Hexahedral mesh of anatomical atlas for construction of computational human brain models: Applications to modeling biomechanics and bioelectric field propagation
<|reference_start|>Hexahedral mesh of anatomical atlas for construction of computational human brain models: Applications to modeling biomechanics and bioelectric field propagation: Numerical simulations rely on constructing accurate and detailed models to produce reliable results - a task that is often challenging. This task becomes notably more difficult when the model is of the human brain. We create an anatomically comprehensive hexahedral mesh of the human brain using an open-source digital brain atlas. Digital atlases are valuable tools currently used by medical professionals, medical students, and researchers for gathering, presenting, and discovering knowledge about the human brain. We demonstrate that the atlas can be used to efficiently create an accurate and detailed hexahedral finite element mesh of the brain for scientific computing. We present two case studies. The first case study constructs a biomechanical model of the brain to compute brain deformations and predict traumatic brain injury risk due to violent impact. In the second case study, we construct a bioelectrical model of the brain to solve the electroencephalography (EEG) forward problem, a frequent simulation process used in electrophysiology to study electromagnetic fields generated by the nervous system. We demonstrate efficient and accurate model construction using the meshed anatomical brain atlas, as well as emphasize the importance of effective communication and contextual analysis of results for enabling multi-disciplinary scientific computing research.<|reference_end|>
@article{huynh2024hexahedral,
    title={Hexahedral mesh of anatomical atlas for construction of computational
  human brain models: Applications to modeling biomechanics and bioelectric
  field propagation},
    author={Andy Huynh, Benjamin Zwick, Mostafa Jamshidian, Michael Halle, Adam
  Wittek and Karol Miller},
    journal={arXiv preprint arXiv:2410.01409},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01409},
    primaryClass={cs.CE}
}