Three-Dimensional Laser Surface Imaging and Geometric Morphometrics Resolve Frontonasal Dysmorphology in Schizophrenia

Hennessy, Robin J.; Baldwin, Patrizia A.; Browne, David J.; Kinsella, Anthony; Waddington, John L.

doi:10.1016/j.biopsych.2006.08.045

Cited by 52 publications

(67 citation statements)

References 68 publications

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“…3 So far, a large spectrum of medical disciplines have utilised these methods in the investigations of facial growth, facial dysmorphology, craniofacial identification, as well as the influence of different medical conditions on facial phenotype. [4][5][6][7][8][9][10][11][12] Therefore, an exciting opportunity has occurred to explore whether facial characteristics can serve as new diagnostic measures of illnesses. Childhood obesity is becoming an epidemic health problem.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

A population-based cross-sectional study of the association between facial morphology and cardiometabolic risk factors in adolescence

et al. 2013

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ObjectiveTo determine whether facial morphology is associated with fasting insulin, glucose and lipids independent of body mass index (BMI) in adolescents.DesignPopulation-based cross-sectional study.SettingAvon Longitudinal Study of Parents and Children (ALSPAC), South West of England.ParticipantsFrom the ALSPAC database of 4747 three-dimensional facial laser scans, collected during a follow-up clinic at the age of 15, 2348 white British adolescents (1127 males and 1221 females) were selected on the basis of complete data on cardiometabolic parameters, BMI and Tanner's pubertal stage.Main outcome measuresFasting insulin, glucose and lipids (triglycerides, high-density lipoprotein cholesterol (HDLc) and low-density lipoprotein cholesterol (LDLc)).ResultsOn the basis of the collection of 63 x, y and z coordinates of 21 anthropometric landmarks, 14 facial principal components (PCs) were identified. These components explained 82% of the variation in facial morphology and were used as exposure variables. With adjustment for age, gender and pubertal stage, seven PCs were associated with fasting insulin, none with glucose, three with triglycerides, three with HDLc and four with LDLc. After additional adjustment for BMI, four PCs remained associated with fasting insulin, one with triglycerides and two with LDLc. None of these associations withstood adjustment for multiple comparisons.ConclusionsThese initial hypotheses generating analyses provide no evidence that facial morphology is importantly related to cardiometabolic outcomes. Further examination might be warranted. Facial morphology assessment may have value in identifying other areas of disease risk.

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Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

A population-based cross-sectional study of the association between facial morphology and cardiometabolic risk factors in adolescence

et al. 2013

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“…Primarily, computational methodologies can be used to better understand the relationship between different features through associations. For example, the link between facial dysmorphology and brain problems has been shown in [5,6]. Relationships between facial dysmorphology and cognitive disorders are also being studied [7].…”

Section: Introductionmentioning

confidence: 98%

Shape-Based Classification of 3D Facial Data to Support 22q11.2DS Craniofacial Research

Wilamowska

Heike

et al. 2011

J Digit Imaging

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3D imaging systems are used to construct high-resolution meshes of patient's heads that can be analyzed by computer algorithms. Our work starts with such 3D head meshes and produces both global and local descriptors of 3D shape. Since these descriptors are numeric feature vectors, they can be used in both classification and quantification of various different abnormalities. In this paper, we define these descriptors, describe our methodology for constructing them from 3D head meshes, and show through a set of classification experiments involving cases and controls for a genetic disorder called 22q11.2 deletion syndrome that they are suitable for use in craniofacial research studies. The main contributions of this work include: automatic generation of novel global and local data representations, robust automatic placement of anthropometric landmarks, generation of local descriptors for nasal and oral facial features from landmarks, use of local descriptors for predicting various local facial features, and use of global features for 22q11.2DS classification, showing their potential use as descriptors in craniofacial research.

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“…Overlapping CF phenotypes in SZ and BP disorder may reflect shared maldevelopmental processes. Previous reports in the literature have described frontonasal variation in SZ, taking the form of an altered conformation of the CF surface 4 and of diminished nasal sinus volumes. 11 Hennessy and colleagues 25 also noted altered shape and size within the frontonasal region of BP disorder subjects.…”

Section: Overviewmentioning

confidence: 99%

“…3,6 More recently, digital imaging and morphometry have been employed to quantify CF variation. 1,4,8,11,12,13 We have attempted to overcome earlier methodologic limitations by adopting quantitative techniques that make the assessment of CF dysmorphology more objective and reliable. Dr Leslie Farkas, a dysmorphologist and plastic surgeon, developed these methods, adapting and expanding classical anthropometric measurement techniques to produce a comprehensive atlas of the head and face.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Measures of Craniofacial Dysmorphology in a Family Study of Schizophrenia and Bipolar Illness

Deutsch

Levy

Price

et al. 2015

SCHBUL

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Several laboratories, including ours, have reported an overrepresentation of craniofacial (CF) anomalies in schizophrenia (SZ). How might this dysmorphology arise in a brain-based disorder? Because the brain and face derive from shared embryologic primordia and morphogenetic forces, maldevelopmental processes may result in both CF and brain dysmorphology. Our approach is 2-pronged. First, we have employed, for the first time in the study of psychiatric disorders, objective measures of CF morphology that utilize an extensive normative database, permitting computation of standardized scores for each subject. Second, we have rendered these findings biologically interpretable by adopting principles of embryology in the analysis of dysmorphology. Dependent measures in this investigation focused on derivatives of specific embryonic primordia and were contrasted among probands with psychotic disorders, their first-degree relatives, and normal controls (NC). Subject groups included patients with a diagnosis of SZ (N = 39) or bipolar (BP) disorder with psychotic features (N = 32), their clinically unaffected relatives (N = 82 and N = 41, respectively), and NC (N = 95) subjects. Anomalies involving derivatives of frontonasal and mandibular embryonic primordia showed a clear association with psychotic illness, as well as familial aggregation in relatives in both diagnostic groups. In contrast, one class of CF anomalies emerged only among SZ probands and their first-degree relatives: dysmorphology arising along the junction of the frontonasal and maxillary prominence derivatives, manifested as marked asymmetries. This class was not overrepresented among the BP patients nor among their relatives, indicating that this dysmorphology appears to be specific to SZ and not a generalized feature of psychosis. We discuss these findings in light of embryologic models that relate brain regions to specific CF areas.

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Three-Dimensional Laser Surface Imaging and Geometric Morphometrics Resolve Frontonasal Dysmorphology in Schizophrenia

Cited by 52 publications

References 68 publications

A population-based cross-sectional study of the association between facial morphology and cardiometabolic risk factors in adolescence

A population-based cross-sectional study of the association between facial morphology and cardiometabolic risk factors in adolescence

Shape-Based Classification of 3D Facial Data to Support 22q11.2DS Craniofacial Research

Quantitative Measures of Craniofacial Dysmorphology in a Family Study of Schizophrenia and Bipolar Illness

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