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
  • [31] CutFEM forward modeling for EEG source analysis
    Erdbruegger, Tim
    Westhoff, Andreas
    Hoeltershinken, Malte
    Radecke, Jan-Ole
    Buschermoehle, Yvonne
    Buyx, Alena
    Wallois, Fabrice
    Pursiainen, Sampsa
    Gross, Joachim
    Lencer, Rebekka
    Engwer, Christian
    Wolters, Carsten
    FRONTIERS IN HUMAN NEUROSCIENCE, 2023, 17
  • [32] EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model
    Rullmann, M.
    Anwander, A.
    Dannhauer, M.
    Warfield, S. K.
    Duffy, F. H.
    Wolters, C. H.
    NEUROIMAGE, 2009, 44 (02) : 399 - 410
  • [33] Impact of skull-to-brain conductivity ratio for high resolution EEG source localization
    Demoulin, Gregoire
    Pruvost-Robieux, Estelle
    Marchi, Angela
    Ramdani, Celine
    Badier, Jean-Michel
    Bartolomei, Fabrice
    Gavaret, Martine
    BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, 2021, 7 (05):
  • [34] The significance of relative conductivity on thin layers in EEG sensitivity distributions
    Wendel, Katrina
    Vaisanen, Juho
    Kybartaite, Asta
    Hyttinen, Jari
    Malmivuo, Jaakko
    BIOMEDIZINISCHE TECHNIK, 2010, 55 (03): : 123 - 131
  • [35] A new method for detection and source analysis of EEG spikes
    Van Hese, P
    Boon, P
    Vonck, K
    Lemahieu, I
    Van de Walle, R
    PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4: A NEW BEGINNING FOR HUMAN HEALTH, 2003, 25 : 2455 - 2458
  • [36] EEG Source analysis of data from paralysed subjects
    Carabali, Carmen A.
    Willoughby, John O.
    Fitzgibbon, Sean P.
    Grummett, Tyler
    Lewis, Trent
    DeLosAngeles, Dylan
    Pope, Kenneth J.
    11TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, 2015, 9681
  • [37] UNCERTAINTIES ASSESSMENT IN GLOBAL SENSITIVITY INDICES ESTIMATION FROM METAMODELS
    Janon, Alexandre
    Nodet, Maelle
    Prieur, Clementine
    INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2014, 4 (01) : 21 - 36
  • [38] Ictal EEG source imaging in presurgical evaluation: High agreement between analysis methods
    Beniczky, Sandor
    Rosenzweig, Ivana
    Scherg, Michael
    Jordanov, Todor
    Lanfer, Benjamin
    Lantz, Goran
    Larsson, Pal Gunnar
    SEIZURE-EUROPEAN JOURNAL OF EPILEPSY, 2016, 43 : 1 - 5
  • [39] Dorsal-Ventral Visual Pathways and Object Characteristics: Beamformer Source Analysis of EEG
    Tiwari, Akanksha
    Pachori, Ram Bilas
    Sanjram, Premjit Khanganba
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 70 (02): : 2347 - 2363
  • [40] EEG Source Imaging With Spatio-Temporal Tomographic Nonnegative Independent Component Analysis
    Valdes-Sosa, Pedro A.
    Vega-Hernandez, Mayrim
    Miguel Sanchez-Bornot, Jose
    Martinez-Montes, Eduardo
    Antonieta Bobes, Maria
    HUMAN BRAIN MAPPING, 2009, 30 (06) : 1898 - 1910