The influence of forward model conductivities on EEG/MEG source reconstruction

被引:0
|
作者
Haueisen, Jens [1 ]
机构
[1] Tech Univ Ilmenau, Inst Biomed Engn & Informat, Ilmenau, Germany
来源
2007 JOINT MEETING OF THE 6TH INTERNATIONAL SYMPOSIUM ON NONINVASIVE FUNCTIONAL SOURCE IMAGING OF THE BRAIN AND HEART AND THE INTERNATIONAL CONFERENCE ON FUNCTIONAL BIOMEDICAL IMAGING | 2007年
关键词
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
In order to reconstruct the neuronal activity underlying measured EEG and MEG data both the forward problem (computing the electromagnetic field due to given sources) and the inverse problem (finding the best fitting sources to explain given data) have to be solved. The forward problem involves a model with the conductivities of the head, which can be as simple as a homogeneously conducting sphere or as complex as a finite element model consisting of millions of elements, each with a different anisotropic conductivity tensor. The question is addressed how complex the employed forward model should be, and, more specifically, the influence of anisotropic volume conduction is evaluated. For this purpose high resolution finite element models of the rabbit and the human head are employed in combination with individual conductivity tensors to quantify the influence of white matter anisotropy on the solution of the forward and inverse problem in EEG and MEG. Although the current state of the art in the analysis of this influence of brain tissue anisotropy on source reconstruction does not yet allow a final conclusion, the results available indicate that the expected average source localization error due to anisotropic white matter conductivity might be within the principal accuracy limits of current inverse procedures. However, in some percent of the cases a considerably larger localization error might occur. In contrast, dipole orientation and dipole strength estimation are influenced significantly by anisotropy. In conclusion, models taking into account tissue anisotropy information are expected to improve source estimation procedures.
引用
收藏
页码:362 / 363
页数:2
相关论文
共 50 条
  • [1] SOFOMORE: COMBINED EEG SOURCE AND FORWARD MODEL RECONSTRUCTION
    Stahlhut, Carsten
    Morup, Morten
    Winther, Ole
    Hansen, Lars Kai
    2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2, 2009, : 450 - 453
  • [2] Influence of the head model on EEG and MEG source connectivity analyses
    Cho, Jae-Hyun
    Vorwerk, Johannes
    Wolters, Carsten H.
    Knoesche, Thomas R.
    NEUROIMAGE, 2015, 110 : 60 - 77
  • [3] Influence of the head model on EEG and MEG source connectivity analysis
    Cho, J-H
    Vorwerk, J.
    Wolters, C. H.
    Knoesche, T. R.
    BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK, 2014, 59 : S631 - S631
  • [4] Efficiently solving the EEG/MEG forward model
    Mosher, JC
    INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY, 1999, 33 (01) : 39 - 39
  • [5] THE INFLUENCE OF MODEL PARAMETERS ON EEG MEG SINGLE DIPOLE SOURCE ESTIMATION
    STOK, CJ
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 1987, 34 (04) : 289 - 296
  • [6] Influence of Population Dependent Forward Models on Distributed EEG Source Reconstruction
    Cuartas-Morales, E.
    Cespedes-Villar, Y. R.
    Martinez-Vargas, J. D.
    Arteaga-Daza, L. F.
    Castellanos-Dominguez, C.
    NATURAL AND ARTIFICIAL COMPUTATION FOR BIOMEDICINE AND NEUROSCIENCE, PT I, 2017, 10337 : 374 - 383
  • [7] Two probabilistic algorithms for MEG/EEG source reconstruction
    Zumer, Johanna M.
    Attias, Hagai T.
    Sekihara, Kensuke
    Nagarajan, Srikantan S.
    2006 3RD IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1-3, 2006, : 940 - +
  • [8] HIERARCHICAL BAYESIAN MODEL FOR SIMULTANEOUS EEG SOURCE AND FORWARD MODEL RECONSTRUCTION (SOFOMORE)
    Stahlhut, Carsten
    Morup, Morten
    Winther, Ole
    Hansen, Lars Kai
    2009 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2009, : 452 - 457
  • [9] Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging
    Ding, Lei
    Yuan, Han
    HUMAN BRAIN MAPPING, 2013, 34 (04) : 775 - 795
  • [10] Bayesian model selection of template forward models for EEG source reconstruction
    Strobbe, Gregor
    van Mierlo, Pieter
    De Vos, Maarten
    Mijovic, Bogdan
    Hallez, Hans
    Van Huffel, Sabine
    David Lopez, Jose
    Vandenberghe, Stefaan
    NEUROIMAGE, 2014, 93 : 11 - 22