The Finite Element (FE) method has increasing application to biological sciences but frequently lacks proper validation by robust experimental research. One aspect of particular biological and bio-mechanical importance is growth of the human infant skull. Specific local changes during growth of the infant skull are largely unknown with only the general rate of cranial increase from 25% at birth to 65% of the adult size by age six. The potential adverse effects of any abnormalities in infant skull growth is difficult to approximate if the isolated local areas likely to be most impacted are not accurately known. If properly validated, computer simulated modelling such as Finite Element methods would be invaluable in surgical settings. A new comprehensive study focusing on human infant cranial vault expansion utilized robust laboratory experiments of a fetal skull (ex-vivo), replicate physical model (in-vitro), several FE models (in-silico) and a sample of micro-CT infant skulls (in-vivo). The first validation tested a physical model against a FE model (A) in which the cranial base and facial bones formed a single structure with only the cranial vault comprising individual bones. The FE model (A) over-predicted size changes to the anterior of the skull especially near the orbits and mediolateral expansion of the skull. The second validation tested in-vivo models against an FE model (B) in which the only the facial bones formed a single structure while the vault and cranial base comprised individual bones. All analyses associated discrepancy between the FE model (B) and the in-vivo models with age-related changes. As age increased, the regions under-predicted by the FE model (B) were first the orbits and upper vault before tending toward the cranial base, while the regions over-predicted by the FE model (B) were focused on the anterior and posterior fontanelles.
This validation study showed that FE modelling could be used to approximate growth in the human skull with only small discrepancies. The differences between the predicted ranges of growth (FE models) and the observed growth (in-vivo models) was explained by assumption of isotropic brain expansion which simplified the highly complex and uneven growth rates in real brain expansion. The artificial construction of a single structure representing the facial bones added further constraints. The development of more advanced simulations could narrow the discrepancy between expected and observed growth patterns allowing a more accurate representation of human skull growth.
The International Encyclopedia of Primatology, a new multi-volume resource suited to an academic audience studying human and non-human primates with topics on evolutionary biology, genetics, behaviour, taxonomy and ecology.
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.
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.
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 showe d 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.
In general, members of the Primate Order possess larger brains for body size than other mammals, with modern humans (Homo sapiens) evolving the largest brains. The Internal Carotid Artery (ICA) provides blood supply to the brain but there are distinct anatomical differences between the primate groups. While vascular and other soft-tissues are very rarely fossilised, evidence of the ICA passage is retained where it was encapsulated in bony tubes or as distinct grooves of the endocranial surface. The ICA evolved in primates from two main pathways in the auditory complex: the promontory artery branched from the cochlear space to supply blood to the brain while the stapedial artery branched from the obturator foramen of the stapes to provide blood to the cerebral meninges and the orbito-facial complex. Primate groups are recognised by ICA anatomy: Within the Strepsirrhini, Lorisiformes and Cheirogaleids both lack the stapedial and promontory arteries with the External Carotid Artery (ECA) supplying blood to the brain instead of the ICA; whereas, non-Cheirogaleids possess the ICA and stapedial artery but often retain a much smaller promontory artery, while the anthropoids (apes and monkeys) lack the stapedial artery entirely retaining only the promontory artery.
The evolution of these distinct differences was examined by comparing living and fossil primates and reconstructing the hypothetical phylogenetic pathways. The size differences between the stapedial and promontory arteries were compared to endocranial volume (ECV) to investigate the influence on brain size. Only the size of the promontory artery had a consistent correlation with brain size. There was no reported correlation between brain size and size of the stapedial artery, with the stapedial only correlating with size of the promontory artery. This suggests that throughout primate evolution, the trend for body and brain size increases caused the stapedial artery to become restricted as head size increased but size of the obturator foramen did not scale equally with the head. Most early primates evolved a reduction in the size of the stapedial artery and quickly accommodated an increase in the promontory artery allowing even greater blood flow to the brain and driving the encephalisation process and the eventual loss of the stapedial artery in anthropoids.
The mature primate brain consists of many layers with the outer layer or cerebral cortex forming folds known as sulci and gyri. During embryonic development, the brain is divided into zones with the inner-most ventricular zone where neurons are formed and a series of cytoarchitecturally distinct layers forming plates radiating outward. The subplate is located between the inner ventricular zone and the outer cortical plate hosting the migration of neurons allowing brain expansion. Most embryonic brain research is conducted on non-primate mammals but there are substantial differences in the development of the non-primate and primate brain. A very recent study utilized existing primate tissue databases to examine the embryonic development of the subplate zone in non-human and human primates. Duque et al. found during that development of the macaque brain, once the neurons have migrated to the subplate they then are pushed downward by axons derived from the subcortical layer before further compression occurs from further axonal development originating from the cortical layer. The implications of this force acting on the neurons within the subplate suggests that thickness of the subplate differs unevenly throughout the brain potentially due to an increased axonal density. Duque et al. suggest the density of axonal fibers increases with demand for more connectivity between brain regions with those areas possessing a high-demand for greater complexity causing a thicker subplate.
Changes at the cellular-level of the subplate also have implications for the development of the cerebral convolutions such as sulci and gyri. It was recently posed that the folding patterns in the human brain are the result of mechanical forces related to the subplate and outer expansion of the cerebral cortex. Tallinen et al. showed through numeric and physical simulations with the support of MRI that during fetal development the subplate stabilizes while the outer cortical plate continues to expand. The final stages of growth see the cortical layer undergo extensive gyrification to form the folding patterns we see in the adult human brain. Overall, a better understanding of human neurobiology informed through non-human primate neurobiology offers a glimpse into the evolutionary pathways which led to the evolution of modern humans.
In a recent study, Eklund et al. sparked an ongoing international debate when it highlighted systemic failures in cluster-based analysis of functional magnetic resonance imaging (fMRI). The fMRI method has been used for decades to investigate correlations between brain region inactivation and task performance. Active regions in the brain are assigned by two methods: voxel-wise and cluster-wise inferences. Voxel-wise inference assigns activity to brain regions based on association of specific voxels. Meanwhile, cluster-wise inference assigns activity based on correlation between specific clusters of voxels usually associated by size. The occurrence of false-positives is controlled in the most commonly used fMRI software packages (SPM, FSL and AFNI) by a function known as the Family-wise error (FWE). The Eklund et al. study examined the reliability of the five FWE analysis tools offered by the main software packages. The results showed that for the FWE in cluster-wise inference, parametric studies gave extremely high false-positives but were within range for the voxel-wise inference. To analyze the data using a nonparametric test, Eklund et al. utilized a permutation test which gave results for the FWE within the boundaries for both cluster-wise and voxel-wise inferences.
An independent post examined the assumptions behind the comparison of the five different FWE tools based on the differences between voxel-wise and cluster-wise thresholds. In short, voxel-wise thresholding relies on making a decision about ‘active’ brain regions at a specific voxel-level, whereas cluster-wise thresholding relies on this decision made about adjacent ‘clusters’ of voxels and is specific to the spatial distribution or size of the clusters. Eklund et al. also examined the in-built auto-correlation functions in the software packages which assign activity to a brain region based on the cluster representing a squared exponential. This is the basic assumption made by the auto-correlation algorithm but in testing this functionality, Eklund et al. found the assumption of spatial smoothness did not follow a Gaussian distribution or was not normally distributed across the entire brain. The lack of spatial smoothness lead the auto-correlation function to incorrectly calculate clusters and in turn, force a false-positive finding.
With the Eklund et al. research actively calling into question the fMRI studies of the past two decades, a heated debate arose around the validity of such a statement and the methods used in the research. Subsequently, the statement was retracted and redefined but this did not go unnoticed. Unfortunately, it does appear that the issue at the heart of this debate has been overlooked and somewhat downplayed which is the matter of reproducibility affecting neuroscience and all science in general. The replication of all results are essential to removing incorrect inferences and misassumptions that lead discoveries to be meaningless without validation. While the debate over the ‘failure’ of fMRI continues to evolve the premise holds that without validation of scientific hypotheses there will never be an opportunity for these to graduate into scientific theories.
Commonly, examining MRI (magnetic resonance imaging) employs volumetric measurements of CSF (cerebral spinal fluid), GM (grey matter), WM (white matter) and CT (cortical thickness). A recent study investigated the effect time-of-day (TOD) had on volume of the whole brain and regional areas. Using a linear mixed effect model, the results indicated a statistically significant correlation between changes in TOD and changes in brain volume. For example, there was a decreasing trend in volume effected by TOD in the CSF, GM and WM, respectively. This implies that the time-of-day an MRI scan is acquired can affect the volume of these common measurements. Importantly, volumetric changes attributed to TOD are not consistent throughout the brain. The cortical areas of the frontal and temporal lobes showed greater effect from TOD while parietal and occipital lobes showed an opposing trend. This highlights a necessity to “factor in” the time-of-day for MRI scan acquisition in order to minimise potential error and account for bias in further statistical analysis.