Betton, Charlie; Hoban, Katie; Valdés Hernández, Maria del C. (2020). Systematic review on the variability of the human brain arterial territories assessed using neuroimaging methods, 1940-2020 [dataset]. University of Edinburgh. Centre for Clinical Brain Sciences. https://doi.org/10.7488/ds/2886.
Imaging methods for brain arterial territories have evolved significantly from cadaver injection techniques to combinations of CT angiography with multiple MRI perfusion techniques. The variability of the brain arterial territories is well acknowledged in the literature. It has been attributed to: (1) haemodynamic factors related to the peripheral resistance of each artery; (2) anomalies of the Circle of Willis; (3) the involvement of leptomeningeal collateral (LMCs) arteries; and (4) enlargement of arterial territories due to previous stroke; among other factors. We separately conducted two systematic literature reviews searching for papers that 1) acknowledge, illustrate or analyse the vascular supply of the brain territories and the variability of their boundaries; 2) identify and discuss the source of this variability; and 3) present or evaluate useful resources to estimate the boundary of the arterial territories from brain MRI data. Each systematic search had slightly different aim and inclusion/exclusion criteria. This dataset contains the result of the searches and the data extracted. From analysing the data extracted, the primary source of variability in the arterial territories was the involvement of LMCs. These provide emergency blood supply across the surface of the brain, between arterial territories, following occlusion. Secondary sources included variation of the Circle of Willis and enlargement of arterial territories due to previous stroke. After analysing non-contrast FLAIR MRI from 45 stroke patients for border zone and LMCs involvement, inter-observer reliability analysis found the prospect of assessing LMCs from conventional structural MRI sequences to estimate the variability of these territories to be very low (Kappa Statistic = 0.1262). This dataset also includes this inter-observer analysis.
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