Superior parietal sulcus

Studies using functional neuroimaging have shown that the superior parietal lobe (SPL) is involved in cognitive processes such as visuospatial integration and attention. The SPL is sometimes separated into two parts, the anterior and posterior regions, by the superior parietal sulcus (SPS). Drudik et al. (2022) conducted a study examining the morphological variability of the SPS in 40 human brain MRIs. Through volumetric and spatial probability maps, the analysis aimed at quantifying the SPS’s spatial position, following the Montreal Neurological Institute (MNI) standard stereotaxic space

When dealing with brain morphological differences, a major challenge is to provide a consistent definition of the elements involved, able to cope with the fuzzy variability of idiosyncratic anatomical features. In this case, the SPS definition, used as identification criteria, was: “the dorsal parietal sulcus located within the SPL, posterior to the superior postcentral sulcus (SPCS), and anterior to the paroccipital segment of the intraparietal sulcus (IPS-PO).” The presence of more than one branch has been interpreted as an “SPS complex.” Three categories were established: Type I for a single sulcus, Type II for multiple sulci, and Type III for a group of dimples forming a complex. Additionally, the first two SPS types were subdivided into five subcategories (a to e), based on their interaction with surrounding sulci.

Their results indicate that Type I SPS was present in 75% of the hemispheres studied, with Type II accounting for 22.5%, and Type III making up the remaining 2.5%. These results differ somewhat from Ono et al. (1990), where SPS could not even be detected in 20% of the left hemispheres and 40% of the right ones.

The deep developmentof the SPS in the precuneus would probably merit further attention. According to the study of Pereira-Pedro & Bruner (2016), in fact, this region displays irregular and variable sulcal patterns.

Rafael Gallareto

Cingulate cortex aging

The cingulate cortex is the largest structure of the limbic lobe, spanning multiple functional units. Recently, Aghamohammadi-Sereshki et al. (2024) performed structural Magnetic Resonance Imaging (sMRI) scans on 126 healthy individuals ranging between 18 and 85 years old. The aim was to study morphological, functional, and demographic patterns of cingulate cortex atrophy in relation to age.

The first part of the research was meant to observe the effects of sex, cerebral hemisphere, and regional anatomy on age-related cingulate cortex deterioration. The authors hypothesized that age would cause a more significant atrophy in the cingulate cortex’s posterior regions than in its anterior regions. Results revealed that all regions of the cingulate cortex, except the right anterior midcingulate cortex (MCC), showed expected bilateral linear volumetric reductions in relation to age. Meanwhile, sex differences suggested a faster age-related atrophy in men than in women.

The second half of the study focused on determining the relationship of age-related volume reduction and functional decline, particularly in emotion recognition accuracy and Theory of Mind (ToM): the correct recognition of others’ cognitive states. Here, it was predicted that functional decline would be linked to age-related volume reduction in two specific anterior regions of the cingulate cortex (the pregenual anterior cingulate cortex (ACC) and the anterior MCC). On the one hand, although emotion recognition accuracy presented a positive correlation with pregenual ACC volume, this relation was found to be independent of age. On the other hand, better ToM capabilities presented a positive correlation with pregenual ACC and anterior MCC volumes. Notwithstanding, in this case, functional decline was indeed associated with age-related atrophy of the aforementioned cingulate subregions.

For a comprehensive overview of neuroimaging’s use in the study of brain aging see Cole & Franke (2017).

Tim Schuurman

Hybrid PET/MR neuroimaging

MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) are two of the most often utilized neuroimaging procedures. Even though their approaches are based on different physical principles, the information each offers is extremely complementary. While MRI uses a strong static field (in the tesla range) to align the magnetic moments of hydrogen nuclei, providing a static structural 3D image, PET uses the detection of the decay of a radiopharmaceutical ligand previously injected at a very low concentration, allowing functional visualization of specific molecular properties.

The combination of these —independently generated— images has proven to be extremely valuable in the study of a range of neurological diseases, including Alzheimer’s and Parkinson’s. However, when combining the two sources of information, some important challenges appear, such as some geometric inaccuracies due to the image quality of the scanners and the patient’s body position in each session. These obstacles were overcome by the creation of a hybrid method that merged PET and MRI in a single scanner. That new approach, unsurprisingly named ‘PET/MRI,’ was approved for public use by the FDA in June 2011, allowing for more accurate imaging results, an advance in medical diagnostic capabilities, and significant cost reductions for both patients and institutions.

The book Hybrid PET/MR Neuroimaging, edited by Ana M. Franceschi and Dinko Franceschi, is a collection of papers about these new methods combining static and functional neuroimages. The first section provides a brief overview of each technique, discusses the difficulties encountered during the merging process, compares this approach with other important hybrid techniques such as PET/CT, and discusses current developments and upcoming trends, with the application of artificial intelligence (AI) being one of the key advancements. The specific applications of PET/MRI in the research of neurological disorders, epilepsy, neuro-oncology, CNS inflammatory and infectious illnesses, its usage in pediatrics, and cerebral vascular imaging, are covered in the sections that follow. This book appears to be an excellent introduction to this technology, which, according to the preface, has come to transform imaging for patients with neurodegenerative diseases.

Rafael Gallareto

Morphological similarity brain networks

In their latest review Cai et al. (2023) transmit a key notion about the brain. The number of  levels at which the brain is interconnected is remarkable (i.e., functional, cellular, histological, and anatomical, both intrinsically and extrinsically). Hence, a common way to represent and investigate the brain is through network graphs and their analysis.

Brain networks can be divided into three main categories: functional, anatomical, and morphological. In all regards, the first stands almost in contrast to the others, making functional networks the most self-explanatory category of the three. Etymologically, although not in practice, the difference between anatomical and morphological networks is more subtle. One is based on neuronal connections among brain regions, while the other generally relies on the measurement of mathematical correlations of specific morphometric features across regions. In short, morphological networks are graphs based on correlation matrices based on structural magnetic resonance imaging (sMRI). Working with models implies by definition engaging in abstraction, but these “morphological” networks are arguably separated from the brain, our actual object of study, by three steps. They are an abstraction of an abstraction of an abstraction. Abstraction is often extremely useful, but it can lead to bias and must thus be kept in mind. Nevertheless, morphological network analysis intends to reveal the structural organizing principles inherent to the brain, which has earned some attention in recent years. More interestingly, such networks seem to overlap very well with functional and anatomical networks. The most recent development in morphological network analysis, its application to single subjects, has proven useful in exploring brain topology under both healthy and pathological conditions.

A small caveat about anatomical and morphological networks: these labels are subject to an additional interpretation, yet a fourth way to build network graphs of the brain reliant on spatial contact (whether through contiguity or continuity). Spatial-contact brain networks, which we explore in-depth in Schuurman & Bruner (2023, 2024), are in this sense left without a clear category. The alternatives for such brain networks are two. On the one hand, since spatial-contact brain networks can also stem from sMRI, they could be included in the category of morphological networks. On the other hand, since spatial-contact brain networks can rely not only on sMRI but on any anatomical sort of data (e.g., dissections), a new name could be used: spatial brain networks.

Tim Schuurman

Rafa

Welcome to a new PhD student! Rafael Gallareto Sande is just beginning his PhD project in human evolution at the University of Burgos. After a first Master in Forensic Anthropology and a second one in Human Evolution, he is now joining this lab to keep on working on the morphology of the parietal cortex. His project does include issues in ontogeny, aging, vascularization and (of course) fossils. At present, the morphology of the parietal lobes is very distinctive in Homo sapiens, in both its size and shape. Besides the increase in curvature, our species display larger size and proportions of the parietal cortex, when compared with extinct hominids, including the large-brained Neandertals. These regions are association regions, crucial hubs for many cognitive functions, and are generated by gradients between primary inputs. Therefore, their expansion should be associated with some kind of behavioral change, and probably with increasing computational complexity in cognitive abilities including visuospatial integration, attention, language, working memory, and self-awareness. It remains to be evaluated what precise areas are involved in this recent evolution of our lineage. Rafa, welcome on board.

Introducing the HumanBrainAtlas

Recently, Schira et al. (2023) have introduced a new in vivo magnetic resonance imaging (MRI) dataset meant to build a fine-detailed, open-access atlas of the human brain. This is only the first step, a presentation of full brain scans of two healthy male individuals, but a powerful proof of a resource that includes multiple contrasts, ideal for studying both white and grey matter. The corresponding datasets, protocols, as well as any future advances, are (or will be) available on the Human Brain Atlas website.

To obtain the level of detail that the authors strive for, several high-resolution scans were performed of each participant that were averaged afterwards. This method allows for a resolution of up to 0.25 mm, an image quality suitable for structural parcellations that parallel those based on histology. However, being performed in vivo and non-invasive, MRI circumvents issues such fixation shrinkage and wrapping. Additionally, where histology requires a new specimen for each anatomical plane (i.e., axial, coronal and sagittal), MRI does not. And although these key advantages of MRI were hitherto insufficient to outbalance its lacking resolution, it is likely that, going forward, only magnified views of small structures will justify the use of a histological approach.

Besides the scans themselves, the results also include the first examples of a parcellation of the brain. Specifically, Schira et al. (2023) present parcellations of an axial and a coronal section at intermediate points in the respective sequences (both at the height of the anterior commissure, for reference). The parcellations include a host of white matter structures, as well as an array of cortical and subcortical grey matter elements. Finally, to push the resolution of the scans as far as possible, they show a magnified version of the coronal section highlighting the detail and contrast obtained for several of the subcortical telencephalic and diencephalic nuclei (e.g., caudate and putamen nuclei, globus pallidus and thalamus).

Tim Schuurman

Modularity and community detection

We have recently published a new paper regarding modularity and community detection in the context of human brain anatomical network analysis (Schuurman & Bruner, 2023a). Humans’ morphologically complex brain is spatially constrained by the physical interactions of its elements, and this aspect can be studied using anatomical network analysis. A crucial issue in this framework is modularity, assessed by means of community detection algorithms: the presence of groups of elements undergoing morphological changes in a concerted manner.

Previous works on the subject had produced mixed results: Bruner et al. (2019) and Bruner (2022) found longitudinal (anterior-posterior) modular partitions of the brain, while Schuurman & Bruner (2023b) found a vertical (superior-inferior) modular partition. In the present study, our aim was to test an array of community detection algorithms on a previously designed anatomical network model (Schuurman & Bruner, 2023b), offering a quantitative exploration of modularity to generate well-founded interpretations of the observed morphological organization of the human brain.

The algorithms that were examined comprise the combined method of the spin-glass model and simulated annealing algorithm (SG-SA), a partitional method with an automatic determination of the number of communities; the Louvain method, a heuristic method reliant on Q optimization with no a priori assumptions on community size or number; Infomap, an algorithm that focuses on information diffusion across the network; the generalized topological overlap measure (GTOM), a hierarchical clustering method based on the level of structural equivalence of the network’s nodes; and agglomerative hierarchical clustering based on maximal clique (EAGLE), which identifies initial subsets of completely connected nodes, then merges these subsets hierarchically according to their topological similarity and identifies the ideal partition using Q optimization.

The algorithms that provide the highest quality partitions are SG-SA and the Louvain method. However, all five of them supply useful information. SG-SA, Louvain and Infomap reveal a clear vertical modular partition of the brain: they yield only two superior modules (these span regions as posterior as the middle occipital gyrus) and three inferior modules. Additionally, Informap goes as far as to subdivide the posterior inferior module vertically again, into a superior and an inferior posterior submodule. On the other hand, GTOM and EAGLE, show a more obvious longitudinal division: firstly, they separate the superior modules longitudinally (these only reach as far as the supramarginal gyrus), generating an additional, more posterior, superior submodule; and secondly, many regions incorporated in the inferior modules by SG-SA, the Louvain method and Infomap, are here considered to be part of the superior modules, contributing even further to this sense of anterior versus posterior. In other words, jointly, the community detection algorithms expose the simultaneous occurrence of a longitudinal and a vertical modular partition. This combination mirrors the morphological organization of the enveloping braincase, separated vertically by the distinct developmental processes of the cranial base and vault, and longitudinally by the particular morphogenetic environments of the three endocranial fossae. Overall, results suggest a level of concerted topological reciprocity in the spatial arrangement of craniocerebral anatomical components. Lastly, they posit questions concerning the degree to which structural constraints of the skull and the modular partition of the brain may channel both evolutionary and developmental trajectories.

Tim Schuurman

Spatial packing in the macaque head

Head morphology reflects, besides function, inherent structural constraints. This is interesting in developmental studies because, as tissues grow and compete for space, structural constraints lead to a modification of the genetically defined adult phenotype. In their latest work, Jeffery and Manson (2023) set out to investigate these geometric alterations in the postnatal stage of head development in the rhesus macaque (Macaca mulatta). Specifically, they used magnetic resonance imaging (MRI) data of 32 male and female individuals aged 0‒3 years old. Their aim was to find the most significant modifier in brain and skull shape out of relative size changes in the brain, eyeballs and masseter muscles, as well as neuronal wiring length, using patterns of covariation.

Results reveal that, in the rhesus macaque, associations of brain and skull shape with eyeball size and neuronal wiring length are weak across the board. However, these findings do not deny the significance of neuronal wiring length as a structural constraint in other species, as the functional threshold for length optimization might not have been reached in the sample in question. Meanwhile, brain and skull shape are shown to be influenced greatly by the relative size of the masseter muscles and, surprisingly, the face and basicranium, rather than the brain. In other words, this research contributes to the idea that skeletal growth should be considered properly in models of spatial packing, instead of focusing almost exclusively on the brain. In conclusion, the trajectory and efficacy of spatial-packing events in the head, as well as the transition from a spatially modular system to a more integrated one, is probably dependent on the combined developmental timings of the brain, masticatory muscles, and cranium.

Tim Schuurman

Towards an all-encompassing brain atlas

The journal Brain Structure and Function is in the process of publishing a new Special Issue entitled “Towards multi-modal, multi-species brain atlases”, with an editorial by Rogier B. Mars (University of Oxford’s Nuffield Department of Clinical Neurosciences) and Nicola Palomero-Gallagher (Research Centre Jülich’s Institute of Neuroscience and Medicine). Some of the most notable entries in this special issue include a triad of articles on the primate thalamus (García-Cabezas et al., 2023; He et al., 2022; Pérez-Santos et al., 2023), a subcortical complex of differing structure, connectivity, function and developmental origin; or a visual demonstration of how Sanides’ (1962, 1970) Hypothesis on the Dual Origin of the Neocortex explains the preservation of cortical types across the brains of rodents and primates, despite the latter order’s neocortical expansion (García-Cabezas et al., 2022).

Neuroanatomical knowledge improves the interpretation of findings in neuroimaging analysis in the context of the brain’s underlying structural and functional segregation. This has never been more patent than now, in this new digital, open neuroscience era. One of this era’s greatest ensuing advancements is the continuous creation of brain maps, obtained using a plethora of different methods. Most interesting is the number of organization levels of these maps, which do not necessarily present the same degree of granularity, within and between species. Understanding how the different maps relate to one another in these two directions, coined vertical and horizontal translation, respectively, are equally ambitious endeavors. Finally, the editorial leaves us with a quote by Passingham and Lau (2022) for us to look forward to the full publication of the issue: “… the aim of neuroscience is not to simply label the brain, but to understand how it works.”

Many of these topics on neuroanatomy and brain functions will be precisely discussed next week in the Cortical Evolution congress in Burgos, Spain.

Tim Schuurman

Neurocompare

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