Faculty Bibliography

Glaucoma is an age-related neurodegenerative disease of the visual system, affecting both the eye and the brain. Yet its underlying metabolic mechanisms and neurobehavioral relevance remain largely unclear. Here, using proton magnetic resonance spectroscopy and functional magnetic resonance imaging, we investigated the GABAergic and glutamatergic systems in the visual cortex of glaucoma patients, as well as neural specificity, which is shaped by GABA and glutamate signals and underlies efficient sensory and cognitive functions. Our study shows that among the older adults, both GABA and glutamate levels decrease with increasing glaucoma severity regardless of age. Further, our study shows that the reduction of GABA but not glutamate predicts the neural specificity. This association is independent of the impairments on the retina structure, age, and the gray matter volume of the visual cortex. Our results suggest that glaucoma-specific decline of GABA undermines neural specificity in the visual cortex and that targeting GABA could improve the neural specificity in glaucoma.

PURPOSE/UNASSIGNED:The structural changes measured by optical coherence tomography (OCT) are related to functional changes in visual fields (VFs). This study aims to accurately assess the structure-function relationship and overcome the challenges brought by the minimal measurable level (floor effect) of segmentation-dependent OCT measurements commonly used in prior studies. METHODS/UNASSIGNED:We developed a deep learning model to estimate the functional performance directly from three-dimensional (3D) OCT volumes and compared it to the model trained with segmentation-dependent two-dimensional (2D) OCT thickness maps. Moreover, we proposed a gradient loss to utilize the spatial information of VFs. RESULTS/UNASSIGNED:Our 3D model was significantly better than the 2D model both globally and pointwise regarding both mean absolute error (MAE = 3.11 + 3.54 vs. 3.47 ± 3.75 dB, P < 0.001) and Pearson's correlation coefficient (0.80 vs. 0.75, P < 0.001). On a subset of test data with floor effects, the 3D model showed less influence from floor effects than the 2D model (MAE = 5.24 ± 3.99 vs. 6.34 ± 4.58 dB, P < 0.001, and correlation 0.83 vs. 0.74, P < 0.001). The gradient loss improved the estimation error for low-sensitivity values. Furthermore, our 3D model outperformed all prior studies. CONCLUSIONS/UNASSIGNED:By providing a better quantitative model to encapsulate the structure-function relationship more accurately, our method may help deriving VF test surrogates. TRANSLATIONAL RELEVANCE/UNASSIGNED:DL-based VF surrogates not only benefit patients by reducing the testing time of VFs but also allow clinicians to make clinical judgments without the inherent limitations of VFs.

Artificial intelligence and machine learning applications are becoming increasingly popular in health care and medical devices. The development of accurate machine learning algorithms requires large quantities of good and diverse data. This poses a challenge in health care because of the sensitive nature of sharing patient data. Decentralized algorithms through federated learning avoid data aggregation. In this paper we give an overview of federated learning, current examples in healthcare and ophthalmology, challenges, and next steps.

Central insulin resistance, the diminished cellular sensitivity to insulin in the brain, has been implicated in diabetes mellitus, Alzheimer's disease and other neurological disorders. However, whether and how central insulin resistance plays a role in the eye remains unclear. Here, we performed intracerebroventricular injection of S961, a potent and specific blocker of insulin receptor in adult Wistar rats to test if central insulin resistance leads to pathological changes in ocular structures. 80 mg of S961 was stereotaxically injected into the lateral ventricle of the experimental group twice at 7 days apart, whereas buffer solution was injected to the sham control group. Blood samples, intraocular pressure, trabecular meshwork morphology, ciliary body markers, retinal and optic nerve integrity, and whole genome expression patterns were then evaluated. While neither blood glucose nor serum insulin level was significantly altered in the experimental or control group, we found that injection of S961 but not buffer solution significantly increased intraocular pressure at 14 and 24 days after first injection, along with reduced porosity and aquaporin 4 expression in the trabecular meshwork, and increased tumor necrosis factor α and aquaporin 4 expression in the ciliary body. In the retina, cell density and insulin receptor expression decreased in the retinal ganglion cell layer upon S961 injection. Fundus photography revealed peripapillary atrophy with vascular dysregulation in the experimental group. These retinal changes were accompanied by upregulation of pro-inflammatory and pro-apoptotic genes, downregulation of anti-inflammatory, anti-apoptotic, and neurotrophic genes, as well as dysregulation of genes involved in insulin signaling. Optic nerve histology indicated microglial activation and changes in the expression of glial fibrillary acidic protein, tumor necrosis factor α, and aquaporin 4. Molecular pathway architecture of the retina revealed the three most significant pathways involved being inflammation/cell stress, insulin signaling, and extracellular matrix regulation relevant to neurodegeneration. There was also a multimodal crosstalk between insulin signaling derangement and inflammation-related genes. Taken together, our results indicate that blocking insulin receptor signaling in the central nervous system can lead to trabecular meshwork and ciliary body dysfunction, intraocular pressure elevation, as well as inflammation, glial activation, and apoptosis in the retina and optic nerve. Given that central insulin resistance may lead to neurodegenerative phenotype in the visual system, targeting insulin signaling may hold promise for vision disorders involving the retina and optic nerve.

PURPOSE/UNASSIGNED:Lamina cribrosa (LC) deformation is hypothesized to play a major role in glaucoma pathogenesis. The purpose of this study was to determine in vivo how varying intraocular pressure (IOP) under fixed intracranial pressure (ICP), and vice versa, deforms the pore paths throughout the LC volume. METHODS/UNASSIGNED:Spectral-domain optical coherence tomography scans of the optic nerve head were acquired from healthy adult rhesus monkeys under different pressures. IOP and ICP were controlled with gravity-based perfusion systems into the anterior chamber and lateral ventricle, respectively. IOP and ICP were modulated from baseline to high (19-30 mmHg) and highest (35-50 mmHg) levels while maintaining a fixed ICP of 8 to 12 mmHg and IOP of 15 mmHg, respectively. After three-dimensional registration and segmentation, the paths of pores visible in all settings were tracked based on their geometric centroids. Pore path tortuosity was defined as the measured distance divided by the minimal distance between the most anterior and posterior centroids. RESULTS/UNASSIGNED:The median pore tortuosity at baseline varied among the eyes (range, 1.16-1.68). For the IOP effect under fixed ICP (six eyes, five animals), two eyes showed statistically significant increased tortuosity and one showed a decrease (P < 0.05, mixed-effects model). No significant change was detected in three eyes. When modulating ICP under fixed IOP (five eyes, four animals), a similar response pattern was detected. CONCLUSIONS/UNASSIGNED:Baseline pore tortuosity and the response to acute pressure increase vary substantially across eyes. TRANSLATIONAL RELEVANCE/UNASSIGNED:LC pore path tortuosity could be associated with glaucoma susceptibility.

BACKGROUND:The development of accurate machine learning algorithms requires sufficient quantities of diverse data. This poses a challenge in health care because of the sensitive and siloed nature of biomedical information. Decentralized algorithms through federated learning (FL) avoid data aggregation by instead distributing algorithms to the data before centrally updating one global model. OBJECTIVE:To establish a multicenter collaboration and assess the feasibility of using FL to train machine learning models for intracranial hemorrhage (ICH) detection without sharing data between sites. METHODS:Five neurosurgery departments across the United States collaborated to establish a federated network and train a convolutional neural network to detect ICH on computed tomography scans. The global FL model was benchmarked against a standard, centrally trained model using a held-out data set and was compared against locally trained models using site data. RESULTS:A federated network of practicing neurosurgeon scientists was successfully initiated to train a model for predicting ICH. The FL model achieved an area under the ROC curve of 0.9487 (95% CI 0.9471-0.9503) when predicting all subtypes of ICH compared with a benchmark (non-FL) area under the ROC curve of 0.9753 (95% CI 0.9742-0.9764), although performance varied by subtype. The FL model consistently achieved top three performance when validated on any site's data, suggesting improved generalizability. A qualitative survey described the experience of participants in the federated network. CONCLUSION/CONCLUSIONS:This study demonstrates the feasibility of implementing a federated network for multi-institutional collaboration among clinicians and using FL to conduct machine learning research, thereby opening a new paradigm for neurosurgical collaboration.

Objective: Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. Design: Review and conference proceedings. Subjects: No human or animal subjects or data therefrom were used in the production of this article. Methods: A summary of the Workshop was produced with input and approval from all participants. Main Outcome Measures: Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. Results: The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. Conclusions: The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied.

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.