RESEARCH ARTICLE Googling Stroke ASPECTS to Determine Disability: Exploratory Analysis from VISTA- Acute Collaboration

The summed Alberta Stroke Program Early CT Score (ASPECTS) is useful for predicting stroke outcome. The anatomical information in the CT template is rarely used for this pur-pose because traditional regression methods are not adept at handling collinearity (related-ness) among brain regions. While penalized logistic regression (PLR) can handle collinearity, it does not provide an intuitive understanding of the interaction among network structures in a way that eigenvector method such as PageRank can (used in Google search engine). In this exploratory analysis we applied graph theoretical analysis to explore the re-lationship among ASPECTS regions with respect to disability outcome. The Virtual Interna-tional Stroke Trials Archive (VISTA) was searched for patients who had infarct in at least one ASPECTS region (ASPECTS9, ASPECTS=10 were excluded), and disability (modi-fied Rankin score/mRS). A directed graph was created from a cross correlation matrix (thre-sholded at false discovery rate of 0.01) of the ASPECTS regions and demographic variables and disability (mRS>2). We estimated the network-based importance of each AS-PECTS region by comparing PageRank and node strength measures. These results wer