1. Craniofacial Dysmorphism & Fetal Alcohol Exposure
Tatiana Foroud & Peter Hammond (PIs)
Leah Wetherill, Mike Suttie

2. Update on Image Collection
Site
3D Images
(# subjects)
Plans for 2013
San Diego
237 (215)
Camera on location
UCLA/USC
52 (52)
Camera brought to site
Atlanta
147 (147)
Minneapolis
50 (50)
Ukraine
47 (34)
Camera to be put at location (training March/April 2013)
South Africa (Jacobson)
223 (223)
Study visit scheduled
(Oct 1-12, 2013)
South Africa (May)
37 (37)
No longer a site
South Africa (PASS)
1,738 (1,260)

3. Update on Image Numbers

4. Coordinating Image Collection
For sites without a dedicated camera, will subjects be brought in for Dysmorphology?
Do we need to develop alternate plans to ensure image collection?
Can we collect longitudinal images at these sites?

5. Update on Saliva Collection
Site
Saliva
Plans for 2013
San Diego
131
Continue to collect
UCLA/USC 45
Atlanta 28
Minneapolis
Ukraine
0 (no approval)
No plans to initiate
South Africa (Jacobson)
225
Only collect if new subjects
South Africa (PASS)
0 (collected as part of parent study)
No need to initiate

6. Saliva Collection Collect saliva from all subjects
Obtain additional DNA for future studies
Appropriate since amount of DNA obtained has varied

7. Use of DNA for Research GWAS completed in 240 individuals
700,000 SNPs genotyped
Variable alcohol exposure and phenotype
Data used for candidate gene studies
Genotype x alcohol interaction

8. Zebrafish Model: Gene Pathways
Collaboration with Johann Eberhart (Pilot Study)
Platelet-Derived Growth Factor (PDGF)
5 genes
Fibroblast Growth Factor (FGF)
4 genes
Bone Morphogenetic Protein (BMP)
2 genes
Examine 5 facial phenotypes related to craniofacial abnormalities seen in the zebrafish model

9. PDGFRB and midfacial depth

10. Use of DNA for Research
DNA will be made available to Dipak Sarkar (Pilot Study)
Plans in place to perform another GWAS or similar technology in later years of the grant
Remain in contact with PASS investigators (Ingrid Holm) to ensure coordination with genetic studies

11. Collaboration with PASS
Submitted request for data from DM-STAT for PASS subjects with images
Received growth measurements which will be used in initial analyses
Cannot receive alcohol exposure data for several years

12. PROGRESS ON 4 OTHER OBJECTIVES
Develop a screening tool that would utilize the data from the 3D facial images and could be widely used to accurately identify individuals with a high likelihood of alcohol exposure
Recruit and analyze facial imaging data from very young populations to develop a screening tool that accurately identifies high risk individuals for future intervention
Combine face images, neurobehavioral data and brain images to identify common pathways and hence improve diagnosis of prenatal alcohol exposure
Extend existing and develop novel techniques and associated software to cope with demands of larger datasets and more diverse comparison of controls, alcohol exposed and other developmentally delayed subjects while accommodating multiple anatomical images per subject

13. PROGRESS ON 4 OTHER OBJECTIVES
Develop ascreening tool that would utilize the data from the 3D facial images and could be widely used to help identify individuals with a high likelihood of alcohol exposure
Recruit and analyze facial imaging data from very young populations to develop a screeniand tool that accurately identifyie high risk individuals for future intervention (PASS/UKRAINE)
Combine face images, neurobehavioral data and brain datas to identify common pathways and hence improve diagnosis of prenatal alcohol exposure
Extend existing and develop novel techniques and associated software to cope with demands of larger datasets and more diverse comparison of controls, alcohol exposed and other developmentally delayed subjects while accommodating multiple anatomical images per subject

14. VISUALISING INDIVIDUAL FACIAL DYSMORPHISM
DYNAMIC MORPH
between individual & average of matched controls
highlights facial dysmorphism
FACE SIGNATURE
quanitifies facial dysmorphism
Red-
contracted
Blue-
expanded
Green-
coincident
(Suttie et al, 2013: Pediatrics, in press)

15. FACIAL DYSMORPHISM ACROSS FASD
(Suttie et al, 2013: Pediatrics, in press)

16. EXAMPLES OF UPPER LIP SIGNATURES
±1.0 SD
FAS
PFAS
±1.5 SD
±2.0 SD
(Suttie et al, 2013: Pediatrics, in press)

17. RECOGNITION OF FAS FACIAL FEATURES
probability of correctly classifying two individuals, one taken from each of the two groups being compared
HC vs FAS
HC vs FAS+PFAS CM
LDA
SVM
Face
0.967
1.00
0.892
0.909
Periorbit
0.983
0.917
0.900
Perioral
0.850
0.884
0.883
Perinasal
0.833
0.934
0.825
0.817
Profile
0.933
0.925
(Suttie et al, 2013: Pediatrics, in press)

18. FACE SIGNATURE GRAPH clusters individuals with similar face signatures
Children with FAS
clustered together
(boxed faces)
Children without FAS
clustered together
Red-
contracted
Blue-
expanded
Green-
coincident
(Suttie et al, 2013: Pediatrics, in press)

19. DETECTION OF HE FACIAL DIFFERENCES presence/absence of FAS like facial differences in HE group concur with neurobehavioral differences
FAS+PFAS
HE2 – control like facial differences
HE1 – FAS/PFAS like facial differences
FAS
PFAS
HE1
HE2 HC
HE1 vs HE2 (t)
WISC IQ
65.4
63.0
65.5
73.3
-1.80†
CVLT-C
test1
42.7
41.5
40.0
47.3
45.8
-2.02*
test2
88.5
88.3
84.3
93.7
93.2
-1.89†
† p < *p <0.05
(Suttie et al, 2013: Pediatrics, in press)

20. PRELIMINARY ANALYSIS OF US CAUCASIAN COHORT
SA Cape coloured
Group n
mean age HC 74
11.8 69
10.1 HE 42
12.3 75
10.4
FAS/PFAS 17
13.6 48
10.3

21. FAS/HE FACE SIGNATURES
SMS54
SMS130
ESL10991 HE

22. CAUCASIAN FACIAL GROWTH (PC1)

23. unblinded closest mean classification
CAUCASIAN HC VS FAS
unblinded closest mean classification
SMS54
ESL10991

24. blinded classification of 3 FAS
CAUCASIAN HC VS FAS
blinded classification of 3 FAS
SMS54
ESL10991
SMS130

25. DISCRIMINATION TESTING blinded drop-1-out
FAS vs HC
Sens
Spec
Accuracy
Reduced face
76.5
76.9
76.8
Thin Profile
70.6
84.6
81.2
Mouth
94.2
88.4

26. SIGNATURE GRAPHS FOR FAS AND HE
ESL10991
SMS54
SMS130
ESL10991
SMS54
SMS130

27. CONTRIBUTION TO MOUSE PROJECT
Lipinski et al, 2012: PLoS ONE 7(8)

28. MULTIPLE COMPONENT MODELLING
-For every image:
Landmarks
Separated regions in 3D space
-For every region we have at least 1 landmark
Select landmark
Use landmark as seed point
Analyse connectivity
Warping (TPS) algorithm now fed individual 3D region
Components put back together for PCA

29. Multi Atlas Segmentation applied to in vivo mouse brain MRI
Manual segmentation
Labor intensive
Requires training and prior knowledge
Inter-operater variability

30. Multi Atlas Segmentation applied to in vivo mouse brain MRI
Da Ma1,2, M. Jorge Cardoso1, Marc Modat1, Nick Powell1,2, Holly Holmes2, Mark Lythgoe2, and Sébastien Ourselin1,3
1 Centre for Medical Imaging Computing, 2 Centre for Advanced Biomedical Imaging, 3 Dementia Research Centre, University College London, UK

31. PARCELLATION PIPELINE
AUTOMATIC STRUCTURAL
PARCELLATION PIPELINE
Non-uniform intensity normalization (N3) [1]
Create brain mask (MAPS) [2]
Image registrations
An iterative approach to estimate the true tissue intensities and normalise their values
Create mask to eliminate everything but the brain region
Perform non rigid registration, warping each image into correspondence with every other
Perform segmentation based on single atlas to produce segmented image
Repeat process optimizing the altas on each iteration.
The parameter of multi-atlas segmentation propagation was optimized by running different combinations within the atlas and find the combination that can achieve the highest segmentation performance
Single-Atlas segmentation propagation
Multi-Atlas Segmentation Propagation
Parameter optimisation
[1] Sled et al IEEE transactions on medical imaging
[2] Leung et al NeuroImage

32. Face screening tools – more details tomorrow