Faculty Bibliography

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 : 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 : 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 : Vivid Vision Perimetry (VVP) is a novel method for performing in-office and home-based visual field assessment using a virtual reality platform and oculokinetic perimetry. The purpose of this study was to examine the test-retest reproducibility of the VVP platform. Methods : Subjects with open-angle glaucoma and glaucoma suspects were prospectively enrolled and underwent visual field analysis across 54 test locations in a 24-2 pattern using the VVP device (Vivid Vision, San Francisco, CA). Each subject was examined in 2 sessions, and the mean sensitivity (dB) was the primary outcome measure obtained for each eye in both sessions. The repeatability of mean sensitivity was assessed through analysis of bias from the differences between the two VVP sessions. A Bland-Altman plot using a mixed effects model (adjusting for average sensitivity and eye correlation) was created to illustrate the level of agreement between repeated measurements. Results : Fourteen eyes of 7 open-angle glaucoma patients and 10 eyes of 5 glaucoma suspects were enrolled (mean age 62.3 +/-9.3 years, 33% female). Based on the data from 24 eyes, the average difference of VVP mean sensitivity between the two sessions was found to be 0.48 dB. Three eyes (12.5%) fell outside the upper and lower limits of agreement (95% CI:-1.15, 2.11). The level of agreement between repeated VVP measurements showed a general trend of increasing precision as mean sensitivity values increased (Figure 1). Conclusions : The VVP platform provides reproducible visual field sensitivity measurements for glaucoma patients and glaucoma suspects and represents a novel approach for glaucoma monitoring. These data suggest that VVP measurement repeatability is consistent with standard automated perimetry

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 : 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 : 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 : Open-angle glaucoma (OAG) is a leading cause of blindness globally, yet the relative contributions of genetic background and the environment to the development of this disease are unclear. As a first step in determining these contributions, we document posterior pole pathology and investigate the association between optic nerve head glaucomatous features, intraocular pressure (IOP), and demographic information, in an exceptionally large cohort of free-ranging rhesus macaques. Methods : We administered ophthalmologicl exams under sedation to 55 female and 54 male animals aged 0-21 years (mean age = 6.08; SD = 4.27). IOP was measured using TonoPen and TonoVet Plus tonometers and measurements adjusted using published calibration equations (McAllister et al Optom Vis Sci. 2018). Cup/Disk ratio (CDR) was calculated from clinical examination aided by optical coherence tomography (OCT; Bioptigen Envisu). Posterior pole pathology was documented using fundus imaging. Association between CDR and age, sex, and IOP was assessed using generalized linear mixed models (GLMM) and likelihood ratio tests (LRTs). Results : The mean (+/-standard deviation) IOP in the 218 eyes measured was 19.32 (+/-6.24) mmHg. Mean CDR was 0.37 (+/-0.15). We detected elevated IOP (> 22mmHg) in 78 eyes (36.62%) and CDR > 0.7 was detected in 13 eyes (5.96 %). CDR values were highly concordant in eyes of the same animal (CDR of left and right eyes within 0.2 for all animals. IOP was a significant predictor of CDR (p<0.001) in models that either included or excluded age, and animal sex. The best fitting model included only IOP as a predictor variable (AIC =-196.3). This model was significantly better than models containing age (AIC =-185.8, p = 0.004) or both age and sex (AIC =-183.4). Additional posterior pole pathology included pigmentary macular changes (8 eyes), macular scars (2 eyes), vessel tortuosity (19 eyes), and retinal hemorrhages (5 eyes). Conclusions : IOP is a significant predictor of CDR in this cohort. Age did not appear to correlate with CDR, but controlling for relatedness may further elucidate impacts of individual biology on OAG. Similarities between rhesus and human glaucomatous phenotypes and the presence of additional retinal pathology in our population may make these animals valuable in the study of other complex human disease, such as age-related macular degeneration