Accuracy of measurements used to quantify cranial asymmetry in deformational plagiocephaly.

OBJECTIVE: Various measurements are used to quantify cranial asymmetry in deformational plagiocephaly (DP), but studies validating cut-off values and comparing the accuracy of such measurements are lacking. In this study, we compared the accuracy of four different measurements in classifying children with and without DP diagnosed by visual assessment, and sought to determine their optimal cut-off values. STUDY DESIGN: Two experts rated 407 3D craniofacial images of children aged between 3 and 36 months old using the Argenta classification. We then measured the following asymmetry-related variables from the images: Oblique Cranial Length Ratio (OCLR), Diagonal Difference (DD), Posterior Cranial Asymmetry Index (PCAI), and weighted Asymmetry Score (wAS). We created receiver operating characteristic curves to evaluate the accuracy of these variables. RESULTS: All variables performed well, but OCLR consistently provided the best discrimination in terms of area under the curve values. Subject's age had no clear effect on the cut-off values for OCLR, PCAI, and wAS; however, the cut-off for DD increased monotonically with age. When subjects with discrepant expert ratings were excluded, the optimal cut-off values for DP (Argenta class ≥ 1) across all age-groups were 104.0% for OCLR (83% sensitivity, 97% specificity), 10.5% for PCAI (90% sensitivity, 90% specificity), and 24.5 for wAS (88% sensitivity, 90% specificity). CONCLUSION: We recommend using OCLR as the primary measurement, although PCAI and wAS may also be useful in monitoring cranial asymmetry. The threshold of relative asymmetry required for a deformation to appear clinically significant is not affected by the child's age, and DD has no additional utility in monitoring DP compared to using only OCLR.

The course of positional cranial deformation from 3 to 12 months of age and associated risk factors: a follow-up with 3D imaging.

Deformational plagiocephaly is reported in up to 46.6 % of healthy infants, with the highest point prevalence at around 3 months of age. Few prospective studies on the natural course of skull deformation have been conducted, and we know of no studies using 3D imaging starting from the highest point prevalence period. In this prospective, population-based cohort study, we describe the course of cranial asymmetry and shape in an unselected population using 3D stereophotogrammetry and investigate factors associated with late cranial deformation and failure to recover from previous deformation. We evaluated 99 infants at 3, 6, and 12 months of age. We acquired 3D craniofacial images and performed structured clinical examinations and parental interviews at each visit. Eight outcome variables, representing different aspects of cranial shape, were calculated from a total of 288 3D images. Scores of asymmetry-related variables improved throughout the observation period. However, the rate of correction for cranial asymmetry decreased as the infants grew older, also in relation to the rate of head growth, and a significant amount of asymmetry was still present at 12 months. Positional preference at 3 months predicted an unfavorable course of cranial asymmetry after 3 months, increasing the risk for DP persisting. What is known: • The prevalence of deformational plagiocephaly spontaneously decreases after the first months of life. • Limited neck range of motion and infant positional preference increase the risk of deformational plagiocephaly during the first months of life. What is new: • Positional preference at 3 months predicts an unfavorable spontaneous course of deformation also from three to 12 months of age, presenting a potential target for screening and treatment. • The spontaneous rate of correction for cranial asymmetry decreases after 6 months of age, also in relation to the rate of head growth.

Analyzing infant head flatness and asymmetry using kernel density estimation of directional surface data from a craniofacial 3D model.

Infant skull deformation is analyzed using the distribution of head normal vector directions computed from a 3D image. Severity of flatness and asymmetry are quantified by functionals of the kernel estimate of the normal vector direction density. Using image data from 99 infants and clinical deformation ratings made by experts, our approach is compared with some recently suggested methods. The results show that the proposed method performs competitively. Copyright © 2016 John Wiley & Sons, Ltd.

Comparing Facial 3D Analysis With DNA Testing to Determine Zygosities of Twins.

The aim of this study was to compare facial 3D analysis to DNA testing in twin zygosity determinations. Facial 3D images of 106 pairs of young adult Lithuanian twins were taken with a stereophotogrammetric device (3dMD, Atlanta, Georgia) and zygosity was determined according to similarity of facial form. Statistical pattern recognition methodology was used for classification. The results showed that in 75% to 90% of the cases, zygosity determinations were similar to DNA-based results. There were 81 different classification scenarios, including 3 groups, 3 features, 3 different scaling methods, and 3 threshold levels. It appeared that coincidence with 0.5 mm tolerance is the most suitable feature for classification. Also, leaving out scaling improves results in most cases. Scaling was expected to equalize the magnitude of differences and therefore lead to better recognition performance. Still, better classification features and a more effective scaling method or classification in different facial areas could further improve the results. In most of the cases, male pair zygosity recognition was at a higher level compared with females. Erroneously classified twin pairs appear to be obvious outliers in the sample. In particular, faces of young dizygotic (DZ) twins may be so similar that it is very hard to define a feature that would help classify the pair as DZ. Correspondingly, monozygotic (MZ) twins may have faces with quite different shapes. Such anomalous twin pairs are interesting exceptions, but they form a considerable portion in both zygosity groups.

Bayesian LASSO, scale space and decision making in association genetics.

BACKGROUND: LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. RESULTS: We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

Family-based clusters of cognitive test performance in familial schizophrenia.

BACKGROUND: Cognitive traits derived from neuropsychological test data are considered to be potential endophenotypes of schizophrenia. Previously, these traits have been found to form a valid basis for clustering samples of schizophrenia patients into homogeneous subgroups. We set out to identify such clusters, but apart from previous studies, we included both schizophrenia patients and family members into the cluster analysis. The aim of the study was to detect family clusters with similar cognitive test performance. METHODS: Test scores from 54 randomly selected families comprising at least two siblings with schizophrenia spectrum disorders, and at least two unaffected family members were included in a complete-linkage cluster analysis with interactive data visualization. RESULTS: A well-performing, an impaired, and an intermediate family cluster emerged from the analysis. While the neuropsychological test scores differed significantly between the clusters, only minor differences were observed in the clinical variables. CONCLUSIONS: The visually aided clustering algorithm was successful in identifying family clusters comprising both schizophrenia patients and their relatives. The present classification method may serve as a basis for selecting phenotypically more homogeneous groups of families in subsequent genetic analyses.

A note on estimating the posterior density of a quantitative trait locus from a Markov chain Monte Carlo sample.

We provide an overview of the use of kernel smoothing to summarize the quantitative trait locus posterior distribution from a Markov chain Monte Carlo sample. More traditional distributional summary statistics based on the histogram depend both on the bin width and on the sideway shift of the bin grid used. These factors influence both the overall mapping accuracy and the estimated location of the mode of the distribution. Replacing the histogram by kernel smoothing helps to alleviate these problems. Using simulated data, we performed numerical comparisons between the two approaches. The results clearly illustrate the superiority of the kernel method. The kernel approach is particularly efficient when one needs to point out the best putative quantitative trait locus position on the marker map. In such situations, the smoothness of the posterior estimate is especially important because rough posterior estimates easily produce biased mode estimates. Different kernel implementations are available from Rolf Nevanlinna Institute's web page (http://www.rni.helsinki.fi/;fjh).