Faculty Bibliography

Glaucoma is a chronic neurodegenerative disease of the visual system, and treatment is targeted toward lowering intraocular pressure. However, some patients fail to respond to treatment and their intraocular pressure levels remain high, risking continuous vision loss. Explainable machine learning provides a mechanism for both individual prognostication and the identification of factors associated with treatment outcome. Here, we used explainable machine learning to predict intraocular pressure for glaucoma patients receiving medication treatment. We accessed the UK Biobank to obtain information on 290 eyes from 161 participants who reported a diagnosis of glaucoma and were receiving treatment. Features were divided into three distinct datasets containing demographic data only, physiometabolic parameters and medication prescription data, and all data combined. We evaluated five machine learning techniques for each feature set in terms of their ability to predict intraocular pressure at a follow-up visit in a classification task. We then calculated SHapley Additive exPlanation (SHAP) values for the best performing model to determine feature importance, stability, and interactions. We found that eXtreme Gradient Boosting (XGBoost) outperformed all other models when trained and tested on the combined feature set with an area under receiver operating characteristic curve (AUC) of 0.708. Insulin-like growth factor 1 (IGF-1), low-density lipoprotein (LDL), and lymphocyte count ranked as the three most important features for this model. LDL and IGF-1 exhibited a low degree of global variability in contribution to the model output across all cross-validation repeats. SHAP values demonstrated the strongest interactions being between LDL and IGF-1. In summary, our studies indicated the importance of blood LDL and IGF-1 in contributing to the outcomes of intraocular pressure lowering treatment and demonstrated the ability of XGBoost to predict these outcomes.

Glaucoma is a chronic neurodegenerative disease of the visual system, and treatment is targeted toward lowering intraocular pressure. However, some patients fail to respond to treatment and their intraocular pressure levels remain high, risking continuous vision loss. Explainable machine learning provides a mechanism for both individual prognostication and the identification of factors associated with treatment outcome. Here, we used explainable machine learning to predict intraocular pressure for glaucoma patients receiving medication treatment. We accessed the UK Biobank to obtain information on 290 eyes from 161 participants who reported a diagnosis of glaucoma and were receiving treatment. Features were divided into three distinct datasets containing demographic data only, physiometabolic parameters and medication prescription data, and all data combined. We evaluated five machine learning techniques for each feature set in terms of their ability to predict intraocular pressure at a follow-up visit in a classification task. We then calculated SHapley Additive exPlanation (SHAP) values for the best performing model to determine feature importance, stability, and interactions. We found that eXtreme Gradient Boosting (XGBoost) outperformed all other models when trained and tested on the combined feature set with an area under receiver operating characteristic curve (AUC) of 0.708. Insulin-like growth factor 1 (IGF-1), low-density lipoprotein (LDL), and lymphocyte count ranked as the three most important features for this model. LDL and IGF-1 exhibited a low degree of global variability in contribution to the model output across all cross-validation repeats. SHAP values demonstrated the strongest interactions being between LDL and IGF-1. In summary, our studies indicated the importance of blood LDL and IGF-1 in contributing to the outcomes of intraocular pressure lowering treatment and demonstrated the ability of XGBoost to predict these outcomes.

Cerebrospinal fluid (CSF), partly driven by sensory stimulation, is crucial for maintaining homeostasis and clearing metabolic waste. Whether such stimulus-driven CSF flow is disrupted in age-related neurodegenerative diseases of the visual system remains unclear. This study examined the CSF flow during visual stimulation in glaucoma patients and healthy older adults using functional magnetic resonance imaging. In glaucoma, CSF inflow becomes progressively decoupled from the visually evoked blood-oxygenation-level-dependent (BOLD) response. Specifically, the characteristic stimulus-locked CSF patterns, which decrease after stimulus onset and increase after offset, diminish with disease severity. Mediation analysis suggests this flattened CSF pattern is driven by a flatter ascending BOLD slope, leading to a shallower CSF trough and a reduced post-stimulus surge. These results indicate that glaucoma-related functional impairments contribute to downstream alterations in CSF dynamics. Overall, this study provides insight into how glaucoma disrupts visually driven CSF inflow and highlights in vivo biomarkers for monitoring CSF dynamics.

Glaucoma patients often have higher injurious fall rates compared to healthy older adults. However, little is known about the underlying neural mechanisms. Recent evidence shows cerebral changes beyond the visual pathway of glaucoma patients, yet it remains unclear whether the cerebellum, which plays an important role in balance and motor control, is involved in glaucoma. In this study, we sought to investigate cerebellar functional connectivity changes in glaucoma by comparing 32 glaucoma subjects and 10 age-matched healthy control subjects who underwent resting-state functional magnetic resonance imaging at 3 Tesla with eyes closed. After conducting both regions-of-interest and seed-to-voxel analyses, we found that the functional connectivity within the cerebellum tended to be weakened in glaucoma patients compared to healthy controls, whereas the functional connectivity between some cerebral and cerebellar regions showed opposite changes in the same glaucoma subjects. Our findings underscore the potential role of cerebellar and cerebro-cerebellar dysfunction in postural and cognitive control in glaucoma patients. Taken together, these observations implicate the widespread brain changes in glaucoma beyond the cerebral regions into the cerebellum that may underlie the neural underpinnings of impaired balance control in this disease.

Currently there are no surgical solutions to restore vision in the irreversibly blind. Whole eye transplantation (WET), is an appealing surgical approach for restoration, replacement, and reconstruction of nonfunctioning eyes. Development of a reliable animal model to test the integrity and functionality of the transplanted eye is an essential step towards clinical whole eye transplantation. This study presents a feasible vascularized orthotopic eye transplantation preclinical rat model to study the structural and functional outcomes of whole eye transplantation. Syngeneic orthotopic transplants were performed in rats, involving anastomoses between carotid arteries, external jugular veins, and optic nerve coaptations of donors and recipients. The transplanted and recipient native eyes were assessed by ocular exam under anesthesia, optical coherence tomography (OCT), histology, magnetic resonance imaging and electroretinography. A 100% surgical survival rate of recipients with maintained long-term health demonstrated this to be a reliable and reproducible model. Assessment from clinical examination under anesthesia revealed that segments of native eyes appeared normal throughout the duration of the study, but transplanted eyes presented mild chemosis of the eye lids, mild ciliary flush of the conjunctiva, cornea neovascularization, mild engorgement of the vessels in the iris, and mild opacities in the lens in some animals. Most of these findings improved over time after transplantation. Doppler optical coherence tomography corroborated the presence of blood flow in transplanted retinas. There was no significant difference in measured IOP between native and transplanted eyes. Both histology and OCT scans demonstrated increased central corneal thickness and decreased total retinal thickness in transplanted eyes. Transplanted eyes exhibit minimal scotopic and photopic ERG responses. To date, no other vascularized orthotopic rodent WET transplantation models have been described in the literature. As functional visual return remains the ultimate goal, this model provides a foundation for future translational strategies and is ideal for testing immunomodulatory, neuroprotective, and neuroregenerative approaches either individually or in combination, as required for total human eye allotransplantation (THEA) to become a clinical reality.

PURPOSE/UNASSIGNED:To determine the efficacy and safety of repetitive transorbital alternating current stimulation (rtACS) treatment by assessing vision-related quality of life and visual function outcome in subjects treated with rtACS versus sham-control. STUDY DESIGN/UNASSIGNED:Double masked, randomized, sham-controlled clinical trial (NCT03188042). SUBJECTS/UNASSIGNED:Sixteen subjects with moderate-to-advanced glaucoma (visual field [VF] mean deviation [MD] ≤-6.00 decibels) randomized into sham (9 subjects) or rtACS intervention (7 subjects) groups. METHODS/UNASSIGNED:Subjects underwent 10 rtACS sessions over 2 weeks. All subjects had comprehensive ocular examination at baseline, 1-week, and 4-weeks posttreatment. MAIN OUTCOME MEASURES/UNASSIGNED:Visual acuity (VA), contrast sensitivity (CS), VF MD, number of threshold sensitivity points that changed or were unchanged, and vision-related quality of life (VR-QoL) questionnaire scores. RESULTS/UNASSIGNED: = 0.04). No significant changes were detected with VA, CS, and VF analyses for either group. No serious adverse events were noted in either study group. CONCLUSIONS/UNASSIGNED:Repetitive transorbital alternating current stimulation therapy showed a significant beneficial effect on several domains of VR-QoL. Further studies will determine its utility in glaucoma. FINANCIAL DISCLOSURES/UNASSIGNED:Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.