A longitudinal study of brain development

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

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

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Longitudinal MRI study of brain development in
autism
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Features of Autism
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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

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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

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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

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Data Available
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Data Available

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

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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
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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, Cerebellar vermis
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Requirements/Contraints
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Requirements/Constraints

• Registration of images to a common atlas
• Inhomogeneities bias correction
• Tissue contrast myelination
• Brain shape changes across development

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Existing Image Processing
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Tissue segmentation
EMS hard segmentations
EMS segmentations overlaid on MRI
Shown here 2 year old
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Lobe parcellation by template warping
Manually-derived parcellation warped to new
subjects
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UNC Longitudinal MRI Study of Autism

• 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

Hazlett et al Arch Gen Psych 2005
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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
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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
• 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

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Segmented Substructures (ROIs)

• Basal ganglia
• Caudate
• Putamen
• Globus pallidus
• Amygdala
• Hippocampus

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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
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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
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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) lt.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
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Other ROIs

• Corpus callosum (midsaggital)
• Ventricles
• Anterior Cingulate
• Cerebellar vermis

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Surface growth maps cortical thickness by lobe
age 2 4
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Resources

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

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NA-MIC Collaboration

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

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NA-MIC Collaboration

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

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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