Experimental validation of the influence of white matter anisotropy on the intracranial EEG forward solution

J Comput Neurosci. 2010 Dec;29(3):371-87. doi: 10.1007/s10827-009-0205-z. Epub 2010 Jan 9.


Forward solutions with different levels of complexity are employed for localization of current generators, which are responsible for the electric and magnetic fields measured from the human brain. The influence of brain anisotropy on the forward solution is poorly understood. The goal of this study is to validate an anisotropic model for the intracranial electric forward solution by comparing with the directly measured 'gold standard'. Dipolar sources are created at known locations in the brain and intracranial electroencephalogram (EEG) is recorded simultaneously. Isotropic models with increasing level of complexity are generated along with anisotropic models based on Diffusion tensor imaging (DTI). A Finite Element Method based forward solution is calculated and validated using the measured data. Major findings are (1) An anisotropic model with a linear scaling between the eigenvalues of the electrical conductivity tensor and water self-diffusion tensor in brain tissue is validated. The greatest improvement was obtained when the stimulation site is close to a region of high anisotropy. The model with a global anisotropic ratio of 10:1 between the eigenvalues (parallel: tangential to the fiber direction) has the worst performance of all the anisotropic models. (2) Inclusion of cerebrospinal fluid as well as brain anisotropy in the forward model is necessary for an accurate description of the electric field inside the skull. The results indicate that an anisotropic model based on the DTI can be constructed non-invasively and shows an improved performance when compared to the isotropic models for the calculation of the intracranial EEG forward solution.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms
  • Anisotropy
  • Brain / physiology*
  • Cerebrospinal Fluid / physiology
  • Data Interpretation, Statistical
  • Diffusion Magnetic Resonance Imaging
  • Electric Conductivity
  • Electrodes
  • Electroencephalography / statistics & numerical data*
  • Finite Element Analysis
  • Head
  • Humans
  • Image Processing, Computer-Assisted
  • Linear Models
  • Models, Neurological
  • Reproducibility of Results
  • Skull / anatomy & histology