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

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

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

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, 0.78125 mm x 0.78125 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, 0.78125 mm x 0.78125 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), 0.9375 mm x 0.9735 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

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

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

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

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

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

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

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

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...

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

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)

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?

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