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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. 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

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

    • 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


Project cortical thickness analysis of pediatric brain
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


Autism

Autism

Neurodevelopmental disorder of language, social communication, and stereotyped behavior

Neuroimaging findings (volumetric studies):

Brain enlargement

Gray & white matter enlargement

Enlargement is present early


Cortical thickness in asd

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)



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


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%


atlas deformable registration

Skull stripped data

Parcellation map


**

*

*

** p<.0001

* p<.05


Next steps

Next steps

Complete pipeline for local cortical thickness

Explore cortical thickness in relation to clinical and genetic data


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


Other collaborations

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)


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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