Category Archives: Brain

Cortical and scalp development

In a recent study, Tsuzuki and colleagues analysed the co-development of the brain and head surfaces during the first two years of life using a sample of 16 infant MRIs, aged from 3 to 22 months. First, they digitized a set of cortical landmarks defined by the major sulci. Then they determined the position of cranial landmarks according to the 10-10 system, a standard method to place electrodes for electroencephalography, using  nasion, inion, and the pre-auriculars as a reference. Besides analysing the spatial variability of the cortical and scalp landmarks with age, they compared the variability of the cortical landmarks to the 10-10 positions, in order to evaluate the validity of the scalp system as a reference for brain development. For that, they transferred a given cortical landmark to the head surface by expressing its position as a composition of vectors in reference to the midpoint between the two pre-auriculars and to the three neighbor 10-10 points. The scalp-transferred landmarks were then transformed to the scalp template of a 12-month-old infant and depicted in reference to the 10-10 system.

Age-related changes in the cortical landmarks were most obvious in the prefrontal and parietal regions. As the brain elongates, the frontal lobe shifts anteriorly and the precentral gyri widen. In addition, the intraparietal sulci and the posterior part of the left Sylvian fissure move forward, suggesting relative enlargement of the parietal region in the anterior direction. The same result was obtained by our team by analyzing cranial and brain landmarks in adults: larger brain size is associated with a relative forward position of the parietal lobe. The scalp showed relative anteroposterior elongation and lateral narrowing with growth. Regarding the contrast between the cortical landmarks and the 10-10 system, the authors observed that the variability in the position of the former was much smaller than the area defined by 10-10 landmarks, indicating this system can be useful to predict the underlying cortical structures. Hence, they conclude that the changes in brain shape during development are well described by cortical landmarks and that the relative scalp positioning based on the 10-10 system can adjust to preserve the correspondence between the scalp and the cortical surfaces.

 

Sofia Pedro


What the brain’s wiring looks like

The world’s most detailed scan of the brain’s internal wiring has been produced by scientists at Cardiff University. The MRI machine reveals the fibres which carry all the brain’s thought processes. It’s been done in Cardiff, Nottingham, Cambridge and Stockport, as well as London England and London Ontario. Doctors hope it will help increase understanding of a range of neurological disorders and could be used instead of invasive biopsies …

[keep on reading this article by Fergus Walsh on BBC News]


Brain partition scaling

A group coordinated by Dr. Vera Weisbecker examined whether the evolution of mammalian brain partitions follows conserved developmental constraints, causing the brain to evolve as an integrated unit in which the partitions scale with brain size. According to this ‘late equals large’ hypothesis, the timing of neurogenesis predicts the size of the partition such that later and more extended neurogenesis produces larger partitions due to the production of more neural precursors. In order to investigate the impact of neurogenesis on patterns of brain partition growth, the volumes of the whole brain and major partitions were reconstructed from soft-tissue diceCT scans of three marsupial species, including individuals with ages ranging from 1 day to adulthood. They tested three hypotheses consistent with a conserved brain partition growth: H1 postulates that partition scaling during development reflects the evolutionary partition scaling, and thus growth patterns should be uniform between species; H2 assumes that a neurogenesis-driven pattern of partition scaling is predictable from adult brain size, i.e. brain partitions scale with brain size; and H3 states that growth with age might differ between species according to brain size and/or neurogenetic events. Regressions of log partition volume against log rest-of-the-brain volume (whole-brain volume minus partition volume) showed significant interspecific differences in slopes and intercepts of most brain partitions, indicating diverse scaling patterns between species, which could not be predicted by adult brain size, as the smallest-brained species had intermediate slope to the other two.  Growth curves of log partition volume against age were similar in all partitions within-species, but differed between species, particularly in growth rates, with the species with intermediate brain size having slower rates than the other two. Differences in growth patterns do not seem to be related to neurogenetic schedule as largest partitions are not especially late in their development and important maturation processes, like eye opening, occur closer to the end of the growth phase. Thus, none of the hypotheses are supported by these results, challenging the conserved neurogenetic schedules behind the evolution of mammal brain partitions. Moreover, the authors found high phylogenetic signal in brain partition scaling, revealing that a large part of the scaling relationship between brain and partition volumes is explained by phylogeny, which is more in agreement with a mosaic evolution of brain partition sizes, stressing its biological meaning and the level of mammalian brain plasticity. However, the intraspecific regular partition growth curves led the authors to contemplate the existence of an early brain partition pattern regulated by regional gene expression, and propose that further studies of brain partition evolution should integrate developmental neuromere expression models, neuron density, and patterns of neuron migration.

 

Sofia Pedro


Cerebellum and Alzheimer

A perspective review on cerebellum and Alzheimer’s disease, coordinated by Heidi Jacobs

Jacobs H.I., Hopkins D.A., Mayrhofer H.C., Bruner E., van Leeuwen F.W., Raaijmakers W., Schmahmann J.D.
The cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline.
Brain, 2017

[link]

(and here a post on cerebellum and paleoneurology …)


Selective brain cooling in modern humans

The human brain is the most expensive and costly organ in terms of energetic resources and management. However, the current understanding of its sophisticated thermal control mechanisms remains insufficient. Wang et al., 2016, have reviewed the most recent studies on brain thermoregulation and examined the anatomical and physiological elements associated with selective brain cooling. Modern humans have a brain that is approximately three times larger than a primate with a similar body size, which uses 20%– 25% of the total body energy compared with a maximum of 10% in other primates and 5% in other mammals. The evolution of a large and expensive brain in modern humans effectively influences critical factors such as temperature, and functional limits can affect cerebral complexity and neural processes. Brain thermoregulation depends on many anatomical components and physiological processes, and it is sensitive to various behavioral and pathological factors, which have specific relevance for clinical applications and human evolution. The anatomical structures protecting the brain, such as the human calvaria, the scalp, and the endocranial vascular system, act as a thermal interface, which collectively maintains and shield the brain from heat challenges, and preserves a stable equilibrium between heat production and dissipation. Future advances in biomedical imaging techniques would allow a better understanding of the physiological and anatomical responses related to the cerebral heat management and brain temperature in modern humans.

Gizéh Rangel de Lázaro

 


Advances in brain imaging

Klein et al 2017The 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.

Alannah Pearson


Structural MRI artifacts

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).

Sofia Pedro


Brain Volume Database

ibvd-brain-coloured

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.

Alannah Pearson


Seasonal skull reduction

dechmann-et-al-2017Braincase 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.

Sofia Pedro


Fractal brains

the-fractal-geometry-of-the-brain

[click here!]