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Lines are the kernel regression trend-lines for each group. These were calculated by resampling with replacement and recomputing the estimates 10 times. The magnitude of dimorphism declines up until about age three, then it increases in two phases between ages five and ten and between ages twelve and sixteen. The magnitude of dimorphism possibly continues to increase beyond age sixteen. The first three columns of Fig.

The first two show the expected heads for boys and girls. Growth patterns and comparison of expected faces. The first three columns describe sexual dimorphism.

The grey heads are the expected images. Red indicates the points are more outwards. The last two columns illustrate the growth patterns of boys and girls. Stronger colours indicate more change. The primary features that distinguish the two groups at sixteen years old are that in boys relative to girls: In general these differences in shape must result from differences between the growth patterns of the two groups.

The growth patterns for each group are plotted in the final two columns of Fig. All growth patterns and rate comparisons use the same colour scale.

These results are condensed into descriptions of how each dimorphic feature emerges below:. Comparison of growth patterns between boys and girls. Red indicates males are changing faster, blue indicates females are changing faster. Red indicates the growth vectors are pointing in the same direction, blue indicates they are pointing in the opposite direction.

Thus the chins of both groups project anteriorly and inferiorly, but boys do so faster. This results in a difference in chin position by age eight. From age six the upper lip differences emerge like the chin differences. That is, by a difference in rate of change along anterior and inferior growth vectors.

The difference is evident from age twelve. This difference is present up until age two and then disappears. While this difference is present at all ages it becomes more exaggerated between ages six and ten mostly due to a difference in growth rate although on a small portion of the cheek there are also differences in growth direction.

Points on the cheeks are displaced inwards for both groups, but boys are displaced faster. Between ages twelve to sixteen this difference continues to become exaggerated, but due to different growth direction of this region at this time. This indicates a more brachycephalic head shape in boys than girls. Over time, the lateral portion of the neurocranium is displaced inwards medially. This occurs faster for males than females, resulting in a reduction of the lateral projection of the neurocranium.

This difference is totally absent by sixteen. Classification was not significantly better than chance for participants less than five years old but was in the 5—10 and 10—15 age brackets. Performance was near perfect in those over The groups overlap almost completely in the bracket 0—5 and become more separated in the older brackets. AUC is the area under the receiver operator characteristic curve. Values outside the brackets are the mean value over the folds. Differences in head shape between groups are most frequently analysed by comparing simple measurements, or single average faces.

However, understanding how these differences emerge and change can provide insights into the underlying biological and genetic mechanisms. In this study we introduce a framework for describing and analysing the emergence of shape differences between populations and apply the method to the question of how differences in craniofacial shape emerge between males and females.

The combination of a large sample size, covering most of childhood and adolescence, and spatially-dense analysis is unique in the literature on craniofacial sexual dimorphism. We first discuss our results in this context, followed by considering the broader applications of the methodology and its relationship to existing approaches. At sixteen we observe a protruding brow ridge and nose; a chin and upper lip that are positioned more anteriorly and inferiorly; and a flatter buccal region in boys relative to girls.

These features have been consistently observed in adults 16 , 22 , Many soft-tissue studies exclude most of the forehead, focussing only on the facial shell but, in those that include it, it is generally found to be sloping backwards more in adult males than females 24 , Given the proximity of frontal bone to the skin, findings on this area can be directly related to the hard tissue anatomy.

In our analysis, the brow ridge prominence develops independently of the superior portion of the forehead, indicating growth by isolated deposition on this region; not by deposition at cranial sutures which would shift the whole frontal bone anteriorly.

Increased soft tissue thickness on the upper lip and chin has been documented in adults 19 , Shape differences in these areas may represent differences in soft tissue depth, in combination with different positioning of the mandible and maxillary bones. The nose differs in both its cartilaginous parts and the bony nasal bridge. This is consistent with previous observations of skulls that revealed males have a more anterior projection of the most inferior point of the nasal bone and a larger anterior nasal aperture Differences in the buccal region probably mostly reflect differences in soft tissue, including the buccal fat pad and facial musculature, rather than the underlying hard-tissue.

With regard to how these differences emerge: Although differences in the nose are also present at age 1. At about age twelve the growth rate of the nose for boys becomes faster than for girls, giving rise to the differences we see at sixteen. Differences in the chin and upper-lip emerge from differences in growth rate by ages eight and twelve respectively.

Differences in the buccal region are present at all time points, but become more exaggerated over time. This occurs first through differences in growth rate and then, around puberty, through differences in growth direction.

Overall, we observe that there are two phases in the emergence of sexual dimorphism. During the first, between ages five and ten, the anterior and inferior positioning of the chin in boys appears and differences in the buccal region and forehead continue to become exaggerated.

The second is from about twelve onwards. During this period differences in the nose, brow ridge and upper-lip emerge and differences in the chin, buccal region and forehead continue to become exaggerated. Accompanying the increased differences between the groups we observe decreasing overlap in the distributions of male-female scores and better classification accuracy.

Traditionally it has been assumed that sexual dimorphism emerges primarily at puberty, resulting from an influx of sex-hormones 31 , Contrary to this view, a few studies have reported sex-differences in facial measurements in pre-pubescent children 33 — They were limited however in that they both used relatively sparse landmark configurations to represent the anatomy.

Furthermore, Bulygina only analysed the shape described in 2d in a lateral cephalogram and only used a very small sample size. Previously our lab demonstrated retro-positioning of the forehead, flattening of the buccal region, a slightly more brachycephalic head shape and an enlarged nose in boys relative to girls at one year-old Our findings at age 1.

We also find that the differences in the buccal region and forehead are maintained and become more exaggerated, gradually developing into adult patterns of dimorphism, whereas the relative brachycephaly disappears entirely.

The differences in the nose disappear, only to reappear during adolescence. Our study adds to the growing body of literature demonstrating that sexual dimorphism is present very early in life. Our results show aspects of this early dimorphism continue to develop during the pre-pubescent period, suggesting that adult sexual dimorphism is not all attributable to sex hormones released at puberty. This is corroborated by evidence that masculinity of facial shape in adult males is correlated with this hormone ratio 37 , The observed differences in the pre-pubescent period may be due to uterine hormone levels, activated prior to puberty, or they may represent sex-linked facial characteristics, not directly attributable to hormone levels.

The growth trajectory of a particular species or group may be adaptive, in an evolutionary sense, and be an object of natural selection This implies that differences between growth trajectories of males and females can inform our understanding of how natural selection, including sexual selection, has operated differently on the two groups.

Although outside the scope of this paper we consider how our findings relate to three important anthropological hypotheses: In general, it is not possible to prove similarity of growth directions. However, our results indicate dimorphism of the nose, chin, forehead and brow-ridge are largely a product of different rates of change, not different directions.

Differences in the buccal region result from differences in growth rate between six and ten and from different growth directions between twelve and sixteen. This study represents an important methodological advance in the description of group level differences, and classification therefrom, in growing populations. Previous studies have used average faces based on samples of mixed ages, ignoring the fact that the differences may change over time 5 — 9.

Others have grouped data into age brackets and constructed mean heads for each age bracket Binning requires decisions about where to divide the data, which may affect the results.

Furthermore, if the brackets are large, it is likely that changes occurring within the age bin will be missed. Since it can be evaluated for any age the expectation is effectively continuous, unlike discrete age bins. They used a weighting system that emphasises those cases closest in age to the target age, maximising how well the expected head represents the target age. In this work, we extend their approach by using a weighted regression model, instead of the weighted mean, to define the expected head for each age.

The regression also predicts change in each point co-ordinate, estimating a 3D growth vector for each point on the head. This allows pointwise comparison of growth rate and growth direction, facilitating a deeper understanding of how the differences between the populations emerge.

It is also more robust to uneven sampling than the weighted mean see ref. Traditional classification models including linear discriminant analysis, Euclidean distance to centroids and support vector machines attempt to discriminate groups based on a set of variables. In a binary classification scenario they fit a single surface through the space of these variables e.

These models can be applied to new cases, which may make them valuable diagnostic aids 5 , 8 , 41 , Beyond simply diagnosing cases categorically, if the discriminating surface is a plane, the signed distance of an observation from it can be calculated easily and interpreted as a continuous measure of phenotype expression. This may identify subclinical cases of disorders that exhibit only a reduced form of the full phenotype.

The classification method applied here produces a score describing where the case sits on the axis between age appropriate expected faces. This is related to traditional models in that this score is proportional to the signed distance from a discriminating plane orthogonal to this axis. In contrast to traditional approaches the location and orientation of this plane adapts to the age of the case being classified. One important application may be in the study and assessment of foetal alcohol spectrum disorders FASDs.

FASDs are a spectrum of neurocognitive deficits observed in children exposed to alcohol in utero. The more severe manifestations are accompanied by specific facial anomalies including a shortened palpebral fissure, thin vermillion line, flat philtrum and reduced head circumference Recent studies have also observed subtle facial differences in alcohol exposed children not diagnosed with a FASD 6 , The classic facial differences diminish in adolescence The time-course of the more subtle aspects is unknown, but could be investigated with our methodology.

Given the emerging evidence for a spectrum of facial effects, a shape based facial phenotype score, like the one described here, may flag potential cases for further assessment that might otherwise be undetected. Although for the current analysis we draw on a very large database of images, such large samples are unlikely to be available for every study. The described approach compensates for small sample size by increasing the width of the kernel of ages used to estimate each expected head.

In Supplementary Text S1 we describe how to optimise this width for any dataset. Increasing the width of the window linearizes the growth trajectory; as the size of the window gets very large the model approaches being a single partial least-squares regression. While this model, being linear, would clearly be a simplification of growth, even in this worst-case scenario, it still improves on the common method of using the first principal component.

Specifically the first principal component is simply the dimension of greatest variation within the sample. It is also linear, but is constructed without any reference to age. Therefore there is no guarantee in principle that it relates to growth.

Our approach, in contrast, always takes age into account in defining the growth trajectories. An important limitation in modelling growth from age is that individuals mature at different rates. Age, therefore, only approximates maturation. A further limitation is that we did not adjust for allometry, the association between centroid size and shape As centroid size is strongly correlated with age data not shown , it is not appropriate to adjust for it in the current study.

Doing so would essentially remove the effect of age on shape growth , which is an important aspect of our analysis. In this paper we describe an approach for analysing the emergence and change in craniofacial differences between two growing populations and for age-adaptive classification. We demonstrate the method by asking how the differences in head shape between boys and girls emerge.

In doing so we comprehensively describe the emergence of craniofacial sexual dimorphism and add to the growing body of research indicating that there is sexual dimorphism long before puberty. Mostly the differences between the sexes emerge through boys growing at a faster rate than girls.

In future the method can be used to chart changes in the distinctive facial appearances that characterise many disorders, including foetal alcohol spectrum disorders FASDs. These changing descriptions can be used to better understand their underlying pathology and facilitate diagnosis from facial shape. We thank all the children and families who took part, the schools that collaborated and the 3D photographers, Robert Reitmaier and Lloyd Ellis.

The kernel regression and classification algorithms used here were designed and implemented by H. All authors contributed to the manuscript content. Supplementary information accompanies this paper at Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. National Center for Biotechnology Information , U. Published online Mar Matthews , 1, 2, 3 Anthony J.

Clement , 1, 4, 5 Nicola M. Kilpatrick , 1, 2, 3 and Peter D. Claes 1, 6, 7. Received Dec 14; Accepted Feb To view a copy of this license, visit http: Abstract Many disorders present with characteristic abnormalities of the craniofacial complex. Introduction Many disorders present with characteristic abnormalities in craniofacial shape, emerging through abnormal pre- or postnatal growth. Sample The sample consisted of boys and girls range: Open in a separate window.

Image acquisition A 3D photograph of the entire head, including the face, ears and neurocranium, was taken of each participant using either the 3dMD 7-pod or mobile 5-pod system http: Image measurement The 3D surface in each image was sampled at 28, points, by gradually warping a generic template head into the shape of each image 2 , 3.

Modelling growth trajectories Further details of the implementation and mathematics are reported in Supplementary Text S1. Data availability As images of heads are potentially identifiable and children are a vulnerable population, images were collected without consent for broad data sharing.

Dimorphic features The first three columns of Fig. Differences in growth rate and direction in the emergence of dimorphice features In general these differences in shape must result from differences between the growth patterns of the two groups. These results are condensed into descriptions of how each dimorphic feature emerges below: Upper lip is positioned anteriorly and inferiorly From age six the upper lip differences emerge like the chin differences.

Nose protrudes anteriorly This difference is present up until age two and then disappears. Buccal region flatter While this difference is present at all ages it becomes more exaggerated between ages six and ten mostly due to a difference in growth rate although on a small portion of the cheek there are also differences in growth direction.

Additional features At age 1. Table 1 Classifier performance statistics. Distributions of scores for the classification analysis for males and females. Discussion Differences in head shape between groups are most frequently analysed by comparing simple measurements, or single average faces. Electronic supplementary material Supplementary Information 6. Movie S1 K, mp4.

Movie S2 K, mp4.

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