The cerebral vasculature facilitates blood flow to maintain normal function in the brain. Vascular injury can impair the ability of the cerebral vasculature to regulate blood flow and preserve the integrity of the blood-brain barrier. Cerebral microhemorrhages (CMH) are an indicator of structural damage in the brain vasculature. Aging and hypertension are the most common risk factors for CMH. In this study, we analyzed the effect of hypertension on resting-state cerebral blood flow and the development of CMH in a mouse model of aging. A reduction in resting-state cerebral blood flow was observed in hypertension mice. CMH were found to appear nearest to capillary-sized vessels. Together, these findings demonstrate hypertension can impair the function (via reduced resting-state cerebral blood flow) and structure (via formation of CMH) of the brain vasculature.
Cerebral microhemorrhages (CMH) are the accumulation of hemosiderin-iron deposits which are associated with cognitive impairments. CMH are suspected to be the result of the deterioration of cerebral microvessels. Despite the clinical significance of CMH, our understanding of CMH formation remains limited. Standard histology for CMH visualization is constrained by thin planar views obtained sparsely throughout brain tissue. This limitation can misrepresent the true size and extent of CMH. Here we incorporate tissue clearing to capture a complete view of CMH and compare it to that of a two-dimensional approach.
Cerebral microhemorrhages (CMHs) are associated with cognitive impairment and several conditions, diseases, and normal aging processes. Current histological methods manually identify and quantify Prussian blue-stained CMHs, which can take months to complete. To speed up this labor-intensive process, we developed a spectroscopic, semi-automated approach. We used the ratio of the red and green intensities relative to the blue intensity squared to discriminate CMH-pixels from background pixels. We calculated a sensitivity and specificity of 83.75% and 99.74%, respectively. The intraclass correlation coefficient was 0.992 (95% confidence interval: 0.989-0.995). Future studies are needed to test if this approach works in other CMH models.
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