NA-MIC National Alliance for Medical Image Computing A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research Center & UNC-CH Dept of Psychiatry NA-MIC AHM Salt Lake City, UTJan 7, 2010

NA-MIC National Alliance for Medical Image Computing DBP-2 PI: Heather Cody Hazlett Co-PI: Joseph Piven CS Programmers: Clement Vachet, Cedric Matthieu Core 1: Martin Styner, UNC Chapel Hill UNC Algorithm: Ipek Oguz, Nicolas Augier, Marcel Prastawa, Marc Niethammer, Clement Vachet, Cedric Mathieu Core 2: Jim Miller, GE Research UNC DBP-2 Team

NA-MIC National Alliance for Medical Image Computing Project: Cortical thickness analysis of pediatric brain Project Goals: –Individual and group analysis of regional and local cortical thickness –Creation of an end-to-end application within Slicer3 –Apply pipeline to our large pediatric dataset of children with ASD

NA-MIC National Alliance for Medical Image Computing Autism Neurodevelopmental disorder of language, social communication, and stereotyped behavior Neuroimaging findings (volumetric studies): Brain enlargement Gray & white matter enlargement Enlargement is present early

NA-MIC National Alliance for Medical Image Computing Cortical thickness in ASD Surfaced based morphetry shows decreased CT in school- age ASD (Chen et al 2009) Regional CT decreased in adults with ASD (Raznahan et al 2009) VBM and CT increased in brain regions associated with autism in young adults with ASD (Hyde et al 2009) Decreased volume and CT over time in small sample of school-aged males with ASD (Hardan et al 2009)

NA-MIC National Alliance for Medical Image Computing Regional cortical thickness

NA-MIC National Alliance for Medical Image Computing Regional Cortical Thickness - Pipeline Overview A Slicer3 high-level module for individual cortical thickness analysis has been developed: ARCTIC (Automatic Regional Cortical ThICkness) Input: raw data (T1-weighted, T2-weighted, PD-weighted images) Three steps in the pipeline: 1. Tissue segmentation 2. Regional atlas deformable registration 3. Cortical Thickness

NA-MIC National Alliance for Medical Image Computing * Percent male at Time 2: ASD 89%, Controls 71% Sample Characteristics Time 1Age (yrs)Time 2Age (yrs)% Male GroupNM (SD)NM (SD)at Time 1* ASD592.7 (.32) (.41)86% Controls382.6 (.52) (.46)74%

NA-MIC National Alliance for Medical Image Computing

NA-MIC National Alliance for Medical Image Computing

NA-MIC National Alliance for Medical Image Computing

NA-MIC National Alliance for Medical Image Computing

NA-MIC National Alliance for Medical Image Computing Skull stripped data Parcellation map atlas deformable registration

NA-MIC National Alliance for Medical Image Computing ** * * ** p<.0001 * p<.05

NA-MIC National Alliance for Medical Image Computing Next steps Complete pipeline for local cortical thickness Explore cortical thickness in relation to clinical and genetic data

NA-MIC National Alliance for Medical Image Computing Local Cortical Thickness - Pipeline Overview Eleven steps in the pipeline: 7. White matter surface inflation 8. Cortical correspondence 9. Label map creation 10. Cortical thickness 11. Group statistical analysis 1. Tissue segmentation 2. Atlas-based ROI segmentation 3. White matter map creation 4. White matter map post-processing 5. Genus zero white matter map image & surface creation 6. Gray matter map creation

NA-MIC National Alliance for Medical Image Computing Other collaborations Caudate shape: Ross Whitaker, Josh Cates, Martin Styner, Michele Poe Grant submission: New statistical models for investigating subcortical shapes (S Marron, UNC stats)

NA-MIC National Alliance for Medical Image Computing Joe Piven, MD Guido Gerig, PhD Martin Styner, PhD Clement Vachet, MS Cedric Matthieu, BA Rachel Smith, BA Mike Graves, MChE Sarah Peterson, BA Matt Mosconi, PhD Parent grant funded by the National Institutes of Health Contributors: NA-MIC Team Jim Miller Ipek Oguz Nicolas Augier Marc Niethammer Brad Davis