On the modeling of brain fibers in the EEG forward problem via a new family of wire integral equations

Lyes Rahmouni , Adrien Merlini , Axelle Pillain , Francesco P. Andriulli
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引用次数: 0

Abstract

Source localization based on electroencephalography (EEG) has become a widely used neuroimaging technique. However its precision has been shown to be very dependent on how accurately the brain, head and scalp can be electrically modeled within the so-called forward problem. The construction of this model is traditionally performed by leveraging Finite Element or Boundary Element Methods (FEM or BEM). Even though the latter is more computationally efficient thanks to the smaller interaction matrices it yields and near-linear solvers, it has traditionally been used on simpler models than the former. Indeed, while FEM models taking into account the different media anisotropies are widely available, BEM models have been limited to isotropic, piecewise homogeneous models. In this work we augment standard BEM with a new wire integral equation to account for the anisotropy of the white matter. The new formulation combines the efficiency of BEM discretization of the boundaries only and modeling of the fibrous nature of the white matter as one-dimensional basis functions which limits the computational impact of their modeling. We compare our scheme against widely used formulations and establish its correctness in both canonical and realistic cases.

用一类新的线积分方程组对脑电正演问题中的脑纤维建模
基于脑电图的脑源定位已成为一种广泛应用的神经成像技术。然而,它的精度已被证明在很大程度上取决于在所谓的正向问题中大脑、头部和头皮的电建模精度。该模型的构造传统上是通过利用有限元或边界元方法(FEM或BEM)来执行的。尽管后者由于产生较小的交互矩阵和接近线性的求解器而在计算上更高效,但传统上它被用于比前者更简单的模型。事实上,尽管考虑不同介质各向异性的有限元模型广泛可用,但边界元模型仅限于各向同性、分段齐次模型。在这项工作中,我们用一个新的线积分方程来增加标准边界元法,以考虑白质的各向异性。新公式将边界的边界元法离散化和白质纤维性质建模的效率结合为一维基函数,这限制了它们建模的计算影响。我们将我们的方案与广泛使用的公式进行了比较,并在规范和现实的情况下证明了它的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Physics: X
Journal of Computational Physics: X Physics and Astronomy-Physics and Astronomy (miscellaneous)
CiteScore
6.10
自引率
0.00%
发文量
7
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