Archive for the 'Methods' Category

Digital Endocasts

A new Springer book: Digital Endocasts: from skulls to brains. Chapter 1 (Holloway) is an introduction to physical casting. Chapter 2 (Ogihara et al.) deals with digital reconstructions of Neandertals and early modern humans’ endocasts. Chapter 3 (Kobayashi et al.) is about inferences on cortical subdivision from skull morphology. Chapter 4 (Beaudet and Gilissen) introduces paleoneurology on non-human primates, and Chapter 5 (Walsh and Knoll) is on birds and dinosaurs. Chapter 6 (Rangel de Lázaro et al.) reviews  craniovascular traits. Chapter 7 (Bruner) is on functional craniology and multivatiate statistics. Chapter 8 (Gómez-Robles et al.) concerns brain and landmarks, and Chapter 9 (Pereira-Pedro and Bruner) concerns endocasts and landmarks. Chapter 10 (Dupej et al.) is on endocranial surface comparisons. Chapter 11 (Kochiyama et al.) presents computed tools to infer brain morphology in fossil species. Chapter 12 (Neubauer and Gunz) deals with brain ontogeny and phylogeny. Chapter 13 (Bruner et al.) is on an application of network analysis to brain parcellation and cortical spatial contiguity. Then, there are chapters dedicated to the evolution of the frontal lobes (Chapter 14 – Parks and Smaers), of the parietal lobes (Chapter 15 – Bruner et al.), of the temporal lobes (Chapter 16 – Bryant and Preuss), of the occipital lobes (Chapter 17 – Todorov and de Sousa) and of the cerebellum (Chapter 18 – Tanabe et al.). The aim of the book is to provide a comprehensive perspective on issues associated with endocasts and brain evolution, and to promote a general overview of current methods in paleoneurology. The book has been published within the series “Replacement of Neanderthals by Modern Humans“. Here on the Springer webpage.

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Surfin’ endocasts

Endocasts and brains are difficult to analyze through traditional anatomical landmarks, because of the smooth morphology, blurred boundaries, and a noticeable individual variation. Currently, semilandmarks and surface analyses are good alternatives. Nonetheless, these two methods analyze the geometry of an “object”, ignoring its anatomical nature. If such geometrical modelling is interpreted too strictly, it may generate speculations and even incorrect conclusions. Numerical transformations behind spatial and geometrical models can be very complex and entangled, and the long chains of algorithms cannot be disentangled in any research article (the same occurs in any other field, like molecular biology, where long chains of reactions and engineering processes can’t be resumed in detail in every single paper and, necessarily, we must blindly rely on their proper functioning). In those many numerical steps, we must be aware that there may be incorrect passages, or simply algebraic assumptions that are not consistent with the real biological and evolutionary processes. More importantly, the brain is formed by so many independent elements, histological components, and cortical areas, and a pooled geometrical analysis can generate hybrid results. Anatomical landmarks are still necessary to mark boundaries and proportions, as to evaluate the real contribution of each element. Of course, anatomical landmarks are difficult to assess, they require experience, and they require inferences: as in every experimental paradigm, as in every field of science. Shape analyses deals with models, not with real anatomical entities. And models only take into considerations some specific properties of those anatomical systems, following algebraic rules that, right or wrong, represent conventional and operational assumptions. Here an opinion paper on all these issues.

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 …

Endocasting

I have found a very useful article published one year ago by Amy Balanoff and colleagues on Journal of Anatomy, a guide on “Best Practice for Digitally Constructing Endocranial Casts”. The paper is a detailed and comprehensive methodological overview on digital endocasting, introducing techniques, parameters, programs, problems, tools, and many suggestions on procedures and operational choices. Although the paper is more focused on birds and dinosaurs, it can be perfectly suited for human paleoneurology as well. The authors have organized the article as a set of replies to essential questions dealing with endocranial cast digital reconstruction. Pretty clear flow charts supply quick solutions for basic technical issues. The paper takes into account technical aspects (machines, physics, programs) as well as biological aspects (bone, skull, brain). Indeed, an extremely useful lecture for those who want to step into digital anatomy and paleoneurology.

Frontal surfaces

beaudet-and-bruner-2017

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.

Surfaces

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.

Imaging brains

Brain glass coasters 2016In the last decade neuroscience has experienced an explosion of brain imaging studies, programs, and databases. The advance has been outstanding, indeed. Nonetheless, we have also been surprised by a large and unexpected number of strange and discordant results. There are so many examples in which similar studies reach different or even opposite conclusions. Differences in raw values, comparisons (from sexual variation to hemispheric asymmetries), or correlations and associations between variables, can be pretty frequent when dealing with specific aspects of brain anatomy. In some cases, these studies apparently deal with “simple” factors, like volumes or linear metrics. We have discovered new kinds of uncertainties on functions, software, and even on basic anatomy. A recent study has discussed the problem of reliability in functional MRI (here a comprehensive post). Of course this is not something strictly associated with neuroanatomy and brain imaging. Any analysis in molecular biology has many more methodological and technical passages which can hide some kind of processing problem and generate noise or even confounding outputs. In neuroimaging, there are at least four main steps which can be problematic. First, data sampling (machines, parameters, and so on). Second, the formatting procedures (databases, archives, platforms). Third, the processing of the data (programs, algorithms, etc.). Fourth, statistics (sample size, statistical power, adequacy of the statistical tools). Whatever it is, it is calling our attention. And this is the good part: we are more and more forced to be less superficial. All these unexpected uncertainties require a critical view, as discussed in this manuscript on future challenges for neuroimaging research. The most patent problem is confusion, and an epidemic spread of wrong information. But there is also another risk: too many discordant results can lead to a consistent loss of confidence in these methods (here another post – in Spanish). And this can have both scientific and economic consequences.


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