Faculty Bibliography

Purpose : In mild glaucoma, RNFL thinning and visual field (VF) loss are often localized, but structure-function modeling is impeded by variability due to individual eye anatomy. We perform high-resolution spatial correlations of RNFLT maps for each VF location to identify relevant areas and study further improvements by geometrically aligning RNFLT maps based on artery trajectories. Methods : In 419 SITA Standard 24-2 Humphrey VFs with at most mild glaucoma (mean deviation >=-3dB) with accompanying circumpapillary Cirrus HD-OCT RNFLT maps, we computed pixel-wise correlations (52 VF locations x 40401 pixels). We then performed an alignment operation, ensuring that the two major retinal arteries follow the same lines in all scans. We piecewise linearly approximated the trajectories of the arteries on 4 concentric circles around ONH (Fig. 1a), determined the necessary rotation for each pixel, and morphed the images accordingly (Fig. 1b). Results : For the pre-alignment RNFLT (correlation maps Fig. 2 top) we observed: (1) relatively high correlations (max 0.29); (2) most of the high-correlation regions are highly localized around the median trajectories of the major arteries at most VF locations, possibly due to the stacked character of the fiber bundles close to ONH, which impedes precise spatial mapping to the VF. This observation suggests general retinal vulnerability zones rather than highly VF location-specific areas as assumed by many previous structure-function models. Accordingly, morphing the RNFLT maps by aligning the eyespecific artery locations increased the maximal correlations on 25 of the 52 VF locations (Fig. 2 bottom, marked in green), particularly in nasal and inferior VF, with improvements of up to 0.1 (inferior arcuate region of VF). At many locations, aligned vulnerability areas become substantially more conspicuous (e.g. the location enlarged on the top left) and might have been missed without aligning. Conclusions : High-resolution structure-function correlations reveal retinal vulnerability zones in mild glaucoma. At many VF locations, these zones become better correlated with VF regions when RNFLT maps are aligned along the arteries. Specific attention to RNFL thinning in these zones in glaucoma suspects may improve the detection of initial VF loss glaucoma

Purpose : Glaucoma is thought to involve neurochemical changes not only in the eye but also the brain's visual system. While excitotoxicity may play a role in glaucoma pathogenesis, it remains controversial whether excess glutamate occurs in this process. In the current study, we investigated alterations in the excitatory-inhibitory balance (E/I balance) in the visual cortex of glaucoma patients. In addition, we examined whether the altered neurochemical balance in the visual cortex is associated with projections of basal nucleus of Meynert (BNM), a major source of cortical cholinergic innervation in the basal forebrain. Methods : 10 glaucoma patients with a wide range of disease severity and 4 age-matched healthy subjects underwent 3-Tesla anatomical MRI, resting-state functional MRI (fMRI), and magnetic resonance spectroscopy (MRS). We used MEGA-PRESS and PRESS sequences to measure the levels of gamma-aminobutyric acid (GABA) and combined glutamate and glutamine (GLX), respectively. Both GABA and GLX were obtained from the same single voxel (2.2x2.2x2.2 cm³) placed along the calcarine sulci and fitted by LCModel software. We normalized the amount of GABA and GLX to N-acetyl-aspartate (NAA) values obtained from MEGA-PRESS, following LCModel guidelines. E/I balance was calculated by dividing the amount of GLX by the amount of GABA. The resting-state fMRI data were analyzed by CONN software. Results : Glaucoma patients had 16.51% higher E/I balance in the visual cortex compared to the healthy control group (Figure 1a). This difference in E/I balance was apparently driven by a 16.85% reduction in GABA (Figure 1b) with no apparent difference in glutamate or glutamine levels between groups (Figure 1c). Furthermore, the E/I balance in the visual cortex was correlated with the functional connectivity between BNM and the visual cortex (Figure 2). Conclusions : The current study shows that the visual cortex of glaucoma patients adopts an excitatory-dominant state that is driven by reduced GABA. This imbalance was associated with the functional connectivity between BNM and the visual cortex, suggesting that weaker projection of BNM to the visual cortex may play a role in the neurochemical changes in the visual cortex of glaucoma patients. Taken together, these findings suggest that widespread functional and metabolic alterations are involved in the brain during glaucoma pathogenesis

Purpose: The purpose of the present study was to quantify test-retest reproducibili-ty of measurements of stiffness of the human trabecular meshwork (HTM) by atomic force microscopy (AFM).
Method(s): Eleven 40 mum radial limbal cryostat sections from a fresh human donor rim were mounted on charged slides and rehydrated at room temperature. Stiffness at four TM locations (anterior to posterior along Schlemm's canal) was measured by AFM. At each location, a 6 x 6 grid was sampled. Indentation points were evenly distributed over a 20 mum x 20 mum area, with a rate of one load/unload cycle per second. Measurements were then repeated for calculation of test-retest variability.
Result(s): The test-retest coefficients of variation for the four measurement locations (anterior to posterior) were 24.39, 25.28, 12.74, and 14.26%, respectively, with a notable drop in the two posterior locations compared to the anterior. The test-retest coefficient for the sections was 19.17%. For the entire eye, the test-retest coefficient of variation for the measurement of the TM stiffness was 17.13%. Young's moduli consistently decreased from anterior to posterior location.
Conclusion(s): Wide regional variation suggests that single value does little to fully describe the complex array of TM stiffness levels within the eye, and future studies of TM stiffness assessed by AFM should include multiple tissue samples from each eye, with documentation of the anterior-posterior location of each measurement.
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Purpose : Optical coherence tomography (OCT) two dimensional (2D) ganglion cell inner plexiform layer (GCIPL) thickness maps often reveal subtle abnormalities that might be washed out with summarized parameters (global or sectoral measurements). Also, the spatial pattern of GCIPL shows useful information to understand the extent and magnitude of localized damages. The purpose of this study was to predict next-visit 2D GCIPL thickness map based on the current and past GCIPL thickness maps. Methods : 346 glaucomatous eyes (191 subjects) with at least 5 visits with OCT tests were included in the study. GCIPL thickness maps were obtained using a clinical OCT (Cirrus HD-OCT, Zeiss, Dublin, CA; software version 9.5.1.13585; 200x200 macular cube scan). Since 83.2% of subjects were stable (average GCIPL change < 2um per year), we simulated progressing cases for diffuse damage pattern and hemifield damage pattern (superior vs. inferior hemifield damage was 50:50) (Figure 1 (c) and (d)). A deep learning based method, time-aware convolutional long short-term memory (TC-LSTM), was developed to handle irregular time intervals of longitudinal GCIPL thickness maps and predict the 5th GCIPL thickness map from the past 4 tests. The TC-LSTM model was compared with a conventional linear regression (LR) analysis. Mean square error (MSE, normalized to pixel intensity) and peak signal to noise ratio (PSNR) between predicted maps and ground truth maps were used to quantify the prediction quality (lower MSE and higher PSNR indicate better results). The Wilcoxon signed-rank test was used to compare TC-LSTM results and LR results. Results : TC-LSTM achieved lower MSE and higher PSNR compared to the LR model (MSE 0.00049 vs. 0.00061, p<0.001, and PSNR 34.45 vs. 32.52 dB, p=0.035). Subjective evaluation by 3 expert ophthalmologists showed that TC-LSTM model had closer representations of the ground truth maps than the LR model (Table 1, Figure 1). Conclusions : The next visit GCIPL thickness maps were successfully generated using TC-LSTM with higher accuracy compared to LR model both quantitatively and subjectively

Purpose : Current GPU memory limitations do not support the analysis of OCT scans at its original resolution, and previous techniques have downsampled the inputs considerably which resulted in a loss of detail. Here, we utilise a new memory management support framework that allows for the training of large deep learning networks and apply it to the detection of glaucoma in OCT scans at its original resolution. Methods : A total of 1110 SDOCT volumes (Cirrus, Zeiss, CA) were acquired from both eyes of 624 subjects (139 healthy and 485 glaucomatous patients (POAG)). A convolutional neural network (CNN) consisting of 8 3D-convolutional layers with a total of 600K parameters and was trained using a cross-entropy loss to differentiate between the healthy and glaucomatous scans. To avoid GPU memory constraints, the network was trained using a large model support library that automatically adds swap-in and swap-out nodes for transferring tensors from GPUs to the host and vice versa. This allowed for the OCT scans to be analysed at the original resolution of 200x200x1024. The performance of the network was gauged by computing the area under the receiver operating characteristic (AUC) curve. The performance of this network was also compared to a previously proposed network that ingested downsampled OCT scans (50x50x128), consisted of 5 3D-convolutional layers and had a total of 222K parameters; and a machine-learning technique (random forests) that relied on segmented features (peripapillary nerve fibre thicknesses). Class activation maps (CAM) were also generated for each of these networks to provide a qualitative view of the regions that the network deemed as important and relevant to the task. Results : The AUCs computed on the test set for the networks that analysed the volumes at the original and downsampled resolutions was found to be 0.92 and 0.91, respectively. The CAMs obtained using the high resolution images show more detail in comparison to the downsampled volume. The random forest technique showed an AUC of 0.85. Conclusions : The performance of the two networks was comparable for glaucoma detection but showed a vast improvement over the random forest that relied on segmented features. The ability to retain detail (as shown in the CAM) will likely allow for improvements in other tasks, such as spatial correspondences between visual field test locations and retinal structure

Purpose : Recent studies have indicated reduced blood flow in not only the eye but also the brain in patients with late glaucoma (LG). In contrast, patients with early glaucoma (EG) appear to show increased ocular blood flow, but little is known about their corresponding brain changes and their specific pathology. This study utilized non-invasive functional and molecular imaging biomarkers to determine cerebral blood flow (CBF) and neurochemical changes in the visual cortex of EG and LG patients. Methods : Four EG (age=67.00+/-5.26 years; 2F), 6 LG (age=65.33+/-2.75 years; 1F), and 5 healthy controls (age=63.00+/-3.11 years; 1F) underwent pseudo-continuous arterial spin labeling (pCASL) functional MRI and MEGA-PRESS magnetic resonance spectroscopy (MRS) at rest using a 3-Tesla MRI scanner. Basal CBF was measured from pCASL in the visual and motor cortices (Figure 1a). For MRS, the level of gamma-aminobutyric acid (GABA) in the visual cortex was quantified through the LCModel software (Figure 2a), and normalized over the N-acetyl aspartate and N-acetyl aspartyl glutamic acid complex (NAA+NAAG) to account for systematic fluctuations following LCModel guidelines. Results : Basal CBF in the white matter (WM) of the visual cortex was significantly higher for EG compared to LG (p=0.021) and controls (p=0.045), whereas basal CBF in the gray matter (GM) of the visual cortex was significantly higher for EG compared to LG (p=0.042) (Figure 1b). No apparent CBF difference was found within the motor cortex across groups (p>0.05). For MRS, normalized GABA levels appeared lower in EG than in controls (p=0.021), while LG had a trending decease compared to controls (p=0.092) (Figure 2b). Within the glaucoma groups, we also found a negative association between basal CBF and normalized GABA levels in both WM (p=0.038) and GM (p=0.039) (Figures 2c-d). Conclusions : The elevated basal CBF and lower baseline GABA levels in the visual cortex of EG suggest that vascular autoregulation dysfunction and/or neurochemical adaptation may be occurring in the brain's visual system apart from the eye during the initial phases of glaucoma pathogenesis. Within glaucoma groups, the inverse correlations demonstrated between basal CBF and baseline GABA levels may also offer a quantitative framework for interrogating inhibitory GABAergic activity and hemodynamic reactivity relationships in the glaucomatous brain during disease progression

Purpose : To develop a method for monitoring the functional deterioration of glaucoma patents using structural surrogates, we used machine learning algorithms to estimate visual field index (VFI) from OCT scans, and evaluated the accuracy of the progression rates calculated from the estimated VFI. Methods : Macular and ONH SDOCT scans (Cirrus HD-OCT, Zeiss, Dublin, CA; 200x200x1024 samplings over 6x6x2mm, downsampled to 64x64x128 voxels) were acquired from both eyes of 1,678 healthy participants, glaucoma suspects, and glaucoma patients over multiple visits (range: 1-14, median=3), forming a dataset of 10,172 pairs of macular+ONH scans. Automated perimetry (Humphrey visual field, SITA 24-2) tests were administered at each visit. Two models were trained to estimate the measured VFI from a pair of macular and ONH scans: the first ("classic model") was a non-linear regression model (multi-layer perceptron) based on 47 thickness measures of retinal layers, while the other ("CNN") was a 5-layer convolutional neural network, trained to learn 3D features in the OCT scans. For both models, MSE was minimized in 5-fold cross-validation, using 80%:10%:10% of the dataset as training, validation and test sets. Data from the same participant were not split across the three sets. For data in the test sets, VFI's for eyes with more than N=3,4,5 visits were estimated for individual visits, and the slopes were calculated using linear regression across N consecutive visits. Median absolute error (MAE) was used to quantify estimation accuracy. Results : For estimating VFI at single visits, the CNN achieved significant lower MAE (2.6+/-0.28; mean and s.d.) than the classic model (2.9+/-0.45). For estimating slopes across 5 visits, the MAE of the CNN (0.73+/-0.12/year) was also lower than the classic model (0.82+/-0.23/year). The errors depended on the measured VFI of the first visit, and on the true slope (Fig. 1). Increasing the number of visits decreased the errors (N=3.6, MAE=1.38/yr, 0.99/yr, 0.73/yr, and 0.63yr) Conclusions : The feature-agnostic CNN was better at estimating VFI and visual field progression rates than the regression method based on thickness measures. Structure-tofunction estimation using neural networks is a promising method for monitoring the visual functions of glaucoma patients

Purpose : To compare the discrimination accuracy for glaucoma diagnosis using the OCT RNFL clock hours compared with average RNFL. Methods : In a large, ongoing, longitudinal cohort of healthy subjects and subjects with glaucoma, all subjects underwent visual field (VF) and OCT testing. Principal component (PC) analysis was used to reduce the dimensionality of clock hour measurements while maintaining maximum information variability for diagnostic performance. The first four PCs with linear regression were used as predictors of VF mean deviation (MD) and to classify glaucoma diagnosis. The prediction accuracy and discrimination power using cross validation were compared to the models using only average RNFL as a predictor. All models were adjusted for age, signal strength, and intra-subject correlation. Results : 1317 healthy and glaucomatous eyes (717 subjects) were included in the study. A PC analysis was built on the 9 clock hours while excluding non-informative sectors (clock hours 3, 4, and 9). The first PC explained 51% of the total variance, and the first four PCs explained 82% of the total variance and thus were used for subsequent regression models. A PC regression for glaucoma discrimination showed that clock hours 1, 5, 6, 7, 10, 11, 12 were significantly association with diagnosis. The PC showed better glaucoma diagnosis performance compared to average RNFL, with 10-fold cross-validation AUCs of 0.898 and 0.877, respectively (p<0.001). The PC regression for MD improved the model fit measured by R² by 9% compared to a regression using average RNFL. PC showed that clock hours 2, 5, 6, 7, 10, 11, 12 were significantly associated with MD. Conclusions : Using PCs with RNFL clock hours improved classification performance for glaucoma diagnosis and model fit for MD, compared to using average RNFL. This method improves discrimination performance by both considering all sectoral RNFL information and removing locations with low diagnostic yield

Purpose : Gradient class activation maps (grad-CAM) generated by convolutional neural networks (CNN) have qualitatively indicated that these networks are able to identify important regions in OCT scans. Here, we quantitatively analyse these regions to improve our understanding of the CNN decision making process when detecting glaucoma in OCT volumes. Methods : A total of 1110 OCT (Cirrus HD-OCT, Zeiss, Dublin, Ca) scans from both eyes of 624 subjects (139 healthy and 485 glaucomatous patients (POAG)). An end-to-end 3D-CNN network was trained directly on 3D-volumes for glaucoma detection. Grad-CAM was implemented to highlight structures in the volumes that the network relied on. Grad-CAM heatmaps were generated for 3 different convolutional layers and quantitatively validated by occluding the regions with the highest grad-CAM weights (12.5% of original input volumes) and then evaluating the performance drop. Further, 8-retinal layers segmentation method was used to compute the average heatmap weights for each segmented layer separately, and used to identify the layers that were deemed as important for the task. Results : The model achieved an AUC of 0.97 for the test set (110 scans). Occlusion resulted in a 40% drop in performance (Fig.1). The RNFL and photoreceptors showed the highest median weights for grad-CAM heatmaps (0.1 and 0.2, respectively). The retinal pigment epithelium (RPE) and photoreceptors showed higher weights in the glaucomatous scans (Fig.2-a). RNFL had wider range of weights in healthy cases versus POAG ones. Analysis of the B-scans showed that central part around the optic disc (# 85-135) had the highest contribution to the network decision and the heatmap weights were much higher in glaucoma cases than healthy ones across all B-scans (Fig.2-b). Conclusions : The occlusion experiment indicates that the regions identified by the grad-CAMs are in fact pertinent to the glaucoma detection task. The increased emphasis on the photoreceptors in the glaucoma cases may be attributed to the atrophy in the superficial layers which in turn increased the brightness of this structure. This technique can be used to identify new biomarkers learned for other ocular diseases