Archive for the 'Brain morphology' Category

Brains and eyes

After our first survey on the morphological relationships between eyes and brains, here a comprehensive second study on this same topic. We have analyzed data from computed tomography (orbits and endocranial space) and magnetic resonance (eyes and brain), investigating modern humans, apes, and fossils. Soft tissue variation mainly deals with the distance between eyes and temporal lobes. Cranial variation mainly concerns the orientation of the orbits, probably influenced by parietal morphology and variation of the head functional axis. Phylogenetic differences are generally associated with the distance between orbits and braincase, with fossil humans showing an intermediate position between modern humans and apes. Here a Skull Box post with more details.


Sulcal imprints

The fuzzy geometry of the brain surface shapes the endocranial wall, and endocasts can show traces and imprints of the cortical sulcal patterns. Individual variation is noticeable, and the precise mechanisms behind these folding schemes are not clear at all. Hence, it is not recommended to use this information in a simplistic “phrenological” fashion, as unfortunately it has been done in many evolutionary studies. At the same time, cortical morphology is the direct result of neurons growth and development, and therefore even the pretentious rejection of this information seems unwise. Many authors dismiss any result based on brain gross morphology, simply because it is “just brain form”. This is probably because they ignore the developmental processes behind that forms, and they don’t take into account that when we talk about “brain form” we are implicitly referring to those processes, and not to a crude geometrical appearance. At least, sulcal patterns are useful (and the only available macroscopic) boundaries to detect the absolute or relative extension of some cerebral districts or cortical areas. So, despite all the uncertainties, they are directly providing information on cortical proportions. Proportions means “some areas are larger and some others are smaller”. Size is not always a matter of more or less neurons, but it is however matter of more or less “something”. Whatever it is, it should be functional, and maybe even adaptive some way, associated with some specific histological factor, or with some indirect physiological consequence. This is why the issue is not trivial.

Sulcal imprints are generally more visible on smaller and younger skulls. A recent study investigates the expression of the sulcal traces in macaques. Anterior folds (frontal and temporal lobes) leave more traces than the posterior ones (parietal and occipital). There are no many differences among young ontogenetic stages but then, during aging, the expression of the traces decreases noticeably, and imprints become more blurred. Local anatomical differences in the barrier between brain and skull (meninges, vessels, etc) can have a role in this size-related differences. Nonetheless, probably it is a matter of growth. In earlier ages, the brain generates a constant pressure on the vault bones, shaping the bone surface. But in later ages, when brain growth is concluded, that intimate physical relationship is looser. During aging, the brain even undergoes a shrinkage of about 7-8%, and the contact is further lost. This study is simple and effective, a good paper to approach the topic. Between an uncritical phrenological approach and a snobbish rejection of the evidence, we should consider an intermediate approach, in which we evaluate what kind of information we can obtain from these traits. To do that, we have to investigate their phenotypic factors and their mechanical influences, their structural associations and their variability.

Brainstorm …

As properly remarked in the prequel of the Planet of the Apes, we know everything about our brain, except how it does work. We are aware of such lack of knowledge, at least in theory. In practice, papers are replete of firm sentences and conclusive statements. But we use complex programs and devices, and we should not forget that these tools can only generate models of reality. Models based on algorithms that are trying to represent and simulate only some specific physical or spatial properties. Our brain models are but statistical outputs, not real “brains”. We identify brain activity through indirect blood or metabolic functions, assuming there is a strict correspondence between those signals and our concept of “at work”. A correspondence that is reasonable, but not that strict. Even basic anatomical issues can be blurred after a more detailed scrutiny, mostly when previous knowledge is based on information that has been copy-and-pasted through decades. We are more and more finding strange factors influencing our results. Apparently, the brain undergoes daily variations, and the braincase may suffer seasonal changes. Brain structure and function can be even influenced by head position and posture. These unexpected effects recommend further caution when making too general conclusions from specific and punctual results. Let’s take into account that we still miss much information on gross neuroanatomical components. For example, we still ignore the function of the cerebellum, that has four time the number of neurons of the brain, and we still don’t know all the functions of the glial cells, that may be nine times more numerous than neurons. And, we don’t know how much brain anatomy and functions are the result of genetic programs or environmental influences. In only few weeks, training can easily improve or demote brain complexity. Nothing new under the sun: science is about hypotheses, and hypotheses need to be tested and validated. Our models are tentatively designed with this scope in mind. This summer post is a summary of articles concerning some methodological limitations and some curious result dealing with brain structure and function. And an invitation to interpret results for what they are: evidences supporting or rejecting hypotheses. Remember that those are not neurons: just pixels! Take it easy …

Precuneus form and folds

bruner-et-al-aa2017One more paper on the morphology of the precuneus. This time we have analyzed a racially heterogenous sample, confirming that precuneus size is a major source of brain form variation also when a wider genetic variability is taken into account. It is a variation that is apparently independent from sex, race, or hemisphere, although males could have slightly larger proportions than females. A larger precuneus can be associated with additional folds, often in its anterior district, although this association is feeble. Geometric models suggest that the areas involved in this variations are the anterior-dorsal ones, roughly corresponding to area 7a. This area is the largest and more variable of the precuneus, and it includes the medial cortex but also the dorsal external cortex of the upper parietal lobule. It is functionally associated with the integration of somatic and visual information, and with self-centered mental imagery. These results also suggest that upper and lower areas of the precuneus should be considered separately when dealing with functional or evolutionary neuroanatomy. Our former papers on this topic concerned the shape of the precuneus, its cortical surface area, its sulcal patterns and  lateral extension, and the differences between humans and chimpanzees. Apart from the relevance in modern neuroanatomy, these same endocranial regions also display a corresponding spatial enlargement in modern human evolution.

Frontal surfaces


More surfaces. This week we have published a surface comparison of the frontal endocranial morphology in OH9, Buia, and Bodo. The methods are the same applied previously by Amélie Beaudet and colleagues. Despite the importance generally assigned to the frontal cortex in our species, paleoneurology has not managed to reveal clear and patent changes in its gross form. Endocasts can only supply information on the general external appearance of the cortical anatomy, so we should expect they cannot be used to trace many aspects  associated with evolutionary variations. Also, the bad habits to defend firm statements based on single (and often reconstructed and fragmented) individuals unpleasantly crashes against the basic scientific principle of hypothesis testing, something that needs quantification, large samples and statistics. In this paper we compare these three specimens with the general scope of discussing some issues about frontal lobe evolution and paleoneurology. When compared with a modern human endocast, the younger fossils (Buia and Bodo) display flatter dorsal-lateral areas, while the older one (OH9) show a more extensive flattening of the whole dorsal surface. They all fit within a general trend observed in humans and hominoids: the more the eyes go below the frontal cortex, the more the frontal lobe bulges. So it seems reasonable to think that the curvature of the frontal lobes is but a structural consequence of the spatial relationships between face and braincase. In paleoneurology, we should exclude structural changes (cranial constraints and secondary consequences) if we want to localize functional ones, or if we want to reveal specific adaptations and primary evolutionary variations. Surface analysis is one more tool to go in that direction.


beaudet-et-al-jhe2016Amélie Beaudet and colleagues have published a comprehensive and detailed paleoneurological study on South African fossil cercopithecoids. The paper supplies three main advances. First, it provides key information on primate paleoneurology, in particular on Plio-Pleistocene monkeys, belonging to the genera Theropithecus, Parapapio, and Cercopithecoides. Paleoneurology is often more focused on humans and hominoids than on monkeys, and therefore this article is particularly welcome. Furthermore, the study is based on a surface-based method, that compares the rough geometry of the object. Surface analyses can represent an additional and interesting alternative for computing endocast comparisons. There are many complex techniques currently available in shape analysis, and we should always carefully consider that their results depend upon their specific criteria and constraints. Morphometric outputs are “ordered representations” of a given sample variation according to specific numerical and logical assumptions. Consequently, methods are crucial in determining the comparative framework. Different methods, different criteria. For example, surface analysis is not constrained by anatomical correspondence, but it is only sensitive to geometrical correspondence. Hence, the approach misses the information on anatomical boundaries between different elements and areas, distributing variation all through a homogeneous and undifferentiated object.This can be an advantage when taking into consideration form alone, or a disadvantage if one want to investigate the contribution of specific anatomical components. Finally, this study presents a semi-automatic approach for sulcal detection, that is a geometry-based method for the identification of surface relieves, curvature lines, and topographical variations. This approach may seriously represent a major advance in paleoneurology. Nonetheless, it should be taken into account that we still ignore many mechanisms behind cortical folding, and that folding patterns could be the result of passive biomechanical constraints with uncertain phylogenetic or functional meaning.

Integrated paleoneurology

Zollikofer et al 2016Together with the recent article on modern vs Neandertal endocranial ontogeny, the team coordinated by Christoph Zollikofer has now published also a large and comprehensive study on endocranial ontogeny in humans and apes. The paper focuses on a specific question: to what extent endocranial differences are due to brain differences, and to what extent they are due to cranial constraints? Definitely, this is a key-paper in paleoneurology. They considered the integration between and within the main cranial districts to evaluate the influence on brain shape of two major cranial effects: spatial packing and facial orientation. Their analyses suggest that endocranial differences between humans and apes, as well as differences among apes, are the result of all those factors, the cerebral and the cranial ones. Therefore, the endocranial form is due to a complex admixture of specific brain differences (already present at birth) and cranial constraints. Comparisons among endocranial ontogenetic patterns of living hominoids, among adult fossil specimens, and among different neuroanatomical aspects of living species, can give different results, suggesting that the relationships between anatomical, morphological, and cytological elements is far from being understood. In my opinion, a limit of many shape analyses in general concerns the use of surface semi-landmarks to analyze brain geometry. Surface landmarks are necessary because of the lack of good anatomical references on the endocasts. Unfortunately, they can’t take into account the contribution of distinct cerebral areas, and as a consequence they consider brain morphology as a single homogeneous surface. The identification of boundaries or distinct and independent elements within this surface might seriously influence the multivariate output. I am particularly interested in the analysis of the parietal districts. When using surface landmarks the analysis of the parietal surface may give different (and sometimes contrasting) results. Hence, we may wonder whether the observed parietal variations are the result of brain differences (cortical expansion/reduction) or of geometry (bulging and flexion). Nonetheless, previous morphological studies based on cortical landmarks suggest that modern humans show an actual (absolute and relative) increase not only of the parietal “surface”, but also and specifically of the parietal “lobe”, when compared with extinct hominids or with living chimps. The localization of anatomical boundaries on endocasts may be difficult, although those results have been replicated on different samples. The identification of anatomical landmarks in living species is, in contrast, definitely more reliable. Therefore, whatever the result of a global surface analysis of the whole endocranium, we should not forget that comparisons of specific areas are suggesting a differential contribution of distinct brain components.

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