Evaluation of numerical techniques for solving the current injection problem in biological tissues

Accurate computational modeling of electric fields in the human head has become important in clinical research to study or influence brain functionality. While existing numerical approaches have been evaluated against simple geometries with known closed form solutions, the relationship between these...

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Published in2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) Vol. 2016; pp. 876 - 880
Main Authors Hyde, Damon E., Dannhauer, Moritz, Warfield, Simon K., MacLeod, Rob, Brooks, Dana H.
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.04.2016
Subjects
BEM
Boundary element method
Brain
Brain models
Computation
Computational modeling
Conductivity
EEG
FDM
FEM
Finite element analysis
Finite element method
Frequency division multiplexing
Head (anatomy)
head model
Mathematical analysis
Mathematical models
Numerical models
source localization
TDCS
Transcranial low-current stimulation
Voltage
EEG
Transcranial low-current stimulation
TDCS
source localization
BEM
head model
FDM
FEM
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Summary:Accurate computational modeling of electric fields in the human head has become important in clinical research to study or influence brain functionality. While existing numerical approaches have been evaluated against simple geometries with known closed form solutions, the relationship between these approaches in more complex geometries has not been studied. Here, we compare the three most commonly used approaches for bioelectric modeling: the finite element method (FEM), the finite difference method (FDM), and the boundary element method (BEM). Using both isotropic and anisotropic conductivity distributions, we construct and compare bioelectric models for a realistic head geometry. Our results suggest that both FEM and FDM are capable of accurately model voltages in the brain, while computations from BEM result in significantly larger errors, due to the increased simplicity and implicit model assumptions.
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ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2016.7493405