1. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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 10, 2008

2. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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 Longitudinal MRI study of brain development in autism

3. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Structural MRI Diffusion Tensor Behavioral, cognitive, developmental Processed pediatric longitudinal data Data Available:

4. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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 Scan data (collected on 1.5T GE MRI scanner):

5. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Tissue Segmentation - EMS GM, WM, CSF Total brain & lobes

6. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Hippocampus Amygdala Basal ganglia (divided Caudate, putamen, & globus pallidus) Corpus callosum (midsaggital) Ventricles Anterior Cingulate Cerebellar vermis Substructure ROIs:

7. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Processed datasets*: Time1 (2 yr old) Time2 (4 yr old) EMS/lobes CN AMYG Autism 49 51 47 29 31 31 DD 12 9 10 6 5 6 Typical 25 22 21 11 12 10 *Note: Volumetric data completed after both intra- & inter-rater reliability testing of ROI protocols.

8. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Blue = growth, red = atrophy, green = no change Surface growth maps & cortical thickness NEED Local & regional Across time (2-4yr) Between groups

9. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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 Component 1: Tissue segmentation

11. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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 Component 2: Cortical thickness

12. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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... Component 3: Cortical correspondence

13. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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 Component 4: Hypothesis testing

14. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Summary of work completed: WM/GM Segmentation Added itkEMS to Slicer3 as module (at UNC) WM/GM segmentation of ped brain with itkEMS in Slicer3 WM/GM segmentation of ped brain with Slicer3 –currently in progress Cortical Thickness Added UNC Cortical Thickness tool to Slicer3 (at UNC) Run Niethammer’s Laplacian cortical thickness code as a Slicer3 module (at UNC)

15. NA-MIC National Alliance for Medical Image Computing http://na-mic.org 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: 1)Quantify shape changes over time to allow for analysis with behavioral data 2)Possible application of new DTI tools?

16. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Joe Piven, MD Guido Gerig, PhD Martin Styner, PhD Clement Vachet, MS 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