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.
The traces of the middle meningeal artery (MMA) can be observed on dry skulls. For this reasons, it is often investigated in paleoneurology. The vessels run between the two layers of dura mater, along with the endosteal (periosteal) layer which is adherent to the inner surface of the skull. The MMA display connections with other vascular networks, but it is largely independent of the cerebral vascular system. Apparently, in adults there is only scarce or absent blood flow in MMA at rest, and activation may be triggered by thermal stress or other emergency responses (see Bruner et al. 2011). In a recent paper, Niknejad and colleagues (2018) test the possibility of using the MMA as a donor vessel in cerebrovascular bypass procedures, as an alternative to the superficial temporal artery (STA) which is standardly used for this purpose. The authors performed cadaveric dissections on 12 specimens and compared size, diameter and feasibility of both the MMA and the STA for the bypass to the middle cerebral artery. Their results confirmed that the MMA can be a suitable donor vessel. The premise of the donor potential of the MMA is based on its dispensability. Nevertheless, the authors note that the MMA may play an important role in case of the moyamoya disease, in which conditions MMA forms an important collateral network. In addition, this study provides valuable empirical data on the MMA morphology. Authors were able to identify three main branches in all specimens, with the dominant anterior petrosquamosal branch in all the cases. The diameter of the MMA was measured at its ostium and was 2.4 mm in average.
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.
European Society for the study of Human Evolution – Faro, 2018
Duan and colleagues developed a new computational method for automatic detection of patterns of cortical folding in large datasets. This method extracts multiple features that characterize the folding patterns, such as sulcal bottoms and gyral crest curvatures. Then, an overall similarity matrix is calculated that contains information on shared patterns and individual variation. Finally, the subjects are clustered on groups that represent a common folding pattern. The authors show their method is more efficient than previous ones in detecting folding patterns and clustering subjects into affinity groups. They validate its reproducibility and reliability in two main samples. They demonstrate the application of their methodology on a large sample of 595 healthy neonate brains, to characterize folding patterns in newborns. Then, they compare their results to a dataset of adult brains from the Human Connectome Project. They focus on four cortical regions, the superior temporal gyrus (STG), the inferior frontal gyrus (IFG), the cingulate cortex, and the precuneus, considering both sex differences and hemispheric asymmetries. Overall, the typical folding patterns of infants were consistent with those of adults, evidencing that cortical folds are largely established from an early age. On the other hand, some differences were also identified. For instance, four folding patterns were recognized in infant STG, while adults have an extra pattern. In contrast, one of the neonates’ IFG pattern is absent in adults. In both samples, there are sex differences in the proportions of some of the folding patterns of STG, IFG, and cingulate cortex. Hemispheric asymmetries were observed in the cingulate and STG, being more significant in the latter, which the authors suggest might reflect language lateralization. Considering the precuneus, their method revealed three main gyral patterns which were not associated with sex differences or hemispheric asymmetries. These gyral patterns appear to be mainly grouped based on the presence of either a gyral structure (patterns 1 and 2, more frequent) or a deep sulcus (pattern 3) in the middle of the precuneus. According to the authors, these groups are similar to the ones described in a previous study by our lab on a sample of adult healthy brains.
From Fossils to Function
Integrative and Taxonomically-Inclusive Approaches to Vertebrate Evolutionary Neuroscience
Brain, Behavior and Evolution, 91
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.
Reardon and colleagues recently published a study on the variation of human brain organization and its relationship with brain size. Using neuroimaging data from more than 3000 individuals they calculated the local surface area and estimated the areal scaling in relation to the total cortical area in order to generate a reference map for areal scaling in cortical and subcortical structures. By using three separate cohorts, three different platforms of image-acquisition, and two distinct imaging processing pipelines, they obtained the same results. Regions with positive scaling, i.e. which area increases with increasing total cortical size, were found with the prefrontal, lateral temporoparietal, and medial parietal cortices, whereas the limbic, primary visual, and primary somatosensory regions showed negative scaling. These patterns of cortical area distribution relatively to normative brain size variation were also reproduced at the individual level in terms of proportion, as, for instance, areas of positive scaling regions were positively correlated with the total cortical area. These patterns of areal scaling distribution are also comparable with patterns of brain expansion during human development and primate evolution (humans vs. macaques). In terms of cytoarchitecture, the regions of positive scaling were concentrated within association cortices, such as the default mode, dorsal attention, and frontoparietal networks, while the negative scaling regions were found within the limbic network. The association of areal scaling patterns with known patterns of mitochondria-related gene expression suggests these regions that are expanded in larger brains might differ in their metabolic profile. The authors concluded that the similarity of the areal scaling maps across development and evolution, and at the individual level, suggests a shared scaling gradient of the primate cortex. Larger brains tend to preferentially expand association cortex, specialized for integration of information, which might point to a need for an increase of the neural subtracts, such as dendrites or synapses, in order to maintain or enhance brain function in an expanded brain. Further study designs are required to investigate the relationship between cortical areal patterns and brain function.
Paleoneurology Lab at Atapuerca, July 2018