Abstract #3345
Multi-Site Normative Modeling and Hierarchical Bayesian Analysis of DKI metrics in Carriers of 16p11.2 Copy Number Variants
Julio Ernesto Villalon-Reina1, Clara Moreau2, Talia M Nir1, Neda Jahanshad1, Simons Variation in Individuals Project Consortium3, Anne Maillard4, David Romascano4, Bodgan Draganski5, Sarah Lippé6, Carrie E Bearden7, Paul M Thompson1, and Sebastien Jacquemont8
1Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States, 2Institut Pasteur, Université de Paris, Paris, France, 3Simons Foundation, New York, NY, United States, 4Service des Troubles du Spectre de l’Autisme et apparentés, Département de psychiatrie, Lausanne University Hospital, Lausanne, Switzerland, 5Laboratoire de Recherche en Neuroimagerie, Université de Lausanne, Lausanne, Switzerland, 6Psychology Department, University of Montreal, Montreal, QC, Canada, 7Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States, 8University of Montreal, Montreal, QC, Canada
Synopsis
We used Hierarchical Bayesian Regression (HBR) to create a normative model of age-effects on Diffusion Kurtosis Imaging (DKI) measures of the white matter. DKI maps from more than 1,300 healthy subjects (3-78 years) from six sites and eight scanners were used to estimate the normative model across the lifespan enabling the multi-site data integration with HBR. 16p11.2 deletions and duplications showed “mirror effects” on diffusion kurtosis metrics.
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