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Abstract #3169

Test-Retest Reliability of Computational Network Metrics Derived from the Structural Connectome of the Human Brain

Julia P. Owen1, Etay Ziv1, Polina Bukshpun2, Nicholas Pojman2, Mari Wakahiro2, Jeffrey I. Berman3, Timothy Roberts4, Elliott Sherr2, Pratik Mukherjee1

1Radiology, UCSF, San Francisco, CA, United States; 2Neurology, UCSF, San Francisco, CA, United States; 3Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States; 4Radiology, CHOP, Philadelphia, PA, United States

In this study, we evaluate the test-retest reliability of graph theory techniques as applied to the structural connectome of the human brain. We explore unweighted and weighted graph metrics, as well as other measures of consistency, such as edge weights and module assignments. Two cohorts are used, one group (n=10) was scanned twice on the same scanner and the other group (n=5) was scanned once at two different sites, on the same model of scanner with identical acquisition parameters.