Two different papers have been published this month on the evolution of the supraorbital anatomy in humans. The first article is on Neanderthal facial morphology, and it was coordinated by Stephen Wroe, of the FEAR lab. Here a comment on the Daily Mail. The second article, by Ricardo Miguel Godinho and coauthors, links supraorbital morphology and social dynamics, and it was commented in a News and Views by Markus Bastir.
Category Archives: Morphometrics
Bruner E. & Ogihara N. 2018. Surfin’ endocasts: the good and the bad on brain form. Palaentologia Electronica 21.1.1A: 1-10.
The primate skull is comprised of complexes including the cranial base, vault and facial region. How these complexes respond to different developmental and growth processes as well as varied selective pressures like diet, locomotion and sexual selection have been investigated in terms of modularity and integration. The concepts of modularity and integration concern the co-variance or independence of these complexes.
Profico et al. used several recent statistical methods to test previous research conclusions suggesting the primate cranial base and facial complex are strongly integrated. The cranium from 11 extant species of the Cercopithecoidea and Hominoidea were studied utilizing geometric morphometrics to investigate shape variation, the presence of evolutionary allometry and modularity or integration.
Shape variation of the primate cranial base and facial complex was assessed by Principal Component Analysis. Among taxa, shape variation of the cranial base reflected patterns in locomotion, cranial base flexion and the size of the foramen magnum. The shape variation of the facial complex reflected size-related and sex-linked morphology, the degree of lower and mid-facial prognathism and associated changes to narrowing of the nasal-orbital regions. Evolutionary allometry was tested by multivariate regression of size on shape and indicated the facial complex but not the cranial base was influenced by evolutionary allometry. Modularity and integration was analyzed using Partial Least Squares to test the degree of co-variation between the facial complex and cranial base which proved to be low. These combined results suggested the cranial base and facial complex complied with the concept of modularity rather than integration contrasting with previous studies.
An important reminder that although a pattern of similarity was found between Pongo pygmaeus and Hylobates lar this does not imply a close biological relationship, rather these taxa share similar cranial base and facial block morphology, potentially as a by-product of orthograde posture and the absence of quadrupedalism found in the other primate taxa with the exception of modern humans which are obligate bipeds. In light of the current findings, a more comprehensive reconsideration may be necessary of the effects from variation in the facial complex and cranial base morphology throughout primate evolution.
Fiorenza L., Bruner E. 2017. Cranial shape variation in adult howler monkeys (Alouatta seniculus). Am. J. Primatol. [link]
Recently, Evin et al. 2016 have published a study comparing the accuracy of the three-dimensional reconstruction of five wolf crania using both photogrammetry and high-resolution surface scanner. For the photogrammetric images acquisition, they used an 8-megapixel (DSLR) Canon EOS 30D camera, mounted with a Canon EF 24–105mmf/4 L IS USMlens. The scanner-based 3D models were created using a Breuckmann StereoScan structured light scanner (http://www.breuckmann.com). The resulting 3D models were compared first through visual observation, and second with the computation of a mesh-to-mesh deviation map. The pairs of models were spatially aligned (using a least-square optimisation best-fit criterion), followed by a 3D landmark-based geometric morphometric approach using corresponding analyses. The results show that photogrammetric 3D models are as accurate in terms of coloration, texture, and geometry, as the highest-end surface scanners. Minimal differences between photogrammetric 3D models and surface scanner-based models have been only identified on intricate topological regions, such the tooth row. Photogrammetry is becoming a common tool in archaeological and anthropological research. The major advantage of this technique is the speed and ease of image acquisition and reconstruction. Photogrammetry is an equally good alternative and less expensive than other more common techniques, such as structured light or surface scanners. In terms of archaeological samples conservation, photogrammetry could be in the future an excellent alternative to provide accurate replica models that can be widely accessible for research, without affecting the original collections.
Gizéh Rangel de Lázaro
Virtual anatomy and inner structural morphology,
from head to toe
A tribute to Laurent Puymerail
Comptes Rendus Palevol 16 (2017)
In a recent study, Tsuzuki and colleagues analysed the co-development of the brain and head surfaces during the first two years of life using a sample of 16 infant MRIs, aged from 3 to 22 months. First, they digitized a set of cortical landmarks defined by the major sulci. Then they determined the position of cranial landmarks according to the 10-10 system, a standard method to place electrodes for electroencephalography, using nasion, inion, and the pre-auriculars as a reference. Besides analysing the spatial variability of the cortical and scalp landmarks with age, they compared the variability of the cortical landmarks to the 10-10 positions, in order to evaluate the validity of the scalp system as a reference for brain development. For that, they transferred a given cortical landmark to the head surface by expressing its position as a composition of vectors in reference to the midpoint between the two pre-auriculars and to the three neighbor 10-10 points. The scalp-transferred landmarks were then transformed to the scalp template of a 12-month-old infant and depicted in reference to the 10-10 system.
Age-related changes in the cortical landmarks were most obvious in the prefrontal and parietal regions. As the brain elongates, the frontal lobe shifts anteriorly and the precentral gyri widen. In addition, the intraparietal sulci and the posterior part of the left Sylvian fissure move forward, suggesting relative enlargement of the parietal region in the anterior direction. The same result was obtained by our team by analyzing cranial and brain landmarks in adults: larger brain size is associated with a relative forward position of the parietal lobe. The scalp showed relative anteroposterior elongation and lateral narrowing with growth. Regarding the contrast between the cortical landmarks and the 10-10 system, the authors observed that the variability in the position of the former was much smaller than the area defined by 10-10 landmarks, indicating this system can be useful to predict the underlying cortical structures. Hence, they conclude that the changes in brain shape during development are well described by cortical landmarks and that the relative scalp positioning based on the 10-10 system can adjust to preserve the correspondence between the scalp and the cortical surfaces.
The diagnosis of human brain abnormalities depends on knowing the norm and yet defining the range of normal variation is still far from resolved. Understanding what is within the normal human range has been limited by samples and the constraints of producing accurate brain mapping. Access to large brain imaging databases has been possible for a while but producing reliable atlases of key structures including folding patterns (sulci, gyri and fundii), volumes and major shape changes has not had large enough sample sizes to reliably grasp the range of normal brain variation. Current approaches have relied on highly skilled professionals to assess neuroanatomy. While this approach is adequate, it does introduce an inherent level of subjectivity and potential bias with each neuroanatomist dependent on the individual level of experience. To begin reducing this error while increasing sample sizes, new computational technologies allow more automated imaging processes that combine speed and quality.
Mindboggle is a new software platform recently released after development through a long-term research project addressing a need for integrating morphometry (measurements of morphology) to assess the quantitative differences in brain structure. Mindboggle relies on specially developed algorithms to segment brain tissue in MRI images, produce volumetric and structural parallelization of the brain and asses shape variation. Klein and colleagues highlighted issues with similar algorithm-based software that produced errors in segmenting brain from non-brain tissue. Freesurfer was shown to underestimate grey matter while overestimating white matter, while ANTs included more grey matter yet sometimes excluded white matter that extended deep in gyral folds. To resolve this issue, Mindboggle employed a hybrid algorithm that overlays the Freesurfer and ANTs segmentation imaging then combines these to produce a more faithful imaging set negating any errors in volume estimates, folding patterns or shape differences. Further results indicated the geodesic algorithm produced an exaggerated depth for brain regions like the insula, while the time depth algorithm unique to Mindboggle produced more valid results for shallow brain structures than other comparable algorithms. Finally, Mindboggle was shown to be reliable with minimal error estimate showing a consistently greater shape difference between left and right hemispheres than the difference between repeated scans of the same individuals.
Mindboggle also introduced many new and innovative features for extracting and measuring fundii but these algorithms have not yet been thoroughly evaluated. Additionally, the Mindboggle algorithms are developed for human brain anatomy and expansion into non-human neuroanatomy has not yet been fully developed. The potential of Mindboggle and similar platforms lies in the allowance to expand knowledge of normal human brain variation by using much larger samples to more accurately capture the normal range in human neuroanatomy to better inform diagnoses of brain abnormalities.