A comprehensive anatomical network analysis of human brain topology

Abstract

A network approach to the macroscopic anatomy of the human brain can be used to model physical interactions among regions in order to study their topological properties, as well as the topological properties of the overall system. Here, a comprehensive model of human brain topology is presented, based on traditional macroanatomical divisions of the whole brain, which includes its subcortical regions. The aim was to localise anatomical elements that are essential for the geometric balance of the brain, as to identify underlying phenotypic patterns of spatial arrangement and understand how these patterns may influence brain morphology in ontogeny and phylogeny. The model revealed that the parahippocampal gyrus, the anterior lobe of the cerebellum and the ventral portion of the midbrain are subjected to major topological constraints that are likely to limit or channel their morphological evolution. The present model suggests that the brain can be divided into a superior and an inferior morphological block, linked with extrinsic topological constraints imposed by the surrounding braincase. This information should be considered duly both in ontogenetic and phylogenetic studies of primate neuroanatomy.

Modularity and community detection in human brain morphology

“…However, community detection algorithms were not created specifically to be employed in anatomy (Fortunato, 2010). Here, we test and compare five community detection algorithms on a previously designed network model of the human brain based on traditional macroanatomical divisions of its whole volume (see Schuurman & Bruner, 2023) to provide a detailed assessment of modularity and community detection in regard to human brain morphology. To build the anatomical network of the human brain, standard knowledge on physical interactions (whether histological continuity or spatial contiguity) among brain elements was employed, derived from standard atlases or, when necessary, from specific anatomical publications.…”

“…The community detection algorithms featured in this study were chosen based on algorithm input requirements (i.e., the least possible a priori assumptions on community size and number), performance on networks of comparable size, and successful use in previous anatomical network analysis studies (Bruner, 2022; Bruner et al, 2018; Esteve‐Altava, 2017b; Esteve‐Altava et al, 2018; Fortunato, 2010; Schuurman & Bruner, 2023). We ran the algorithms using R (R Core Team, 2013) in RStudio (RStudio Team, 2020), employing functions of the packages: ape (Paradis et al, 2004), cba (Buchta & Hahsler, 2019) and igraph (Csardi & Nepusz, 2006).…”

“…A second model, based on Brodmann's map, also found a longitudinal modular partition, but here it was the cingulate gyrus that acted as a crucial longitudinal bridge between the two modules (Bruner, 2022). Lastly, the most recent study, which included the subcortical regions, found the most representative modular partition to be a vertical (superior–inferior) division of the brain, matching extrinsic topological constraints which were imposed by the enveloping braincase (namely, the cranial vault versus cranial base; Schuurman & Bruner, 2023). These results highlight that the possibility to localize distinct morphological partitions is influenced by the biological and mathematical criteria involved.…”

“…Here we set out to expand upon the aforementioned work by offering a quantitative exploration of modularity through community detection, a main target in network analysis, as to generate a valid interpretation of the observed morphological organization of the human brain. To do this, we test an array of community detection algorithms on a previously designed network model of the human brain, based on traditional macroanatomical divisions of its whole volume (Schuurman & Bruner, 2023). Our aim is to provide a detailed assessment of modularity and community detection in regard to human brain morphology, thereby further developing the network approach to this field of research.…”

“…Recently, patterns of physical interaction, or topology, have been employed to investigate the morphological properties of anatomical systems through the implementation of network theory (Dos Santos et al, 2017; Esteve‐Altava et al, 2011; Kerkman et al, 2018; Murphy et al, 2018). Anatomical network analysis can be used to evaluate the topological structure of a given anatomical system in order to assess the amount and kinds of spatial constraints associated with specific elements or regions (Bruner, 2022; Bruner et al, 2019; Schuurman & Bruner, 2023).…”