A longitudinal study of brain development in autism

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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

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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

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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

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Autism

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

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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)

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Regional cortical thickness
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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
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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
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atlas deformable registration
Skull stripped data
Parcellation map
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plt.0001 plt.05
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Next steps

• Complete pipeline for local cortical thickness
• Explore cortical thickness in relation to
  clinical and genetic data

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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
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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)

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Contributors

• 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

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