The process by which the brain develops from a smooth surface at early weeks of gestation to a
folded surface with sulci and gyri at the neonatal stage is still an unsolved question. Despite the
hypotheses proposed by several theories, the lack of large data at different developmental stages
and limitations in computational resources have made it difficult to develop mechanical models
of brain growth to better understand which mechanisms control the cortical folding process and
test different ...
The process by which the brain develops from a smooth surface at early weeks of gestation to a
folded surface with sulci and gyri at the neonatal stage is still an unsolved question. Despite the
hypotheses proposed by several theories, the lack of large data at different developmental stages
and limitations in computational resources have made it difficult to develop mechanical models
of brain growth to better understand which mechanisms control the cortical folding process and
test different hypothesis about the most relevant factors guiding this phenomenon. An additional
challenge is to work with finite-element meshes (FEM) in this application, due to the large
deformations undergone in brain development that can generate overlapping elements, which
hamper obtaining the correct solutions and prevent the simulations to converge. Thus, meshless
modelling techniques could give a solution to that issue since they are based on particles, not
requiring node connectivity and being robust against complex mechanisms such as brain
folding. The main goal of this TFG is setting the first steps that could allow modelling cortical
folding through meshless methods. To do so, a Smoothed Particle Hydrodynamics (SPH)
environment built for modelling cardiac mechanical motion was adapted in order to incorporate
the mechanical equations that describe the gyrification process. Then, the results were assessed
by making a comparison with the current FEM-based implementations in simplified synthetic
scenarios.
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