Recent advances in modeling and analysis of bioelectric and biomagnetic sources

Abstract Determining the centers of electrical activity in the human body and the connectivity between different centers of activity in the brain is an active area of research. To understand brain function and the nature of cardiovascular diseases requires sophisticated methods applicable to non-invasively measured bioelectric and biomagnetic data. As it is difficult to solve for all unknown parameters at once, several strains of data analysis have been developed, each trying to solve a different part of the problem and each requiring a different set of assumptions. Current trends and results from major topics of electro- and magnetoencephalographic data analysis are presented here together with the aim of stimulating research into the unification of the different approaches. The following topics are discussed: source reconstruction using detailed finite element modeling to locate sources deep in the brain; connectivity analysis for the quantification of strength and direction of information flow between activity centers, preferably incorporating an inverse solution; the conflict between the statistical independence assumption of sources and a possible connectivity; the verification and validation of results derived from non-invasively measured data through animal studies and phantom measurements. This list already indicates the benefits of a unified view.

[1]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[2]  B. Roth,et al.  Electrically silent magnetic fields. , 1986, Biophysical journal.

[3]  John J. Bray,et al.  Lecture Notes on Human Physiology , 1986 .

[4]  J. Sarvas Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[5]  S. Ernè Brainstem auditory evoked magnetic fields , 1988 .

[6]  M. Scherg Akustisch evozierte Potentiale: Grundlagen - Entstehungsmechanismen - Quellenmodell , 1991 .

[7]  J. Malmivuo,et al.  Sensitivity distributions of EEG and MEG measurements , 1997, IEEE Transactions on Biomedical Engineering.

[8]  L. Trahms,et al.  Magnetocardiography and 32‐Lead Potential Mapping , 1997, Journal of cardiovascular electrophysiology.

[9]  Sylvain Baillet,et al.  Influence of skull anisotropy for the forward and inverse problem in EEG: Simulation studies using FEM on realistic head models , 1998, Human brain mapping.

[10]  F. H. Lopes da Silva,et al.  Biophysical aspects of EEG and magnetoencephalogram generation , 1998 .

[11]  U Leder,et al.  Source localization in an inhomogeneous physical thorax phantom. , 1999, Physics in medicine and biology.

[12]  Jens Haueisen,et al.  Reconstruction of extended current sources in a human body phantom applying biomagnetic measuring techniques , 2000 .

[13]  R Grave de Peralta Menendez,et al.  Imaging the electrical activity of the brain: ELECTRA , 2000, Human brain mapping.

[14]  A. Schnitzler,et al.  Dynamic imaging of coherent sources: Studying neural interactions in the human brain. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[16]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[17]  J. Haueisen,et al.  The Influence of Brain Tissue Anisotropy on Human EEG and MEG , 2002, NeuroImage.

[18]  G. R. Barnes,et al.  A Quantitative Assessment of the Sensitivity of Whole-Head MEG to Activity in the Adult Human Cortex , 2002, NeuroImage.

[19]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[20]  Jens Haueisen,et al.  Dipole models for the EEG and MEG , 2002, IEEE Transactions on Biomedical Engineering.

[21]  W. Hackbusch,et al.  Efficient Computation of Lead Field Bases and Influence Matrix for the FEM-based EEG and MEG Inverse Problem. Part I: Complexity Considerations , 2003 .

[22]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[23]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[24]  Jens Haueisen,et al.  Three component magnetic field data: Impact on minimum norm solutions in a biomedical application , 2005 .

[25]  Jens Haueisen,et al.  Evaluation of the distortion of EEG signals caused by a hole in the skull mimicking the fontanel in the skull of human neonates , 2005, Clinical Neurophysiology.

[26]  Jens Haueisen,et al.  Vortex Shaped Current Sources in a Physical Torso Phantom , 2005, Annals of Biomedical Engineering.

[27]  Alois Schlögl,et al.  Analyzing event-related EEG data with multivariate autoregressive parameters. , 2006, Progress in brain research.

[28]  Jens Timmer,et al.  Handbook of time series analysis : recent theoretical developments and applications , 2006 .

[29]  Y. Okada,et al.  Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals , 2006, The Journal of physiology.

[30]  Thomas R. Knösche,et al.  Influence of anisotropic conductivity on EEG source reconstruction: investigations in a rabbit model , 2006, IEEE Transactions on Biomedical Engineering.

[31]  Luiz A. Baccalá,et al.  Computer Intensive Testing for the Influence Between Time Series , 2006 .

[32]  Alois Schlögl,et al.  A comparison of multivariate autoregressive estimators , 2006, Signal Process..

[33]  Xavier Tricoche,et al.  Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling , 2006, NeuroImage.

[34]  Matthias M. Müller,et al.  Directed Cortical Information Flow during Human Object Recognition: Analyzing Induced EEG Gamma-Band Responses in Brain's Source Space , 2007, PloS one.

[35]  M. Peters,et al.  Computation of neuromagnetic fields using finite-element method and Biot-Savart law , 2007, Medical and Biological Engineering and Computing.

[36]  Harald Köstler,et al.  Numerical Mathematics of the Subtraction Method for the Modeling of a Current Dipole in EEG Source Reconstruction Using Finite Element Head Models , 2007, SIAM J. Sci. Comput..

[37]  Martin Burghoff,et al.  Application of decorrelation-independent component analysis to biomagnetic multi-channel measurements , 2007, Biomedizinische Technik. Biomedical engineering.

[38]  Carsten H. Wolters,et al.  Geometry-Adapted Hexahedral Meshes Improve Accuracy of Finite-Element-Method-Based EEG Source Analysis , 2007, IEEE Transactions on Biomedical Engineering.

[39]  J. Haueisen,et al.  An Experimental Study on the Effect of the Anisotropic Regions in a Realistically Shaped Torso Phantom , 2008, Annals of Biomedical Engineering.

[40]  Karen O. Egiazarian,et al.  Measuring directional coupling between EEG sources , 2008, NeuroImage.

[41]  Jens Haueisen,et al.  Boundary Element Computations in the Forward and Inverse Problems of Electrocardiography: Comparison of Collocation and Galerkin Weightings , 2008, IEEE Transactions on Biomedical Engineering.

[42]  E. Oja,et al.  BSS and ICA in Neuroinformatics: From Current Practices to Open Challenges , 2008, IEEE Reviews in Biomedical Engineering.

[43]  Jens Haueisen,et al.  Influence of anisotropic compartments on magnetic field and electric potential distributions generated by artificial current dipoles inside a torso phantom , 2008, Physics in medicine and biology.

[44]  O. Väisänen,et al.  Improving the SNR of EEG generated by deep sources with weighted multielectrode leads , 2009, Journal of Physiology-Paris.

[45]  S. Makeig,et al.  Improved EEG source analysis using low‐resolution conductivity estimation in a four‐compartment finite element head model , 2009, Human brain mapping.

[46]  C H Wolters,et al.  Accuracy and run-time comparison for different potential approaches and iterative solvers in finite element method based EEG source analysis. , 2009, Applied numerical mathematics : transactions of IMACS.

[47]  Asaid Khateb,et al.  Electrophysiological correlates of affective blindsight , 2009, NeuroImage.

[48]  Simon K. Warfield,et al.  EEG source analysis of epileptiform activity using a 1 mm anisotropic hexahedra finite element head model , 2009, NeuroImage.

[49]  Carsten H. Wolters,et al.  A full subtraction approach for finite element method based source analysis using constrained Delaunay tetrahedralisation , 2009, NeuroImage.

[50]  Dorothea Kolossa,et al.  Non-independent BSS: A Model for Evoked MEG Signals with Controllable Dependencies , 2009, ICA.

[51]  Julia P. Owen,et al.  Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG , 2010, NeuroImage.

[52]  Stefan Skare,et al.  A variational approach for the correction of field-inhomogeneities in EPI sequences , 2010, Medical Imaging.