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 AHM Salt Lake City, UTJan 11, 2007

2. Overview Summary of structural imaging studies of autism Findings from our longitudinal autism study Challenges & benefits to imaging across development Future projects & goals for NA-MIC

3. Structural Imaging in Autism

4. MRI Studies of Brain Volume in Autism StudyBrain FindingSubject Age Piven et al. (1992) mid-sagittal area 18 - 53 yrs Piven et al (1995) total brain volume14 – 29 yrs Courchesne et al (2001)cerebral. gray and white2 – 4 yrs only Sparks et al (2002)total cerebral 3 - 4 yrs Aylward et al (2002)TBV (HFA) under 12 yrs Lotspeich et al (2004)cerebral gray (N=52) 7 – 18 yr Herbert et al (2004)cerebral white5 – 11 yrs Hazlett at al (2005)gray matter volume14 - 29 yrs Palmen et al (2005)TBV, cerebral gray (N=21)7 – 15 yrs Limitations: no developmental studies, heterogeneity of samples

5. When compared to typically developing individuals…. increased brain weight in autism macrocephaly in 20% increased brain volume on MRI enlarged tissue volumes (both WM & GM) age effects present

6. Longitudinal MRI study of brain development in autism

7. 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

8. 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

9. 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

10. 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

12. autism typical autism typical mean (SE)mean (SE) % diff p mean (SE)mean (SE) % diff p cerebrum 941.5 (10.5) 903.1 (17.4) 4.2 0.06 gray 676.7 (7.7)652.7 (12.2) 3.7 0.1 gray 676.7 (7.7)652.7 (12.2) 3.7 0.1 white 264.7 (3.1)250.4 (5.4) 5.7 0.02 white 264.7 (3.1)250.4 (5.4) 5.7 0.02 autism dev delayed autism dev delayed mean (SE)mean (SE) % diff p mean (SE)mean (SE) % diff p cerebrum 941.5 (10.5) 874.4 (17.2)7.7 0.0008 gray 676.7 (7.7)633.5 (12.4)6.8 0.003 gray 676.7 (7.7)633.5 (12.4)6.8 0.003 white 264.7 (3.1)240.9 (5.1)9.9 0.0001 white 264.7 (3.1)240.9 (5.1)9.9 0.0001 UNC Longitudinal MRI Study of Autism

13. Substructures of interest

14. Relationship between Brain Volume and Autistic Features Social Communication Atypical Behaviors

15. Substructures of interest Basal ganglia –Caudate –Putamen –Globus pallidus AmygdalaHippocampus

16. Caudate Enlargement in Autism Nage t p Nage t p Study 1 autism3512-292.45.01 controls3612-29 Study 2 autism15m = 27.73.19.003 controls15m = 30.3 (Sears, Vest, Bailey, Ransom, Piven 1999)

17. Clinical Correlates of Caudate Volume ADI Domain Spearman r p social 0.19 ns communication 0.05 ns ritualistic/repetitive -0.360.02 (Sears, Vest, Bailey, Ransom, Piven 1999)

18. Hollander et al. Biological Psychiatry 2005 Clinical Correlates of Caudate Volume

19. 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

20. 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

21. Clinical Correlates of Basal Ganglia Volume in 2 year olds with Autism Caudate Globus Pallidus Putamen B (SE) p * B (SE) p B (SE) p ADI Item Minor Change-.35 (.230).034 -.115 (.071).055-.439 (.135).001 Rituals - -- Body Mvt.413 (.150).004.126 (.049).007.140.140.163 * one-sided t-test

22. MRI Studies of Amygdala Volume in Autism Sparks (2002)45 ASDinc vs. TYP and DD controls (3-4 yr olds) Schumann (2004)61 ASDincreased in 7-12 year olds, not increased 12-17 year olds

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. Imaging Development

25. Challenges to 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

26. total cerebral To total cerebral white matter frontal grayparietal gray temporal gray occipital gray Longitudinal Studies: Brain Development During Childhood and Adolescence Age in years Peak 12 y 12 yrs 16 yrs 20 yrs 4 more sensitive for detecting growth patterns, even in the presence of large inter-individual variation and non-linear growth Longitudinal Methods time 1  time 2 Giedd et al., Nature Neuroscience, 1999

27. Gray matter maturation Gogtay, Giedd et al PNAS 2004. N = 13 (7 male, 6 female) typical subjects

28. Time Course of Critical Events in the Determination of Human Brain Morphometry Neurodevelopmental processes, cortical synapse density, and their relationship to gray and white matter volumes on MRI. Giedd et al. 1999, Sowell et al. 1999.

29. Neonatal Brain MRI T2 T1 non- myelinated white matter early myelinated white matter gray matter

30. Corpus Callosum Neonate (2 wks) Adult Corpus callosum: FA along Commissural bundles Infant (1 year)

31. Infancy to Childhood Hermove et al., NeuroImage 2005.

32. Data

33. Data Structural MRI Diffusion Tensor Behavioral, cognitive, developmental Processed longitudinal data

34. 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

35. Image Processing

36. Tissue segmentation –2 yr old EMS hard segmentations EMS segmentations overlaid on MRI

37. Automatic parcellation by template warping Manually-derived parcellation “warped” to new subjects

38. Substructures of interest Basal ganglia –Caudate –Putamen –Globus pallidus AmygdalaHippocampus

39. 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 *As of Nov06 Also have segmented data for: Put/GP, Hipp, CC area, Ventricles, Ant Cing

40. Challenges to Image Processing

41. Registration of images to a common atlas Inhomogeneities – bias correction Tissue contrast – myelination Brain shape changes across development Challenges to Image Processing

42. Future Directions

43. Examination of longitudinal data e.g., 2-4 years old, follow-ups at 6-8 Development & application of novel image processing methods e.g., shape, cortical thickness

44. Change from 2 to 4 years These frames show the evolution from 2 year old to 4 year old using high dimensional fluid warping (Joshi)

45. Surface growth maps age 2 4

46. NA-MIC Collaboration Our site can offer NAMIC collaborators: 1) Pediatric dataset of sMRI & DTI 2) Longitudinal data 3) Segmented datasets (e.g., substructures, ROIs) to be used as validation tools

47. NA-MIC Collaboration Goals/Projects for NAMIC collaborators: 1) Pipelines for growth-rate analysis 2) Longitudinal analysis of cortical thickness, cortical folding patterns, etc. 3) Automating DTI processing, creating more regionally defined DTI analysis (?) 4) Development of new segmentation protocols (e.g., dorsolateral prefrontal cortex) 5) Quantify shape changes over time to allow for analysis with behavioral data

48. 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 Many thanks to the families that have generously participated !