Inverse localization of electric dipole current sources in finite element models of the human head

Büchner, Helmut; Knoll, Gunter; Fuchs, Manfred; Rienäcker, Adrian; Beckmann, Rainer; Wagner, Michael; Silny, Jiri; Pesch, Jörg

doi:10.1016/s0013-4694(96)95698-9

Cited by 161 publications

(167 citation statements)

References 21 publications

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“…Ᏺ was calculated using the "boundary element method" (BEM) for a three-shell realistic head model (Geselowitz, 1967; Hä mä lä inen and Sarvas, 1989;Meijs et al, 1989;de Munck, 1992;Schlitt et al, 1995;Ferguson and Stroink, 1997;Buchner et al, 1997). A structural MR image of the head was segmented (Ashburner and Friston, 1997) and divided into three volumes with homogeneous isotropic conductivity: the brain, the skull, and the scalp volume.…”

Section: Head and Source Modelmentioning

confidence: 99%

Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

2002

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Distributed linear solutions of the EEG source localization problem are used routinely. Here we describe an approach based on the weighted minimum norm method that imposes constraints using anatomical and physiological information derived from other imaging modalities to regularize the solution. In this approach the hyperparameters controlling the degree of regularization are estimated using restricted maximum likelihood (ReML). EEG data are always contaminated by noise, e.g., exogenous noise and background brain activity. The conditional expectation of the source distribution, given the data, is attained by carefully balancing the minimization of the residuals induced by noise and the improbability of the estimates as determined by their priors. This balance is specified by hyperparameters that control the relative importance of fitting and conforming to prior constraints. Here we introduce a systematic approach to this regularization problem, in the context of a linear observation model we have described previously. In this model, basis functions are extracted to reduce the solution space a priori in the spatial and temporal domains. The basis sets are motivated by knowledge of the evoked EEG response and information theory. In this paper we focus on an iterative "expectationmaximization" procedure to jointly estimate the conditional expectation of the source distribution and the ReML hyperparameters on which this solution rests. We used simulated data mixed with real EEG noise to explore the behavior of the approach with various source locations, priors, and noise levels. The results enabled us to conclude: (i) Solutions in the space of informed basis functions have a high face and construct validity, in relation to conventional analyses. (ii) The hyperparameters controlling the degree of regularization vary largely with source geometry and noise. The second conclusion speaks to the usefulness of using adaptative ReML hyperparameter estimates. © 2002 Elsevier Science (USA)

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Section: Head and Source Modelmentioning

confidence: 99%

Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

2002

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“…Dirichlet boundary condition is applied in inner surfaces Γ I of the boundary Γ(x k ) for the specified potentials (α) [15] ( )…”

Section: Fe Forward Modelingmentioning

confidence: 99%

EEG Analysis on Skull Conductivity Perturbations Using Realistic Head Model

Bashar¹,

Li²,

Wen

2009

Rough Sets and Knowledge Technology

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Abstract. Measurement of electroencephalogram (EEG) requires accurate estimation of tissue conductivity. Among the head tissues, skull compartment has less conductivity due to compacta and spongiosa, which impacts on EEG measurement. Therefore, skull conductivity plays a vital role in head modeling, forward computation and source localization. In this study, we have investigated the effects of scalp potentials due to skull conductivity perturbations in realistic head models using different skull to brain and/or scalp conductivity ratio (σ ratio ). Several studies used this σ ratio as 1/80, however, other studies found the values of σ ratio between 1/20 and 1/72. Each head model constructed from the values of different σ ratio ranging from 1/20 to 1/72 is compared to the head model constructed from σ ratio = 1/80. The obtained results demonstrated that the skull conductivity perturbations have effects on EEG and the head model constructed from less σ ratio generates larger errors due to higher potential differences.

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“…The dipole approximation is a convenient representation of the source in the case of synchronous polarization of a few square centimeters of cortical surface (29,30). Head models can be calculated analytically in simple volume conductors with spherical symmetries and homogenous conductors (31) or numerically in more realistic models, by segmented head models from MRI, with BEM or FEM (19,20). The inverse problem addresses the rela-tionship between the sources and the resulting potentials for source localization and evaluation of their time dynamics.…”

Section: Source Reconstructionmentioning

confidence: 99%

“…The nonlinear L1-norm was used for regularization, minimizing the absolute values, because this norm is known to result to most focal reconstructions (20,41,42). The weighting between the minimum-norm condition and the goodness of fit of the data, Lambda, was iteratively adjusted until a desired fit error of 1/SNR was achieved (X 2 -criterion) (43).…”

Section: Procedures Iv: Current Density Reconstruction (Cdr)mentioning

confidence: 99%

“…The recent progress in the development of volume conductor models on the basis of individual anatomical information such as the boundary element method (BEM) (18,19) and the finite element method (FEM) (20), as well as multiple source reconstruction by current density reconstruction (CDR), may give an additional impulse to EEG-based source analyses in epilepsy patients (21). Although it is evident that the application of individual realistically shaped head models leads to a more precise localization of temporal epileptiform activity in single dipole solutions (16,18,19,22), it has yet to be evaluated to what extent the implementation of advanced source reconstruction methods such as CDR improves source reconstruction in this application.…”

Section: Introductionmentioning

confidence: 99%

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Source Reconstruction of Mesial‐Temporal Epileptiform Activity: Comparison of Inverse Techniques

2000

Epilepsia

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Summary:Purpose: To evaluate whether advanced source reconstruction such as current density reconstruction (CDR) provides additional hints for clinical presurgical evaluation, different source reconstruction techniques with idealized spherical as well as realistically shaped head models (boundary element method, BEM) were applied on interictal and ictal epileptiform activity in presurgical evaluated patients with temporal lobe epilepsy. It is discussed whether CDR and BEM give additional information for presurgical evaluation compared to "conventional" strategies, such as single moving, and spatio-temporal dipole modeling with spherical head models.Methods: A variety of source reconstruction procedures were applied to the data of five patients with pharmacoresistent temporal lobe epilepsy with probable mesial origin: (1) singlemoving dipole in a spherical head model and (2) in BEM, (3) spatio-temporal dipole modeling in a spherical head model and (4) in BEM; and (5) deconvolution with fixed locations and orientations and (6) with cortically constrained L1-norm CDR in BEM. In addition, simulated sources of temporal lobe origin were calculated in each subject with CDR to prove the basic feasibility of this technique in the particular application.Results: Source activity was correctly localized within the affected temporal lobe by all source reconstruction techniques used. Neither single moving dipole, spatio-temporal modeling, nor CDR was able to localize sources at a sublobar level. In the case of two sources, single moving dipole solutions showed changes in dipole orientation in time and spatio-temporal modeling separated two sources, whereas CDR at the peak latency failed to distinguish among different origins. BEM enhanced localization accuracy.Conclusion: There was no advantage of using CDR. Single moving dipole as well as spatio-temporal dipole modeling in BEM leads to more precise localization within the individual anatomy and provides a simple algorithm, which is capable of indicating both the time course and the number of sources.

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Inverse localization of electric dipole current sources in finite element models of the human head

Cited by 161 publications

References 21 publications

Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

EEG Analysis on Skull Conductivity Perturbations Using Realistic Head Model

Source Reconstruction of Mesial‐Temporal Epileptiform Activity: Comparison of Inverse Techniques

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