Faculty Bibliography

Purpose : Development and pre-clinical testing of glaucoma neurotherapeutics have been obfuscated by limited experimental models that provide chronic elevation of intraocular pressure (IOP) while preserving optical media clarity for structural and functional assessments over time. In this study, we developed an in vivo model system involving the use of non-invasive tonometry, optical coherence tomography (OCT) and optokinetics to characterize retinal integrity and visual behavior in a novel hydrogel-induced chronic IOP elevation model. Methods : Six adult C57BL/6J mice underwent unilateral intracameral injection of an optically clear, chemically cross-linked hydrogel composed of hyaluronic acid functionalized with vinyl sulfone and thiol groups. IOP was measured with a rebound tonometer at baseline and 1, 3, 7, 10 and 14 days after hydrogel injection. The optic nerve head (ONH) region was scanned for each eye using OCT at baseline and 2 weeks after injection, and total retinal thickness (TRT) was measured within a 0.26-0.36 mm radius ring centered on the ONH using custom-written software (Fig 1). Visual acuity (VA) was measured for each eye using an optokinetic virtual-reality system at baseline and 2 weeks after injection. Data are presented as mean+/-SEM. Results : Intracameral hydrogel injection resulted in mild-to-moderate IOP elevation throughout the 2-week experimental period (Fig 2a). TRT in the hydrogel-injected eye was 10.06+/-3.61% thinner at 2 weeks post-injection compared to baseline (p<0.01) (Fig 2b). IOP elevation also led to a decline in VA by 58.12+/-7.22% at 2 weeks post-injection compared to baseline (p<0.001) (Fig 2c). Interestingly, among the hydrogel-injected eyes, cumulative IOP measured from 0 to 14 days post-injection did not correlate with TRT or VA (p>0.05) (Fig 2d-e), whereas TRT was positively associated with VA at 2 weeks post-injection (r=0.824, p<0.05) (Fig 2f). No significant change in IOP, TRT or VA was found in the non-injected eye. Conclusions : An in vivo glaucoma model system was developed that showed a positive correlation between retinal thinning and visual behavioral deficits after chronic IOP elevation. The weak association between cumulative IOP and TRT or VA suggests additional factors apart from IOP level in contributing to glaucomatous damage after chronic IOP elevation

Purpose : To develop a novel glaucoma progression model with deep learning methods incorporating four major glaucoma biomarkers: VFI, MD, cRNFL and GCIPL. Methods : 1023 eyes from 596 glaucoma/glaucoma-suspect patients were included from clinic. Two types of deep learning (DL) models were developed using Keras: an artificial neural network (ANN) and a recurrent neural network (RNN). The ANN contained five fullyconnected (FC) layers, with a leaky rectified linear unit activation function and a dropout layer with a rate of 0.2. The RNN contained two long short-term memory layers, followed by a FC layer and a dropout layer with a rate of 0.2. Both models were trained to predict four major clinical biomarkers for glaucoma: visual field index (VFI), mean deviation (MD), circumpapillary retinal nerve fiber layer (cRNFL) thickness, and ganglion cell inner plexiform layer (GCIPL) thickness. The models were trained using the first three visits to predict the fourth one year later. Train/validation/test splits were 65/15/20. A linear regression (LR) model was trained and evaluated on the same data for baseline comparison. Evaluation of the actual and predicted values were measured by mean absolute error (MAE). Statistical testing of each biomarker was performed between the DL models and LR model by paired Wilcoxon rank sum test. Results : The mean patient age was 62.4 +/- 12.9 years. The baseline mean cRNFL: 76.9 +/- 13.4 mum, GCIPL: 70.3 +/- 9.9 mum, VFI: 90.3 +/- 17.8%, and MD: -3.76 +/- 6.13 dB. Table shows the MAE between the actual and predicted values of each of the four biomarkers across all three models. The ANN and RNN models showed statistically significantly smaller MAE compared to the LR model. In particular, the ANN model had the lowest MAE and was able to predict all four biomarkers significantly better than the LR model. Conclusions : By harnessing the power of deep learning, we were able to accurately predict future values of both structural and functional measures of glaucomatous change one year later. This is possible as neural networks are able to recognize the intricate interplay between structural and functional changes in glaucoma that otherwise cannot be well captured in a conventional linear regression model

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

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: 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.
Copyright

Purpose : Oxygen concentration in retinal blood vessels (sO ) can be critical biomarkers for diabetic retinopathy and glaucoma, leading causes of blindness worldwide. We previously demonstrated sO2measurements in rodent and human retinas with spectroscopic visible-light optical coherence tomography (vis-OCT). However, reliable measurements of sO2in a clinical setting remains an open challenge due to constraints on light exposure, imaging time, patient motion, and variation in eye geometry. Spectral calibration to optimize sO2measurements under these non-ideal imaging conditions is needed. Here, we investigate, develop, and implement such calibration. Methods : We developed vis-OCT processing software to optimize sO2measurements in humans. First, we identified an optimal spectral range for spectral measurement in which sO2was most stable. Next, we developed methods to account for alterations induced by the imaging system and eye optics. Specifically, we accounted for depth-dependent variations in the measured spectrum, such as absorption contrast, spectrally-dependent roll-off, chromatic aberrations, and eye morphology. We then imaged the retinas of 12 healthy subjects aged 22 to 60 at Northwestern Medical Hospital in Chicago, IL, and Langone Medical Center in New York, NY. All imaging was approved by the respective IRBs and strictly adhered to the Declaration of Helsinki. Light exposure in the eye was no higher than 250 muW and imaging time was no longer than 5 s. We extracted sO2from vessels larger than 50 mum in diameter using an automated version of our vis-OCT processing software. Results : We measured the sO2in 89 vessels (53 arteries and 36 veins). We found the mean sO2in arteries was 97.70 +/-4.75 % in arteries and mean sO2in veins was 53.11 +/-6.85 %. Conclusions : We developed analytical methods for depth-dependent alterations to the measured spectrum in vis-OCT retinal oximetry. Our measurements yielded spectra that are highly consistent with those reported in literature, despite variations in imaging conditions. Our results indicate a clear path forward for clinical adoption of vis-OCT

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 : The presence of s zone peripapillary atrophy (PPA) has been associated with glaucoma. We performed a retrospective longitudinal study to evaluate s zone PPA area as a predictor for glaucomatous structural and functional progression. Methods : Subjects with glaucoma and >4 visits were included. Subjects had Humphrey visual field (Zeiss, Dublin, CA) testing, spectral-domain OCT (Cirrus HD-OCT; Zeiss) optic nerve head (ONH) and macula scans. s zone PPA was manually delineated on the baseline en face ONH scan as the area contiguous with the optic disc with the presence of hyper-and hyporeflectivity. Mixed effects linear models accounting for intra-subject correlation, follow-up time, scan's signal strength and ethnicity, were performed to determine if baseline PPA area was associated with glaucoma severity. Subsequent models incorporating the interaction term between time and baseline PPA area were performed to determine if baseline PPA area affected the rate of change in parameters of glaucoma over time. Results : 81 eyes (56 subjects) aged 62.8+/-14.1 years with an average follow-up time 3.9+/-1.3 years were analyzed. PPA was significantly associated with mean deviation (MD), visual field index (VFI), and inferior retinal nerve fiber layer (RNFL), (p=0.033, 0.038, and 0.034, respectively), but not with average RNFL, or macular ganglion cell inner plexiform layer (GCIPL) global and sectoral measurements and ONH parameters. No significant association was detected between s zone PPA area and the rate of progression for any parameter except for VFI (p =0.035). Conclusions : Although baseline s zone PPA area is associated with some indicators of glaucoma severity, it is not a significant predictor of the rate of glaucomatous progression (except for VFI)

Purpose : Glaucoma adversely affects subjects' ability to accomplish daily activities, engage in social roles, and contributes to disability. Yet methods to evaluate glaucoma-related disability are limited. To identify daily activities and social roles associated with glaucoma, this study (1) tests the association between visual field (VF) and the Assessment of Life Habits (LIFE-H), a questionnaire developed to measure the degree of difficulty and the level(s) of assistance subjects require in order to accomplish daily activities and social roles, and (2) identifies LIFE-H items with high differential capability of person functional ability. Methods : We recruited 101 subjects aged 50 years and older diagnosed with glaucoma who underwent comprehensive ophthalmic evaluation and VF testing (Humphrey Field Analyzer, Zeiss, Dublin, CA) whom were administered the LIFE-H. Better-eye VF mean deviation (MD) was used to measure severity of visual impairment. Multivariable regression analyses determined the association between MD and 11 LIFE-H domains (totaling 37 daily activity and 38 social role items), adjusting for the covariates age, gender, race, comorbidities, and depressive symptoms. Domains not significantly associated with MD and items not applicable to 10% of subjects were excluded from further analyses, resulting in 64 qualified subjects and 40 LIFE-H items. Rasch analysis was used to determine the item hierarchical order based on the level of person ability. Results : 64 subjects of average age 66+/-10 years and better-eye MD of -5.0+/-7.4 dB qualified for the analysis. All LIFE-H domains except interpersonal relationships were significantly associated (p <= 0.05) with MD. Overall, average domain scores were high (range, 8.7+/-1.5 to 9.7+/-0.4) with the lowest scoring domains being mobility, employment, and recreation. Of the 40 LIFE-H items, 29 were daily activities and 11 were social roles. 21 items across 6 domains were detected to have high differential capability; of which 11 items were daily activities and 10 items were social roles. 11 of the 21 items were significantly associated with MD; 8 of which were social roles and 3 daily activities. Conclusions : The large impact of the social role items among the LIFE-H questionnaire highlight the psychosocial factors for subjects with glaucoma. Further evaluation of daily activities and social roles that constitute when and how glaucoma affects subjects is needed