1 / 48

A longitudinal study of brain development in autism

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 11, 2007. Overview. Summary of structural imaging studies of autism

damian
Download Presentation

A longitudinal study of brain development in autism

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 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 volume 14 – 29 yrs Courchesne et al (2001) cerebral. gray and white 2 – 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 white 5 – 11 yrs Hazlett at al (2005) gray matter volume 14 - 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. 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

  8. UNC Longitudinal MRI Study of Autism N % male years (SD) IQ-SS (SD)* Autism51 88% 2.7 (0.3) 54.2 (9.4) Controls25 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 Hazlett et al Arch Gen Psych 2005

  9. UNC Longitudinal MRI Study of Autism autism controls 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. UNC Longitudinal MRI Study of Autism autism controls mean (SE) mean (SE) % diff p TBV 1264.6 (13.4) 1208.1 (16.2) 4.7 0.008 cerebrum941.5 (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

  11. UNC Longitudinal MRI Study of Autism autism typical mean (SE) mean (SE) % diff p cerebrum 941.5 (10.5) 903.1 (17.4) 4.2 0.06 gray676.7 (7.7) 652.7 (12.2) 3.7 0.1 white 264.7 (3.1) 250.4 (5.4) 5.7 0.02 autism dev delayed mean (SE) mean (SE) % diff p cerebrum941.5 (10.5) 874.4 (17.2) 7.7 0.0008 gray676.7 (7.7) 633.5 (12.4) 6.8 0.003 white 264.7 (3.1) 240.9 (5.1) 9.9 0.0001

  12. Substructures of interest

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

  14. Substructures of interest Basal ganglia • Caudate • Putamen • Globus pallidus Amygdala Hippocampus

  15. Caudate Enlargement in Autism age t p Study 1 autism 35 12-29 2.45 .01 controls 36 12-29 Study 2 autism 15 m = 27.7 3.19 .003 controls 15 m = 30.3 (Sears, Vest, Bailey, Ransom, Piven 1999)

  16. Clinical Correlates of Caudate Volume ADI Domain Spearman r p social 0.19 ns communication 0.05 ns ritualistic/repetitive -0.36 0.02 (Sears, Vest, Bailey, Ransom, Piven 1999)

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

  18. Descriptives % Years Cognitive* Adaptive** Group N Male M (SD) M (SD) M (SD) autism 52 87% 2.7 (0.3) 54.1 (9.3) 60.8 (5.9) controls 33 70% 2.6 (0.5) 87.4 (28.6) 850.4 (21.1) developmental delay 12 67% 2.8 (0.4) 55.5 (6.7) 65.8 (14.0) typically developing 21 71% 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

  19. Basal Ganglia Volumes in 2 Year Olds with Autism(adjusted for TBV) Aut v Total ControlsAut v TYPAut 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

  20. 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

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

  22. Amygdala/Hippocampus Volume 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 % 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

  23. FXS-autism vs autism-nonFXS FXS (N=35); Controls (N=38); FXS + autism (N=12); Autism - nonFXS (44)

  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. To total cerebral white matter Longitudinal Studies: Brain Development During Childhood and Adolescence total cerebral frontal gray parietal gray Longitudinal Methods time 1  time 2 12 yrs 12 yrs more sensitive for detecting growth patterns, even in the presence of large inter-individual variation and non-linear growth Peak 12 y temporal gray occipital gray 16 yrs 20 yrs Age in years 4 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 gray matter non-myelinated white matter early myelinated white matter T2 T1

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

  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. Data • 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 (+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 *As of Nov06

  36. Image Processing

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

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

  39. Challenges to Image Processing

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

  41. Future Directions

  42. Future Directions 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

  43. 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)

  44. Surface growth maps age 2 4

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

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

  47. Contributors 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 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 Many thanks to the families that have generously participated !

More Related