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

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

doi:10.3390/jimaging6050034

Cited by 7 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, another multivariate method that has previously been used to study human shapes in particular is given by multilevel principal components analysis (mPCA) [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], which is a generalization of PCA that allows for us to account for groupings or clusters in our population of shapes. Indeed, mPCA allows us to isolate (to some extent at least) competing effects at different levels of the model.…”

Section: Introductionmentioning

confidence: 99%

“…The authors note that mPCA “offers more flexibility and “allows deformations” (i.e., changes in shape) that “classical statistical models cannot generate” [ 9 ]. We previously applied mPCA to investigate (in humans): facial shape changes by ethnicity and sex [ 10 , 11 ]; the act of smiling [ 12 , 13 ]; facial shape changes in adolescents due to age [ 14 , 15 ]; and, maternal smoking and alcohol intake on the facial shape of children [ 16 ]. Here, we wish to extend these calculations to study otter cranial morphology.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

Three-dimensional (3D) surface scans were carried out in order 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 using a graphical user interface (GUI) and distance measurements (i.e., the length, height, and width of the crania) were found by using the landmark points. Male otters had significantly larger skulls than females (P < 0.001). Differences in size also occurred 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 standardized component scores. Such clustering in a cranial shape by geographical area might reflect genetic differences in otter populations in Great Britain, although other potentially confounding factors (e.g., population age-structure, diet, etc.) might also drive regional differences. 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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Betweengroup (bgPCA) [8,9] is an extension of standard PCA that carries out separate PCAs on (between-group) covariance matrices based on "group means" and (within group) covariance matrices based on individual shapes around these means. Multilevel PCA (mPCA) has been used by us [10][11][12][13][14][15][16] to analyse 3D facial shapes obtained from 3D facial scans; note that two-level multilevel PCA (mPCA) is equivalent to bgPCA. mPCA has been used by us to investigate changes by ethnicity and sex [10,11], the act of smiling [12,13], facial shape changes in adolescents due to age [14,15], and the effects of maternal smoking and alcohol consumption on the facial shape of English adolescents [16].…”

Section: Introductionmentioning

confidence: 99%

An Exploration of Pathologies of Multilevel Principal Components Analysis in Statistical Models of Shape

Farnell¹

2022

Preprint

Self Cite

View full text Add to dashboard Cite

3D facial surface imaging is a useful tool in dentistry and in terms of diagnostics and treatment planning. Between-groups PCA (bgPCA) is a method that has been used to analyse shapes in biological morphometrics, although various “pathologies” of bgPCA have recently been proposed. Monte Carlo (MC) simulated datasets were created here in order to explore “pathologies” of multilevel PCA (mPCA), where mPCA with two levels is equivalent to bgPCA. The first set of MC experiments involved 300 uncorrelated normally distributed variables, whereas the second set of MC experiments used correlated multivariate MC data describing 3D facial shape. We confirmed previous results of other researchers that indicated that bgPCA (and so also mPCA) can give a false impression of strong differences in component scores between groups when there is none in reality. These spurious differences in component scores via mPCA reduced strongly as the sample sizes per group were increased. Eigenvalues via mPCA were also found to be strongly effected by imbalances in sample sizes per group, although this problem was removed by using weighted forms of covariance matrices suggested by the maximum likelihood solution of the two-level model. However, this did not solve problems of spurious differences between groups in these simulations, which was driven by very small sample sizes in one group here. As a “rule of thumb” only, all of our experiments indicate that reasonable results are obtained when sample sizes per group in all groups are at least equal to the number of variables. Interestingly, the sum of all eigenvalues over both levels via mPCA scaled approximately linearly with the inverse of the sample size per group in all experiments. Finally, between-group variation was added explicitly to the MC data generation model in two experiments considered here. Results for the sum of all eigenvalues via mPCA predicted the asymptotic amount for the total amount of variance correctly in this case, whereas standard “single-level” PCA underestimated this quantity.

show abstract

“…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%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 7 publications

References 38 publications

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

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

An Exploration of Pathologies of Multilevel Principal Components Analysis in Statistical Models of Shape

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

Contact Info

Product

Resources

About