Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Farnell, Damian J. J.; Galloway, Jennifer; Zhurov, Alexei I.; Richmond, Stephen

doi:10.1007/978-3-030-39343-4_9

Cited by 7 publications

(13 citation statements)

References 11 publications

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“…Farnell et al [ 5 , 6 , 7 , 8 , 9 ] describe the mathematical concepts and various applications of mPCA, including nested, non-nested and mixed approaches. A non-nested approach was used here, with median averaging of the covariance matrices used to attempt to deal with outliers.…”

Section: Methodsmentioning

confidence: 99%

“…mPCA has been described by Lecron et al [ 3 ] and used in the segmentation of spine radiographs. Subsequently, mPCA has been used to assess dental radiographs [ 4 ] and has been successful in assessing the influence of various covariates on facial shape, including ethnicity, sex, smiling and age [ 5 , 6 , 7 , 8 , 9 ]. These papers document the progression of the mPCA technique and provide the mathematical background.…”

Section: Introductionmentioning

confidence: 99%

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Multilevel Analysis of the Influence of Maternal Smoking and Alcohol Consumption on the Facial Shape of English Adolescents

et al. 2020

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This cross-sectional study aims to assess the influence of maternal smoking and alcohol consumption during pregnancy on the facial shape of non-syndromic English adolescents and demonstrate the potential benefits of using multilevel principal component analysis (mPCA). A cohort of 3755 non-syndromic 15-year-olds from the Avon Longitudinal Study of Parents and Children (ALSPAC), England, were included. Maternal smoking and alcohol consumption during the 1st and 2nd trimesters of pregnancy were determined via questionnaire at 18 weeks gestation. 21 facial landmarks, used as a proxy for the main facial features, were manually plotted onto 3D facial scans of the participants. The effect of maternal smoking and maternal alcohol consumption (average 1–2 glasses per week) was minimal, with 0.66% and 0.48% of the variation in the 21 landmarks of non-syndromic offspring explained, respectively. This study provides a further example of mPCA being used effectively as a descriptive analysis in facial shape research. This is the first example of mPCA being extended to four levels to assess the influence of environmental factors. Further work on the influence of high/low levels of smoking and alcohol and providing inferential evidence is required.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilevel Analysis of the Influence of Maternal Smoking and Alcohol Consumption on the Facial Shape of English Adolescents

et al. 2020

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“…Multilevel principal components analysis (mPCA) [50][51][52][53][54][55][56][57] provides another multivariate method to fit multilevel models to shape coordinate data. mPCA has been used to investigate facial shape changes by ethnicity and sex [51,52], the act of smiling [53,54], maternal smoking and alcohol intake [55], and facial shape changes during adolescence [56,57]. Group centroids were found in [56,57] at (integer) ages 12 to 17 (i.e., 6 groups) in order to explore component scores at an appropriate level of the model.…”

Section: Introductionmentioning

confidence: 99%

“…mPCA has been used to investigate facial shape changes by ethnicity and sex [51,52], the act of smiling [53,54], maternal smoking and alcohol intake [55], and facial shape changes during adolescence [56,57]. Group centroids were found in [56,57] at (integer) ages 12 to 17 (i.e., 6 groups) in order to explore component scores at an appropriate level of the model. A limitation of this approach was that age was treated indirectly as a discrete rather continuous quantity.…”

Section: Introductionmentioning

confidence: 99%

An exploration of adolescent facial shape changes with age via multilevel partial least squares regression

Farnell

Richmond

Galloway

et al. 2021

Computer Methods and Programs in Biomedicine

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Background and Objectives: Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males and females.Methods: 3D facial images were obtained for Welsh and Finnish male and female subjects at multiple ages from 12 to 17 years old. 1000 3D points were defined regularly for each shape by using "meshmonk" software. A three-level model was used here, including level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The mathematical formalism of mPLSR is given in an Appendix.Results: Differences in facial shape between the ages of 12 and 17 predicted by mPLSR agree well with previous results of multilevel principal components analysis (mPCA); buccal fat is reduced with increasing age and features such as the nose, brow, and chin become larger and more distinct. Differences due to ethnicity and sex are also observed. Plausible simulated faces are predicted from the model for different ages, sexes and ethnicities. Our models provide good representations of the shape data by consideration of appropriate measures of model fit (RMSE and R 2 ).Conclusions: Repeat measures in our dataset for the same subject at different ages can only be modelled indirectly at the lowest level of the model at discrete ages via mPCA. By contrast, mPLSR models age explicitly as a continuous covariate, which is a strong advantage of mPLSR over mPCA. These investigations demonstrate that multivariate multilevel methods such as mPLSR can be used to describe such age-related changes for dense 3D point data. mPLSR 3 might be of much use in future for the prediction of facial shapes for missing persons at specific ages or for simulating shapes for syndromes that affect facial shape in new subject populations.

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“…It allows one to account for groupings or clustering when analysing multivariate data such as shapes or image texture. It has been applied previously to investigate (in humans): facial shape changes by ethnicity and sex [18,19]; the act of smiling [20,21]; and facial shape changes in adolescents due to age [22,23]; maternal smoking and alcohol intake on the facial shape of children [24].…”

Section: Introductionmentioning

confidence: 99%

Initial Investigations of the Cranial Size and Shape of Adult Eurasian Otters (<em>Lutra lutra</em>) in Great Britain

Farnell¹,

Khor²,

Doyle³

et al. 2020

Preprint

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3D surface scans were carried out to determine the shapes of the upper sections of (skeletal) crania of adult Eurasian otters (Lutra lutra) from Great Britain. Landmark points were placed on these shapes by using a graphical user interface (GUI) and distance measurements (i.e., the length, height, and width of the crania) could be found by using the landmark points. These “GUI-based” distances were shown to be accurate and reliable in comparison to physical measurements taken on the crania directly by using a digital calliper. The crania of males were 6.85mm, 5.44mm, 1.66mm larger in terms of length, width and height, respectively, than females in our sample (P < 0.001), i.e., male otters had significantly larger skulls than females. Significant differences in size occurred also by geographical area in Great Britain (P < 0.05). Multilevel Principal Components Analysis (mPCA) indicated that sex and geographical area explained 31.1% and 9.6% of shape variation in “unscaled” shape data and that they explained 17.2% and 9.7% of variation in “scaled” data. The first mode of variation at level 1 (sex) correctly reflected size changes between males and females for “unscaled” shape data. Modes at level 2 (geographical area) also showed possible changes in size and shape. Clustering by sex and geographical area was observed in standardised component scores. Such clustering in cranial shape by geographical area might reflect genetic differences that are known to occur in otter populations in Great Britain, although other potentially confounding factors (e.g. population age-structure, diet, etc.) might also drive regional differences. Furthermore, sample sizes per group were small for geographical comparisons. However, this work provides a successful first test of the effectiveness of 3D surface scans and multivariate methods such as mPCA to study the cranial morphology of otters.

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Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Cited by 7 publications

References 11 publications

Multilevel Analysis of the Influence of Maternal Smoking and Alcohol Consumption on the Facial Shape of English Adolescents

Multilevel Analysis of the Influence of Maternal Smoking and Alcohol Consumption on the Facial Shape of English Adolescents

An exploration of adolescent facial shape changes with age via multilevel partial least squares regression

Initial Investigations of the Cranial Size and Shape of Adult Eurasian Otters (<em>Lutra lutra</em>) in Great Britain

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