric tests were used. The only exceptions were for some correlation analyses, which will be described in the next paragraph. Mann-Whitney was used to compare the differences in age, spherical equivalent and axial length between the 2 groups. Kruskall-Wallis and Dunn’s multiple comparisons test was used to compare RNFL thickness between emmetropes and myopes. Spearman correlation was used to correlate RNFL thickness with age and with axial length.

Depending on the outcome of the normality testing, Pearson or Spearman correlation was used to correlate PAF intensities with RNFL thickness for each of the subjects. PAF intensities sampled from Photoshop CS 6 using the medium, large and arc segment methods were correlated with the average RNFL from each of the 12 clockhour segments provided by OCT (Figure 6.2F). For correlating PAF intensities sampled using the small square method with RNFL thickness, the corresponding RNFL thickness was then obtained from the TSNIT graph of the OCT report (Figure 6.2G). Figure 6.3

181 shows a sample scatterplot for PAF intensity versus RNFL thickness from 12 clockhour segments.

Figure 6.3. Example scatterplot from subject 63E showing correlation between PAF intensity and RNFL thickness in one observer. The numbers (1 - 12) beside each data point represent the RNFL clockhour. The arc segment method was used to obtain the autofluorescence signal in this example.

In order to evaluate the 4 PAF sampling methods to determine the most appropriate method in the study of PAF and RNFL, the repeatability of the PAF intensities was compared. Repeatability of PAF intensities (expressed as a percentage) was using a formula that normalised the differences in intensity to the average (Delori et al., 2011):

퐶표푒푓푓𝑖푐𝑖푒푛푡 표푓 푅푒푝푒푎푡푎푏𝑖푙𝑖푡푦 (𝑖푛푡푒푛푠𝑖푡푦)

= 1.96 푥 푆퐷 푑푖푓푓푒푟푒푛푐푒 표푓 1푠푡 푎푛푑 2푛푑 푚푒푎푠푢푟푒푚푒푛푡푠 푥 100 (1) ( ) 푎푣푒푟푎푔푒 표푓 2 푚푒푎푠푢푟푒푚푒푛푡푠

Once we had established the most appropriate sampling method for sampling PAF, linear regression was performed to determine the magnitude by which PAF could be attenuated by the overlying RNFL.

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6.4. Results

6.4.1. Subjects

The average age, spherical equivalent and axial length of the emmetropes and myopes are found in Table 6.2. There was no significant difference in age between the 2 groups (Mann-Whitney test, p = 0.460). The myopic group had more negative spherical equivalent (Mann-Whitney test, p < 0.001) and longer axial length (Mann- Whitney test, p < 0.001) than the emmetropic group.

Age (year) Spherical equivalent (D) Axial length (mm) Emmetropes 27 plano 23.50 (n = 39) (18 - 58) (+0.63 – -1.50) (22.00 - 25.90) Myopes 26.5 -7.57 27.05 (n = 50) (18 - 58) (-6.00 – -14.63) (24.88 - 29.82) p-value 0.460 < 0.001 < 0.001

Table 6.2. Median (range) age, spherical equivalent and axial length of the emmetropes and myopes.

6.4.2. RNFL Thickness Profile

Figure 6.4 shows the RNFL profile for emmetropes and myopes obtained from the 12 clockhour segment RNFL thickness chart in OCT. RNFL thicknesses have not been compensated for magnification differences due to refractive error.

RNFL was thickest at the 7 and 11 o’clock segments and thinnest at the 3 and 9 o’clock segments. RNFL thickness was thinner at 12, 1, 2, 5 and 6 o’clock segments compared to emmetropes (Kruskall-Wallis test, H(23) = 794.8, p < 0.0001) (Table 6.3). Average RNFL thickness was negatively correlated with axial length (Spearman’s ρ = -0.510, p < 0.0001) (Figure 6.5).

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Figure 6.4. RNFL thickness profile in emmetropes and myopes along the clockhour segments. RNFL is thickest at the 7 and 11 o’clock segments and thinnest at the 3 and 9 o’clock segments. Myope RNFL was significantly thinner at 12, 1, 2, 5 and 6 o’clock segments compared to emmetrope (Kruskall-Wallis test, H(23) = 794.8, p < 0.0001). Symbols and error bars represent the median and interquartile range. Asterisks indicate statistical significance using Dunn’s multiple comparisons test (Table 6.3).

Dunn’s multiple comparisons test Clockhour segment Mean rank difference Adjusted p-value 12 207.1 * 0.020 1 197.3 * 0.033 2 209.1 * 0.018 3 20.0 >0.999 4 72.2 >0.999 5 225.3 * 0.008 6 222.8 * 0.009 7 45.4 >0.999 8 -131.7 0.547 9 -89.3 >0.999 10 -97.2 >0.999 11 47.0 >0.999

Table 6.3. Results of Dunn’s multiple comparisons test comparing emmetrope and myope RNFL thickness.

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Figure 6.5. Scatterplot of average RNFL thickness against axial length. RNFL decreases with increasing axial length (Spearman’s ρ = -0.510, p < 0.0001).

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Average RNFL was not correlated with age (r = -0.132, p = 0.216); however an age- related decrease in RNFL thickness was observed for superior (r = -0.453, p < 0.0001), inferior (r = -0.382, p = 0.0002) and nasal quadrant RNFL (Spearman’s ρ = - 0.302, p = 0.004) (Figure 6.6).

Figure 6.6. Scatterplots showing correlation between (A) average, (B) superior, (C) inferior, (D) nasal and (E) temporal RNFL thickness and age. An age-related decrease in RNFL thickness was observed in the superior, inferior and nasal regions, but not in the temporal one.

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6.4.3. Repeatability of PAF Intensity using Different Sampling Methods

Fifty-four subjects had a second PAF image available for repeatability analysis. The second image was aligned as described in section 6.3.5 and the PAF intensities were sampled again using Photoshop CS 6. Repeatability of PAF intensities using different methods of sampling was calculated. The arc segment method was the most repeatable, followed by the large, small and medium square methods (Figure 6.7).

Figure 6.7. Coefficient of repeatability of PAF intensity sampled using different methods (n = 54). Bar and error bars show median and interquartile range. Kruskall-Wallis test returned significant differences in repeatability among the sampling methods (H(3) = 79.26, p < 0.0001). * indicates statistical significance.

Repeatability of PAF intensities were similar between the small and medium square methods (p = 0.101), and between the large square and arc segment methods (p = 0.676). Repeatability of PAF intensities using the small square method was poorer than the large square (p = 0.0004) and arc segment (p < 0.0001) methods. Similarly, repeatability of PAF intensities using the medium square method was poorer than the large square (p < 0.0001) and arc segment (p < 0.0001) methods.

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6.4.4. Consistency of the Correlation Analysis

To test the consistency of the correlation analysis between PAF intensity and RNFL thickness, correlation was repeated using a second image from 54 subjects. The correlation results were most consistent with the arc segment method where 100% the eyes that showed significant correlation in the first image also showed significant correlation in the analysis in the second image. Similarly, all the images that did not show correlation in the first image also showed no correlation in the second image. The large sampling method returned consistent correlation results in 93% of the images, while the small method and medium sampling methods were consistent in 91% and 83% of the images respectively.

6.4.5. Correlation between PAF Intensity and RNFL Thickness using Different Sampling Methods

The results of the correlation analysis were dependent on the sampling method. Of the 39 emmetropic eyes analysed, 77% to 97% of eyes show negative correlation between PAF intensity and RNFL thickness. Of the 50 myopic eyes analysed, 60% to 90% of eyes demonstrated negative correlation (Figure 6.8).

Figure 6.8. Percentage of eyes showing significant correlation between PAF intensity and RNFL thickness using each sampling method (n = 39 emmetropes and 50 myopes, referred to as ‘Emm’ and ‘My’ in the bar graph).

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The most number of subjects with a negative correlation between PAF intensity and RNFL thickness was found when the small square and arc segment sampling methods were used. The medium square sampling method returned the fewest number of statistically significant correlations. This is most likely because among the 4 methods, the medium square had the greatest mismatch between location of PAF sampling and the averaged RNFL thickness. The medium sampling area represented just under half of the area of the clockhour segment from which RNFL was measured. The large method showed better ‘matching’ as it covered approximately three-quarters of the area of the clockhour segment. For the small square and arc segment methods, the PAF sampling matched the area where RNFL thickness was measured.

The correlation results using the small square and arc segment methods were generally in agreement. Subject 38M showed no correlation using the small sampling method because PAF intensities from the inferior-temporal region were sampled on the border of PPA. Re-sampling PAF intensity at a further eccentricity to avoid the pigmentary changes associated with the border of the PPA returned a statistically significant negative correlation.

Subject 79E failed to demonstrate a statistically significant correlation between PAF intensity and RNFL thickness when PAF was sampled using the small method but not the arc segment method. Inspection of the correlation scatterplots revealed that when using the small square method, PAF intensity at the 10 and 11 o’clock segments were much brighter than expected for the given RNFL thickness. This was not seen in the scatterplot using arc segment PAF. Comparison of the PAF profiles using the small and arc segment methods of sampling revealed a markedly brighter superior and temporal region using the small method. Hence, the lack of correlation in 79E can be attributed to an unusually strong PAF superior-temporally at the eccentricity of the RNFL scan circle.

Subjects 32M, 90M and 92E also failed to demonstrate significant correlation between PAF and RNFL using the small square method but correlated using the arc segment method. Inspection of the scatterplots suggest that the arc segment method, which sampled closer to the optic disc, picked up larger differences in the PAF intensity around the optic disc. This allowed for stronger correlation with RNFL thickness that reached statistical significance.

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Since the arc segment method sampled PAF intensities with greatest repeatability and gave the most consistent correlation results between PAF intensity and RNFL thickness, we used the arc segment method for further analysis.

6.4.6. Correlation between PAF Intensity and RNFL Thickness using Arc Segment Method

Using PAF intensities obtained using the arc segment method, Pearson correlation was used for analysis for all subjects except the following myopes: 25M, 24M, 52M, 77M, 82M and 86M. This was because the PAF intensity for 25M and the RNFL thickness for the other 5 myopes were not normally distributed. Table 6.4 shows the correlation coefficients and corresponding p-values using Spearman and Pearson correlation for these subjects.

Table 6.4 shows that for all subjects except 24M, using Spearman or Pearson correlation did not affect the conclusion of the analysis. Subject 24M’s RNFL data was not normally distributed because RNFL at the 6 o’clock segment was exceptionally thick (187µm) while the remaining segments were 45µm to 118µm thick.

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Subject Spearman Pearson Difference in ID correlation correlation correlation coefficient ρ = -0.452 r = -0.830 24M 0.378 p = 0.142 p = 0.001 ρ = -0.302 r = -0.457 52M 0.155 p = 0.338 p = 0.135 ρ = -0.759 r = -0.790 77M 0.031 p = 0.006 p = 0.002 ρ = -0.804 r = -0.756 82M -0.048 p = 0.003 p = 0.005 ρ = -0.909 r = -0.902 86M -0.007 p = 0.0001 p <0.0001 ρ = -0.727 r = -0.855 25M 0.128 p = 0.010 p = 0.0004

Table 6.4. Correlation coefficients using Spearman and Pearson correlation for subjects with non-normally distributed data. There was no marked difference in correlation coefficient for subjects 77M, 82M and 86M, with differences between the 2 analysis being < 0.05. Correlation coefficient increased by > 0.1 for 52M and 25M. However there was no difference in the conclusion of the analyses (whether a significant correlation exists or not) for all subjects except 24M. For subject 24M, significant negative correlation was found using Pearson correlation but not Spearman correlation.

Among the 38 emmetropes and 43 myopes with significant correlation between PAF intensity and RNFL thickness using the arc segment method, the correlation coefficient was (mean ± SD) -0.802 ± 0.103 and -0.795 ± 0.082 respectively. The correlation coefficients were similar between the 2 refractive groups (unpaired t-test, t = 0.354, p = 0.724).

The 7 eyes that did not show correlation are listed in Table 6.5. Except for 67E and 89M, lack of correlation could be attributed to vitreous floater, PPA, exceptionally thick RNFL at the 6 o’clock region, and RNFL thinning. Weiss ring in 97M obstructed both the nasal PAF intensities and RNFL measurements. PPA in subjects 53M and 88M extended from the 7 to 10 o’clock segments. When correlation analyses was repeated without the affected segments, a statistically significant negative correlation was found (53M: r = -0.858, p = 0.006 and 88M: r = -0.887, p = 0.003). Subject 52M had 191 significant inferior RNFL loss that was diagnosed by an ophthalmologist to be non- glaucomatous.

Both 67E and 89M had normal RNFL thickness. However both eyes had PAF intensities brighter than usual in the superior parapapillary region. The PAF images of 67E and 89M are good quality images and did not demonstrate problems with defocus. The lack of correlation in both eyes was repeatable and reproducible using different sampling methods and the optic discs did not appear to be tilted (an effect that may cause image non-uniformity).

Spherical Axial Subject Age Correlation p- equivalent length Remarks ID (yr) coefficient value (D) (mm) 97M 43 -7.25 24.88 -0.463 0.130 Weiss ring 53M 40 -10.13 27.61 0.037 0.908 PPA 88M 52 -7.00 26.17 -0.530 0.076 PPA Thick RNFL at 6 24M 20 -7.38 26.93 -0.452 0.142 o’clock segment 52M 52 -6.13 27.27 -0.302 0.338 RNFL thinning Strong PAF superior- 67E 34 0.25 24.68 -0.426 0.167 temporally Strong PAF 89M 43 -7.50 26.36 -0.497 0.100 superiorly

Table 6.5. Six myopic eyes and 1 emmetropic eye did not show correlation between PAF intensity and RNFL thickness using the arc segment method. The reasons for the lack of correlation could be attributed to Weiss ring (97M), parapapillary atrophy (PPA) (53M, 88M), exceptionally thick inferior RNFL (24M) and RNFL thinning (52M). 67E and 89M did not exhibit any of those features, however superior PAF was unusually bright for the given RNFL thickness.

6.4.7. Attenuation of PAF Intensity by RNFL

The gradients of the linear regression line between PAF intensity and RNFL thickness data were calculated from 78 samples that had normally distributed data and showed significant correlation between PAF and RNFL. The average (± SD) gradient was -0.32 192

± 0.11 (n = 39) and -0.29 ± 0.10 (n = 39) for emmetropes and myopes. This means that for every 10µm of RNFL, PAF for both groups was attenuated by about 3 ± 1GL. This is a substantial effect, considering RNFL thickness can be 150µm to 200µm at its thickest. It translates to a potential attenuation of 34 - 51% (30 - 60GL) at the 7 o’clock position where RNFL is 150µm thick.

6.5. Discussion

In this study, we found thinner superior and inferior RNFL and thicker temporal RNFL in myopes compared to emmetropes. There was a negative correlation between RNFL and age. Autofluorescence intensity around the optic disc was observed to be lower than autofluorescence intensity in the macula. In this study, we evaluated 4 PAF sampling techniques and determined that the arc segment method was most appropriate for sampling PAF. We also calculated the magnitude by which RNFL will attenuate PAF intensity (approximately 3GL for every 10µm of RNFL).

6.5.1. RNFL Thickness between Emmetropes and High Myopes

It is well-documented that measured RNFL decreases with increasing myopia, primarily due to reduction in magnification (Bae et al., 2016, Kang et al., 2010). As Cirrus HD- OCT does not compensate the diameter of the scan circle for refractive error, the scan circle is relatively larger in myopic eyes than in emmetropic eyes. As a result, RNFL thickness may be measured further from the optic disc in myopes than emmetropes. Due to this magnification discrepancy, we found a negative correlation between average RNFL thickness and axial length. We estimate that the RNFL from the subject with longest axial length (58M) could be up to 24% thinner than if it had been measured at 1.7mm eccentricity (Kang et al., 2010).

We found that myope RNFL was significantly thinner at 12, 1, 2, 5 and 6 o’clock segments compared to emmetrope RNFL. This is in agreement with published studies. Using Cirrus HD-OCT and without adjusting for magnification differences, high myopes had thinner RNFL along the 12,1, 2, 5 and 6 o’clock regions, and thicker RNFL along the 8, 9 and 10 o’clock segments (Seo et al., 2017). Leung et al. (2006) found thinner RNFL at 12, 1, and 7 o’clock segments. Similar findings were found in 269 young healthy participants, where longer axial length was associated with thicker RNFL at 8 –

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10 o’clock segments, and thinner RNFL at 12 to 2 and 5 – 6 o’clock segments (Kang et al., 2010). It may be due to optic disc tilting, in which the nerve fibres are redistributed so that the superior, inferior and nasal RNFL thickness decreases while temporal RNFL thickness increases (Kang et al., 2010, Hwang et al., 2012a, Leung et al., 2012a).

6.5.2. RNFL Thickness with Age

We found significant negative correlation between superior, inferior and nasal RNFL thickness and age. Superior and inferior quadrant age-related RNFL thinning was relatively greater than nasal and temporal RNFL. This is in agreement with other studies (Lee et al., 2012, Poinoosawmy et al., 1997, Leung et al., 2012b). It has also been reported that the thicker the baseline RNFL measurement, the faster the rate of age-related decay (Leung et al., 2012b). This was observed in our study, where the superior and inferior RNFL had steeper rates of RNFL thinning than the nasal and temporal RNFL.

Small but statistically significant age-related reductions in RNFL thickness using larger sample sizes have been reported (Poinoosawmy et al., 1997, Leung et al., 2012b, Lee et al., 2012, Celebi and Mirza, 2013, Kim et al., 2011). The rate of RNFL thinning ranged from -0.205um/year to -0.365um/year (Celebi and Mirza, 2013, Lee et al., 2012, Leung et al., 2012b). Leung et al. (2012b) reported RNFL thinning of - 0.325µm/year in 100 subjects in the 4th to 7th decades of life. We failed to find any significant correlation between average RNFL and age due to the limited age range (18 – 58 years) as well as the relatively small sample size in our study. The wide variation in RNFL thickness in the normal population and in myopic eyes, along with regional variation of RNFL thickness dilutes the overall thinning effect in average RNFL when the sample size is small.

6.5.3. Correlation between PAF Intensity and RNFL Thickness

PAF intensities using 4 sampling methods were correlated with RNFL thickness (RNFL values were not compensated for magnification differences due to refractive error). Due to this difference in magnification, RNFL from the longest eye in our study was sampled at 2.2µm instead of the 1.7µm from the centre of the optic disc in an

194 emmetropic eye. Since the PAF and OCT scans were aligned prior to PAF sampling for small, medium and large square sampling methods, the sampling squares were positioned at the same position as the RNFL scan circle. For arc segment sampling method, the arc was fixed at 150 pixel radius. As a result, the arc segment was 0.050mm to 0.092mm smaller in radius compared to the RNFL scan circle.

Of the 4 sampling methods used, the arc segment method produced the most repeatable results in terms of PAF intensity. Consequently this method allowed the strongest and most consistent correlation coefficient between PAF intensity and RNFL thickness to be obtained.

Using the arc segment sampling method, we found that PAF intensity and RNFL thickness were negatively correlated. The median (IQR) R2 value was 0.673 (0.494 - 0.776) and 0.644 (0.528 - 0.724) in emmetropes and myopes, indicating that RNFL thickness explains about two-thirds of the variability in the autofluorescence intensity around the optic disc. This is the first experiment to report negative and linear correlations between RNFL thickness and PAF in both emmetropic and high myopic eyes.

Reznicek et al. (2013) conducted a study involving primary open angle eyes. Autofluorescence intensity in various sectors of the retina was compared to the corresponding sectoral RNFL thickness around the optic disc. They found that stronger autofluorescence intensity was associated with thinner RNFL in primary open angle in the inferior-temporal (r = 0.324; p = 0.036) and nasal (r = 0.376; p = 0.014) sectors, and this implied that ‘peripapillary RNFL degeneration in glaucoma patients may be accompanied by corresponding RPE alterations’. Although we sampled parapapillary autofluorescence instead of FAF and recruited healthy subjects instead of glaucomatous patients, we also found a similar relationship between PAF intensity and RNFL thickness.

We therefore estimate that every 10µm of RNFL thickness will attenuate PAF intensity by 3 ± 1GL. The RNFL can attenuate PAF in 2 ways. First, RNFL can scatter the excitation laser as it enters the retina, resulting in less laser energy reaching RPE lipofuscin. With lower incident energy, the excitation of lipofuscin is less and the resultant autofluorescence weaker. Secondly, it can scatter emitted autofluorescence from RPE lipofuscin as it travels through the neurosensory retina to the vitreous. As a result, less autofluorescence signal reaches the detector.

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6.6. Limitations of the Correction Factor between PAF Intensity and RNFL Thickness

The correction factor for PAF attenuation by RNFL (3GL for every 10µm of RNFL) should only be applied to images captured using our imaging system with the standard protocol described. The correlation between signal of PAF and RNFL thickness may be different when other protocols or cSLOs are used. Further research is needed to measure the PAF attenuation in cSLO equipped with an internal autofluorescence standard. This will allow the pooling of all subjects’ PAF intensities and RNFL thicknesses in order to calculate a ‘universal’ correction factor. It may also allow other variables, which could affect the correction factor, to be determined.

The correction factor we report only applies to parapapillary region of healthy eyes. We have not tested the relationship between PAF and RNFL thickness at other retinal locations such as the macula where RNFL is thinner than the parapapillary region, nor have we tested it in diseased eyes. We had one subject with very thin RNFL and no significant correlation between PAF and RNFL was found. Further study is needed (such as at different RNFL thickness or different retinal locations) to evaluate the relationship between PAF and RNFL in different retinal locations with different RNFL thickness. Establishment of correction factor is recommended for those who conduct PAF studies.

6.7. Conclusion

The arc segment sampling method of PAF produces the most consistent and reliable results for studying the correlation between PAF intensity and RNFL thickness. We have shown that the thick RNFL around the optic disc significantly attenuates PAF intensity. We estimate that every 10µm of RNFL attenuated PAF intensity by (mean ± SD) 3 ± 1GL. This is a significant amount, considering the RNFL around the optic disc can be 150µm to 200µm thick in the superior/inferior RNFL bundles. Quantitative studies of autofluorescence around the optic disc therefore need to take this into account. The development of an attenuation factor between RNFL thickness and PAF intensity allows the intensity of RPE autofluorescence to be calculated. This opens up more applications of autofluorescence imaging in the clinical investigations of ocular diseases, especially that of optic neuropathies.

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6.8. References

ACH, T., HUISINGH, C., MCGWIN, G., MESSINGER, J. D., ZHANG, T., BENTLEY, M. J., GUTIERREZ, D. B., ABLONCZY, Z., SMITH, R. T., SLOAN, K. R. & CURCIO, C. A. 2014. Quantitative autofluorescence and cell density maps of the human retinal pigment epithelium. Invest Ophthalmol Vis Sci, 55, 4832-41. ASLI DINC, U., TATLIPINAR, S., GORGUN, E. & YENEREL, M. 2009. Fundus Autofluorescence in optic disc drusen: comparison of confocal scanning laser ophthalmoscope and standard fundus camera. Neuro-Ophthalmology, 33, 318- 321. BAE, S. H., KANG, S. H., FENG, C. S., PARK, J., JEONG, J. H. & YI, K. 2016. Influence of Myopia on size of optic nerve head and retinal nerve fiber layer thickness measured by spectral domain optical coherence tomography. Korean J Ophthalmol, 30, 335-343. BINDEWALD, A., BIRD, A. C., DANDEKAR, S. S., DOLAR-SZCZASNY, J., DREYHAUPT, J., FITZKE, F. W., EINBOCK, W., HOLZ, F. G., JORZIK, J. J., KEILHAUER, C., LOIS, N., MLYNSKI, J., PAULEIKHOFF, D., STAURENGHI, G. & WOLF, S. 2005. Classification of fundus autofluorescence patterns in early age-related macular disease. Invest Ophthalmol Vis Sci, 46, 3309-14. BLAND, J. M. & ALTMAN, D. G. 1986. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1, 307-10. CELEBI, A. R. & MIRZA, G. E. 2013. Age-related change in retinal nerve fiber layer thickness measured with spectral domain optical coherence tomography. Invest Ophthalmol Vis Sci, 54, 8095-103. CHANG, M. Y., VELEZ, F. G., DEMER, J. L., BONELLI, L., QUIROS, P. A., ARNOLD, A. C., SADUN, A. A. & PINELES, S. L. 2017. Accuracy of Diagnostic Imaging Modalities for Classifying Pediatric Eyes as Papilledema Versus Pseudopapilledema. Ophthalmology. 124(12):1839-1848. COHEN, S. Y., DUBOIS, L., GRENET, T., NGHIEM-BUFFET, S., JUNG, C., FAJNKUCHEN, F., DELAHAYE-MAZZA, C., QUENTEL, G. & TADAYONI, R. 2016. Peripapillary retinal pigment epithelium changes in age-related macular degeneration. Retina, 36, 458-64. CUKRAS, C. A., WONG, W. T., CARUSO, R., CUNNINGHAM, D., ZEIN, W. & SIEVING, P. A. 2012. Centrifugal expansion of fundus autofluorescence patterns in Stargardt disease over time. Arch Ophthalmol, 130, 171-9. CURTIN, B. J. & KARLIN, D. B. 1970. Axial length measurements and fundus changes of the myopic eye. I. The posterior fundus. Trans Am Ophthalmol Soc, 68, 312-34. DELORI, F. C., GOGER, D. G. & DOREY, C. K. 2001. Age-related accumulation and spatial distribution of lipofuscin in RPE of normal subjects. Invest Ophthalmol Vis Sci, 42, 1855-66. GREENBERG, J. P., DUNCKER, T., WOODS, R. L., SMITH, R. T., SPARROW, J. R. & DELORI, F. C. 2013. Quantitative fundus autofluorescence in healthy eyes. Invest Ophthalmol Vis Sci, 54, 5684-93. HOLDEN, B. A., FRICKE, T. R., WILSON, D. A., JONG, M., NAIDOO, K. S., SANKARIDURG, P., WONG, T. Y., NADUVILATH, T. J. & RESNIKOFF, S. 2016. Global Prevalence of myopia and high myopia and temporal trends from 2000 through 2050. Ophthalmology, 123, 1036-42. HOOD, D. C., FORTUNE, B., ARTHUR, S. N., XING, D., SALANT, J. A., RITCH, R. & LIEBMANN, J. M. 2008. Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography. J Glaucoma, 17, 519-28. HWANG, Y. H., YOO, C. & KIM, Y. Y. 2012a. Characteristics of peripapillary retinal nerve fiber layer thickness in eyes with myopic optic disc tilt and rotation. J Glaucoma, 21, 394-400. 197

HWANG, Y. H., YOO, C. & KIM, Y. Y. 2012b. Myopic optic disc tilt and the characteristics of peripapillary retinal nerve fiber layer thickness measured by spectral-domain optical coherence tomography. J Glaucoma, 21, 260-5. KANG, S. H., HONG, S. W., IM, S. K., LEE, S. H. & AHN, M. D. 2010. Effect of myopia on the thickness of the retinal nerve fiber layer measured by Cirrus HD optical coherence tomography. Invest Ophthalmol Vis Sci, 51, 4075-83. KIM, E. J., HONG, S., KIM, C. Y., LEE, E. S. & SEONG, G. J. 2011. Attenuated age- related thinning of peripapillary retinal nerve fiber layer in long eyes. Korean J Ophthalmol, 25, 248-51. LAEMMER, R., HORN, F. K., VIESTENZ, A., LINK, B., JUENEMANN, A. G. & MARDIN, C. Y. 2007. Measurement of autofluorescence in the parapapillary atrophic zone in patients with ocular hypertension. Graefes Arch Clin Exp Ophthalmol, 245, 51-8. LEE, J. Y., HWANG, Y. H., LEE, S. M. & KIM, Y. Y. 2012. Age and retinal nerve fiber layer thickness measured by spectral domain optical coherence tomography. Korean J Ophthalmol, 26, 163-8. LEUNG, C. K., MOHAMED, S., LEUNG, K. S., CHEUNG, C. Y., CHAN, S. L., CHENG, D. K., LEE, A. K., LEUNG, G. Y., RAO, S. K. & LAM, D. S. 2006. Retinal nerve fiber layer measurements in myopia: An optical coherence tomography study. Invest Ophthalmol Vis Sci, 47, 5171-6. LEUNG, C. K., YU, M., WEINREB, R. N., MAK, H. K., LAI, G., YE, C. & LAM, D. S. 2012a. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: interpreting the RNFL maps in healthy myopic eyes. Invest Ophthalmol Vis Sci, 53, 7194-200. LEUNG, C. K., YU, M., WEINREB, R. N., YE, C., LIU, S., LAI, G. & LAM, D. S. 2012b. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a prospective analysis of age-related loss. Ophthalmology, 119, 731-7. MCGILL, T. J., RENNER, L. M. & NEURINGER, M. 2016. Elevated Fundus autofluorescence in monkeys deficient in lutein, zeaxanthin, and omega-3 fatty acids. Invest Ophthalmol Vis Sci, 57, 1361-9. OHNO-MATSUI, K., LAI, T. Y., LAI, C. C. & CHEUNG, C. M. 2016. Updates of pathologic myopia. Prog Retin Eye Res, 52, 156-87. OHNO-MATSUI, K., SHIMADA, N., YASUZUMI, K., HAYASHI, K., YOSHIDA, T., KOJIMA, A., MORIYAMA, M. & TOKORO, T. 2011. Long-term development of significant visual field defects in highly myopic eyes. Am J Ophthalmol, 152, 256-265.e1. OISHI, A., OGINO, K., MAKIYAMA, Y., NAKAGAWA, S., KURIMOTO, M. & YOSHIMURA, N. 2013. Wide-field fundus autofluorescence imaging of retinitis pigmentosa. Ophthalmology, 120, 1827-34. POINOOSAWMY, D., FONTANA, L., WU, J. X., FITZKE, F. W. & HITCHINGS, R. A. 1997. Variation of nerve fibre layer thickness measurements with age and ethnicity by scanning laser polarimetry. Br J Ophthalmol, 81, 350-4. REZNICEK, L., SEIDENSTICKER, F., MANN, T., HÜBERT, I., BUERGER, A., HARITOGLOU, C., NEUBAUER, A. S., KAMPIK, A., HIRNEISS, C. & KERNT, M. 2013. Correlation between peripapillary retinal nerve fiber layer thickness and fundus autofluorescence in primary open-angle glaucoma. Clin Ophthalmol, 7, 1883-8. SAMARAWICKRAMA, C., MITCHELL, P., TONG, L., GAZZARD, G., LIM, L., WONG, T. Y. & SAW, S. M. 2011. Myopia-related optic disc and retinal changes in adolescent children from singapore. Ophthalmology, 118, 2050-7. SEEHAFER, S. S. & PEARCE, D. A. 2012. Lipofuscin: the "wear and tear" pigment. In: LOIS, N. & FORRESTER, J. V. (eds.) Fundus autofluorescence. Lippincott Williams & Wilkins. p.3-13. SEO, S., LEE, C. E., JEONG, J. H., PARK, K. H., KIM, D. M. & JEOUNG, J. W. 2017. Ganglion cell-inner plexiform layer and retinal nerve fiber layer thickness

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according to myopia and optic disc area: a quantitative and three-dimensional analysis. BMC Ophthalmol, 17, 22. VON RÜCKMANN, A., FITZKE, F. W. & BIRD, A. C. 1995. Distribution of fundus autofluorescence with a scanning laser ophthalmoscope. Br J Ophthalmol, 79, 407-12. VON RÜCKMANN, A., FITZKE, F. W. & BIRD, A. C. 1997. Fundus autofluorescence in age-related macular disease imaged with a laser scanning ophthalmoscope. Invest Ophthalmol Vis Sci, 38, 478-86. VON RÜCKMANN, A., FITZKE, F. W. & GREGOR, Z. J. 1998. Fundus autofluorescence in patients with macular holes imaged with a laser scanning ophthalmoscope. Br J Ophthalmol, 82, 346-51. WING, G. L., BLANCHARD, G. C. & WEITER, J. J. 1978. The topography and age relationship of lipofuscin concentration in the retinal pigment epithelium. Invest Ophthalmol Vis Sci, 17, 601-7. YE, C., YU, M. & LEUNG, C. K. 2016. Impact of segmentation errors and retinal blood vessels on retinal nerve fibre layer measurements using spectral-domain optical coherence tomography. Acta Ophthalmol, 94, e211-9.

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Chapter 7. Parapapillary Autofluorescence (PAF) in Emmetropes and High Myopes

Contribution My work included collaboration in the design of the study together with my supervisor and co-supervisor. Participant recruitment was done by me with the help of 6 Diploma in Optometry students at Singapore Polytechnic (Singapore). I was solely responsible for data collection and data analysis.

Publication Nil

Conference Presentation The Association of Research in Vision and Ophthalmology (ARVO) 2017. Baltimore, US (poster).

Acknowledgments Supported by the Singapore Polytechnic Final Year Project fund (CLS-15A154, CLS- 16A023).

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7.1. Abstract

Purpose

Autofluorescence imaging of the eye captures fluorescence signal from RPE lipofuscin, a by-product of photoreceptor outer segment phagocytosis that is linked to oxidative stress. High myopia is characterised by axial elongation, tilting of the optic disc, and parapapillary atrophy (PPA). Increased oxidative and mechanical stresses have been hypothesised as possible mechanisms in the development of cataract, glaucoma and retinal degeneration associated with high myopia. Therefore parapapillary autofluorescence (PAF) changes may accompany the structural changes at the optic disc in high myopia. It may also reveal potential links to oxidative and mechanical stress at the optic disc. The purpose of this experiment is to compare PAF intensities around the optic disc between emmetropes and high myopes without ocular disease, and to document age-related change in PAF intensity.

Method

Thirty-degree PAF images and 55° FAF images from 35 emmetropic and 38 high myopic eyes were captured using Spectralis HRA+OCT after pupillary dilation. PAF grey level intensities were sampled in an annulus with an inner and outer radius of 1.1mm and 1.7mm centred on the optic disc. Perifoveal intensities were sampled in an annulus with an inner and outer radius of 1.0mm and 1.5mm centred on the fovea. Areas corresponding to major retinal vasculature were excluded using Photoshop CS 6. Spearman correlation was used to test for associations between autofluorescence intensity and age. Z statistics were used to compare rates of age-related increase in PAF intensity between emmetropes and myopes, and to compare age-related increase in PAF and perifoveal autofluorescence.

Results

PAF was strongest nasally and weakest inferior-temporally in both emmetropes and myopes. After correcting for RNFL attenuation, the PAF profile was relatively uniform around the optic disc with marginally lower intensity along the inferior-temporal regions. The RNFL-corrected PAF profile was similar between emmetropes and myopes (p > 0.9). PAF intensities increased with age and were similar between emmetropes 201 and myopes (z = -1.18, p = 0.238). The rate of age-related PAF increase was 63% that of perifoveal autofluorescence (z = -2.71, p = 0.007).

Conclusion

We have documented the PAF profile in healthy eyes of emmetropes and high myopes. PAF intensity varies around the optic disc and was strongest nasally (median: 93GL, interquartile range: 77 – 117GL) and weakest inferior-temporally (median: 51GL, interquartile range: 42 – 67.5GL) due to RNFL attenuation. After correcting for RNFL attenuation, PAF profile was relatively uniform around the optic disc. PAF profile was similar between emmetropes and myopes. In highly myopic eyes without ocular disease, PAF intensity and age-related increase in PAF are similar to emmetropic eyes. PAF increased at a slower rate than perifoveal autofluorescence, reflecting the lower phagocytic load of parapapillary RPE. Quantitative PAF imaging and OCT angiography can be performed on high myopia eyes with glaucoma to shed light on the relationship between autofluorescence and glaucoma and whether autofluorescence imaging can be used to predict prognosis or monitor progression in glaucoma.

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7.2. Introduction

Fundus autofluorescence (FAF) is a retinal imaging modality that captures fluorescence from endogenous ocular fluorophores. In the normal eye, autofluorescence signal originates from retinal pigment epithelium (RPE) lipofuscin (Delori et al., 1995, Wing et al., 1978). The RPE supports photoreceptors by phagocytosing shed outer segments and participating in the recycling of visual pigment. A by-product of this is lipofuscin, which accumulate as small granules in the cytoplasm of RPE cells. When RPE lipofuscin is irradiated with short-wavelength light, it absorbs the energy and releases it in the form of fluorescence of a longer wavelength (Seehafer and Pearce, 2012).

Autofluorescence in the central retina has been studied in healthy and diseased eyes (Ach et al., 2014, Delori et al., 2001, Sepah et al., 2014, Yung et al., 2016). FAF signal is relatively low at the fovea and peaks in the normal macula. The lower levels of autofluorescence in the fovea may be attributed to macular pigment and RPE melanin screening, and lower visual pigment recycling load for the RPE from cone photoreceptors. As a result, less RPE lipofuscin is generated in the fovea and autofluorescence is weaker (Ach et al., 2014, Weiter et al., 1986). The distribution of FAF in the normal eye reflects rod photoreceptor distribution (Delori et al., 2001, Ach et al., 2014).

FAF imaging has been documented in a variety of eye diseases, such as age-related macular degeneration, Stargardt’s disease and retinitis pigmentosa (Bindewald et al., 2005, Oishi et al., 2013, Cukras et al., 2012) to understand disease mechanisms and progression. FAF imaging has also been used in other retinal diseases as an auxiliary imaging tool to aid in disease detection and management (Kolomeyer et al., 2013, Dinc et al., 2011, Marmor et al., 2011).

The autofluorescence in the parapapillary region is not well studied (Delori et al., 2001, Greenberg et al., 2013, von Rückmann et al., 1995). There are only a few studies, discussed below, that described hyperautofluorescent patches or patterns of altered autofluorescence adjacent to the optic disc. In ocular hypertensive eyes, discrete regions of parapapillary hyperautofluorescence have been correlated with greater peak latency of blue-on-yellow visual evoked potential, which is correlated with glaucomatous damage (Laemmer et al., 2007). In eyes with primary open angle glaucoma, small areas of hyperautofluorescence along the optic disc margin have been correlated with thinner retinal nerve fibre layer (RNFL) (Plouznikoff and Harasymowycz, ARVO meeting abstract 2013). In eyes with age-related macular

203 degeneration, 5 types of autofluorescence patterns have been documented around the optic disc, although their significance remains to be elucidated (Cohen et al., 2016).

In recent years, the increasing global prevalence of myopia and high myopia has alarmed eye care providers due to the increased risks of blindness (Holden et al., 2016). High myopes may experience higher levels of oxidative stress compared to emmetropes (Francisco et al., 2015, Simonelli et al., 1989, Micelli-Ferrari et al., 1996). The oxidative stress comes as a consequence of retinal hypoxia due to lower ocular pulse amplitude (the difference between systolic and diastolic pressure in the eye). In myopic eyes, lower ocular pulse amplitude may be due to the larger volume of the myopic eye, a difference in eye rigidity in longer eyes, or may reflect a true difference in the pulsatile blood flow in larger eyes (James et al., 1991). The lower ocular pulse amplitude implies reduced pulsatile blood flow in the choroid (Schwenn et al., 2002), and this in turn is thought to produce ischaemia/reperfusion phenomena and oxidative stress (Francisco et al., 2015). Oxidative stress has been proposed as a mechanism of glaucomatous damage. Malondialdehyde, a product of lipid peroxidation, has also been found to be higher in the cataracts of high myopes than controls (Simonelli et al., 1989). In vitro studies have shown that oxidative stress on photoreceptors is associated with increased RPE lipofuscin formation and increased autofluorescence (Lei et al., 2013). Besides oxidative stress, the myopic eye also experiences structural changes secondary to axial elongation. This includes the development of parapapillary atrophy (PPA), optic disc pits, and optic disc tilting (Curtin and Karlin, 1970, Samarawickrama et al., 2011, Ohno-Matsui et al., 2012). Optic disc pits form from the schisis of the scleral tissue at the optic disc margin (Ohno-Matsui et al., 2012) and are associated with papillomacular bundle damage in glaucoma (Kimura et al., 2012).

Autofluorescence around the optic disc is likely to differ from macula autofluorescence due to the different RPE and photoreceptor distribution in the region (Ach et al., 2014). Autofluorescence imaging of the myopic optic disc may have application in ocular disease like glaucoma or pathological myopia (Ohno-Matsui et al., 2016, Francisco et al., 2015, Kimura et al., 2012). In this experiment we documented and compared parapapillary autofluorescence (PAF) profile and age-related changes of PAF in emmetropic and high myopic eyes.

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7.3. Methods

7.3.1. Ethics and Subjects

This study was approved by the institutional review board of Khoo Teck Puat Hospital (Singapore) and Singapore Polytechnic. Ninety-nine participants were screened and written consent was obtained from the subjects. The inclusion and exclusion criteria are stated in Table 7.1. If both eyes met the inclusion criteria, the dominant eye was chosen as the study eye. One myopic participant withdrew from the study after the first visit. In total, 98 participants completed the study (39 emmetropes and 59 myopes).

Inclusion criteria Exclusion criteria Aged 18 – 60 years Best-corrected visual acuity worse than 6/12 Emmetropia (within Astigmatism more than -2.00D ±0.75D), or History of macular or refractive surgery High myopia (at least - Diagnosed with diabetes 6.00D) Diagnosed with ocular disease (excluding myopia-related retinal changes) Contraindication to pupil dilation Use of medication with known ocular side effects Sensitivity to bright light Inability to provide informed consent

Table 7.1. Study inclusion and exclusion criteria.

7.3.2. Refraction and Biometry

The first study visit took place in Singapore Polytechnic. Keratometry was measured using Tonoref II autorefractor (Nidek Co., Japan). Refractive error was determined using subjective refraction and expressed in spherical equivalent. Axial length of the study eye was measured using IOLMaster 500 (Zeiss Meditec, Germany).

7.3.3. Autofluorescence Imaging

The second study visit was done at Khoo Teck Puat Hospital, Singapore. The study eye was dilated with one drop of 1% tropicamide. An additional drop of 2.5%

205 phenylephrine was instilled if the pupil was not mid-dilated after 10 minutes. Image capture commenced when the pupil was dilated to > 5mm. Thirty degree autofluorescence images of the optic disc were captured using Spectralis HRA+OCT (Heidelberg Engineering, Germany). All images were captured with automatic real-time (ART) averaging at 49 scans. Detector sensitivity was fixed at 95 units except for 16 subjects, whose images were captured with sensitivity between 90 - 94 or 96 - 101 units. In infra-red mode, the external fixation light was used as the fixation target to position the optic disc close to the centre of the imaging field. Focus was adjusted according to the clarity of the blood vessels on the neuroretinal rim. The HRA+OCT was then switched to blue light autofluorescence mode and focus was readjusted. After 20 seconds of photobleaching, the autofluorescence image was recorded and saved as 768x768 pixel TIFF files, giving a scale factor of approximately 12µm/pixel. Autofluorescence images were not captured for 4 myopes as they were too sensitive to the intense blue light and resulted in poor fixation. Images from 5 myopes were deemed to be of poor image quality and excluded from analysis. In order to facilitate comparisons of the autofluorescence intensities, only images captured at sensitivity of 95 units were used. Hence, autofluorescence images from 35 emmetropes and 38 myopes were available for analysis (Figure 7.1).

Macula-centred autofluorescence images were captured on the same visit using HRA+OCT. Automatic real-time (ART) averaging was fixed at 49 scans and detector sensitivity at 105 units. The internal central fixation target was used. Focus was adjusted in infra-red mode and fine-tuned after switching to autofluorescence mode. Image capture began after a 20 – 30 second photobleaching period. Autofluorescence images were also saved as 768x768 pixel TIFF files, giving a scale factor of approximately 20µm/pixel.

7.3.4. Optical Coherence Tomography (OCT)

Optical coherence tomography (OCT) scans of the optic disc were captured using Cirrus HD-OCT (ver. 5.1.1.6 and 7.0.1.290; Carl Zeiss Meditec, Germany). All scans had a scan quality of at least 7 except for subject 10M, who had a scan quality of 6. In order to determine the amount of grey level (GL) correction from RNFL attenuation, linear regression line was fitted to the 12 clockhour segment RNFL thickness and PAF intensities around the optic disc (Chapter 6).

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Figure 7.1. Flow chart of number of participants recruited and with images available for analysis.

7.3.5. Sampling PAF

Autofluorescence images and OCT reports were exported to Photoshop CS 6 (version 13; Adobe Systems Inc., CA, USA). PAF images were aligned with the RNFL deviation map from the OCT optic disc volume scan report, which shows the centre of the optic disc identified by OCT (Figure 7.2B). PAF intensities were sampled in an arc segment (2945 pixels) using Photoshop CS 6 (Figure 7.2C). The arc segment method involved placing an annulus with an internal and external radius of 1.1mm and 1.7mm that was centred on the optic disc. The annulus was divided into 12 segments for documentation of PAF profile. The clockhour positions were referenced to the right eye such that the 12, 3, 6 and 9 o’clock positions correspond to superior, nasal, inferior and temporal regions respectively. Pixels corresponding to PPA and blood vessels > 48µm in width were excluded. Average PAF was obtained by averaging the PAF intensity from all 12 arc segments.

PAF intensities were compensated for RNFL attenuation using data from Chapter 6. To summarise, linear regression was performed on PAF intensity and its corresponding RNFL thickness along the 12 clockhour positions. The gradient of the linear regression line quantified the relationship between PAF and RNFL thickness for each subject. This individual compensation factor was then applied to the 12 clockhour PAF intensities for each subject. 207

Figure 7.2. Method of PAF sampling. (A) The original PAF image of subject 70E. (B) The infrared image from OCT scan, with the centre of the optic disc and 12 clockhour segments identified is superimposed over the PAF image in (A). (C) PAF intensities are sampled from 12 arc segments ( ) with an inner and outer radius of 1.1mm and 1.7mm.

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7.3.6. Sampling Perifoveal Autofluorescence

Fifty-five degree macula-centred FAF images were captured for 56 subjects (28 emmetropes and 28 myopes). Perifoveal autofluorescence intensities were sampled in an annulus with an inner and outer radius of 1.0mm and 1.5mm centred on the fovea (Figure 7.3).

Figure 7.3. Method of perifoveal autofluorescence sampling. Perifoveal autofluorescence intensities were sampled in an annulus between radius 1.0 – 1.5mm eccentricity. Pixels corresponding to blood vessels > 80µm were excluded. The white cross in the centre represents the fovea.

As the axial length (magnification) of the eye will affect the incident power of the laser and the resultant amount of autofluorescence elicited, the intensity data were compensated using the formula below (Delori et al., 2011).

Aging results in loss of media transparency. Intensity data were compensated for media transmission accordingly (Delori et al., 2011):

−5 2 푀푒푑𝑖푎 푡푟푎푛푠푚𝑖푠푠𝑖표푛 = 10 5.56푥10 푥(퐴푔푒 −400) (2)

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Thus, the raw GL intensities from the autofluorescence images were compensated for data analysis using the formula below:

The zero reference is the GL intensity recorded in the absence of autofluorescence.

7.3.7. Statistical Analysis

Statistical analyses were performed with Prism 7.01 (Graphpad Software Inc, CA, USA). Normality testing was performed using D'Agostino & Pearson normality test. Data that were normally distributed were presented in mean and standard deviation (SD). Non-normally distributed data were presented in median and interquartile range (IQR), unless otherwise stated. Mann-Whitney test was used to compare the age and spherical equivalent between emmetropes and myopes. Kruskall-Wallis and Dunn’s multiple comparisons test was used to compare PAF intensities between emmetropes and myopes. Pearson and Spearman correlation was used to determine the relationship of PAF intensity with age in emmetropes and myopes respectively. To compare age-related PAF increase with age between emmetropes and myopes, exponential functions were fitted to the PAF data of each group. The z score was then calculated for the rate constant, k, of each curve:

푘 −푘 1 2 (4) 푧 = 2 2 √푆퐸(푘1) +푆퐸(푘2)

Statistical significance was set at 0.05.

7.4. Results

7.4.1. Subjects

Average age, spherical equivalent and axial length of the emmetropes and myopes are found in Table 7.2. There was no significant difference in age between the 2 groups (Mann-Whitney test, p = 0.573). The spherical equivalent and axial length of myopic eyes were higher and longer than those of emmetropic eyes (Mann-Whitney test, p < 0.001).

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Emmetrope (n = 35) Myope (n = 38) p-value

Age (years) 28 (18 – 58) 32.5 (18 – 58) 0.573

Spherical Plano (+0.63 to -1.50) -7.44 (-6.00 to -14.63) < 0.001 equivalent (D)

Axial length (mm) 23.48 (22.19 to 25.85) 26.98 (24.88 to 28.54) < 0.001

Table 7.2. Median (range) age, spherical equivalent and axial length of the emmetropes and myopes. There was no significant difference in age between the emmetropic and myopic groups (Mann-Whitney test, p = 0.573). The myopic group had more negative spherical equivalent (Mann- Whitney test, p < 0.001) and longer axial length than emmetropic group (unpaired t-test, p < 0.001).

7.4.2. PAF Profile

PAF around the optic disc was plotted in Figure 7.4. PAF intensity was lowest at the 7 o’clock segment and highest at the 3 o’clock segment. PAF increases and peaks at the 3 and 9 o’clock segments before reducing again towards the inferior and superior regions. The mean PAF intensities of myopic eyes were consistently higher than those of emmetropes from 12 to 6 o’clock segments but these were not statistically significant (p > 0.9). Myopic eyes had more variable PAF intensity compared to emmetropic eyes as shown by the larger IQR in Figure 7.4.

Since RNFL thickness significantly attenuates PAF intensity (Chapter 6), we compensated each subject’s PAF intensity around the optic disc for their RNFL thickness (Figure 7.4B). The RNFL-adjusted PAF profile was relatively uniform around the optic disc with marginally lower intensity along the inferior-temporal regions. There was no significant difference in PAF intensity at any of the clockhours between emmetropes and myopes (Dunn’s multiple comparisons test, p > 0.9).

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Figure 7.4. PAF profile of emmetropic and highly myopic eyes captured at detector sensitivity of 95 units before adjustment (A) and after adjustment (B) for RNFL attenuation. In the unadjusted PAF profile of both emmetropic and myopic eyes (A), PAF intensity is lowest inferiorly-temporally (7 o’clock position) and highest nasally (3 o’clock position). There was no statistically significant difference in PAF intensity between emmetropes and myopes (Dunn’s multiple comparisons test, p > 0.5). After adjustment for RNFL attenuation (B), the compensated PAF profile is similar between emmetropes and myopes (Dunn’s multiple comparisons test, p > 0.9) and shows uniform intensity around the optic disc. Symbols and error bars represent the median and interquartile range. The orientation of the clockhour segments is showed as in a right eye.

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7.4.3. Age-related Change in PAF

Figure 7.5 shows the average PAF intensity (± SD) at young, middle and old age groups for emmetropes (n = 35) and myopes (n = 38). PAF intensities were similar between emmetropes and myopes but were significantly different among different age groups (One-way ANOVA, F(5, 67) = 5.588, p = 0.0002). Old emmetropes and old myopes had stronger PAF than their young counterparts (Holm-Sidak’s multiple comparisons test, p = 0.030 and p = 0.0003 respectively). Correlation analyses returned a positive correlation coefficient for both emmetropes (Pearson r = 0.488, p = 0.003) and myopes (Spearman’s rho = 0.653, p < 0.0001).

Figure 7.5. Average PAF intensity at young (≤ 25 years), middle (26 – 39 years) and old (≥ 40 years) age. Emmetropes and myopes of the same age group had similar PAF intensities. Old emmetropes and old myopes had stronger PAF than their young counterparts (One-way ANOVA, F(5, 67) = 5.588, p = 0.0002). Bar and error bars represent the mean and standard deviation. * indicates statistically significant post-hoc Holm-Sidak’s multiple comparisons test.

An exponential function was fitted to the average PAF data for emmetropes and myopes (Figure 7.6). The exponential fit was used because it was found to be a better fit to the data than a linear equation (data not shown). In addition, we adopted the same formula for compensating age-related reduction in crystalline lens transmission 213 as Greenberg et al., and just as their data were presented with exponential fits, this formula also introduced an exponential trend to our data. Emmetropes and myopes between the ages of 18 – 58 years exhibited similar rate of PAF increase with age (z = -1.18, p = 0.238).

Figure 7.6. Age-related PAF increase in emmetropes (n = 35) and myopes (n = 38). Emmetrope and myope PAF increased at similar rates with age (z = - 1.18, p = 0.238).

7.4.4. Comparison with Perifoveal Autofluorescence

Fifty-six subjects had both FAF and PAF images that were of good quality and captured at fixed detector sensitivities of 105 and 95 units respectively. Perifoveal autofluorescence intensities extracted from an annulus with radius 1 – 1.5mm eccentricity of the fovea was sampled. An exponential best fit curve was fitted to the intensity data (Figure 7.7). The rate constant for autofluorescence increase with age was 0.022 for perifoveal and 0.014 for parapapillary data. In other words, the rate of PAF increase was only 63% of the perifoveal rate and this was statistically significant (z = -2.71, p = 0.007).

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Figure 7.7. Age-related increase in perifoveal and RNFL-adjusted PAF intensity of the same subjects (n = 56; 28 emmetropes and 28 myopes). Rate of autofluorescence increase was greater in the perifovea than in the parapapillary region (z = -2.71, p = 0.007).

7.5. Discussion

We reported a number of original findings in this study. We have documented the PAF profile around the optic disc with and without adjusting for the attenuation of autofluorescence intensity by RNFL. We have studied age-related increase in PAF intensity between emmetropes and myopes and found no significant difference. Lastly, we found the rate of age-related increase in PAF intensity was 63% slower than that of perifoveal autofluorescence.

7.5.1. PAF Profile

PAF intensity before adjustment for RNFL attenuation shows regional variations and was most intense nasally and least intense inferior-temporally. RNFL distribution around the optic disc is thickest along the superior and inferior nerve bundles, followed 215 by nasal and temporal. After correcting for RNFL attenuation, the PAF intensities increased, revealing a PAF profile that was uniform around the optic disc with marginally lower intensity along the inferior-temporal regions. PAF profile has not been documented in published literature. Characterising normal PAF profile is important so that further study of PAF in optic neuropathies can be carried out.

Autofluorescence in the normal eye mainly originates from RPE lipofuscin and is closely associated with rod photoreceptor and RPE distribution (Delori et al., 2001, Ach et al., 2014). In our study, RNFL-adjusted PAF intensity was marginally weaker along the inferior-temporal region. This could be partly attributed to the lower number of rods per RPE cell in the temporal region compared to the other regions (Curcio et al., 1990, Ach et al., 2014). Another explanation for the marginally lower PAF intensity temporally is the presence of alpha parapapillary atrophy (ɑ-PPA). ɑ-PPA is characterised as irregular hyper- and hypopigmentary changes at the margin of the optic disc and also beyond the border of ẞ-PPA (Jonas et al., 1989). ɑ-PPA was observed in 67% of the PAF images in our study and appeared hypoautofluorescent relative to background autofluorescence.

7.5.2. Age-related Increase in PAF

FAF has been documented to increase with age but data were only collected in the macular region (von Rückmann et al., 1997, Greenberg et al., 2013, Delori et al., 2001). This is the first study to report the age-related change in PAF. The PAF also increases with age, but at a different rate compared to the perifovea. Although we could not compare the actual grey level intensities between the macula and optic disc autofluorescence images, we could compare the rate of autofluorescence change between these 2 distinct regions in the retina. Our data suggested that age-related PAF increase was slower than that of the perifovea. It may be due to the lower number of rods per RPE cell in the parapapillary retina compared to the perifovea. Rod density in the central 2 – 3mm of the macula is about 131000 rods/mm2 while RPE density is 4500 cells/mm2 (equivalent to approximately 29.1 rods/RPE) (Ach et al., 2014, Curcio et al., 1990, Snodderly et al., 2002). Meanwhile, the parapapillary region has an average rod density of 91000 rods/mm2 and RPE density of 4700 RPE/mm2 (equivalent to approximately 19.4 rods/RPE). This implies that the phagocytic load of parapapillary RPE is 67% that of perifoveal RPE. The rate constant, k, for the age- related increase in parapapillary autofluorescence is 63% that of perifoveal

216 autofluorescence, and proportionally reflects the lighter phagocytic load of parapapillary RPE compared to perifoveal RPE.

The contribution of cone photoreceptors to our findings is likely to be small. Cones also contribute to lipofuscinogenesis, albeit at a much lower rate than rods (Ach et al., 2014) due to the presence of a cone-specific visual cycle (Wang and Kefalov, 2011). The cone-specific visual cycle bypasses RPE cells and works via Muller cells. In Muller cells, all-trans retinol binds with cellular retinoid binding protein. It is isomerised into 11-cis retinol by Isomerase II and returned to the cone photoreceptor. Within the cone photoreceptor, 11-cis retinol is oxidised to 11-cis retinal, which then combines with opsin to form visual pigment. Furthermore, the number of cone photoreceptors per RPE cell is similar in the perifovea and parapapillary retina. Cone density at the perifovea and around the optic disc is 10000 cells/mm2 and 10500 cells/mm2 respectively (Curcio et al., 1990). Using the same RPE densities of 4500 cells/mm2 in the perifovea and 4700 cells/mm2 around the optic disc (Ach et al., 2014), the perifoveal and parapapillary cone:RPE ratio are similar, at about 2.2 cones/RPE.

7.5.3. Rate of PAF Increase in Emmetropes and High Myopes

It is believed that high myopes may experience higher levels of oxidative stress compared to emmetropes (Francisco et al., 2015, Simonelli et al., 1989, Micelli-Ferrari et al., 1996). The oxidative stress comes as a consequence of retinal hypoxia due to lower ocular pulse amplitude (the difference between systolic and diastolic pressure in the eye). In cell cultures, incubating rod outer segments exposed to oxidative stress with RPE cells has been associated with increased RPE lipofuscin formation and therefore increased RPE autofluorescence (Lei et al., 2013). However, our results did not show a difference between emmetropes and high myopes with respect to PAF intensity or age-related increase in PAF. This may be because 1) any difference in oxidative stress levels in high myopes compared to emmetropes is too small to be detected by clinical autofluorescence imaging, 2) the potential difference in PAF intensity manifest at older age (> 60 years old) than our recruited subjects, or 3) despite being high myopes, our subjects’ eyes can be assumed to be healthy (non- pathological) and therefore no significant increase in PAF was detected. We may need to study an older and/or more myopic population in order to detect changes that could imply differences in oxidative stress levels between emmetropes and high myopes.

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7.6. Limitations

The semi-quantitative nature of the autofluorescence images limits the comparison of GL intensities between individual subjects. However, we have shown that our data is comparable to quantified autofluorescence in the published literatures (Chapters 4 and 5). Although our study was not longitudinal in nature, our results confirm previous studies on the age-related changes in autofluorescence (Chapter 5). Our results on the rate of autofluorescence increase also show striking agreement with phagocytic load at the perifoveal and parapapillary regions, and further reinforce the strong relationship between rod distribution and autofluorescence.

In this experiment, we found that PAF profile was relatively uniform around the optic disc once RNFL attenuation had been compensated for. There is an 8 – 10% reduction in PAF intensity along the inferior-temporal region. This reduction may be due to PPA, optic disc tilting and capillary density. PPA is usually found temporal to the optic disc due to axial elongation. Further study is needed to determine how PPA affects the PAF profile. Capillary density is greater in the superior-temporal and inferior-temporal RNFL bundles compared to other parapapillary regions (Lommatzsch, Rothaus, Koch, Heinz, & Grisanti, 2018). Further study using only eyes without any PPA and incorporating OCT angiography (OCT-A) to map capillary density could help us to better understand the effect of these variables on PAF intensity.

We found no significant difference in the age-related increase in PAF intensity between emmetropes and high myopes. This finding should be limited to the age range between 18 and 58 years old. Greater difference in rate of age-related increase in PAF between the 2 refractive groups may be found at older age. In fact, changes in FAF could coincide with the retinal changes in myopic degeneration, which also increases with age.

7.7. Future Work and Conclusion

We have documented the PAF profile in healthy eyes of emmetropes and high myopes. PAF was most intense nasally and least intense inferior-temporally due to RNFL attenuation. Adjusted for RNFL attenuation, the PAF profile is relatively uniform around the optic disc. Myopia without pathological changes did not affect age-related PAF change compared to emmetropia in the age range that we studied. Age-related increase in PAF was almost two-thirds the rate of perifoveal autofluorescence and

218 reflected the proportionally lower phagocytic load of parapapillary RPE. PAF and perifoveal autofluorescence therefore reflect the metabolic activities of the RPE cells.

Future work could extend the study of PAF into older eyes and myopic eyes with overt pathological changes such as pathological myopia or chorioretinal atrophy. Quantitative PAF imaging and OCT angiography can be performed on high myopia eyes with glaucoma to shed light on the relationship between autofluorescence, RNFL and glaucoma. Autofluorescence imaging might be used to predict prognosis or monitor glaucoma progression since loss of RNFL would increase PAF intensity. The use of PAF imaging in pathological myopia can also be explored to study the effect of mechanical forces induced by axial elongation around the optic nerve head.

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7.8. References

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KOLOMEYER, A. M., BAUMRIND, B. R., SZIRTH, B. C., SHAHID, K. & KHOURI, A. S. 2013. Fundus autofluorescence and colour fundus imaging compared during telemedicine screening in patients with diabetes. J Telemed Telecare, 19, 209- 12. LAEMMER, R., HORN, F. K., VIESTENZ, A., LINK, B., JUENEMANN, A. G. & MARDIN, C. Y. 2007. Measurement of autofluorescence in the parapapillary atrophic zone in patients with ocular hypertension. Graefes Arch Clin Exp Ophthalmol, 245, 51-8. LEI, L., TZEKOV, R., MCDOWELL, J. H., SMITH, W. C., TANG, S. & KAUSHAL, S. 2013. Formation of lipofuscin-like material in the RPE Cell by different components of rod outer segments. Exp Eye Res, 112, 57-67. LOMMATZSCH, C., ROTHAUS, K., KOCH, J. M., HEINZ, C., & GRISANTI, S. (2018). Vessel density in OCT angiography permits differentiation between normal and glaucomatous optic nerve heads. Int J Ophthalmol, 11(5), 835-843. MARMOR, M. F., KELLNER, U., LAI, T. Y., LYONS, J. S., MIELER, W. F. & OPHTHALMOLOGY, A. A. O. 2011. Revised recommendations on screening for chloroquine and hydroxychloroquine retinopathy. Ophthalmology, 118, 415-22. MICELLI-FERRARI, T., VENDEMIALE, G., GRATTAGLIANO, I., BOSCIA, F., ARNESE, L., ALTOMARE, E. & CARDIA, L. 1996. Role of lipid peroxidation in the pathogenesis of myopic and senile cataract. Br J Ophthalmol, 80, 840-3. OHNO-MATSUI, K., AKIBA, M., MORIYAMA, M., SHIMADA, N., ISHIBASHI, T., TOKORO, T. & SPAIDE, R. F. 2012. Acquired optic nerve and peripapillary pits in pathologic myopia. Ophthalmology, 119, 1685-92. OHNO-MATSUI, K., LAI, T. Y., LAI, C. C. & CHEUNG, C. M. 2016. Updates of pathologic myopia. Prog Retin Eye Res, 52, 156-87. OHNO-MATSUI, K., SHIMADA, N., YASUZUMI, K., HAYASHI, K., YOSHIDA, T., KOJIMA, A., MORIYAMA, M. & TOKORO, T. 2011. Long-term development of significant visual field defects in highly myopic eyes. Am J Ophthalmol, 152, 256-265.e1. OISHI, A., OGINO, K., MAKIYAMA, Y., NAKAGAWA, S., KURIMOTO, M. & YOSHIMURA, N. 2013. Wide-field fundus autofluorescence imaging of retinitis pigmentosa. Ophthalmology, 120, 1827-34. SAMARAWICKRAMA, C., MITCHELL, P., TONG, L., GAZZARD, G., LIM, L., WONG, T. Y. & SAW, S. M. 2011. Myopia-related optic disc and retinal changes in adolescent children from singapore. Ophthalmology, 118, 2050-7. SCHWENN, O., TROOST, R., VOGEL, A., GRUS, F., BECK, S. & PFEIFFER, N. 2002. Ocular pulse amplitude in patients with open angle glaucoma, normal tension glaucoma, and ocular hypertension. Br J Ophthalmol, 86, 981-4. SEEHAFER, S. S. & PEARCE, D. A. 2012. Lipofuscin: The "wear and tear" pigment. In: LOIS, N. & FORRESTER, J. V. (eds.) Fundus autofluorescence. Lippincott Williams & Wilkins. p.3-13. SEPAH, Y. J., AKHTAR, A., SADIQ, M. A., HAFEEZ, Y., NASIR, H., PEREZ, B., MAWJI, N., DEAN, D. J., FERRAZ, D. & NGUYEN, Q. D. 2014. Fundus autofluorescence imaging: Fundamentals and clinical relevance. Saudi J Ophthalmol, 28, 111-6. SIMONELLI, F., NESTI, A., PENSA, M., ROMANO, L., SAVASTANO, S., RINALDI, E. & AURICCHIO, G. 1989. Lipid peroxidation and human cataractogenesis in diabetes and severe myopia. Exp Eye Res, 49, 181-7. SNODDERLY, D. M., SANDSTROM, M. M., LEUNG, I. Y., ZUCKER, C. L. & NEURINGER, M. 2002. Retinal pigment epithelial cell distribution in central retina of rhesus monkeys. Invest Ophthalmol Vis Sci, 43, 2815-8. VON RÜCKMANN, A., FITZKE, F. W. & BIRD, A. C. 1995. Distribution of fundus autofluorescence with a scanning laser ophthalmoscope. Br J Ophthalmol, 79, 407-12.

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Chapter 8. Overall Discussion and Concluding Comments

8.1. Overall Discussion and Concluding Comments

This study explores the utility of fundus autofluorescence (FAF) imaging in high myopia and identifies the retinal nerve fibre layer (RNFL) as one of the major factors affecting autofluorescence intensity around the optic disc. Qualitative autofluorescence studies of pathologic myopia have been published but not in highly myopic eyes without pathology. It was hypothesized that autofluorescence of healthy, high myopic eyes would reveal early changes in the outer retina that are associated with axial elongation, and these early change(s) might ultimately help us to understand the pathophysiology of myopic degeneration.

FAF imaging captures autofluorescence signal from retinal pigment epithelium (RPE) lipofuscin. Alterations in the FAF signal have been associated with dysfunction of the outer retina. Quantitative analysis of FAF signal was recently developed and allows FAF intensity to be compared among different FAF images (Delori et al., 2011) and is therefore important in longitudinal studies. However it is important to determine whether semi-quantitative autofluorescence data can contribute useful and reliable clinical information as well. It follows that important baseline data are needed so as to describe the expected distribution of FAF signals in the normal eye and in common abnormalities. It might be expected that under some circumstances (e.g. when autofluorescence intensity changes are diffused or mild), quantitative autofluorescence is essential whilst in others (e.g. when specific patterns of FAF, such as ‘patchy’ and ‘reticular’, can be identified), a semi-quantitative or qualitative approach would be appropriate.

This thesis had 3 main aims. First, to understand how FAF signal captured from young healthy eyes using cSLO is affected by different image acquisition settings. Second, to document FAF between emmetropes and high myopes and to relate it with contrast sensitivity. Third, to develop a methodology to study the effect of RNFL on parapapillary autofluorescence (PAF) and compare PAF between emmetropes and high myopes.

Chapter 3 discussed how automatic real-time (ART) averaging and detector sensitivity of the HRA+OCT affected the quality and brightness of the FAF image captured. The findings indicated that for young emmetropic subjects with steady fixation, it was not necessary to include a large number of scans for signal averaging. The locations of

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FAF peaks could be reliably identified even without complete photobleaching of the retina prior to image capture.

In chapter 4, FAF distribution in the central 55˚ of young healthy eyes was reported. It was to our knowledge, the first study to explore widefield FAF images to document FAF intensity along all 8 meridians of the eye. Previous studies used smaller-field of view or focused on the vertical and horizontal meridians only (Delori et al., 2001, Lois et al., 2000). Characterisation of RPE autofluorescence signals using widefield 55˚ lenses showed a superior-nasal peak not reported in previous clinical studies. The peaks detected along all 8 meridians mirrored the distribution of rod photoreceptors and RPE lipofuscin published in the literature. The findings presented here reinforce the close relationship between FAF and rod distribution and illustrate the advantages of wider field data collection and the processing of additional meridians.

Chapter 5 compared FAF peak location and rate of age-related increase in FAF between emmetropes and high myopes. FAF peaks were similar between the 2 groups. There was no difference in contrast sensitivity at low and medium spatial frequencies between emmetropes and high myopes and likewise, no correlation between FAF and contrast sensitivity. This suggests that either FAF differences between emmetropes and high myopes were too small to be picked up by this technique or that the retinae in this group of high myopes were anatomically normal despite having long axial lengths. It is well-established that high myopia is associated with an increased risk of retinal degeneration (Curtin and Karlin, 1970). This risk is greater with increasing axial length and age. It may be worthwhile to explore FAF in eyes with more severe myopia and in older age in order to understand the transition from ‘normal’ high myopia to pathological myopia. Here we have identified a technique for defining ‘normal’.

In Chapter 6, a method to sample PAF was described. It was found that RNFL significantly attenuated the PAF signal around the optic disc. The magnitude of PAF attenuation averaged 3 ± 1GL for every 10µm of RNFL. The conversion factor is only applicable to autofluorescence images captured using the same imaging protocol as described in this experiment as quantified autofluorescence (qAF) was not available. Regardless, this attenuating effect is significant in normal eyes and future quantitative studies of PAF should take this into account. Qualitative studies of PAF should also consider the effects of RNFL as it could mask subtle changes in PAF.

Chapter 7 documented PAF profile in emmetropes and high myopes. Age-related increase in PAF was also observed, albeit at a slower rate compared to that of

224 perifoveal autofluorescence. The rate of PAF increase with age was 63% that of the perifovea. The difference in rates of age-related increase in PAF compared to perifoveal autofluorescence matched the difference in phagocytic load faced by each RPE cell at the 2 retinal locations. Each perifoveal RPE cell supports 29.1 rods whereas each parapapillary RPE cell supports 19.4 rods (Ach et al., 2014, Curcio et al., 1990); therefore the phagocytic load faced by parapapillary RPE is 67% that of perifoveal RPE. Again, these observations agree with current knowledge about RPE lipofuscinogenesis and autofluorescence. The study of age-related increase in PAF is important because it confirms the direct relationship between RPE autofluorescence and rod photoreceptors in normal eyes. This may allow the prediction of the expected autofluorescence intensity at a particular age or retinal location based on the RPE cell to rod photoreceptor ratio.

In conclusion, the overall aim of the work, which was to compare FAF in normal and in high myopes, was achieved. The location of FAF peaks, PAF distribution, and rate of age-related autofluorescence increase were documented between emmetropes and high myopes. The findings suggest that autofluorescence in highly myopic eyes of subjects under 60 years old are comparable to emmetropia. It has been shown that the effect of RNFL on autofluorescence intensity around the optic disc is significant. Accounting for RNFL attenuation will improve the precision and accuracy of autofluorescence imaging in the study of optic neuropathies.

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8.2. References

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Annex 2.1. Good quality FAF images

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Annex 2.2. Poor quality FAF images

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Annex 2.3. Good quality PAF images

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Annex 2.4. Poor quality PAF images

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Annex 3.1 MATLAB Code

The sampling grid (5x5 pixel sampling area spaced at 10 pixel intervals along 8 cardinal meridians of the eye) was proposed by me. The purpose of the grid was to obtain FAF spatial profile along 8 meridians. The MATLAB code for sampling autofluorescence intensities was then developed with the help of Dr Jeremiah M. F. Kelly, who wrote the basic code for sampling pixel intensities from a 5x5 pixel square. The code was expanded by me to sample pixel intensities along 8 meridians at 10 pixel intervals. I also edited the code to provide the standard deviation of the gray level intensities from the 5x5 pixel sampling squares (Chapter 3 ART experiment).

The code was validated by drawing the sampling grid and overlapping it with a sample FAF image in Photoshop CS 6. The grey level intensities from the test image were sampled manually using Photoshop CS 6. The manually-sampled grey level intensities and the FAF spatial profile was compared to the data obtained using the MATLAB code and found to be in good agreement.

%this code automates the extraction of GL intensity from images in a folder files = dir('*.tif'); for k = 1:numel(files) files(k) original = imread(files(k).name); for Radius=10:10:420 run extractdataold end end

%extracts sum of GLs in a 5x5 square along 8 cardinal meridians of image %output displays the average GLs of 25 pixels in one 'ring'/radius. format compact I=rgb2gray(original); [r c]=size(I); % define centre of image centre=floor([r c]/2);% assume this is the fovea

%% Create a mesh r=1:r;c=1:c; % for all rows and columns, [C R]=meshgrid(c,r); % create a mesh grid spanning all rows and columns

%% Create a mask T=~(((R-centre(1)).^2+(C-centre(2)).^2)>(Radius^2)) ; % code for big circle, code is like a closed loop. I2=immultiply(T,I); % apply as a filter innerRadius=Radius-5; % create 'donut' T2=(((R-centre(1)).^2+(C-centre(2)).^2)>(innerRadius^2)); Fluor2=immultiply(T2,I2); 233

%%%% crop centre of fovea into 5x5 square for calculation of Foveal SD T3=~(((R-centre(1)).^2+(C-centre(2)).^2)>(2.9^2)); % code for small circle Fluorc=immultiply(T3,I2); %% Meridian=0; I=Fluor2; Slope=tan(Meridian*pi/180);

S1=tan((Meridian)*pi/180); S2=tan((Meridian+90)*pi/180);

Y1up=S1*(c-centre(2))+centre(1)+r(3); Y1down=S1*(c-centre(2))+centre(1)-r(3); Y2=S2*(c-centre(2))+centre(1);

[a1 ~]=meshgrid(Y1up,r); [a2 ~]=meshgrid(Y1down,r); [b ~]=meshgrid(Y2, r);

%for meridian=0 idxL= Ra2; idxP= R>b; idx=immultiply(idxL,idxU); idx2=immultiply(idx,idxP); arc0=immultiply(idx2,I);

%for meridian=135 Meridian=45; I=Fluor2; Slope=tan(Meridian*pi/180); S1=tan((Meridian)*pi/180); S2=tan((Meridian+90)*pi/180);

Y1up=S2*(c-centre(2))+centre(1)+r(3); Y1down=S2*(c-centre(2))+centre(1)-r(4); Y2=S1*(c-centre(2))+centre(1);

[a1 ~]=meshgrid(Y1up,r); [a2 ~]=meshgrid(Y1down,r); [b ~]=meshgrid(Y2, r); idxL= Ra2; idxP= R

%for meridian=180 Meridian=180; I=Fluor2; Slope=tan(Meridian*pi/180); S1=tan((Meridian)*pi/180); S2=tan((Meridian+90)*pi/180);

Y1up=S1*(c-centre(2))+centre(1)+r(2); Y1down=S1*(c-centre(2))+centre(1)-r(3); Y2=S2*(c-centre(2))+centre(1);

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[a1 ~]=meshgrid(Y1up,r); [a2 ~]=meshgrid(Y1down,r); [b ~]=meshgrid(Y2, r); idxL= Ra2; idxP= R

%for meridian=315 Meridian=270; I=Fluor2; Slope=tan(Meridian*pi/180); S1=tan((Meridian+45)*pi/180); S2=tan((Meridian+90)*pi/180);

Y1up=S1*(c-centre(2))+centre(1)+r(4); Y1down=S1*(c-centre(2))+centre(1)-r(3); Y2=S2*(c-centre(2))+centre(1); [a1 ~]=meshgrid(Y1up,r); [a2 ~]=meshgrid(Y1down,r); [b ~]=meshgrid(Y2, r); idxL= Ra2; idxP= R>b; idx=immultiply(idxL,idxU); idx2=immultiply(idx,idxP); arc315=immultiply(idx2,I);

%for meridian=45 Meridian=315; I=Fluor2; Slope=tan(Meridian*pi/180); S1=tan((Meridian)*pi/180); S2=tan((Meridian+90)*pi/180);

Y1up=S2*(c-centre(2))+centre(1)+r(3); Y1down=S2*(c-centre(2))+centre(1)-r(4); Y2=S1*(c-centre(2))+centre(1);

[a1 ~]=meshgrid(Y1up,r); [a2 ~]=meshgrid(Y1down,r); [b ~]=meshgrid(Y2, r); idxL= Ra2; idxP= R

%for meridian=225 Meridian=315;% I=Fluor2; Slope=tan(Meridian*pi/180); S1=tan((Meridian)*pi/180); S2=tan((Meridian+90)*pi/180);

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Y1up=S2*(c-centre(2))+centre(1)+r(4); Y1down=S2*(c-centre(2))+centre(1)-r(3); Y2=S1*(c-centre(2))+centre(1);

[a1 ~]=meshgrid(Y1up,r); [a2 ~]=meshgrid(Y1down,r); [b ~]=meshgrid(Y2, r); idxL= Ra2; idxP= R>b; idx=immultiply(idxL,idxU); idx2=immultiply(idx,idxP); arc225=immultiply(idx2,I);

% Ir=imrotate(Fluor2, 90); %for meridian=************* '90' Meridian=180;% the line along which we sample the data

Slope=tan(Meridian*pi/180); S1=tan((Meridian)*pi/180); S2=tan((Meridian+90)*pi/180);

Y1up=S1*(c-centre(2))+centre(1)+r(3); Y1down=S1*(c-centre(2))+centre(1)-r(2); Y2=S2*(c-centre(2))+centre(1);

[a1 ~]=meshgrid(Y1up,r); [a2 ~]=meshgrid(Y1down,r); [b ~]=meshgrid(Y2, r); idxL= Ra2; idxP= R>b; idx=immultiply(idxL,idxU); idx2=immultiply(idx,idxP); arc90=immultiply(idx2,Ir);

%for meridian=270 Meridian=180; I=Fluor2; Slope=tan(Meridian*pi/180); S1=tan((Meridian)*pi/180); S2=tan((Meridian+90)*pi/180);

Y1up=S1*(c-centre(2))+centre(1)+r(3); Y1down=S1*(c-centre(2))+centre(1)-r(2); Y2=S2*(c-centre(2))+centre(1);

[a1 ~]=meshgrid(Y1up,r); [a2 ~]=meshgrid(Y1down,r); [b ~]=meshgrid(Y2, r); idxL= Ra2; idxP= R

236 notzero0170=nonzeros(arc0); r10_0=mean(notzero0170); notzero45170=nonzeros(arc45); r10_45=mean(notzero45170); notzero90170=nonzeros(arc90); r10_90=mean(notzero90170); notzero135170=nonzeros(arc135); r10_135=mean(notzero135170); notzero180170=nonzeros(arc180); r10_180=mean(notzero180170); notzero225170=nonzeros(arc225); r10_225=mean(notzero225170); notzero270170=nonzeros(arc270); r10_270=mean(notzero270170); notzero315170=nonzeros(arc315); r10_315=mean(notzero315170);

%CALCULATE FOVEAL AF notzeroFluorc=nonzeros(Fluorc); fovealAF=mean(notzeroFluorc);

%calculate SD for each sampled area %'SD of each sampled area'

%notzero0170=nonzeros(arc0); %find all nonzero elements in the matrix %r10s_0=std2(notzero0170); %display std dev for those elements %notzero45170=nonzeros(arc45); %find all nonzero elements in the matrix %r10s_45=std2(notzero45170); %display std dev for those elements %notzero90170=nonzeros(arc90); %find all nonzero elements in the matrix %r10s_90=std2(notzero90170); %display std dev for those elements %notzero135170=nonzeros(arc135); %find all nonzero elements in the matrix %r10s_135=std2(notzero135170); %display std dev for those elements %notzero180170=nonzeros(arc180); %find all nonzero elements in the matrix %r10s_180=std2(notzero180170);%display std dev for those elements %notzero225170=nonzeros(arc225); %find all nonzero elements in the matrix %r10s_225=std2(notzero225170) ;%display std dev for those elements %notzero270170=nonzeros(arc270); %find all nonzero elements in the matrix %r10s_270=std2(notzero270170); %display std dev for those elements %notzero315170=nonzeros(arc315); %find all nonzero elements in the matrix %r10s_315=std2(notzero315170) ;%display std dev for those elements

%% display results results=horzcat(r10_0, r10_45, r10_90, r10_135, r10_180, r10_225, r10_270, r10_315, fovealAF); %results=horzcat(r10s_0, r10s_45, r10s_90, r10s_135, r10s_180, r10s_225, r10s_270, r10s_315; disp(results)

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Annex 6.1.

Figure A6.1. PAF intensities of all 89 subjects (39 emmetropes, 50 myopes) involved in this experiment.

Clockhour segment K2 P value 12 2.88 0.238

1 6.49 0.039

2 6.22 0.045

3 3.04 0.218

4 6.03 0.049

5 5.51 0.064

6 7.87 0.020

7 28.72 <0.0001

8 11.55 0.003

9 8.13 0.017

10 9.24 0.010

11 7.20 0.027

Table A6.1. D’Agostino & Pearson normality test of PAF intensities from each segment (n = 89). PAF intensities at segments 1, 2, 4, 6, 7, 8, 9, 10, 11 were not normally distributed.

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Figure A6.2. RNFL thickness of all 89 subjects (39 emmetropes, 50 myopes) in this experiment.

Clockhour segment K2 P value 12 7.82 0.020

1 8.76 0.013

2 9.25 0.010

3 3.62 0.164

4 48.75 <0.0001

5 5.19 0.075

6 0.67 0.715

7 14.35 0.001

8 27.80 <0.0001

9 4.24 0.120

10 6.37 0.041

11 0.39 0.822

Table A6.1. D’Agostino & Pearson normality test of RNFL thickness (n = 89). RNFL in clockhour segments 12, 1, 2, 4, 7, 8 and 10 were not normally distributed.

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