Mechanical hierarchy in the formation and modulation of cortical folding patterns

Abstract

The important mechanical parameters and their hierarchy in the growth and folding of the human brain have not been thoroughly understood. In this study, we developed a multiscale mechanical model to investigate how the interplay between initial geometrical undulations, differential tangential growth in the cortical plate, and axonal connectivity form and regulate the folding patterns of the human brain in a hierarchical order. To do so, different growth scenarios with bilayer spherical models that features initial undulations on the cortex and uniform or heterogeneous distribution of axonal fibers in the white matter were developed, statistically analyzed, and validated by the imaging observations. The results showed that the differential tangential growth is the inducer of cortical folding, and in a hierarchal order, high-amplitude initial undulations on the surface and axonal fibers in the substrate regulate the folding patterns and determine the location of gyri and sulci. The locations with dense axonal fibers after folding settle in gyri rather than sulci. The statistical results also indicated that there is a strong correlation between the location of positive (outward) and negative (inward) initial undulations and the locations of gyri and sulci after folding, respectively. In addition, the locations of 3-hinge gyral folds are strongly correlated with the initial positive undulations and locations of dense axonal fibers. As another finding, it was revealed that there is a correlation between the density of axonal fibers and local gyrification index, which has been observed in imaging studies but not yet fundamentally explained. This study is the first step in understanding the linkage between abnormal gyrification (surface morphology) and disruption in connectivity that has been observed in some brain disorders such as Autism Spectrum Disorder. Moreover, the findings of the study directly contribute to the concept of the regularity and variability of folding patterns in individual human brains.

A Deep Learning Framework for Predicting the Heterogeneous Stiffness Map of Brain White Matter Tissue

“…By adopting equivalent anisotropic and continuum material properties across multiple loading cases simultaneously and leveraging DL, we can accurately predict the material properties of complex composites without explicitly modeling numerous fibers. Understanding the multiscale mechanics of structureproperty linkages is crucial to developing unbiased models for accurate predictions of skin aging 77 , brain folding [78][79][80][81][82] , DAI, TBI, neurosurgery [83][84][85] , and neuroglial disorders. This study established a new multidisciplinary framework to link the mechanical properties across the scales.…”

Review of the Brain’s Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

“…While most of these effects (especially biological processes) outgrow the mechanical perspective in itself, they do have an influence over (bio)mechanical properties; thus, they must be taken into consideration, which is not straight-forward in the slightest. Brain growth, for instance, has been characterised as a morphological process during development [ 381 ] and through micro-structural modelling as a soft tissue [ 382 , 383 ], which is not limited to pure kinematics.…”

“…On top of biological considerations, the brain possesses some mechanical properties which make it more complicated to model than many bulk materials. For instance, stiffness varies in time and space: it increases globally with age, especially during cortical folding formation (modelled as mechanistic growth [ 381 , 384 ]). Some brain regions are more robust than others [ 385 ], being positively correlated with collagen [ 386 ] and myelin content [ 387 ], with the latter being also used as a marker for disease [ 388 ].…”

Axonal tension contributes to consistent fold placement

“…However, a limitation of that study was that each point in the model could only have a single axonal orientation, meaning that distributed fiber anisotropy [45][46][47] or intersections between multiple axonal bundles couldn't be captured. More recently, the role of heterogeneous axonal distribution has been explored using the embedded element method in both 2D 48 and 3D domains; 49 however, these models were limited to straight, radially aligned axons without prestretch. Another recent model has focused on how the patterns of axons seen in the brain emerge as a result of their stretch-driven growth.…”