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A longitudinal study of A longitudinal study of brain development brain development
in autismin autism
Heather Cody Hazlett, PhDHeather Cody Hazlett, PhD
Neurodevelopmental Disorders Research CenterNeurodevelopmental Disorders Research Center& UNC-CH Dept of Psychiatry& UNC-CH Dept of Psychiatry
NA-MIC Core1 Mtg Boston, MANA-MIC Core1 Mtg Boston, MA May 30, 2007May 30, 2007
OverviewOverview
Summary of hypothesesSummary of hypotheses
Data available to NA-MICData available to NA-MIC
Specific requirements/constraints of projectSpecific requirements/constraints of project
Existing image processing of dataExisting image processing of data
ResourcesResources
Longitudinal MRI study of brain Longitudinal MRI study of brain development in autismdevelopment in autism
Features of AutismFeatures of Autism
Social deficits
Communicationdeficits
AtypicalBehaviors
Longitudinal MRI study of brain Longitudinal MRI study of brain development in autismdevelopment 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
Longitudinal MRI study of brain Longitudinal MRI study of brain development in autismdevelopment 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
Developmental StudiesDevelopmental Studies
Difficult for very young children and/or Difficult for very young children and/or lower functioning children to remain stilllower functioning children to remain still
May need to remain motionless for long May need to remain motionless for long periods of timeperiods of time
Sleep studies vary in success ratesSleep studies vary in success rates
Subjects may require training and practice Subjects may require training and practice – this adds to expense of project– this adds to expense of project
Data Available Data Available
Data Available Data Available
Structural MRI Structural MRI
Diffusion TensorDiffusion Tensor
Behavioral, cognitive, developmentalBehavioral, cognitive, developmental
Processed pediatric longitudinal dataProcessed pediatric longitudinal data
Data* Data*
Structural MRIStructural MRI
TI:TI: coronal 3D SPGR IRprep, 0.78 x 0.78 x 1.5 mm, coronal 3D SPGR IRprep, 0.78 x 0.78 x 1.5 mm, 124 124 slices, 5 TE/12 TR, 20 FOV, 1 NEX, 256x192slices, 5 TE/12 TR, 20 FOV, 1 NEX, 256x192
PD/T2: coronal FSE, 0.78 x 0.78 x 3.0 mm, 128 slices, PD/T2: coronal FSE, 0.78 x 0.78 x 3.0 mm, 128 slices, 20 FOV, 17 TE/7200 TR, 1 NEX, 256x16020 FOV, 17 TE/7200 TR, 1 NEX, 256x160
DTI DTI
axial oblique 2D spin echo EPI, 0.93 x 0.97 x 3.8 axial oblique 2D spin echo EPI, 0.93 x 0.97 x 3.8 mm, 30 slices, 24 FOV, 12 dirmm, 30 slices, 24 FOV, 12 dir
*All scans collected on 1.5T GE scanner
Data Data
Processed datasets 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 6Typical 25 22 21 11 12 10FX 45 47 47 11 11 10
Also have segmented data for:
Put/GP, Hipp, CC area, Ventricles, Ant Cing, Cerebellar vermis
Requirements/Contraints Requirements/Contraints
Registration of images to a common atlasRegistration of images to a common atlas
Inhomogeneities – bias correctionInhomogeneities – bias correction
Tissue contrast – myelinationTissue contrast – myelination
Brain shape changes across developmentBrain shape changes across development
Requirements/ConstraintsRequirements/Constraints
Existing Image Processing Existing Image Processing
Tissue segmentationTissue segmentation
EMS hard segmentations
EMS segmentations overlaid on MRI
Shown here – 2 year old
Lobe parcellation by template warpingLobe parcellation by template warping
Manually-derived parcellation “warped” to new subjects
NN % male years (SD)% male years (SD) IQ-SS (SD) IQ-SS (SD)**
AutismAutism 5151 88% 88% 2.7 (0.3)2.7 (0.3) 54.2 (9.4) 54.2 (9.4)
ControlsControls 2525
DDDD 1111 55% 55% 2.7 (0.4)2.7 (0.4) 59.7 (9.4) 59.7 (9.4)
TYPTYP 1414 64% 64% 2.4 (0.4) 2.4 (0.4) 107.5 (18.7) 107.5 (18.7)
* IQ-SS = Mullen composite Standard Score* IQ-SS = Mullen composite Standard Score
UNC Longitudinal MRI Study of AutismUNC Longitudinal MRI Study of Autism
Hazlett et al Arch Gen Psych 2005
UNC Longitudinal MRI Study of AutismUNC Longitudinal MRI Study of Autism
autismautism controls controls
mean (SE)mean (SE) mean (SE)mean (SE) % diff % diff p p
TBV TBV 1264.6 (13.4) 1208.1 (16.2) 4.7 0.008 1264.6 (13.4) 1208.1 (16.2) 4.7 0.008
cerebrumcerebrum 941.5 (10.5) 890.5 (12.3) 5.7 941.5 (10.5) 890.5 (12.3) 5.7 0.0020.002
cerebellum cerebellum 114.1 (1.5)114.1 (1.5) 114.4 (2.2)114.4 (2.2) 0.3 0.3 0.9 0.9
Adjusted for Gender and AgeAdjusted for Gender and Age
autismautism controls controls
mean (SE)mean (SE) mean (SE)mean (SE) % diff % diff p p
TBV TBV 1264.6 (13.4) 1208.1 (16.2) 4.7 1264.6 (13.4) 1208.1 (16.2) 4.7 0.008 0.008
cerebrumcerebrum 941.5 (10.5) 890.5 (12.3) 5.7 0.002941.5 (10.5) 890.5 (12.3) 5.7 0.002
graygray 676.7 (7.7) 676.7 (7.7) 644.2 (8.8)644.2 (8.8) 5.0 5.0 0.005 0.005
whitewhite 264.7 (3.1)264.7 (3.1) 246.2 (3.7)246.2 (3.7) 7.5 7.5 0.0001 0.0001
cerebellum 114.1 (1.5) 114.4 (2.2)cerebellum 114.1 (1.5) 114.4 (2.2) 0.3 0.3 0.9 0.9
UNC Longitudinal MRI Study of AutismUNC Longitudinal MRI Study of Autism
Segmented Substructures (ROIs)Segmented Substructures (ROIs)
Basal gangliaBasal ganglia– CaudateCaudate– PutamenPutamen– Globus Globus
palliduspallidus
AmygdalaAmygdala
HippocampusHippocampus
DescriptivesDescriptives
%% YearsYears Cognitive* Cognitive* Adaptive**Adaptive**GroupGroup NN MaleMale M (SD)M (SD) M (SD) M (SD) M (SD)M (SD)
autismautism 5252 87%87% 2.7 (0.3)2.7 (0.3) 54.1 (9.3) 54.1 (9.3) 60.8 (5.9)60.8 (5.9)
controlscontrols 3333 70%70% 2.6 (0.5)2.6 (0.5) 87.4 (28.6) 87.4 (28.6) 850.4 (21.1)850.4 (21.1)
developmental delaydevelopmental delay 1212 67%67% 2.8 (0.4)2.8 (0.4) 55.5 (6.7) 55.5 (6.7) 65.8 (14.0)65.8 (14.0) typically developingtypically developing 2121 71%71% 2.4 (0.5)2.4 (0.5) 106.6 (16.8) 106.6 (16.8) 98.3 (13.4)98.3 (13.4)
* Cognitive estimate from Mullen Composite Standard Score* Cognitive estimate from Mullen Composite Standard Score** Adaptive behavior estimate from Vineland Adaptive Behavior Composite** Adaptive behavior estimate from Vineland Adaptive Behavior Composite
Basal Ganglia Volumes in 2 Year Olds with AutismBasal Ganglia Volumes in 2 Year Olds with Autism(adjusted for TBV)(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
Amygdala/Hippocampus Amygdala/Hippocampus Volume in 2 Year Olds with AutismVolume 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)(adjusted for TBV)
Other ROIs Other ROIs
• Corpus callosum (midsaggital)
• Ventricles
• Anterior Cingulate
• Cerebellar vermis
Surface growth maps &Surface growth maps &cortical thickness by lobecortical thickness by lobe
age 2 4
ResourcesResources
CS programmer – Clement VachetCS programmer – Clement Vachet
Image processing RA support (unfunded)Image processing RA support (unfunded)
Image processing lab at UNC and existing Image processing lab at UNC and existing NA-MIC CoresNA-MIC Cores
NA-MIC CollaborationNA-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)
NA-MIC CollaborationNA-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)
ContributorsContributorsJoe Piven, MDGuido Gerig, PhD
Sarang Joshi, PhDMichele Poe, PhD Chad Chappell, MAJudy Morrow, PhDNancy Garrett, BS, OTA
Robin Morris, BARachel Smith, BAMike Graves, MChESarah Peterson, BAMatthieu Jomier, MSCarissa Cascio, PhDMatt Mosconi, PhDMatt Mosconi, PhD
Martin Styner, PhDAllison Ross, MD James MacFall, PhD
Alan Song, PhDValerie Jewells, MD James Provenzale, MD Greg McCarthy, Ph.D.John Gilmore, MDAllen Reiss, MD
UNC Fragile X CenterNDRC Research Registry
Funded by the National Institutes of Health