Mechanisms of Cerebral Microbleeds

Abstract

Cerebral microbleeds (CMB) are a common MRI finding, representing underlying cerebral microhemorrhages (CMH). The etiology of CMB and microhemorrhages is obscure. We conducted a pathological investigation of CMH, combining standard and immunohistological analyses of postmortem human brains. We analyzed 5 brain regions (middle frontal gyrus, occipital pole, rostral cingulate cortex, caudal cingulate cortex, and basal ganglia) of 76 brain bank subjects (mean age ± SE 90 ± 1.4 years). Prussian blue positivity, used as an index of CMH, was subjected to quantitative analysis for all 5 brain regions. Brains from the top and bottom quartiles (n = 19 each) were compared for quantitative immunohistological findings of smooth muscle actin, claudin-5, and fibrinogen, and for Sclerosis Index (SI) (a measure of arteriolar remodeling). Brains in the top quartile (i.e. with most extensive CMH) had significantly higher SI in the 5 brain regions combined (0.379 ± 0.007 vs 0.355 ± 0.008; p < 0.05). These findings indicate significant coexistence of arteriolar remodeling with CMH. While these findings provide clues to mechanisms of microhemorrhage development, further studies of experimental neuropathology are needed to determine causal relationships.

Insights Into the Mechanisms of Brain Endothelial Erythrophagocytosis

“…Further, we found no change in the blood–brain barrier tight junction protein ZO-1 seven days after tBHP-RBC injection ( Figure 7 ). This is in line with our recent work showing no association between blood–brain barrier changes with CMH development in humans ( Wadi et al, 2020 ). Accordingly, our in vitro data and other reports ( Catan et al, 2019 ) show no change in brain endothelial cell viability and permeability with erythrophagocytosis ( Figure 3 and Supplementary Figure 3 ).…”

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages

“…Third, images of the identified CMHs are digitally acquired. Finally, a user manually outlines and adjusts RGB thresholds 13 to quantify Prussian blue positivity or CMH area 20 . Each step is prone to error, variability, and subjectivity, and demands extensive personnel time.…”

“…One advantage that deep learning could provide that we did not investigate is to determine areas within images that contain CMHs and use the ratiometric or phasor approach for quantitation. Fifth, although Prussian blue is commonly used to detect CMHs, it does not detect newly formed CMHs, nor distinguish between hemoglobin-derived (hemosiderin) and nonhemoglobin-derived iron 13 . Combining our Prussian blue-developed approaches with future algorithms that use fresh CMHs stained with hematoxylin and eosin (H&E) 45 may help detect non-acute and acute CMHs.…”

“…Current methods that identify and quantify CMHs are typically subjective and require tedious microscopic inspection. They rely on manual selection of Prussian blue-stained regions or use manual thresholding 13 , which is time and labor intensive. Recent advances in digital pathology address these limitations by eliminating the need for laborious staining 14 and automating the quantitative analysis of the samples 15 , 16 .…”

Spectroscopic and Deep Learning-Based Approaches to Identify and Quantify Cerebral Microhemorrhages

“…Using a slide scanner in conjunction with one of the developed approaches could drastically decrease the personnel time required to identify and quantify CMHs. Fifth, although Prussian blue is commonly used to detect CMHs, it does not detect newly formed CMHs, nor distinguish between hemoglobin-derived (hemosiderin) and nonhemoglobinderived iron 13 . Combining our Prussian blue-developed approaches with future algorithms that use fresh CMHs stained with hematoxylin and eosin (H&E) 43 may help detect non-acute and acute CMHs.…”