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A longitudinal study of brain development in autism

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, UT Jan 7, 2010. UNC DBP-2 Team. DBP-2 PI: Heather Cody Hazlett Co-PI: Joseph Piven

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A longitudinal study of brain development in autism

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  1. 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, UT Jan 7, 2010

  2. UNC DBP-2 Team • 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

  3. 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

  4. Autism Neurodevelopmental disorder of language, social communication, and stereotyped behavior Neuroimaging findings (volumetric studies): Brain enlargement Gray & white matter enlargement Enlargement is present early

  5. 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)

  6. Regional cortical thickness

  7. 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

  8. Sample Characteristics Time 1 Age (yrs) Time 2 Age (yrs) % Male Group N M (SD) N M (SD) at Time 1* ASD 59 2.7 (.32) 38 5.04 (.41) 86% Controls 38 2.6 (.52) 21 4.69 (.46) 74% * Percent male at Time 2: ASD 89%, Controls 71%

  9. atlas deformable registration Skull stripped data Parcellation map

  10. ** * * ** p<.0001 * p<.05

  11. Next steps Complete pipeline for local cortical thickness Explore cortical thickness in relation to clinical and genetic data

  12. Local Cortical Thickness - Pipeline Overview Eleven steps in the pipeline: 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 7. White matter surface inflation 8. Cortical correspondence 9. Label map creation 10. Cortical thickness 11. Group statistical analysis

  13. 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)

  14. 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 Contributors: NA-MIC Team Jim Miller Ipek Oguz Nicolas Augier Marc Niethammer Brad Davis Parent grant funded by the National Institutes of Health

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