duneuro - A software toolbox for forward modeling in neuroscience

This paper describes duneuro, a software toolbox for forward modeling in neuroscience. Its purpose is to provide extendible and easy-to-use interfaces and enable a closer integration into existing analysis pipelines. It provides implementations of fitted and unfitted finite element methods and makes use of the Dune framework. The forward problems consist of the electroencephalography (EEG) and magnetoencephalography (MEG) forward problems. For the incorporation into existing analysis pipelines, Python and Matlab interfaces are provided. The practical use is demonstrated on a source analysis example of evoked potentials.

[1]  L. R. Scott,et al.  The Mathematical Theory of Finite Element Methods , 1994 .

[2]  L. Beltrachini,et al.  A Finite Element Solution of the Forward Problem in EEG for Multipolar Sources , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[4]  R. T. Hart,et al.  Finite-element model of the human head: scalp potentials due to dipole sources , 1991, Medical and Biological Engineering and Computing.

[5]  Stephen J. Jones,et al.  Potentials evoked in human and monkey cerebral cortex by stimulation of the median nerve. A review of scalp and intracranial recordings. , 1991, Brain : a journal of neurology.

[6]  Romain Brette,et al.  Handbook of neural activity measurement , 2012 .

[7]  Robert Oostenveld,et al.  The Discontinuous Galerkin Finite Element Method for Solving the MEG and the Combined MEG/EEG Forward Problem , 2018, Front. Neurosci..

[8]  Carsten H. Wolters,et al.  A Mixed Finite Element Method to Solve the EEG Forward Problem , 2016, IEEE Transactions on Medical Imaging.

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

[10]  Oliver Sander,et al.  The dune-subgrid module and some applications , 2009, Computing.

[11]  Christian Engwer,et al.  Dune-UDG: A Cut-Cell Framework for Unfitted Discontinuous Galerkin Methods , 2012 .

[12]  Carsten H. Wolters,et al.  A Discontinuous Galerkin Method to Solve the EEG Forward Problem Using the Subtraction Approach , 2015, SIAM J. Sci. Comput..

[13]  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.

[14]  Lucas C. Parra,et al.  Subject position affects EEG magnitudes , 2013, NeuroImage.

[15]  Andreas Galka,et al.  Combining EEG and MEG for the Reconstruction of Epileptic Activity Using a Calibrated Realistic Volume Conductor Model , 2014, PloS one.

[16]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[17]  Ross T. Whitaker Reducing aliasing artifacts in iso-surfaces of binary volumes , 2000, VVS.

[18]  Romain Brette,et al.  Handbook of Neural Activity Measurement: Intracellular recording , 2012 .

[19]  C. E. Acar,et al.  Sensitivity of EEG and MEG measurements to tissue conductivity , 2004, Physics in medicine and biology.

[20]  Thomas R. Knösche,et al.  A guideline for head volume conductor modeling in EEG and MEG , 2014, NeuroImage.

[21]  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.

[22]  Théodore Papadopoulo,et al.  A Trilinear Immersed Finite Element Method for Solving the Electroencephalography Forward Problem , 2010, SIAM J. Sci. Comput..

[23]  Carsten H. Wolters,et al.  The Unfitted Discontinuous Galerkin Method for Solving the EEG Forward Problem , 2016, IEEE Transactions on Biomedical Engineering.

[24]  Peter Bastian,et al.  The Iterative Solver Template Library , 2006, PARA.

[25]  Bart Vanrumste,et al.  Review on solving the forward problem in EEG source analysis , 2007, Journal of NeuroEngineering and Rehabilitation.

[26]  Sampsa Pursiainen,et al.  Forward simulation and inverse dipole localization with the lowest order Raviart—Thomas elements for electroencephalography , 2011 .

[27]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[28]  Scott Makeig,et al.  Simultaneous head tissue conductivity and EEG source location estimation , 2016, NeuroImage.

[29]  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.

[30]  Andreas Dedner,et al.  A generic grid interface for parallel and adaptive scientific computing. Part II: implementation and tests in DUNE , 2008, Computing.

[31]  L. Parra,et al.  Optimized multi-electrode stimulation increases focality and intensity at target , 2011, Journal of neural engineering.

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

[33]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[34]  Peter J.C. Brown,et al.  A robust efficient algorithm for point location in triangulations , 1997 .

[35]  Robert Oostenveld,et al.  The FieldTrip-SimBio pipeline for EEG forward solutions , 2018, Biomedical engineering online.

[36]  J. Vorwerk,et al.  Electroencephalography (EEG) forward modeling via H(div) finite element sources with focal interpolation , 2016, Physics in medicine and biology.

[37]  Peter Hansbo,et al.  CutFEM: Discretizing geometry and partial differential equations , 2015 .

[38]  Andreas Dedner,et al.  A generic grid interface for parallel and adaptive scientific computing. Part I: abstract framework , 2008, Computing.

[39]  Denis Schwartz,et al.  FEM Method for the EEG Forward Problem and Improvement Based on Modification of the Saint Venant's Method , 2015 .

[40]  Martin Burger,et al.  An Optimization Approach for Well-Targeted Transcranial Direct Current Stimulation , 2015, SIAM J. Appl. Math..

[41]  Christian Engwer,et al.  Geometric Reconstruction of Implicitly Defined Surfaces and Domains with Topological Guarantees , 2017, ACM Trans. Math. Softw..

[42]  Peter Bastian,et al.  Generic implementation of finite element methods in the Distributed and Unified Numerics Environment (DUNE) , 2010, Kybernetika.

[43]  A. Nakamura,et al.  Somatosensory Homunculus as Drawn by MEG , 1998, NeuroImage.

[44]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[45]  H. Rentz-Reichert,et al.  UG – A flexible software toolbox for solving partial differential equations , 1997 .

[46]  Leonid Zhukov,et al.  Influence of head tissue conductivity in forward and inverse magnetoencephalographic Simulations using realistic head models , 2004, IEEE Transactions on Biomedical Engineering.

[47]  Carsten H. Wolters,et al.  A realistic, accurate and fast source modeling approach for the EEG forward problem , 2019, NeuroImage.

[48]  Leonid Zhukov,et al.  Lead-field Bases for Electroencephalography Source Imaging , 2000, Annals of Biomedical Engineering.

[49]  André Massing,et al.  A stabilized cut discontinuous Galerkin framework: I. Elliptic boundary value and interface problems , 2018, Computer Methods in Applied Mechanics and Engineering.

[50]  Sampsa Pursiainen,et al.  Zeffiro User Interface for Electromagnetic Brain Imaging: a GPU Accelerated FEM Tool for Forward and Inverse Computations in Matlab , 2018, Neuroinformatics.

[51]  Leandro Beltrachini,et al.  Sensitivity of the Projected Subtraction Approach to Mesh Degeneracies and Its Impact on the Forward Problem in EEG , 2019, IEEE Transactions on Biomedical Engineering.

[52]  Martin Luessi,et al.  MEG and EEG data analysis with MNE-Python , 2013, Front. Neuroinform..

[53]  C. Engwer,et al.  An unfitted finite element method using discontinuous Galerkin , 2009 .

[54]  Martin Bauer,et al.  Comparison Study for Whitney (Raviart–Thomas)-Type Source Models in Finite-Element-Method-Based EEG Forward Modeling , 2015, IEEE Transactions on Biomedical Engineering.