Category Archives: Brain

Brain regional scaling

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.

Sofia Pedro


Modelling brain-skull interface

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.


Alannah Pearson

Seasonal brain changes

As we already discussed, small mammal species with short life spans and high metabolisms which experience seasonal fluctuation of resources tend to undergo seasonal changes in skull size and morphology. More recently, Lázaro et al. examined how this seasonal variation affected brain size and organization in the common shrew (Sorex araneus). They collected specimens in Southern Germany, covering all seasons and the whole lifespan of the shrew, which is about 18 months. The sample was divided into three age groups: summer juvenile, winter subadult, and spring-summer adult (sexually mature). Right hemispheres were used to investigate the volumes of the different brain regions, and the left hemispheres for examining neuron morphology. They confirmed the patterns of seasonal volume variation, and observed there were differences between brain regions and between sexes. The overall volume decrease from summer to winter was more pronounced in females, while spring regrowth was similar for both sexes, thus resulting in adult sexual dimorphism with females having smaller brain volumes. Regarding the brain regions, most significant changes were observed in the hypothalamus and the thalamus, both in the winter decrease and spring regrowth. Neocortex and striatum (mostly caudoputamen) volumes decreased in winter but did not regrow in spring. Cerebellar volumes were smaller in females during winter, but reached the same volumes as males during spring regrowth. According to the authors, as the volume reduction from juveniles to subadults occurs before winter, it is more likely genetically encoded than a direct result of temperature or resources fluctuation. Furthermore, the independent variation of the different brain regions suggests a mosaic adaptation of each structure to the cognitive requirements and energetic limitations of each season. Other explanations for the different patterns of variation between the different regions might be associated with differences in energetic demands and in the potential for plasticity between brain structures. However, the authors could not find correlations between seasonal volume changes and functional demands, developmental timing, or metabolic consumption of the different brain regions. They conclude the variation in each brain structure might be influenced by functional adaptations and plasticity to different degrees. The authors also registered the variation in neuron size and morphology in three regions. The caudoputamen showed a decline in dendritic length and volume, in soma size, and in spine number and density, from juvenile to adult. The somatosensory cortex displayed only decline in soma size from summer to winter and in spine density from winter to adult. In the anterior cingulate cortex there was a reduction in soma size from juvenile to adult but in dendrite volume only from juvenile to subadult. These results cannot explain adequately the observed volumetric changes in the respective regions, and other factors that might affect volume, such as the space between cells and neuron density, should be considered in further studies. Moreover, changes in axonal innervations and myelin, and in the density of microvessels should be considered as these can also influence energetic costs.

Sofia Pedro

Chimpanzee Sulci

Studying the evolution of brain form requires paleoneruologists to rely on casts from the cranial cavity from fossil species. Due to the lack of soft-tissue preservation in fossils, descriptions of macroanatomy and cytoarchitecture  are taken from comparative non-human primates to serve as hypothetical models of early hominin brain form. Using extant non-human primates as models for fossil species ignores the separation of lineages, any specific adaptations and lineage-specific evolution since divergence. Furthermore, extant species risk being relegated as ‘living fossils’ with the issue worsened by the absence of identifiable fossils for either Pan or Gorilla. The untenable assumption is that extant chimpanzee anatomy should resembles  the original form prior to the PanHomo split. Nonetheless, comparison among living hominoids is still mandatory to investigate the evolutionary radiation of this taxon.

Previous published descriptions of chimpanzee sulcal patterns occur in classic literature but were based on only a few post-mortem dissections. Recently, Falk and colleagues aimed to increase knowledge of chimpanzee sulcal variation by describing sulcal patterns present in in-vivo Magnetic Resonance Imaging (MRI) from eight chimpanzees. Results suggested that, contrary to previous opinion, two sulci do occur in both chimpanzees and humans. To elaborate, these two sulci are the middle-frontal sulcus located in the frontal lobe, and lunate sulcus located between the parietal and occipital lobes.

No quantitative analyses were conducted in this study, but Falk et al. (2018) provide detailed descriptions of the variation between individuals, highlighting why descriptions based on only one or two individuals cannot be used to reliably describe the brain anatomy of a species. The authors argue the presence of the middle-frontal sulcus and lunate sulcus in chimpanzees invalidates previous claims that these sulci represent derived states found only in the human lineage. Further quantitative analyses with much larger samples, including both extant and fossil species will aid in a better understanding of the brain anatomy of humans and other great ape species.

Alannah Pearson

Brain and Muscles

Among mammals, primates exhibit a trend toward increasing encephalisation. Attempts to explain the development of this trend focus on the energetic and metabolic trade-offs required to increase brain mass. The most widely discussed are variants of the Expensive Tissue Hypothesis (ETH) which proposes for any increase in brain mass other metabolically expensive tissues must decrease in size. The brain is metabolically costly with primates having larger brain sizes than other mammals and devoting up to 20% more basal metabolic rate to brain maintenance. Brain maintenance relies on aerobic cellular respiration processes, thus requiring oxygen to efficiently function. In a resting-state, up to 90% of brain maintenance is sourced from aerobic respiration. The brain does not source oxygen directly but relies on aerobic cellular respiration, converting glucose into adenosine triphosphate (ATP) to produce energy. In humans, the brain consumes, on average, around 30% of total glucose allocation. Skeletal muscle is another expensive tissue type. Muscle consumes up to 30% of resting energy expenditure with nearly 100-fold increase during high activity. Mammals have nearly 50% of their total body mass accounted for by muscle mass while primates have only 35% of total body mass accounted for by muscle mass. Of primates, humans possess 50% less muscle mass than expected for body size. Skeletal muscle comprises a mixture of fibers known as Type I (slow-twitch for prolonged activity) and Type II (fast-twitch for short, sudden activity). Both fiber types require constant oxygen supply and glucose to convert to ATP via mitochondria. Although Type II fibers consume a higher net-amount of glucose than Type I, this is done for short periods of time. Type I fibers used for prolonged activities possess greater capillary density and more mitochondria than Type II, potentially allowing significantly more efficient conversion of glucose to ATP. This could suggest muscle mass is in direct competition with the brain through glucose requirement and that any increase in brain size could require a corresponding decrease in muscle mass as evidenced in primates, especially humans.

Muchlinski et al. (2018) examined the potential trade-off between muscle mass and brain size in non-human primates. Several skeletal muscles were dissected from primate cadavers and immunohistochemistry used to isolate muscle fiber types. Body mass strongly influenced endocranial volume and muscle mass in the primate species so variables were size adjusted. Results indicated an increase in endocranial volume was associated with a decrease in muscle mass. Type I muscle fibers were negatively correlated with endocranial volume but a positive correlation between Type II and endocranial volume was not statistically significant. In general, the primates sampled possessed more Type II than Type I muscle fibers. These results are encouraging but potential bias could be introduced from the small sample size and muscle selection with larger postural and locomotor muscles, erector spinae and scalenes, not examined as the minimum sample content for immunohistochemistry could not be dissected in very small primate species. The use of published literature for endocranial volumes and body mass may introduce additional issues. Despite this, the assumption that muscle may be in direct competition with the brain appears metabolically and energetically viable and a potential avenue for proper consideration in evolutionary primatology.

Alannah Pearson

Infections of the brain and meninges


infectionsIntracranial infections represent serious brain diseases that occur in various forms and often may be hard to recognize in their earlier stages. A fast diagnosis is crucial for an effective treatment. Various technique of CT and MRI imaging have been developed to distinguish the symptoms in the brain and its associated tissues (see for example Hsu 2010). Radiologists recognize several categories of infections according to the origin (e.g. congenital and neonatal), location (intra-axial, extra-axial), or characters. In general, infections of the brain parenchyma, meninges and ventricles can have bacterial, viral, fungal, or parasitic origins. Bacterial infections usually develop from early cerebritis (inflammation of the cerebrum) to formation of abscesses (accumulation of infectious material and microorganisms) within the cranium. Some bacteria have more specific effects. Streptococcus pneumonia and Neisseria meningitides are common cause of bacterial meningitis (inflammation of the meninges and the cerebrospinal fluid). Tuberculomas, abscesses and tuberculous meningitis indicate presence of Mycobacterium tuberculosis (TB). Frequent viral infections are Herpes Simplex Encephalopathy involving Herpes simplex virus 1 (HSV-1), or infections induced by Human Immunodeficiency Virus (HIV) leading to cerebral atrophy and white matter disease. Fungal infections usually generate abscesses filled with fungi. Cryptococcosis and fungal meningitis are frequent fungal infections in some specific geographical regions. Examples of parasites causing intracranial infections are Toxoplasma gondii, or Taenia solium causing cysticercosis, which also leads to acquired epilepsy (see Vaccha et al. 2016). Consequences of intracranial infections could be in some cases lethal, in other cases they can cause a severe damage. For instance, infections of meninges and cerebrospinal fluid leading to meningitis can further evolve into subdural (between the dura mater and the underlying arachnoid layers) and epidural (between the meninges and the bone) abscesses, hydrocephalus (excessive accumulation of fluid in the brain), ventriculitis (inflammation of the ventricles containing and circulating cerebrospinal fluid throughout the brain), and venous thrombosis. Brain infection can be extended into the bone tissue and cause cranial pathologies like osteomyelitis or mastoiditis. Sometimes the infections can even lead to bone fractures. The origin of brain infection is often associated with traumas, when the microorganism spread from the wound into the soft tissues of the endocranium.

Stáňa Eisová

Digital Endocasts

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Parietal lobes and tool use

The parietal lobe has a unique central location in the brain, and it is involved in higher cognitive functions. Investigating its functions and connectivity is essential to understand its role in uniquely human abilities. Two recent works have put emphasis on the importance of the parietal lobe for tool use.

Catani and colleagues investigated the intralobar parietal connectivity in human and monkey brains, using diffusion imaging tractography. In general, the patterns of white matter connectivity are similar in both species, although with some differences for areas that are distinct in humans. The larger tract connects the superior parietal lobule (SPL) to the angular and supramarginal gyri of the inferior parietal lobule, within the IPS. The authors suggest it might act to mediate the interaction between the two lobules during object manipulation, and to coordinate both dorsal and ventral visuospatial networks. The second and third larger tracts link the postcentral gyrus to the inferior parietal lobule and to the SPL, respectively. These might transmit tactile and proprioceptive information on the body orientation relatively to an object for guiding motor actions and grasp. The connection between the postcentral and the angular gyri was only observed in humans, leading the authors to highlight its role in specific cognitive functions. Particularly, its connections to SPL are key for tool use, mathematical thinking, and language and communication.

Kastner and coworkers reviewed the organization and function of the dorsal pathway of the visual system of monkey and human brains, focusing on the areas of the posterior parietal cortex within and adjacent to the intraparietal sulcus (IPS). Monkeys and humans have diverged in the functional contributions of the IPS since their last common ancestor, as some functionally-defined areas that are located within the IPS in monkeys have been partially relocated outside this sulcus in humans. The authors suggest this relocation might be due to expansion for accommodation of human-specific abilities, such as tool use. They hypothesize that humans might have developed a derived and advanced tool network from the modification of the macaque circuit for object manipulation. First, the human dorsal vision pathway must provide object shape information regardless of size and viewpoint, facilitating object recognition and mental manipulation. Second, object information is integrated with cognitive information such as working memory, allowing maintaining the information over a period of time. Finally, humans have areas that respond specifically to tool use, some of which also integrate frontal and temporal networks involved in action recognition and semantic knowledge related to tools and actions, respectively.

Both studies point at the parietal lobes and visuospatial integration as key elements for human cognitive capacity, as suggested by evidence in paleoneurology, evolutionary neuroanatomy, and cognitive archaeology.


Sofia Pedro

Sliding brains

Bruner E. & Ogihara N. 2018. Surfin’ endocasts: the good and the bad on brain form. Palaentologia Electronica 21.1.1A: 1-10.

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Neuroanatomy and Tractography

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