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Abstract #2100

Age-associated White Matter Microstructure and Connectome Abnormalities in Autism Spectrum Disorder

Clara F Weber1, Stefan P Haider1, Pratik Mukherjee2, Evelyn MR Lake1, Dustin Scheinost1, Nigel S Bamford3, Laura Ment3, Todd Constable1, and Sam Payabvash1
1Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States, 2University of California, San Francisco, San Francisco, CA, United States, 3Pediatrics, Neurology, Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT, United States


Analyzing four study cohorts spanning from infancy to adulthood, we compared DTI-derived diffusion metrics as well as connectome Edge Density between subjects with Autism Spectrum Disorders (ASD) and neurotypical controls. Additionally, we explored the performance of several machine learning algorithms applied to tract-based values for prediction of ASD. We found age- and ASD-related alterations in white matter microstructure and connectome in both voxel-wise and tract-based analyses that show how ASD-associated abnormalities emerge and change over time. Our machine learning analysis evaluated several different approaches and identified a model that achieved 0.75 AUC in the prediction of an ASD diagnosis.

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