Archive for the 'Methods' Category

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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