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 10, 2008

2. Longitudinal MRI study of brain development in autism
Study Aims:
To characterize patterns of brain development longitudinally (from ages 2 yr – 4 yr) in children with autism versus controls (TYP, DD)
To examine cross-sectional & longitudinal relationships between selected brain regions and behavioral characteristics associated with autism

3. Behavioral, cognitive, developmental
Data Available:
Structural MRI
Diffusion Tensor
Behavioral, cognitive, developmental
Processed pediatric longitudinal data

4. Scan data (collected on 1.5T GE MRI scanner):
Structural MRI
TI: coronal 3D SPGR IRprep, 0.78 x 0.78 x 1.5 mm, 124 slices, 5 TE/12 TR, 20 FOV, 1 NEX, 256x192
PD/T2: coronal FSE, 0.78 x 0.78 x 3.0 mm, 128 slices, 20 FOV, 17 TE/7200 TR, 1 NEX, 256x160
DTI
axial oblique 2D spin echo EPI, 0.93 x 0.97 x 3.8 mm, 30 slices, 24 FOV, 12 dir
T1: 3D SPGR IR Prep, coronal, mm x mm x 1.5 mm, 124 slices, field of view = 20 cm x 20 cm scan time = 7:10, NEX = 1 flip angle = 20, TE = 5, TR = 12, TI = 300, acquisition matrix = 256 x 192 PD/T2: 2D Fast Spin Echo (FSE), coronal, mm x mm x 3.0 mm, interleave acquisition, slice number to cover brain (usually 128 slices), field of view = 20 cm x 20 cm scan time = 9:36, NEX = 1 TE = 17, 75, TR = 7200, ETL = 8, acquisition matrix = 256 x 160 DTI: 2D Spin Echo-EPI, axial oblique (to ACPC), mm x mm x 3.8 mm, 0.4 mm gap, 30 slices, field of view = 24 cm x 24 cm, 4 acquisitions, baseline plus 12 directions (6 directions and their inverses) scan time = 2:38 per acquisition (10:32 total) TE = min, TR = 12200, acquisition matrix = 128 x 128

5. Tissue Segmentation - EMS
GM, WM, CSF
Total brain & lobes

6. Substructure ROIs: Hippocampus Amygdala
Basal ganglia (divided Caudate, putamen, & globus pallidus)
Corpus callosum (midsaggital)
Ventricles
Anterior Cingulate
Cerebellar vermis

7. Processed datasets: Time1 (2 yr old) Time2 (4 yr old)
EMS/lobes CN AMYG EMS/lobes CN AMYG
Autism DD
Typical
Data as of Nov ’06

8. Surface growth maps & cortical thickness
NEED
Local & regional
Across time (2-4yr)
Between groups
Blue = growth, red = atrophy, green = static
Blue = growth, red = atrophy, green = no change

9. Cortical thickness analysis of pediatric brains
Goals:
Individual and group analysis of regional and local cortical thickness
Creation of an end-to-end application within Slicer3
Workflow applied to our large pediatric dataset
Basic components:
Tissue segmentation
Cortical thickness
Cortical correspondence
Statistical analysis - Hypothesis testing

10. Component: Tissue segmentation
Characteristics:
Multi-modality data: T1-weighted, T2-weighted, PD
Non skull-stripped data
Intensity inhomogeneity correction
Tools:
UNC segmentation tool: itkEMS
Slicer3 module: EMSegment
Goal:
Testing the tools on a pediatric dataset
Comparison study

11. Component: Cortical thickness
Characteristics:
Regional as well as local cortical thickness
Tools:
UNC tool: CortThick
Sparse, non-symmetric method
Method suited for regional analysis
Slicer3 technique: Measure Thickness Filter
Symmetric method
Method suited for local analysis: specific ROI
Goals:
Testing the tools on our pediatric dataset
Possible improvement necessary GM WM

12. Component: Cortical correspondence
Characteristics:
Regional as well as local cortical correspondence is suited
Tools:
Regional correspondence:
Slicer3 module: B-spline based registration
Local correspondence
No tool available within Slicer yet....
Goal:
Development of local analysis techniques
Comparison with current existing tools: FreeSurfer...

13. Component: Hypothesis testing
Regional analysis:
Use of standard analysis tools
Local analysis:
Standard analysis tools are not applicable
Framework is needed with
Multiple comparison
Generalized linear model computation
No tool available within Slicer yet...
Goal
Development of local analysis techniques

14. Summary of work completed:
WM/GM Segmentation
Added itkEMS to Slicer3 as module
WM/GM segmentation of ped brain with itkEMS in Slicer3
WM/GM segmentation of ped brain with Slicer3 –90%done
Cortical Thickness
Added UNC Cortical Thickness tool to Slicer3
Added Niethammer’s Laplacian cortical thickness code as a Slicer3 module (at AHM)

15. Goals/Projects for the upcoming year:
Complete work on local cortical thickness measures for pediatric brain (e.g., insert UNC algorithm in Slicer3)
Complete work on local correspondence (e.g., point to point correspondence of GM/WM on cortex)
Explore other projects
Quantify shape changes over time to allow for analysis with behavioral data
Possible application of new DTI tools?

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