Retinal vein occlusion (RVO) is one of the leading causes of sudden vision loss in adults, affecting an estimated 16 million people worldwide. It has historically been treated as a disease focalizing the eye, yet vision depends on the entire pathway from retina to cortex. My research investigates whether RVO produces measurable affects in the lateral geniculate nucleus (LGN) — the brain's first relay for visual information — and whether those affects helps explain why some patients lose vision permanently while others recover.
Supervisor/Team Members
My supervisor is Dr. Carol Troy, Professor of Pathology & Cell Biology and Neurology at Columbia University Irving Medical Center, where her lab studies the molecular mechanisms of neuronal injury in retinal vascular disease.
Project Background
Light captured by the retina is converted into electrical signals that travel along the optic nerve to the LGN, a thalamic structure that acts as the gateway between the eye and the visual cortex. Anterograde transsynaptic degeneration, the spread of injury from the retina to central visual structures, has been well documented in glaucoma and optic nerve injury, where retinal ganglion cell loss is followed by neuronal loss in the LGN. In RVO specifically, fMRI studies in human patients have revealed altered functional connectivity and network centrality in the primary visual cortex, suggesting that the consequences of retinal vascular injury extend beyond the eye. However, the cellular and molecular state of the LGN following RVO has not been directly characterized. The Troy Lab has developed a murine RVO model that recapitulates key features of the human condition, including filamentous edema within hours of injury that parallels findings in human cerebral stroke, and has generated matched retinal pathology and optomotor reflex behavioral data across cohorts of these animals. This project extends the lab's work beyond the retina by asking whether the LGN — the first brain structure to receive retinal input — also bears the imprint of RVO injury.
Research Questions
Through this research, I aim to investigate (1) whether RVO produces measurable neuronal loss, programmed cell death, or loss of retinal axon terminals in the LGN; (2) whether LGN damage correlates with the severity of retinal injury and vision loss in the same animals; and (3) whether LGN integrity statistically mediates the relationship between retinal injury and behavioral vision loss, which would implicate the brain in RVO-related visual deficits.
Objectives
I will integrate immunohistochemistry and computational analysis of retinal and behavioral data to build a three-level framework — brain, eye, and behavior — that characterizes the downstream effects of RVO on the central visual pathway. By leveraging hemispheric comparison (contralateral versus ipsilateral to the injured eye) as a within-animal control, I aim to produce a statistically rigorous account of whether and how retinal vascular injury becomes embedded in central visual structures.
Methodology
(1) Immunohistochemistry:
Using brain tissue collected from the Troy Lab's RVO cohorts, I will section and stain LGN tissue for three markers: NeuN (mature neuron survival), cleaved caspase-3 (active programmed cell death), and VGLUT2 (integrity of retinogeniculate axon terminals). I may decide to alter these chosen antibodies later in my research. Tissue will be imaged on a confocal microscope and quantified in FIJI/ImageJ.
(2) Integrated Statistical Analysis:
Using Python and R, I will integrate the LGN imaging and Western data with existing retinal pathology and optomotor reflex data for the same animals. The central analysis is a mediation model testing whether LGN damage explains the statistical link between retinal injury and vision loss.
Impact
If RVO produces measurable injury in the LGN, it would reframe RVO as a disease of the visual pathway rather than the eye alone — with implications for how clinicians evaluate visual prognosis and whether retinal-only therapies can fully restore vision. More broadly, this project contributes to a growing understanding of how local vascular injury in one tissue propagates into the central nervous system, and how computational analysis can extract clinical patterns from multi-level biological data.