Faculty Bibliography

Purpose/UNASSIGNED:The purpose of this study was to develop a machine learning model to forecast future circumpapillary retinal nerve fiber layer (cpRNFL) thickness in eyes of healthy, glaucoma suspect, and glaucoma participants from multimodal temporal data. Design/UNASSIGNED:Retrospective analysis of a longitudinal clinical cohort. Participants/UNASSIGNED:Longitudinal clinical cohort of healthy, glaucoma suspect, and glaucoma participants. Methods/UNASSIGNED:(LTBE). Main Outcome Measures/UNASSIGNED:The mean absolute difference and Pearson's correlation coefficient between the true and forecasted values of the cpRNFL in the healthy, glaucoma suspect, and glaucoma patients. Results/UNASSIGNED:< 0.01) for the 3 groups, respectively. Conclusions/UNASSIGNED:The performance of the proposed forecasting model for cpRNFL is consistent across glaucoma suspect and glaucoma patients, which implies the robustness of the developed model against the disease state. These forecasted values may be useful to personalize patient care by determining the most appropriate intervisit schedule for timely interventions.

Purpose : Falls risk increases with glaucoma. The inability to see obstacles such as steps or stairs is one mechanism of falls. Another potential mechanism is reduced postural control. The impact of glaucoma on the ability to centrally integrate sensory information relevant for balance has not been systematically investigated. The goal of this study is to assess the influence of glaucoma severity on sensory integration abilities for balance. Methods : Eleven adults diagnosed with glaucoma were recruited. Glaucoma severity was determined using two measures: (1) a functional measure, specifically visual field mean deviation (MD) assessed by automated Humphrey perimetry and (2) a structural measure, specifically retinal nerve fiber layer (RNFL) thickness as measured by OCT. Standing balance was assessed using an adapted version of the Sensory Organization Test (SOT) that probes the ability to integrate visual, somatosensory and vestibular information for balance control (Nashner, 1997). The six SOT postural conditions were used, each lasting 3 min. Underfoot center of pressure was used to compute sway speed. Statistical analyses consisted of mixed linear models performed within each postural condition, with glaucoma severity as a fixed effect and subject as the random effect. The dependent measure was sway speed. Statistical significance was set at 0.05. Results : A worse visual field deficit, as reflected by MD, in the better eye was associated with increased sway speed in the first four SOT conditions (p<0.05), i.e. conditions involving altered or absent visual OR somatosensory information. This effect was not found in conditions when the postural control system relies solely on the vestibular system to maintain balance (SOT Conditions 5-6, p>0.2). Visual field deficits in the worse eye and structural damage in either eye, as reflected by RNFL thickness, were not associated with sway speed under any of the postural conditions. Conclusions : Balance is impacted by glaucoma under conditions where sensory integration is challenged. Interestingly, visual field severity and sway speed were associated even during the eyes closed condition. This may suggest a central sensory integration mechanism. Further research is warranted. Reference. Nashner, L. M. (1997). Computerized Dynamic Posturography. In G. P. Jacobson, et al. (Eds.), Handbook of balance function testing. San Diego, CA: Singular Publishing Group, Inc

Purpose : The glymphatic system has been postulated to play a crucial role in the central nervous system via metabolic waste removal from brain tissues by the cerebrospinal fluid (CSF). However, it remains unclear whether there is a direct glymphatic pathway in the visual pathway, partly due to limited in vivo methods for assessing the physiology of CSF dynamics in the optic nerve (ON). Contrast-enhanced MRI has been shown to be capable of monitoring the dynamics of glymphatic system in the brain using paramagnetic contrast agents. Investigating the same in and around the ON might give insights into the mechanisms of vision-related diseases such as glaucoma. Methods : In the present study, we infused a small molecular weight gadolinium-DTPA contrast agent intrathecally into the lumbar region (L4-L5) of 3 healthy adult C57BL/6J mice and imaged its flow, accumulation and clearance in the brain and the optic nerve over time using a 7-Tesla MRI scanner under isoflurane anesthesia. Contrast dynamics was monitored using a 3D T1-weighted imaging sequence at an isotropic resolution of 78x78x78 mum . Each scan lasted 10 min and a total of 12 continuous scans were acquired. These scans included 3 baseline acquisitions followed by 30 min of gadolinium contrast infusion using an automated pump while the scanning continued until the 12th time point. The intensity-time curves of the ON parenchyma, ON subarachnoid space (SAS), olfactory bulb, lateral ventricles and muscle tissues were generated and compared quantitatively. Data are represented as mean+/-SEM. Results : The ON parenchyma, ON-SAS, olfactory bulb and lateral ventricles showed a gradual increase in contrast enhancement (Figures 1 and 2A) with peak intensities at 92.03+/-16.21% (p<0.05), 440.50+/-39.41% (p<0.01), 210.54+/-20.69% (p<0.01) and 196.63+/-38.63% (p<0.05) respectively relative to baseline (Figure 2B). Peak intensity 3 occurred first in the olfactory bulb followed by ON-SAS, ON parenchyma and finally the lateral ventricles (Figure 2B). No apparent contrast uptake was observed in the nearby muscle tissues. Conclusions : This study illustrates direct communications between CSF and ON parenchyma and supports the evidence of the glymphatic system in the ON. In vivo imaging of CSF dynamics in and around the ON may open up new avenues for understanding ON function in health and disease with the possibility of devising novel drug delivery routes and therapeutic targets

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 : 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 : 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 : 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

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