Human arachnoid granulations Part I: a technique for quantifying area and distribution on the superior surface of the cerebral cortex

Abstract

Abstract Background The arachnoid granulations (AGs) are herniations of the arachnoid membrane into the dural venous sinuses on the surface of the brain. Previous morphological studies of AGs have been limited in scope and only one has mentioned surface area measurements. The purpose of this study was to investigate the topographic distribution of AGs on the superior surface of the cerebral cortex. Methods En face images were taken of the superior surface of 35 formalin-fixed human brains. AGs were manually identified using Adobe Photoshop, with a pixel location containing an AG defined as 'positive'. A set of 25 standard fiducial points was marked on each hemisphere for a total of 50 points on each image. The points were connected on each hemisphere to create a segmented image. A standard template was created for each hemisphere by calculating the average position of the 25 fiducial points from all brains. Each segmented image was mapped to the standard template using a linear transformation. A topographic distribution map was produced by calculating the proportion of AG positive images at each pixel in the standard template. The AG surface area was calculated for each hemisphere and for the total brain superior surface. To adjust for different brain sizes, the proportional involvement of AGs was calculated by dividing the AG area by the total area. Results The total brain average surface area of AGs was 78.53 ± 13.13 mm2 (n = 35) and average AG proportional involvement was 57.71 × 10-4 ± 7.65 × 10-4. Regression analysis confirmed the reproducibility of AG identification between independent researchers with r2 = 0.97. The surface AGs were localized in the parasagittal planes that coincide with the region of the lateral lacunae. Conclusion The data obtained on the spatial distribution and en face surface area of AGs will be used in an in vitro model of CSF outflow. With an increase in the number of samples, this analysis technique can be used to study the relationship between AG surface area and variables such as age, race and gender.