The pioneering work by Korbinian Brodmann published in 1909, detailed the correspondence between brain macro anatomy and function which became the classic neuroanatomy reference known as Brodmann’s Map. Over a hundred years later, mapping the human brain continues but the underlying reasons for the highly variable folding patterns is still unclear. Inferences from non-human primates and fossil taxa suggest an increasing complexity of folding patterns which is also associated with an increase in total brain size. This has led to three hypotheses proposed to explain brain folding in primates with underlying genetic causes and species-specific traits, neuronal an axonal tensioning with cellular redistribution and proliferation, and lastly, larger total brain size and increased folding complexities. To date, no single hypothesis seems to adequately explain the variation of folding patterns found in primates.
Heuer and colleagues conducted a study on neocortical folding patterns in 34 primate species using MRI and phylogenetic comparative methods. Despite a small sample size of, on-average, only one individual per species, high-resolution ex vivo MRI of the primate brain was used to test several measures of folding pattern complexity. Results confirmed brain surface area and volume were strongly correlated as was allometry. Estimates of the number of brain folds and the length were also strongly correlated. A commonly used 2D algorithm of folding complexity (gyification index) was expanded for use in 3D models. Folding pattern results showed deeper but fewer brain folds in primate species with smaller brain volumes so that modern humans with much larger brain volumes had more folds, but these are shallower than the smaller brained new world or old world monkeys. The authors suggest caution when interpreting the folding patterns in very small-brained species like lemur species where brain folds were poorly recognized. Phylogenetic signal tests were used to see if primate brain folding patterns were different to those expected under the commonly used Brownian Motion model of evolution. Results showed the influence of phylogeny on primate brain folding patterns was very strong but there are good reasons for caution when using only phylogenetic signal as a test to estimate the impact of evolutionary process on phenotype which can give misleading results.
The conclusions by Heuer et al. (2019) are relevant for the brain folding mechanisms with the mechanical bending and stiffness properties of the brain likely thought to constrain neocortex thickness, which differs little across mammalian species. There was similarity between primate phylogeny and brain size with a rapid increase in brain size from lemurs to old world monkeys associated with more brain folds before a stabilizing in great apes where mechanical properties force doubling within folds, resulting in shallower but greater number of brain folds. As discussed recently, primate neocortical folding patterns might be more the result of brain size increase and the mechanical properties of neural tissues, where increasing size has been mathematically, physically and geometrically simulated to show neocortical buckling and wrinkling generating folding patterns similar to those observed in real brains.
The prefrontal cortex is the most anterior region in the frontal lobe of the brain and, in humans, it is associated with higher-order tasks involving complex cognition and behaviour. Throughout primate evolution there has been a trend for increasing brain size in which modern humans are the most extreme living example. Among all mammals, absolute brain size consistently increases with body mass, but many primates seem to have relatively larger brains than expected. In modern humans, our complex cognitive abilities have been long been associated with bigger brain size but some regions of the brain are suspected of being disproportionately larger than expected for our body size. Precisely which regions of the brain are disproportionately larger in modern humans compared to other primates remains unresolved and highly contentious. The prefrontal cortex is one of the regions suspected of being relatively larger in our species, although no consensus has been reached on this issue, most of all when fossils are included in the debate.
In 2018, Donahue and colleagues used Magnetic Resonance Imaging (MRI) to investigate relative prefrontal cortex size in modern humans, chimpanzees and macaques. The prefrontal cortex was defined using two different approaches from previous studies where opposing conclusions were found. Macromorphology defined the prefrontal cortex as all regions anterior to the genu of the corpus callosum, while cytoarchitecture used neuron densities, specifically, granular (high-density neurons) and dysgranular (modest-density neurons) to define the prefrontal cortex. Limitations of only including three species meant statistical regressions were unsubstantiated and allometry could not be investigated. However, relative prefrontal cortex size was examined by comparing the prefrontal cortex to total brain size and the size of the primary visual and primary motor cortices. Investigation of the prefrontal cortex was further subdivided into gray and white matter volumes. Results indicated modern humans had a larger prefrontal cortex, absolutely and relatively, compared to macaques and chimpanzees, with a specific increase in the proportion of white matter. Results comparing the two approaches of delineating the prefrontal cortex indicated that macromorphology consistently underestimated prefrontal cortex size in all species compared to cytoarchitectural definitions. It was concluded that modern humans possessed a disproportionately large prefrontal cortex, mostly argued on results from allometric regressions which were limited to three species. A reply questioned the conclusion that modern humans have a disproportionately enlarged prefrontal cortex on the basis of allometric regressions which were statistically unsound. Concerns were raised over the anthropocentric focus of these conclusions, instead arguing there was no evidence to refute a generalized scaling rule applicable to all primates when only three species were studied and regressions were inappropriate to indicate humans deviate from chimpanzees and macaques.
The most substantial findings seemed under-emphasised with findings of previous studies confirmed and macromorphology consistently underestimating prefrontal cortex size. Future research to better define the prefrontal cortex in primates is important as is the inclusion of more primate species to determine whether modern humans do have disproportionately enlarged prefrontal cortices compared to other primate species.
Platyrrhines (or New World Monkeys – NWM) inhabit South America and there are currently 5 families and 151 species, possessing traits not found in Catarrhines (Apes and Old World Monkeys of Africa and Asia). The NWM fossil record is fragmentary, with the earliest fossil specimens found in Argentina and dated to the middle Miocene (~ 22 million years ago), and more recent fossil remains found on the Caribbean islands and dated to the Late Pleistocene or early Holocene (~ 20-5 thousand years ago). The evolutionary relationships among living NWM and fossil species remain highly speculative. However, Woods et al. (2018) reported the successful recovery of ancient DNA from a Jamaican fossil species Xenothrix, closely related to living species of the Callicebinae, the Titi monkeys. The continuing uncertainty surrounding NWM evolutionary history has resulted in several Caribbean fossil NWM assigned as tentative ancestral species to living howler monkeys (genus Alouatta) based on similarities of highly prognathic faces, robust crania and smaller than expected brain size or endocranial volume (ECV).
A recent study by Halenar-Price & Tallman (2019) examined cranial shape and potential correlation with ECV in three Caribbean fossil and four living NWM genera. Patterns of cranial shape were determined for each living NWM species using geometric morphometrics and, once controlling for absolute size and phylogeny, the correlation with ECV was investigated using an encephalization quotient (EQ). Results from statistical tests for a correlation between cranial shape and brain size indicated no strong support for common trend for cranial shape describing the entire NWM clade, with the overall effect of cranial shape change in living NWM only slightly associated with brain size or ECV (less than 10%). Instead, cranial shape change was very species-specific, with species often differing in cranial width, cranial base flexion and globularity of the cranial vault. The howler monkeys had the lowest association between ECV and cranial shape, while the saki monkeys (genus Pithecia), showed greater links between ECV and cranial shape change associated with seed-eating diet and presence of cranial crests.
To examine fossil NWM and the role of encephalization on cranial shape, phylogeny was accommodated and fossil NWM added to the analyzes. Results indicated that Dominican Republic fossil NWM Antillothrix had a higher encephalization quotient (EQ) than living howler monkeys and was instead within range of titi monkeys (genus Callicebus), while Brazilian fossil NWM Cartelles was within the range of living howler monkeys. However, the Cuban fossil NWM Paralouatta was below the range of living howler monkeys. This study highlighted that the combined presence of facial prognathism, robust cranial form and smaller than expected brain size in NWM was strongly influenced by species-specific patterns related to diet, physiological and ecological adaptations, where, in very generalized terms, similarities between fossil and living new world monkeys do not necessarily indicate shared evolutionary associations.
Here a recent study on the cranial morphology of howler monkeys, and an article on atelids and ethnozoology.
The brain is a soft-tissue organ surrounded by the bony structure of the skull, where changes in one require changes in the other. From infancy, the bones of the skull are separated by membranous sutures and with rapid brain expansion, these membranous regions of the skull are replaced by bone, fusing the skull into a protective structure around the adult brain. Ontogeny describes changes in the same anatomical structure throughout the life cycle, including the differences between age groups, within a species and across species, while allometry can explain size-related changes to skull shape, particularly between species. The individual bones of the skull join at sutures to form modules which include the facial block, the cranial vault and the cranial base.
A new paper by Scott et al. (2018) examined allometry and ontogeny in the hominid skull. The skulls from three hominid (great ape) species included the Bornean orangutan, the Western lowland gorilla and the common chimpanzee from several age groups were analyzed, and geometric morphometrics was used to capture shape change and allometry in the facial block and endocranium (as an indirect proxy for brain form). Covariation between the facial block and endocranium was tested using 2-block partial least-squares analysis. Results for ontogeny suggested endocranial change was lesser in younger age groups but with increasing age, orangutans separated from gorillas and chimpanzees, showing the greatest difference in face-to-brain shape. Results for allometry indicated that changes in facial shape were mostly related to size differences. However, the endocranium was not entirely influenced by changes in size, suggesting shape change in the endocranium is somewhat independent.
Ultimately, Scott and colleagues have shown the covariation between the facial block and the endocranium was more conserved in all three ape species in younger age groups, but the facial block continued to change shape into adulthood even after the brain growth had stopped. This suggests the endocranium is driven by changes to brain form during earlier stages of life before the cranial vault exerts a greater influence in late adolescence. However, the greatest change to skull morphology occurred during adulthood in facial shape.
In evolutionary biology, microevolution and macroevolution impact on the variation and covariation between genotype and phenotype. A related concept is the biological ability of an organism to adapt and evolve, or its evolvability, which is of keen interest to evolutionary biologists. The quantification of genetic change is analysed via the genetic variance-covariance matrix (G-matrix) while phenotypic change is analysed via the phenotypic variance-covariance (P-Matrix). Under the assumption of a neutral evolutionary model with the absence of genetic drift, the G-matrix should be proportional to a P-matrix. Although there is potential for theoretical complications arising from organisms with higher evolvability biasing the rates of evolutionary change, this is not fully investigated and seems to warrant further empirical studies.
The diversity of craniofacial form observed in fossil species of genus Homo and modern humans has been examined in terms of craniofacial adaptation to various biomechanical and environmental stressors. The absence of recovered genomes from species of fossil Homo beyond Homo neanderthalensis and fossil Homo sapiens has required studies of fossil human phylogenetics to rely on high uncertainties in the estimation of fossil hominin phylogeny and further restricted by small sample sizes.
In a recent study, Baab (2018) used the rate of evolutionary change in populations of modern Homo sapiens to estimate evolutionary rates in species of fossil Homo, analyzing craniofacial shape change, diversification and evolvability in the genus Homo. Results were consistent with independent conclusions that a neutral evolutionary model was adequate to generate the diversity in craniofacial form observed in the genus Homo. Once accounting for the small fossil sample size and the degree of evolutionary rate being higher than chance, there was no statistically significant support for higher rates of evolvability generating more rapid rates of evolutionary change across the entire genus Homo.
In contrast, the more recent lineages showed some evidence for selection acting at a greater magnitude in H. neanderthalensis and early H. sapiens, generating a more rapid rate of evolutionary change. Baab (2018) suggests brain expansion may be a likely contributor influencing the more rapid evolutionary rate change in craniofacial shape as observed in early H. sapiens and H. neanderthalensis and why only the more recent lineages of the genus Homo were affected by such rapid changes in craniofacial form.
The gray mouse lemur (Microcebus murinus) is a small Madagascan primate, averaging 12 cm length and weighing between 60-120 grams. Despite the diminutive size, mouse lemurs are increasingly used in medical studies of Alzheimer’s disease and similar neurological disease processes found in humans. Mouse lemurs often live to 12 years or more in the wild, which combined with torpor (a form of short-term hibernation) may be associated with the longer lifespans. Considering mouse lemurs have prolonged lifespans, it is probably not surprising that they also experience age-related brain atrophy. Nadkarni et al. (2019) address the absence of a dedicated mouse lemur brain atlas through in-vivo MRI scanning 34 mouse lemurs, investigating age-related brain atrophy and the neuroanatomy of Microcebus murinus in a comparative context. Results showed that most of the cerebral cortex was affected with age-related brain atrophy including the primary visual cortex and, although the remainder of the primary sensory areas were unaffected by atrophy, an even higher amount of atrophy was found in the sub-cortical brain regions including the thalamus, hippocampus and amygdala. All previous studies of mouse lemur neuroanatomy have been conducted with histological atlases. However, Nadkarni and colleagues compared mouse lemur cerebral to cortical volumes using high-quality MRI, finding that contrary to histology studies, mouse lemurs had similar cortical to cerebral volume indices to other primate species and were not to be considered a “lesser primate” species as has been previously argued. The proportion of cerebral white matter was the highest in humans, before a continual decrease in macaques and smaller monkeys with the lowest white matter volumes observed in mouse lemurs. The trend for increasing white matter volumes in primates, culminating with the highest values in humans, has often been argued as necessary for reinforcing intra-cerebral connectivity, hypothesized as an important process in primate brain evolution.
Included with this study of mouse lemurs, Nadkarni and colleagues also produced an accompanying MRI in-vivo brain atlas which includes 120 labelled brain structures specific to Microcebus murinus which to-date, has been unavailable. The accessibility of a brain atlas specific for mouse lemurs removes the time-consuming process of manual MRI segmentation, allowing quick and direct comparison of brain regions with other primates for a comparative evolutionary context and in medical research for Alzheimer’s disease.
Since the early 2000s, the expansion of digital anatomy tools has been aided by advances in computational power and accessibility of medical imaging such as Computed Tomography (CT). The greater accessibility to digital imaging of fossil material has allowed the reconstructions of inner cranial cavities (endocasts), sinuses cavities, and dental reconstructions of the enamel-dentine junctions (EDJ) of fossilized teeth. Despite great accessibility, the segmentation processes used to generate digital reconstructions of inner cavities remain time-consuming and require specific expertise in computer analysis, anatomy, digital imaging.
Profico et al. (2018) provide two fully-automated digital methods to minimize these time-consuming digital segmentation tasks. Both of these methods rely on point-of-views (POVs) to delineate a region-of-interest (ROI). In the CA-LSE method, POVs were located outside a ROI and all areas beyond are subtracted from the final reconstruction. In contrast, the AST-3D method relies on a ROI defined by POVs placed inside a cavity and all external areas, subtracted from the final reconstruction. While both methods are similar and can be used to generate reconstructions of the inner cavities, each method has slightly different benefits. Profico and colleagues conducted a comparison of both methods to determine strengths and weaknesses of each approach. While both of these methods are available through the Cran R network, two different R packages were tested: Morpho and Arothron.
Results indicated that in the Morpho package, CA-LSE had no restrictions on where POVs could be placed, but using AST-3D method in Morpho, POVs had to be manually placed inside the internal cavity for successful reconstruction. In the Arothron package, CA-LSE method allowed fully-automated placement of POVs outside the ROI surface, however, the AST-3D method a ROI must be defined by manually placed POVs within the inner cavity. In general, accuracy of the AST-3D and CA-LSE methods were determined by each method, with AST-3D more reliable generating reconstructions of inner cavities (such as endocasts), while the CA-LSE was more suited to reconstructions of outer structures (such as skulls).
Although, automatic approaches offer time-efficiency and often allow larger sample sizes to be more quickly processed, many fossilized skulls are highly fragmentary and automated methods remain limited when fossilized remains are partially or entirely matrix-filled with anatomical and digital expertise still requiring manual segmentation. In these complex scenarios, further fine-tuning of automated methods would be invaluable with inclusion of fully-automated, semi-automatic and manual options.
Comparative neurobiology has traditionally been used to describe and quantify the macro-and micro-anatomical changes to the brain between human and non-human primates. Research literature commonly refers to the Stephen dataset with brain volumes measured from a small sample of ex-vivo non-human primate brains with species often represented by only one or two individuals. Although this is common limitation inherent to many physical neuroanatomy collections, caution should be used with such limited samples not representative of a species and the actual variation unknown. The consequences for such assumptions on quantifying the neuroanatomical differences between humans and non-human primates have broader implications for human evolution. Despite the increasing accessibility of primate neuroimaging datasets, many comparative studies still rely on the Stephen brain volumes. This is despite the necessary factoring of numerous bias including cerebral tissue damage from the delay between the post-mortem interval and brain preservation, potential introduction of artifacts from tissue preservation processes causing shrinkage, some cellular destruction and occasional damage during brain extraction.
Navarette and colleagues recently compared digital neuroanatomical volumes from ex-vivo brain MRI with the Stephen data using the same primate species but including an extra 20 species. Results showed differences between the Stephen data and those obtained by Navarrete et al. with larger brain volumes measured in the pre-fixed state versus post-fixed, indicating fixation did noticeably affect brain volume measurement. Although Navarrete et al. aimed to quantify primate brain volume variation by increasing the number of primate species to 39, there were still 29 species represented by only one individual, while the maximum of for the entire sample was never greater than three individuals per species. Although Navarrete et al. argued a lack of larger in-vivo primate brain neuroimaging datastes, several are accessible as part of the National Chimpanzee Research Center. More broadly, Navarrete and colleagues have shown quantifiable differences between pre- and post-fixed brain volumes and emphasised the need for caution in the suitability of ex-vivo brain collections to provide reliable volumetric measurements for comparative primate neuroanatomy.
Precise computational modelling of the brain-skull interface is necessary for the prediction, prevention and treatment of acquired brain-injuries. The brain-skull interface comprises complex layers including the osseous cranial tissues, meninges, sub-arachnoidal space and tissues, cerebrospinal fluid (CSF), pia mater and the gray and white cerebral matter. While the tissue properties of the brain-skull interface are known, there is no consensus on how these layers interact during head impact. To generate computational models of the brain-skull interface with greater accuracy, knowing the boundary conditions or constraints is necessary. Previous experimental studies have relied on modelling the deformation of the brain-skull interface using neural density targets (NDTs) implanted into the cadaver brain, collecting information on tissue displacement during front and rear impact in motor vehicle crash-tests.
Wang et al. (2018) utilized computational bio-mechanics and finite element analysis (FEA), placing nodes in the 3D model in close approximation to the position of the experimental NDTs. Four hypotheses of the brain-skull interface were modeled, each approach placing different boundary conditions to model deformation during simulated head impact. All analyses were validated against previous experimental studies. Results showed that how the brain-skull interface was modeled appreciably affected the results. The 3D model showing the closest agreement with the experimental data, included all tissues of the brain-skull interface, allowed for displacement without separation of the skull and brain tissues, and strongly corresponded with known neuroanatomy. This 3D model indicated that non-linear stress-strain associations between brain and skull tissues best matched experimental results. Further, this 3D model could be closely predicted using an Ogden Hyperviscoelastic Constitutive model which did not over- or under-estimate deformations during head impact. The risks of over- and under-estimating head impact during motor vehicle accidents has implications for vehicle construction and prevention of serious brain trauma during accidents. Ultimately, a better understanding of the interaction between layers of the brain-skull interface can produce more accurate predictions of the likely impact during motor vehicle accidents and prevent violent head injury. Extrapolation of this research into paleoneurology could allow investigations into the structural interaction between the brain and braincase, testing if the resistence of brain-skull tissues during deformation evolved in human species as primary adaptations or secondary adjustments such as allometric responses.
The Chirurgeon’s Apprentice is a popular blog on the website of medical historian Dr Lindsey Fitzharris who received her doctorate from University of Oxford in medical, technology and science history. Dr Fitzharris discusses the apt naming of the blog with the word ‘chirurgeon‘ the first historical reference to a practitioner of surgery. The website illuminates the often grisly but fascinating historical developments in Medicine and Surgery with focus on the Victorian era and the rapidly developing techniques and methods occurring in all scientific disciplines at this time. Under the Knife is a well-researched and often darkly humorous video series delivered by Fitzharris where each episode details different aspects of the history of Surgery and Medicine. Dr Lindsey Fitzharris is also the author of a recent book The Butchering Art about the Victorian surgical pioneer Jospeh Lister and the development of antiseptic practices.