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Abstract #0432

Mapping Threshold-Independent Drug Effects in Graph Theoretic Analyses of Functional Connectivity Networks: the Opioid Analgesic Buprenorphine Preferentially Modulates Network Topology in Pain-Processing Regions

Adam J. Schwarz1,2, Jaymin Upadhyay, 2,3, Alexandre Coimbra, 2,4, Richard Baumgartner, 2,5, Julie Anderson, 2,3, James Bishop, 2,3, Ed George, 2,6, Lino Becerra, 2,3, David Borsook, 2,3

1Translational Imaging, Eli Lilly and Company, Indianapolis, IN, United States; 2Imaging Consortium for Drug Development, Boston, MA, United States; 3PAIN Group, Brain Imaging Center, McLean Hospital, Belmont, MA, United States; 4Imaging, Merck, West Point, PA; 5Biometrics Research, Merck, Rahway, NJ, United States; 6Anesthesiology and Critical Care, Massechussets General Hospital, Boston, MA, United States

Graph theoretic analyses of functional connectivity networks report on topological properties of the brain and may provide a useful probe of disease or drug effects. However, verifying node-wise effects over a range of binarization thresholds is inconvenient and often subjective for large, voxel-scale networks. We present a straightforward method for calculating graph theoretic node parameters that are robust to binarization threshold and suitable for image analysis in the study of functional connectivity. The method is applied to mapping drug modulation of localized functional network topology by the opioid analgesic buprenorphine in healthy human subjects.