Physics-informed deep generative learning for quantitative assessment of the retina.

Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks with no human input and which out-performs human labelling. Segmentation of DRIVE and STARE retina photograph datasets provided near state-of-the-art vessel segmentation, with training on only a small (n = 100) simulated dataset. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.

Analysis of DNM3 and VAMP4 as genetic modifiers of LRRK2 Parkinson's disease.

The LRRK2 gene has rare (p.G2019S) and common risk variants for Parkinson's disease (PD). DNM3 has previously been reported as a genetic modifier of the age at onset in PD patients carrying the LRRK2 p.G2019S mutation. We analyzed this effect in a new cohort of LRRK2 p.G2019S heterozygotes (n = 724) and meta-analyzed our data with previously published data (n = 754). VAMP4 is in close proximity to DNM3, and was associated with PD in a recent study, so it is possible that variants in this gene may be important. We also analyzed the effect of VAMP4 rs11578699 on LRRK2 penetrance. Our analysis of DNM3 in previously unpublished data does not show an effect on age at onset in LRRK2 p.G2019S carriers; however, the inter-study heterogeneity may indicate ethnic or population-specific effects of DNM3. There was no evidence for linkage disequilibrium between DNM3 and VAMP4. Analysis of sporadic patients stratified by the risk variant LRRK2 rs10878226 indicates a possible interaction between common variation in LRRK2 and VAMP4 in disease risk.