Histologic studies have shown that ischemia due to BRVO is characterized by loss of pericytes and endothelial cells, acellularity of the capillary bed, and loss of neuroglial structures in the inner retinal layers.
64–66 Key retinal elements that control retinal perfusion include these pericytes, smooth muscle cells, and glia.
17,67,68 Smooth muscle α-actin is a key protein that confers contractile properties to some of these cells in response to regional changes in glutamate, lactate, and nitric oxide concentrations.
69,70 Tomasek and colleagues
71 utilized knockout mice to demonstrate that the lack of smooth muscle α-actin in pericytes and smooth muscle cells altered the properties of the blood–retina barrier, including vascular permeability. It is therefore plausible that histologically proven loss of cellular elements within the collective apparatus that serves to fine-tune retinal perfusion can lead to rapid fluctuations in blood flow as correlated clinically by increased microvasculature CoV measurements. The relative absence of pericytes and smooth muscle cells around venules may explain why CoV measurements within retinal veins were not significantly altered within regions of ischemia.
17 The lack of autonomic innervation of retinal arterioles limits the ability to rapidly couple blood flow to metabolic demands, this may explain why no alteration in arteriole CoV measurements was detected in ischemic regions.
72 The process of autoregulation predominantly occurs at the level of capillaries, and our findings here are consistent with that concept.
44 In addition to loss of smooth muscle α-actin–expressing cells, the upregulation of VEGF within regions of ischemia may also account for changes in CoV measurements. Previous studies have shown that VEGF can alter the permeability of vascular structures as well as alter blood flow within the circulation.
73,74 Other factors and metabolites affected in response to ischemia such as lactate and nitric oxide may similarly affect capillary perfusion as found in this report.
44,46 One published abstract has investigated a similar principle to ours with quantification of CoV to map perfusion heterogeneity between DR and control patients, finding variation in regions of both DR and healthy patients (Yuan PHS, et al.
IOVS 2020;61:ARVO E-Abstract PB0020). The method described quantified CoV on a pixel-wise basis, reporting the mean and standard deviation over the whole image. In comparison, our method computes the mean CoV for vessel segments (which decreases susceptibility to artifacts) and also computes ROI-based CoV (better sensitivity to localized differences).