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.
A surface analysis on the frontal lobes in archaic humans …
[A post] [The Paper]
As the use of virtual anatomy increases, awareness of different 3D mesh (digital model) formats is useful. Simply, a 3D mesh is the geometrical representation of an anatomical structure such as a cranium, endocast or tooth. Below, are the main differences, benefits, and uses of common PLY and STL 3D formats.
PLY (Stanford Polygon Format) is a 3D file format that was commonly developed from 3D surface scanners and photogrammetry software to allow the preservation of information on surface geometry while retaining information on RGB colour. STL (Stereolithography) is a 3D format commonly generated from software using only grayscale images such as raw CT (Computed Tomography) where RGB colour is not captured. 3D Printers only require preserved information on surface geometry, not colour, leaving STL to be a preferred format for 3D printing technologies.
Even though there is no discernible difference between the quality of the 3D mesh types, PLY format offers binary encoding of all information (including RGB colour). This results in a smaller file size, allowing less space occupied on a hard-drive or cloud-storage and faster loading of the 3D mesh into software programs as employed in 3D-geometric morphometrics.
So far, the majority of new quantitative methods and approaches to investigate sexual dimorphism have focused primarily on the morphology of the five most commonly studied sexually dimorphic traits of the skull (glabella, orbital margins, mastoid process, nuchal crest, and mental eminence), while other cranial traits are still being evaluated in terms of simple subjective descriptions. One of the cranial regions showing great potential for further development of sex estimation techniques is the frontal region. Recently, Bulut and colleagues quantified the shape differences between male and female frontal bones using a novel and landmark-free 3D modeling method. Their new finding that the male frontal bone is actually more spherical than the female is in opposition to the common perception. In their study, CT scans of 80 male and 80 female Caucasian frontal bones from a Turkish population between the age of 25 and 40 years were obtained. The frontal bone was isolated by carrying out the “selection tool” in the GOM Inspect software using STL models. The frontal bone model is aligned to the CAD sphere model, using the best-fit registration method in the GOM Inspect software. Next, the difference in surface morphology between the frontal bone data and the CAD sphere was quantified, using the sphere model as the reference surface. Also, color maps were generated to show the deviations between frontal bone surface and the CAD sphere surface. Deviations of ±1mm were calculated as the overlapping areas. Color maps show that, for males, the areas exhibiting the largest discrepancy between frontal bone and CAD sphere surface are glabella, the supraorbital margins, the zygomatic processes, the superciliary arc, and the temporal face.
The area displaying most overlap with the sphere is the upper frontal region, including the frontal squama and the frontal eminences. For females, the frontal squama showed the main congruence with the sphere surface, while the largest deviations were observed for glabella, the supraorbital margins, the zygomatic processes, the superciliary arch, the frontal eminences, and the temporal face. The amount of frontal bone overlapping with the sphere was found to range from 30.1% to 56.1% for males, and from 19.6% to 48.3% for females. The difference in average values between males (43.2 ± 6.5%) and females (33.9 ± 6.6%) was found to be statistically significant, i.e. p < 0.0001, using the double-sided version of student’s t-test. This finding is in opposition to the common perception that the male frontal bone is more inclined than the female, which is described as more vertical and rounded, convex, smooth, and broad. Using the overlapping surface parameter to develop linear discriminant functions, sex was accurately predicted for 61 of 80 females (76.3%) and 63 of 80 males (78.8%) after leave-one-out cross-validation, yielding an average of 77.5% correct classifications.
Sex assessment is crucial in any survey on human remains. Musilová et al, have recently published a new method for sexual identification using virtual scans of both male and female individuals. They found that the size of the cranial surface was significantly different between both sexes, being the male skulls larger than the females in some areas, such as the nasal root, external occipital protuberance and mastoids. The most pronounced areas with sexual cranial differences are those linked to muscle attachment, such as supraorbital, frontal and nuchal regions. Sexual dimorphism was significantly lower in senile skulls. This article provides a new and successful method using 3D techniques and geometric morphometrics, interesting for different applications in anthropology.
Gizéh Rangel de Lázaro
Dimitri Neaux and colleagues have published a series of comprehensive analyses on the influence of the cranial base in facial morphology of humans and apes. In one of the papers, they assessed the integration between the face and the two basicranial modules: the sagittal and the lateral cranial base. They tested the covariation between the three sets of 3D landmarks (face vs. midline base and face vs. lateral base) on modern humans and chimpanzees, separately. Only the correlation between the face and the lateral cranial base was significant, confirming the important role of the lateral cranial base in facial morphology. Though the levels of covariation were comparable, the patterns differed between the two species, as taller faces were associated with wider and shorter cranial fossae in chimps and with longer and narrower cranial fossae in humans. In another article, they assessed the relationship between cranial base flexion, facial orientation, and facial shape in modern humans, chimpanzees, and gorillas. Using 3D landmark analysis, they evaluated the within-species patterns of covariation, confirming the intraspecific relationship between facial structures and base flexion. Base flexion is associated with downward rotation of the facial block in both humans and chimps (confirming previous works) but not in gorillas. On the other hand, an upward rotation of the facial block is associated with anterior face vertical elongation on the three species. In humans, facial elongation is also associated with base flexion, which might have been selected during evolution to match the elongation of the nasomaxillary complex, as proposed before. The authors further tested whether increased base flexion is associated with the shortening of the facial length or with the decrease of facial projection. The relationship between base flexion and facial length was only observed in chimps, while facial projection was not related with cranial base flexion in chimpanzees and gorillas. In humans, contrary to what was expected, basicranial flexion was associated with increasing facial projection, which the authors attribute to sexual dimorphism, as males have increased base flexion and facial projection. Again, the main patterns of correlation differed between the species. Cranial base angle is negatively correlated with facial projection in modern humans, with facial length in chimps, and with the angle between the posterior-maxillary plane and the anterior facial plane in gorillas. As the authors conclude, these differences in the patterns of integration might reflect changes in the structural relationships between the face and the cranial base during hominoid evolution.
The cranial vault is composed by three bone layers (inner table, diploe, outer table), and its principal function is to safeguard the brain from impacts. Bone thickness is a crucial parameter to understand the biomechanical factors contributing to skull deformations and fractures after head injury. It is therefore important to establish an accurate measurement system to quantify its variation. Lillie et al., 2015 analyzed microCT scans of two cadavers to evaluate the accuracy of the estimated cortical thickness from clinical CT data. Microscans were acquired at 25-microns, while CT scans had a resolution of 0.48-0.62 mm. The skull average thickness in both cases was below 4 mm. Cortical thickness measurements obtained from CT scans are more accurate compared with traditional physical methods, although results are comparable with those available in literature. The average cortical thickness discrepancy between microCT scans (higher resolution) and CT scans (lower resolution) is 0.078+ 0.58 mm. Such methodological validation is necessary when dealing with age-related changes in distribution of the skull cortical thickness, and to identify species-specific or population differences.
Gizéh Rangel de Lázaro
Encephalization quotients (EQ) have been extensively used to characterize brain evolution, but this univariate metric only includes information on relative size. Marugán-Lobón and his colleagues recently analysed the association between endocranial shape changes and EQ by applying geometric morphometrics to a sample of modern bird endocasts. A Principal Component Analysis accounting for phylogenetic history showed that the bird endocasts varied essentially in the relative expansion of the forebrain and in the degree of flexion of the braincase. The distribution of the specimens in the morphospace has a phylogenetic structure, with morphological affinity between close evolutionary clades, particularly the landbirds, which display larger forebrains. Size explains 10% of the shape variation. EQ accounts for changes in relative forebrain expansion, with larger EQs associated with larger forebrains. A second study was computed correcting for phylogeny, i.e. computing regression analyses on the phylogenetic independent contrasts of shape and size against EQ. When allometric and phylogenetic signals were removed, shape variation was mostly associated with the degree of flexion of the endocasts, and EQ was not significantly correlated with these morphological changes. The authors conclude that, excluding the general effect of size, EQ does not explain shape differences among birds’ endocasts. Therefore, other factors are probably responsible for brain variation in birds.