MRI-based Computational Modeling of Human Cortical Folding
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Multiple factors and scales are at play in the development of the human brain during gestation and especially in the emergence of cortical folds. Considering complex interconnections between microstructure and mechanics can enrich our understanding of main factors leading to human brain folding. Computational modeling is a promising way to explore the brain growth biomechanics. In this work, we investigate the use of anatomical and diffusion MRI data to inform the 3D computational model. The key contribution of this work lies in the joint use of a dynamical brain growth computational model and MRI-based features to simulate cortical folding. Cortical and inner layers are modeled as nearly-incompressible Neo-Hookean materials. The accurate delineation of the cortical layer is obtained from MRI data segmentation maps. The tangential cortical growth rate is defined as a spatio-temporal function of Fractional Anisotropy (FA), taken as a measure of neuronal maturation in the cortex. The results show that heterogeneous data-driven growth rate leads to significantly different folding patterns than using uniform pre-defined growth rate. The presented approach proposes to couple the mechanical deformation to the microstructural behavior of both cortical and inner layers via MRI data. It also opens to the use of specific parameters at voxel scale in 3D brain growth models.
