A Model of Brain Folding Based on Strong Local and Weak Long-Range Connectivity Requirements

Abstract

Throughout the animal kingdom, the structure of the central nervous system varies widely from distributed ganglia in worms to compact brains with varying degrees of folding in mammals. The differences in structure may indicate a fundamentally different circuit organization. However, the folded brain most likely is a direct result of mechanical forces when considering that a larger surface area of cortex packs into the restricted volume provided by the skull. Here, we introduce a computational model that instead of modeling mechanical forces relies on dimension reduction methods to place neurons according to specific connectivity requirements. For a simplified connectivity with strong local and weak long-range connections, our model predicts a transition from separate ganglia through smooth brain structures to heavily folded brains as the number of cortical columns increases. The model reproduces experimentally determined relationships between metrics of cortical folding and its pathological phenotypes in lissencephaly, polymicrogyria, microcephaly, autism, and schizophrenia. This suggests that mechanical forces that are known to lead to cortical folding may synergistically contribute to arrangements that reduce wiring. Our model provides a unified conceptual understanding of gyrification linking cellular connectivity and macroscopic structures in large-scale neural network models of the brain.

A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits

“…Furthermore, the method can in principle be used to implement, by defining a reference frame for each cell, the polarization of connectivity observed in most brain areas 64,65 . Although the use of probability clouds www.nature.com/scientificreports/ is not novel in the construction of network connectivity 18,24,25 , the PMA method has the main advantage of accounting for asymmetries in neuronal morphologies that can be mimicked by combining a variable number of ellipsoidal and conical shapes. Moreover, the choice of shaping probability clouds rather than adopting full morphologies allows the unequivocal identification of axes that can be used as reference frames to orient every neuron in the network and differently from models based on touch algorithms and realistic morphologies 63 , our approach allows the separate reorientation of axons and dendrites without geometrical constraints imposed by fixed morphologies.…”

“…However, despite the use of experimental morphologies to describe extended dendritic and axonal arborization, most brain regions show peculiar anatomical structures (e.g. surface bending or sulci) that strongly limit the possibility to further customize the model through tailored neuronal reorientation 24 . The customization of morphological orientation according to the anatomical constraints of the modelled region would require additional and dedicated computational effort compared to a random neuronal morphological orientation 24 .…”

“…surface bending or sulci) that strongly limit the possibility to further customize the model through tailored neuronal reorientation 24 . The customization of morphological orientation according to the anatomical constraints of the modelled region would require additional and dedicated computational effort compared to a random neuronal morphological orientation 24 . An alternative connection strategy, based on the generation of spherical probability clouds representing the axonal and dendritic volumes can be adopted to estimate the connectivity between neurons through isotropic or distance-dependent criteria 18 , 25 , 26 .…”

Systematic cortical thickness and curvature patterns in primates

“…Fiber connections in convex gyri are significantly denser than the ones in concave sulci ( Nie et al, 2012 ; Chavoshnejad et al, 2021 ), which could further contribute to thickness differences. Moreover, it is well-known that axonal connectivity is more elaborate, and axonal connections more dense, in higher-order species ( Chen et al, 2013b ; Groden et al, 2020 ). The increasing density of axonal connections in larger brains might additionally explain the progressive increase in cortical thickness ratio among the primate species we have investigated in this study.…”

Full-scale scaffold model of the human hippocampus CA1 area

“…Moreover, neuronal connectivity can be derived from the conversion of axons and dendrites into grids of voxels generating density fields of neurites whose intersections determine the probability of contact 27 . However, procedures customizing morphological orientation according to specific constraints are particularly complicated in human brain circuits, due to the complexity of anatomical organization 28 .…”