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 advantages of brain gyrification are well established, but the mechanisms behind this process are yet matter of discussion. However, early this month, a group of researchers published two models for brain gyrification based on the mechanical stress generated by the differential growth of the cortical layer. They created a physical model of a brain in three steps: (1) 3D printing a plastic replica from a MRI of a smooth fetal brain; (2) build a silicon negative mould to cast the core of a gel brain, which would represent the white matter; and after cooled, (3) deposited the same gel polymer in the surface of the core to form the cortical layer. These polymer layers act as elastic solids. The mimicking of fetal brain growth was accomplished by placing the gel brain in a substrate of hexanes that would cause swelling and differential growth of the outer cortical layer, in respect to the core of the model. Starting from the same MRI, they also built a numerical model based on finite element and parameters like cortical thickness, brain growth and tissue stiffness, creating functions for folding and unfolding simulations. The combined results of the physical and numerical simulations showed that the pattern of gyrification depends on the overall shape of the brain, and the primary sulci are formed perpendicularly to the largest compressive stress. Their models are robust and reproducible, capturing the main gyral scheme and even account for variability and hemispherical differences. Furthermore, when comparing the simulated brain to a real one, they were able to find a correspondence with all the primary folds.
In their last review Jean-Jacques Hublin, Simon Neubauer and Philipp Gunz address the effects of hominins’ life histories in brain evolution. Encephalization in humans involved energetic costs that were sustained through changes in social structure and metabolic adaptations, including changes in the diet quality, as explained by the Expensive Tissue Hypothesis, and the ability to store energy in fat tissue, the primary source of energy for neonate brains. Because of the constraints of birth-giving associated with bipedalism, human brains develop mainly after birth. Also because of this prolonged development, the brain is exposed to a rich environment during its wiring process, with the child furthermore protected by the social community.
The study of fossil hominins’ brain development is only possible through the analysis of their endocranial casts, using cross-sectional samples. To establish their life histories it is necessary to attribute an age at death, generally according to the eruption timing of the teeth. Beyond variation in brain size, signs of brain reorganization can be investigated in fossil species to get insights about their cognitive capacities. Regarding Australopithecus, it is not yet clear how their brain development and life histories were more like that of humans or that of chimps. Homo erectus had body proportions and social structures closer to that of modern humans, but different brain organization and smaller brain capacity than that of latter Homo, pointing to a faster life history. Homo sapiens and H. neanderthalensis separately evolved similar brain capacities, although with different morphologies, and had different life histories, faster in the latter. Brain organization differs soon after birth, as modern humans undergo the so-called globularization phase, which does not exist in chimps or Neanderthals. This globular shape of the brain is characterized by the bulging of the parietal areas which may be linked to reorganization of the internal regions, like the precuneus.
There is still a debate about whether or not life histories in fossils can be investigated in terms of brain growth and development. The different brain morphology of Australopithecus when compared with African apes suggests that brain reorganization could have pre-dated encephalization. At last, our patterns of socio-cultural evolution might have been fundamentally responsible for the adaptive changes essential for the evolution of our big brains.
Cranial vault consists of two cortical tables (inner and outer) sandwiching a layer of trabecular bone (diploe). Cranial vault thickness (CVT) is the distance between endocranial and ectocranial surfaces of vault bones. Several studies have pointed out that CVT differs not only between hominids but also among modern human populations. This morphological trait is mainly influenced by systemic and local stimuli, such as brain growth and development, mechanical forces (at the muscles attachments), circulating hormone levels, formation of sutures etc. Marisol Anzelmo and colleagues have recently published a study of ontogenetic changes in CVT in a modern sample of Homo sapiens. They tested age differences in CVT and if these changes are associated with changes in endocranial volume (EV), which reflect brain size. CT cranial images of 143 individuals (males and females) from 0 to 31 years were used to obtain, among others, a thickness mean measure (TMM), a measure of endocranial volume (EV), and a 3D topographic mapping of CVT, which indicates thickness distribution at different regions through a chromatic scale. A topographic mapping is very useful for picturing differences across vault regions in every age group, and it also reveals development of the regions during ontogeny and the onset of adulthood. The results of this study show that TMM increases during ontogeny without sex differences. Most accelerated growth rates of TMM occur during the first 6 years of life. Also, association between TMM and EV was significant only in this period (infants and children). Furthermore, adult pattern of thickness distribution seems to begin early in ontogeny. Increase of CVT in early ontogeny is directly linked to brain protection. However several mechanisms are involved in CVT formation, such as sutures patterning and vessels development. The vault bones dynamic in later ontogeny and in adulthood may be then influenced by different type of muscular activity and mechanical demands or, most commonly, by systemic factors associated with hormones, physical activity, nutrition.