Global sensitivity of EEG source analysis to tissue conductivity uncertainties

被引:3
作者
Vorwerk, Johannes [1 ]
Wolters, Carsten H. [2 ,3 ]
Baumgarten, Daniel [1 ]
机构
[1] UMIT TIROL Private Univ Hlth Sci & Hlth Technol, Inst Elect & Biomed Engn, Hall In Tirol, Austria
[2] Univ Munster, Inst Biomagnetism & Biosignalanalysis, Munster, Germany
[3] Univ Munster, Otto Creutzfeldt Ctr Cognit & Behav Neurosci, Munster, Germany
来源
FRONTIERS IN HUMAN NEUROSCIENCE | 2024年 / 18卷
基金
奥地利科学基金会;
关键词
EEG; forward modeling; finite element method; source analysis; sensitivity analysis; uncertainty quantification; FINITE-ELEMENT MODEL; SOURCE LOCALIZATION; HEAD MODELS; SKULL; MEG; POTENTIALS; ACCURACY; ERRORS;
D O I
10.3389/fnhum.2024.1335212
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Introduction To reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial-but often neglected-aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface.Methods We employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions.Results For sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity.Discussion Our results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] EEG source localization using independent residual analysis
    Tan, G
    Zhang, LQ
    ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 2, 2004, 3174 : 442 - 447
  • [42] Continuous EEG source imaging enhances analysis of EEG-fMRI in focal epilepsy
    Vulliemoz, S.
    Rodionov, R.
    Carmichael, D. W.
    Thornton, R.
    Guye, M.
    Lhatoo, S. D.
    Michel, C. M.
    Duncan, J. S.
    Lemieux, L.
    NEUROIMAGE, 2010, 49 (04) : 3219 - 3229
  • [43] Accuracy and run-time comparison for different potential approaches and iterative solvers in finite element method based EEG source analysis
    Lew, S.
    Wolters, C. H.
    Dierkes, T.
    Roeer, C.
    MacLeod, R. S.
    APPLIED NUMERICAL MATHEMATICS, 2009, 59 (08) : 1970 - 1988
  • [44] EEG human head modelling based on heterogeneous tissue conductivity
    Wen P.
    Li Y.
    Australasian Physics & Engineering Sciences in Medicine, 2006, 29 (3): : 235 - 240
  • [45] Uncertainty and sensitivity analysis for anisotropic inhomogeneous head tissue conductivity in human head modelling
    M. R. Bashar
    Y. Li
    P. Wen
    Australasian Physical & Engineering Sciences in Medicine, 2010, 33 : 145 - 152
  • [46] Functional connectivity analysis in EEG source space: The choice of method
    Barzegaran, Elham
    Knyazeva, Maria G.
    PLOS ONE, 2017, 12 (07):
  • [47] Global sensitivity analysis of asymmetric energy harvesters
    Norenberg, Joao Pedro
    Cunha Jr, Americo
    da Silva, Samuel
    Varoto, Paulo Sergio
    NONLINEAR DYNAMICS, 2022, 109 (02) : 443 - 458
  • [48] Global sensitivity analysis of biological multiscale models
    Renardy, Marissa
    Hult, Caitlin
    Evans, Stephanie
    Linderman, Jennifer J.
    Kirschner, Denise E.
    CURRENT OPINION IN BIOMEDICAL ENGINEERING, 2019, 11 : 109 - 116
  • [49] SENSITIVITY ANALYSIS: ADVANCING THE EFFECTIVENESS OF GLOBAL SENSITIVITY ANALYSIS
    Sun, XIifu
    BULLETIN OF THE AUSTRALIAN MATHEMATICAL SOCIETY, 2023, 107 (02) : 351 - 352
  • [50] Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation
    Maksymenko, Kostiantyn
    Clerc, Maureen
    Papadopoulo, Theodore
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2020, 39 (04) : 888 - 897