This month we have published a review on craniovascular traits and anthropology, freely available to download from the Journal of Anthropological Sciences. The article describes many vascular traits that can be analyzed on skulls, through the traces they leave on the bone surface or within the bone itself. The traces of the middle meningeal vessels, the traces of the venous sinuses, the diploic channels, and the endocranial foramina, can provide information on the vascular networks and, indirectly, on the physiological processes associated with their growth and development. The functional information available from these imprints is partial and incomplete, but it is the only one we have on blood flow when dealing with fossils, archaeological remains, or forensic cases. Methods are an issue, because of the difficulties with small samples, scoring procedure, statistics of ordinal and nominal variables, and with an intrinsic limitation in current anatomy: we still ignore the variations and processes behind many macroanatomical features, even in our own species. Previous articles on this topic deal with middle meningeal artery, vessels and thermoregulation, diploic channels, and parietal bone vascularization. Most of these papers are part of a project funded by the Wenner-Gren Foundation through an International Collaborative Research Grant, entitled “Cranial anatomy, anthropology, and the vascular system”. This beautiful drawing of a sectioned skull is by Eduardo Saiz.
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.
Magnetic resonance imaging (MRI) is a valuable and increasingly used method for studying brain anatomy as it allows large-scale, high-quality in vivo analyses. However, some artifacts might influence the digital results, and thus require cautious interpretation. In a recent review, these issues are addressed along with possible solutions. First, we need to keep in mind that the images acquired are not mere photographs of the brain, but reflect some biophysical properties of the tissues, by measuring the radio-frequency signals emitted by hydrogen atoms (present in water and fat) after being excited by magnetic waves. Thus, MRI is an indirect analysis of the brain anatomy and depends greatly on specific tissue properties. Second, researchers can choose from a variety of methods, depending on the aim of the survey. Macrostructure, i.e. the size and shape across voxels, can be studied through manual volumetry or automatic segmentation, voxel- or deformation-based morphometry, surface- based algorithms, or diffusion tractography. Microstructure, i.e. within-voxel contents, is usually analyzed through diffusion MRI, but also magnetization transfer imaging, or quantitative susceptibility mapping.
When making inferences on the biological significance of the outputs, the researcher must account for the possible digital artifacts. These can occur both during image acquisition and processing and can be subject-related and methodological-related. A common problem is subject motion, which might contaminate or influence the results, as the amount of motion varies with other factors influencing brain changes (age, sex, and disease status), or can even correlate with a specific effect being studied. For instance, motion induces gray matter reduction, which might be perceived as brain atrophy. Subject motion is unavoidable, but its influence can be reduced by using a motion detector during acquisition, or by estimating the amount of motion allowing statistical adjustments, also useful to detect outliers. The difficulty in manipulating the magnetic and radio-frequency fields might also introduce deformation. The main magnetic field should be spatially uniform, but it is dispersed by brain tissue while concentrated by air. This can be partially compensated by applying additional fields. The radiofrequency field is not homogeneous, which affects MRI contrast and intensity. Combining multiple transmit coils might help reduce this caveat, although the contribution and sensitivity of each coil must be taken into account when processing the image.
A particular case that can affect estimates of cortical volume and thickness is the difficulty in discriminating the dura and gray matter due to the similar intensity and anatomical proximity. In this case, MRI parameters can be manipulated in order to increase the contrast between these tissues, without reducing the contrast between gray and white matter. Individual variability in folding patterns is a further major issue in voxel-based morphometry studies because it complicates the mapping of correspondences between subjects. Registration might be enhanced by analyzing regions with larger variation to find possible anatomical alterations, aligning cortical folding patterns to locate corresponding areas, and mapping sulcal changes to improve sulci identification. Finally, researchers should continuously keep track of the constant advances and innovations in the field. The authors conclude acknowledging the importance of structural MRI when coupled with other biological information, like genetic expression (Allen Brain Atlas), cytoarchitecture (JuBrain), and cognitive associations (Neurosynth).
Anatomists on the Edge
27-29 June, 2017
The Internet Brain Volume Database (IBVD) is an online collection of neuroimaging data funded as a part of the international initiative, the Human Brain Project. The IBVD provides access data for both individual and among-group comparisons that allow total volume comparisons with parallelization of the brain into hemispheres, specific lobes or grey matter volumes. While the database contains data on humans, there is also non-human primate (macaque) and rat studies. A summary search provides information on sex, age and handedness as well as age-related pathology, neuro-psychiatric disease, structural disease and twin-studies (monozygotic and dizygotic). These selected individuals can be compared to normal studies or pooled into user-specified group results. For example, it is very easy to generate a plot of left vs. right temporal lobe volume compared to age in normal human in vivo males and females.
Braincase shrinkage during winter was firstly described in shrews by Dehnel in 1949, and is known as the Dehnel’s Phenomenon. Recently, Dechmann et al. investigated the seasonal size variation in the skulls of shrews (Sorex araneus) and least weasels (Mustela nivalis). They measured skull length and braincase depth on specimens previously collected from a Polish National Park, sampling all seasons. Both species showed an initial juvenile growth until the first winter, followed by shrinkage until spring in the adults, and a subsequent re-grow on the second summer, though never reaching the initial size. Heat maps built from high resolution CT scans demonstrated that size changes also involved changes in shape and in bone thickness, with the thinnest skulls coinciding with the smallest braincase size. Interestingly, these patterns differed between sexes, especially in weasels as only males were observed to re-grow. Despite phylogenetically distant, both species have similar life histories, having short life spans and high metabolisms, and inhabiting an environment with seasonal fluctuation of resources availability. Winter shrinkage would reduce energetic requirements and prepare individuals for the harder conditions, and re-growth during the resources-abundant season would prepare the males for reproduction while females would allocate the energy into caring for the offspring. The authors conclude these seasonal reversible size changes are genetically fixed and exclusive of animals with such life histories, as an adaptation to extreme environmental conditions. Future investigation shall clarify the potential drivers and consequences of this phenomenon, including how the variation in size affects brain size and reorganization.
The Finite Element Method (FEM) was developed within the framework of Engineering but has become a popular tool in bio-mechanical studies. It is natural that computational bio-mechanics and Finite Element Analysis (FEA) became increasingly promising in fossil studies where there are no examples of some taxa still living. To study the bio-mechanical responses of fossil hominids, modern humans and non-human primates are often used as comparative samples for which there are already known values. Despite this, precisely how accurately computational bio-mechanics compares with physical studies is still not well understood. The biological composition of bone and dentition is hard to replicate in computational terms with the cranium a mixture of trabecular and cortical bone while teeth comprise variable layers of enamel and dentine. The resolution required from Computed Tomography (CT) scans to accurately capture these finer biological compositions is not feasible for the heavy demands on software to analyze such FEA models with flow-effects for the number of specimens that can be included into any single study.
Godinho et al investigated the validity and sensitivity of Finite Element (FE) models using a direct comparison with a human cadaver. Results were particularly affected if the model was simplified by assigning all materials as cortical bone, including dentition and trabecular bone components. Results showed that the real and virtual skull showed no differences in strain magnitude; differences in strain pattern (high or low strain distribution) were only partially different; simplifying the virtual model decreased the strain magnitude; simplifying the virtual model partially affected the strain pattern with the regions near the dentition, particularly the alveolar ridge, most affected.
For bio-mechanical studies, by not simplifying virtual models and attempting to designate dental and bone tissues properly acknowledges the underpinning biology of the cranium while potentially revealing sensitive adaptations of this biological structure. By adopting these changes, new variations between living and fossil humans, that have so-far been obscured by less time-consuming computational methods, could reveal unique adaptational trends that have real significance for human evolution.
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.