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

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)

Update on Image Numbers

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?

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

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

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

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

PDGFRB and midfacial depth

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

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

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

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

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)

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

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

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)

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)

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 < 0.08 *p <0.05 (Suttie et al, 2013: Pediatrics, in press)

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

FAS/HE FACE SIGNATURES SMS54 SMS130 ESL10991 HE

CAUCASIAN FACIAL GROWTH (PC1)

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

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

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

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

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

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

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

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

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. 1998 IEEE transactions on medical imaging [2] Leung et al. 2011 NeuroImage

Face screening tools – more details tomorrow