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