Meeting Banner
Abstract #0603

Normalization of inter-site Structural Connectivity Data for Regression analysis

Takanori Watanabe1, Birkan Tunc1, Drew Parker1, Jean-Philippe Fortin2, Mark A. Elliott1, Kosha Ruparel3, Ruben C. Gur3, Raquel E. Gur3, Robert Schultz4, Russell T. Shinohara2, and Ragini Verma1

1Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 2Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 4Center for Autism Research, Children’s Hospital of Philadelphia

Diffusion tensor imaging (DTI) and tractography have revealed many critical insights about how the human brain is organized as a large-scale complex network. As multisite imaging studies are becoming increasingly popular within the neuroimaging field, it is imperative to develop methods that can correct for inter-site differences, facilitating the combination of data from multiple sites. In this study, we present a normalization scheme that will correct for site-specific differences in diffusion-based structural connectivity data, and demonstrate its efficacy through multivariate regression experiments using the normalized structural connectivity features to predict subject’s age.

This abstract and the presentation materials are available to members only; a login is required.

Join Here