Diffusion MRI Consistent Wire Models for Efficient Solutions of the Anisotropic Forward Problem in Electroencephalography

The surface Boundary Element Method (BEM) is one of the most commonly employed formulations to solve the forward problem in electroencephalography, but the applicability of its classical incarnations is lamentably limited to piecewise homogeneous media. Several head tissues, however, are strongly anisotropic due to their complex underlying microstructure. This implies that standard boundary integral formulations oversimplify the electrical properties of the head and produce unrealistic solutions, something that drastically limits the suitability and impact of BEM technologies to brain imaging. This contribution addresses this issue by observing that the brain anisotropy in the white matter is due to the presence of neuronal wire-like structures. We then extend the well known wire integral equations used for high frequency problems to the imperfectly conducting quasi-static case and we propose a new hybrid wire/surface/volume integral equation. When applied on multimodal magnetic resonance images combined with tractography, this new approach can flexibly and realistically handle the conductivity anisotropy in any head compartment providing high level of accuracy and efficiency. The beneficial properties of the new formulation together with its impact on brain imaging is demonstrated via numerical results on both canonical and realistic case scenarios.

[1]  Thomas R. Knösche,et al.  A guideline for head volume conductor modeling in EEG and MEG , 2014, NeuroImage.

[2]  Guy B. Williams,et al.  QuickBundles, a Method for Tractography Simplification , 2012, Front. Neurosci..

[3]  Olivier D. Faugeras,et al.  A common formalism for the Integral formulations of the forward EEG problem , 2005, IEEE Transactions on Medical Imaging.

[4]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[5]  Bart Vanrumste,et al.  Review on solving the forward problem in EEG source analysis , 2007, Journal of NeuroEngineering and Rehabilitation.

[6]  Andre L. Simon's Champagne , 1907, The Hospital.

[7]  Donald R. Wilton,et al.  Evaluation and integration of the thin wire kernel , 2006, IEEE Transactions on Antennas and Propagation.

[9]  Denis Le Bihan,et al.  Looking into the functional architecture of the brain with diffusion MRI , 2003, Nature Reviews Neuroscience.

[10]  Peng Wen,et al.  Numerical investigation of white matter anisotropic conductivity in defining current distribution under tDCS , 2013, Comput. Methods Programs Biomed..

[11]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[12]  Théodore Papadopoulo,et al.  Handling white-matter anisotropy in BEM for the EEG forward problem , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[13]  L. Parra,et al.  Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation , 2017, Brain Stimulation.

[14]  Lucas C. Parra,et al.  Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation , 2017, Brain Stimulation.

[15]  Rajendra Mitharwal,et al.  Two volume integral equations for the inhomogeneous and anisotropic forward problem in electroencephalography , 2017, J. Comput. Phys..

[16]  Alvaro Pascual-Leone,et al.  Modeling fiber-like conductivity structures via the boundary element method using thin-wire approximation. I construction of basis functions , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  Alan Connelly,et al.  MRtrix: Diffusion tractography in crossing fiber regions , 2012, Int. J. Imaging Syst. Technol..