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A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research Center &

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Presentation on theme: "A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research Center &"— Presentation transcript:

1 A longitudinal study of brain development in autism Heather Cody Hazlett, PhD Heather Cody Hazlett, PhD Neurodevelopmental Disorders Research Center & UNC-CH Dept of Psychiatry NA-MIC Core1 Mtg Boston, MAMay 30, 2007

2 Overview Summary of hypotheses Data available to NA-MIC Specific requirements/constraints of project Existing image processing of data Resources

3 Longitudinal MRI study of brain development in autism

4 Features of Autism Social deficits Communication deficits Atypical Behaviors

5 Longitudinal MRI study of brain development in autism AIMS To characterize patterns of brain development longitudinally in autism cases versus controls (TYP, DD) To examine cross-sectional & longitudinal relationships between selected brain regions and behavioral characteristics associated with autism

6 Longitudinal MRI study of brain development in autism Hypotheses Brain enlargement will be present in autism cases compared to controls (TYP, DD) Brain differences in specific substructures of interest will be seen in autism cases compared to controls, and these differences will correlate with symptoms of autism and/or severity of features

7 Developmental Studies Difficult for very young children and/or lower functioning children to remain still May need to remain motionless for long periods of time Sleep studies vary in success rates Subjects may require training and practice – this adds to expense of project

8 Data Available

9 Structural MRI Diffusion Tensor Behavioral, cognitive, developmental Processed pediatric longitudinal data

10 Data* 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 *All scans collected on 1.5T GE scanner

11 Data Processed datasets Time1 (2 yr old) Time2 (4 yr old) EMS/lobes CN AMYG Autism 49 51 47 29 31 31 (+2 CS) DD 12 9 10 6 5 6 Typical 25 22 21 11 12 10 FX 45 47 47 11 11 10 Also have segmented data for: Put/GP, Hipp, CC area, Ventricles, Ant Cing, Cerebellar vermis

12 Requirements/Contraints

13 Registration of images to a common atlas Inhomogeneities – bias correction Tissue contrast – myelination Brain shape changes across development Requirements/Constraints

14 Existing Image Processing

15 Tissue segmentation EMS hard segmentations EMS segmentations overlaid on MRI Shown here – 2 year old

16 Lobe parcellation by template warping Manually-derived parcellation “warped” to new subjects

17 N% male years (SD) IQ-SS (SD) * Autism 51 88% 2.7 (0.3) 54.2 (9.4) Controls 25 DD 11 55% 2.7 (0.4) 59.7 (9.4) TYP 14 64% 2.4 (0.4) 107.5 (18.7) * IQ-SS = Mullen composite Standard Score UNC Longitudinal MRI Study of Autism Hazlett et al Arch Gen Psych 2005

18 UNC Longitudinal MRI Study of Autism autism controls autism controls mean (SE)mean (SE) % diff p mean (SE)mean (SE) % diff p TBV 1264.6 (13.4) 1208.1 (16.2) 4.7 0.008 cerebrum 941.5 (10.5) 890.5 (12.3) 5.7 0.002 cerebellum 114.1 (1.5)114.4 (2.2) 0.3 0.9 Adjusted for Gender and Age

19 autism controls autism controls mean (SE)mean (SE) % diff p mean (SE)mean (SE) % diff p TBV 1264.6 (13.4) 1208.1 (16.2) 4.7 0.008 cerebrum 941.5 (10.5) 890.5 (12.3) 5.7 0.002 gray 676.7 (7.7)644.2 (8.8) 5.0 0.005 white 264.7 (3.1)246.2 (3.7) 7.5 0.0001 cerebellum 114.1 (1.5) 114.4 (2.2) 0.3 0.9 UNC Longitudinal MRI Study of Autism

20 Segmented Substructures (ROIs) Basal ganglia –Caudate –Putamen –Globus pallidus AmygdalaHippocampus

21 Descriptives %Years Cognitive* Adaptive** GroupNMaleM (SD) M (SD)M (SD) autism5287%2.7 (0.3) 54.1 (9.3)60.8 (5.9) controls3370%2.6 (0.5) 87.4 (28.6)850.4 (21.1) developmental delay1267%2.8 (0.4) 55.5 (6.7)65.8 (14.0) developmental delay1267%2.8 (0.4) 55.5 (6.7)65.8 (14.0) typically developing2171%2.4 (0.5) 106.6 (16.8)98.3 (13.4) typically developing2171%2.4 (0.5) 106.6 (16.8)98.3 (13.4) * Cognitive estimate from Mullen Composite Standard Score ** Adaptive behavior estimate from Vineland Adaptive Behavior Composite

22 Basal Ganglia Volumes in 2 Year Olds with Autism (adjusted for TBV) Aut v Total Controls Aut v TYP Aut v DD diff (SE) p % diff (SE) p % diff (SE) p % Caudate.50 (.29).094 7% 0.8 (.31).013 12%.20 (.43).65 3% Globus Pallidus.16 (.29).09 6%.17 (.10).094 6%.16 (.12).20 6% Putamen -.16 (.20).410 - 2% -.19 (.22).380 -2% -.14 (.25).594 -2% Note - all comparisons also adjusted for age and gender

23 Amygdala/Hippocampus Volume in 2 Year Olds with Autism Aut v Total Controls Aut v TYP Aut v DD diff (SE) p % diff (SE) p % diff (SE) p % amygdala.35 (.12).004 10%.55 (.11) <.001 16%.16 (.17).336 3% hippocampus.03 (.11).78 1% -.03 (.14).841 0%.09 (.15).55 2% *Note – all comparisons also adjusted for age and gender (adjusted for TBV)

24 Other ROIs Corpus callosum (midsaggital) Ventricles Anterior Cingulate Cerebellar vermis

25 Surface growth maps & cortical thickness by lobe age 2 4

26 Resources CS programmer – Clement Vachet Image processing RA support (unfunded) Image processing lab at UNC and existing NA-MIC Cores

27 NA-MIC Collaboration Possible Goals/Projects : 1) Pipelines for growth-rate analysis 2) Longitudinal analysis of cortical thickness, cortical folding patterns, etc. 3)Quantify shape changes over time to allow for analysis with behavioral data 4) Development of new segmentation protocols (e.g., dorsolateral prefrontal cortex)

28 NA-MIC Collaboration Our site can offer NAMIC collaborators: 1) Existing pediatric dataset of sMRI & DTI 2) Longitudinal data (imaging & behavioral) 3) Segmented datasets to be used as validation tools (e.g., comparison to FreeSurfer) 4) Already collaborating with NA-MIC (e.g., multiple shape analysis papers at MICCAI, shape analysis component already in Slicer)

29 Contributors Joe Piven, MD Guido Gerig, PhD Sarang Joshi, PhD Michele Poe, PhD Chad Chappell, MA Judy Morrow, PhD Nancy Garrett, BS, OTA Robin Morris, BA Rachel Smith, BA Mike Graves, MChE Sarah Peterson, BA Matthieu Jomier, MS Carissa Cascio, PhD Matt Mosconi, PhD Martin Styner, PhD Allison Ross, MD James MacFall, PhD Alan Song, PhD Valerie Jewells, MD James Provenzale, MD Greg McCarthy, Ph.D. John Gilmore, MD Allen Reiss, MD UNC Fragile X Center NDRC Research Registry Funded by the National Institutes of Health

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