Description
Leber's Hereditary Optic Neuropathy (LHON) causes visual dysfunction due to impaired signal transduction in the optic nerve fibers. The initial damage occurs in the retinal ganglion cells that serve the central visual field, which is supported by the most delicate and vulnerable fibers. From there, the dysfunction spreads centrifugally, gradually affecting the broader visual field and, in most cases, eventually involving both eyes.
Visual impairment precedes the degeneration of retinal ganglion cells, and previous models have successfully captured the progression of LHON in the optic nerve cross-section, aligning with histological data. However, a key question remains: can findings from patient visual field testing, reflecting different disease trajectories, be used to develop a computational model that confirms the optic chiasm as a transmission point for sequential LHON and a potential starting point for simultaneous cases?
This project aims to incorporate temporal data from visual field tests and spatial insights from the limited anatomical understanding of the anterior optic pathway. By refining this model, researchers hope to better understand the mechanisms of LHON progression and the role of the chiasm in disease transmission.
Tasks
1. Develop a computational model of LHON progression based on visual dysfunction patterns and limited patient data.
2. Integrate temporal visual field test data and spatial anatomy of the anterior optic pathway into the model.
3. Investigate the role of the optic chiasm in sequential and simultaneous LHON transmission.
Requirements
The final outcome should be a stand-alone application (environment to be discussed depending on the prior experience of the student). The student should have completed successfully at least one course on (Medical) Visualization and/or Computer Graphics. Additional knowledge and/or interest in developing models of biological systems, especially for disease progression, on the basis of diverse data sources (visual field test results, anatomical knowledge). Proficiency in programming languages (e.g., Python) for simulating the disease process and analyzing data. Skills in statistical methods to validate the model using patient data.
Environment
The project should be implemented as a standalone application, desktop or web-based (to be discussed).