Experimental Design

3.3.1 Participants

Thirty-eight participants (29 with primary open angle glaucoma (POAG), 9 healthy controls) were recruited from the University Hospital of Wales, Cardiff and from within the staff, student and friends population at the School of Optometry and Vision Sciences, Cardiff University. Subjects were

71 recruited and imaged by BF (n=7), and KM (n = 31). Where possible, both eyes from each participant were included in the study. Demographics of the eyes included are summarised in Table 3.1.

Demographic Control (n=16) Preperimetric glaucoma (n=20) Early glaucoma (n=19) Advanced glaucoma (n=10)

mean ± SD mean ± SD mean ± SD mean ± SD

Age (years) 65.36 ± 11.33 63.25 ± 9.97 69.16 ± 10.44 59.30 ± 10.33 Gender 4F : 10M 13F : 7M 12F : 7M 5F : 5M MS (D) -1.25 ± 1.25 0.00 ± 1.00 -1.25 ± 2.00 -1.50 ± 2.00 IOP (mmHg) 17.35 ± 2.25 15.64 ± 3.66 15.24 ± 3.11 14.52 ± 3.09 Axial length (mm) 24.57 ± 0.69 23.53 ± 1.09 24.01 ± 1.15 24.64 ± 0.81 MD (dB) 0.57 ± 0.98 0.00 ± 0.82 -3.39 ± 1.55 -8.65 ± 1.61

Table 3.1: Demographics for control and glaucomatous subjects. Gender: F = female, M = male. MS = mean spherical refractive error, IOP = intraocular pressure, MD = mean deviation on visual fields.

3.3.2 Clinical assessments

Participants’ clinical ocular assessments included intra-ocular pressure (IOP) measurements by Goldmann contact tonometry (Haag Streight AG, Switzerland) or Non-Contact Tonometry-80 (Topcon Europe Medical B.V., Netherlands). Axial eye length, refractive error and visual field status were determined as described previously in section 2.3.1. Inclusion criteria for the study were a mean spherical refractive error within ±6.00D and the absence of other (non-glaucomatous) ocular pathology or systemic pathology with ocular side effects. Control data were excluded if the IOP was found to be ≥21 mmHg or visual field loss (as defined by Hodapp et al. (1993)) was identified following visual field testing.

3.3.3 OCT data and image processing

18.7° scans centred on the macula were acquired as described in section 2.3.2. The Initial data processing was undertaken as described in section 2.4. Briefly, spectral OCT data were acquired and processed into tomographic images using custom-written MATLAB software (FDProcessing v1.0). Images were then further processed in ImageJ (v1.47n) in order to align the tomograms, remove artefacts from small eye movements, and improve image quality and contrast.

72 The centre of the fovea was identified in each 3D image stack. Every four b-scans were block averaged to improve definition of retinal layers. A custom-written MATLAB-based software (ManSeg_Anylayer v1.0) was used to manually demarcate the boundaries of the three innermost retinal layers, namely the macular nerve fibre layer (mNFL), ganglion cell layer (GCL) and inner plexiform layer (IPL).

3.3.4 Glaucoma classification

Participant eyes were classified into glaucoma disease stage according to the presence of glaucoma optic disc/nerve head features and degree of visual field loss (classification adapted from Hodapp et al. (1993)) as indicated in section 2.4.6. Thus eyes were categorised into preperimetric glaucoma (n=20), early glaucoma (n=19), advanced glaucoma (n=10) and non-glaucomatous control (n=16) groups. Of the 78 potential eyes for this study, 11 eyes were excluded from the study, either due to their not meeting the inclusion criteria (e.g. ‘suspect glaucoma’, as described in section 2.4.6, or IOP above normal limits in a healthy control; n=9) or as a result of poor image quality (n=2).

3.3.5 Generation of colour-coded thickness maps of inner retinal layers

A custom-written MATLAB program (F Rakebrant, Cardiff University) was used to create colour- coded thickness maps of the mNFL, GCL, IPL and ganglion cell complex (GCC; consisting of the mNFL, GCL and IPL). The colour maps for each dataset were visually compared to the corresponding visual field plot in order to determine whether areas of retinal thinning correlated with visual field loss patterns. Thicker regions are depicted as warmer colours (i.e. orange-red), while thinner areas are cooler colours (i.e. blue-turquoise). The colour coded thickness maps for each eye were arranged in order of increasing visual field loss, allowing for any trends in the data to become more apparent.

73

3.3.6 Regional analysis of retinal layer thickness and volume

In turn, the colour-coded thickness maps were regionally divided for analysis by placing a grid centred on the fovea to create a central 1.5° (fovea), and 3 concentric rings at 2.5°, 5.5° and 8.6° radial eccentricity. These rings were subdivided (4 regions in each), by superior - inferior and temporal - nasal divisions (Figure 3.1a). The mean thickness and volume for each inner retinal layer, as well as for the GCC, was calculated for each of the 13 individual regions (R1-R13). Additionally, the individual regions were grouped to form quadrants (Figure 3.1e), inner-quadrants (Figure 3.1f), hemizones (Figure 3.1g), and inner-hemizones (Figure 3.1h).

The mNFL, GCL, IPL and GCC thicknesses and volumes were quantified in each individual or grouped regions to determine what, if any changes were present between eyes as a function different stages of glaucoma. The IPL:mNFL and IPL:GCL ratios were also calculated for each region in each retinal layer. Additionally, regions of equal eccentricity from the fovea were compared (inner, Figure 3.1b, mid, Figure 3.1c; and outer, Figure 3.1d) for each stage of glaucoma to determine whether damage occurred primarily in any specific area.

3.3.7 Statistical analysis

Statistical analyses were performed using RStudio, an open source platform for R Statistics, as described in section 2.9. Briefly, the normality of data was determined using Shapiro-Wilk tests, QQ plots and histograms, and the majority of data was shown to be non-parametric. Where possible, both eyes from each participant were used for analysis. Thus, in order to compensate this, generalised linear mixed-effects models (LMM) with repeated measures component were used to assess differences between stages of glaucoma for each individual region, included in each model. Where differences were determined using the LMMs, Tukey post-hoc analysis was applied to determine within which stage of glaucoma the differences lay.

74 Additionally, differences between regions of equal eccentricity from the fovea were investigated for each inner retinal layer, for each stage of glaucoma, again using a repeated measures multivariate approach with a Tukey post-hoc correction.