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Abstract #1688

Connectome Based Predictive Modeling: Relating Social Measures to Functional Brain Organization

Evelyn MR Lake1, Emily S Finn2, Monica D Rosenberg3, Xilin Shen4,5, Dustin Scheinost4,5, Marvin M Chun2,3,6, and R Todd Constable1,2,7

1Radiology and Biomedical Imaging, Yale University, Cambridge, MA, United States, 2Interdepartmental Neuroscience Program, Yale University, 3Department of Psychology, Yale University, 4Department of Diagnostic Radiology, Yale School of Medicine, 5Radiology and Biomedical Imaging, Yale University, 6Department of Neurobiology, Yale University, 7Department of Neurosurgery, Yale School of Medicine

We examine the relationship between functional brain networks and behaviour in individuals with autism from the Autism-Brain-Imaging-Data-Exchange. We find a difference in attention network strength between groups (autism vs. control) using an a priori defined network for high-attention [1]. In addition, connectome based predictive modeling (CPM) successfully predicted inattention and communication scores. Finally, on a test group of autistic patients (not used in the CPM), we found network strengths to be more similar to individuals used the CPM with autism than controls. Together, these results indicate that connectivity may prove to be a valuable tool in the diagnosis and treatment of autistic individuals.

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